Analysis of Macroscopic Traffic Flow Parameters

Abstract –
Keywords: Traffic flow, macroscopic parameter, capacity, level of service.
Transportation refers as a movement of persons, animals and goods from one place (origin) to another place (destination). Now a days, transportation is going to be a part of our life to achieve our necessity. Increase in transportation is because of increase in population basically. The population of India is growing rapidly with a national average growth rate of 2.7 percent per annum (Census of India, 2010). The growing cities have generated the high levels of demand for travel by motor vehicles in the cities. This has resulted in tremendous increase in the population of automobiles in the cities. The Indian population increased with a decadal growth of 17.64% (census 2011) and annual growth rate of 1.2% (World Bank report). Next to this, the revolution in the automobile industry, liberalized economy and change in people’s life has led to tremendous increase in the vehicle ownership levels. This has resulted in changing in nature of traffic characteristics on road network and ultimately it affects the capacity of roadway, level of service on stream and congestion on roadway. Hence reduce in speed, unwanted traffic delays, road accidents, traffic jam, increase in travel time etc. are resulted. Therefore, the analysis of traffic stream parameter is needed to study for the effective planning, design, operation and maintenance of roadway system.
Homogeneous traffic has strict lane discipline and has traffic entity types whose physical dimensions do not vary much. In the nonhomogeneous traffic they loose lane discipline prevails. The physical dimensions of the traffic entities vary greatly. Operationally, acceleration and deceleration characteristics vary greatly because nonmotorized traffic entities exist along with motorized vehicles on the road. The most of the studies in such traffic make use of the methods and concepts developed for homogeneous traffic.
In India, it seems that the traffic is greatly differ due to vehicular and road user characteristics. The interaction between different size vehicles and their drivers as well as the infrastructure gives rise to many complex phenomena on our roads. To understand traffic flow, relationships have been established between the two main characteristics: flow and velocity. Flow, speed, and density are the critical parameters used to describe characteristics of traffic flow. A traffic flow fundamental diagram is used to characterize the relation between these three parameters, and plays an important role in traffic flow theory and traffic engineering. In capacity analysis, speed-flow relation models are used to determine the level of service. The time gap between successive vehicle arrivals, namely, time headway between vehicles is an important traffic flow characteristic that affects the safety, level of service and road capacity. Understanding time headways and their distributions will enable better management of traffic.
The aim of study is to analyse the macroscopic traffic flow parameters of heterogeneous traffic on selected stretch of Dakor. The objectives of study are as following,

To estimate the basic traffic flow parameters for different traffic stream under study.
To develop analytical relationship among traffic flow parameters.
To determine the congestion, capacity and level of service of selected road stretch under study.
To suggest the suitable solution for the observed problem of congestion.

Dakor is a pilgrim area and it is observed that a large amount of trip attraction takes place. The surrounding area comprise of large numbers of quarries, as a result of this major traffic observed at the site are multi axle trucks, resulting into considerable congestion. Hence it is necessary to understand the traffic behavior at the chosen site. Dakor, in its earlier phases as pilgrimage center in Gujarat, was famous for the Danknath temple, a place of Shiva worship. Recently, Dakor is included in one of the six major pilgrimage places under “Yatradham Vikas Board” by Government of Gujarat for development as a well-planned and well organized pilgrimage place to facilitate the lacs and lacs of visiting pilgrims. More than 70-80 lacs of pilgrims visit the place every year and a continuous increase is witnessed every year. Dakor is located at 22.75°N 73.15°E. By visual observation and pilot survey, it is examine that the traffic density increases to jam density.
METHODOLOGY

DATA COLLECTION
The study consist of conducting various surveys on selected stretches of Dakor. Data collection is carried out carefully as it is the raw data for final analysis. There are two types of data collected in data collection namely Primary Data and Secondary Data. Primary data is collected from spot speed survey, classified volume count survey and road geometry data by self-measurement of road stretch. Whenever secondary data is collected from the maps given by Road & Building Department of Kheda District.
Primary data collection
Road inventory, traffic volume count and spot speed study is carried out manually.
Classified volume count
Number of vehicles passing through a point or entering a stretch is considered in the analysis of roadway operations. Traffic volume can be counted by manual or video graphic techniques. Here manual traffic survey is carried out for 09:00 am to 7:00 pm with 15 minute time interval and volume of traffic is calculated using tally counter on mid-block section of Dakor to Umreth road.
Analysis of traffic volume data has been done and following results shows the composition if traffic on road and variation of traffic on road.

Figure: Traffic volume analysis for Dakor to Umreth
Figure: Traffic volume analysis for Umreth to Dakor
Spot speed study
Speed is one of the most important characteristic of traffic as measure of effectiveness of traffic system performance. Speed is highly sensitive to the interaction among vehicles in the stream. The spot speed study is carried out on Dakor to Umreth road. The average speed, time mean speed, space mean speed, standard deviation is calculated from spot speed data.

Spot speed study data analysis on Umreth to Dakor road

standard deviation

Space mean speed
(km/hr)

time mean speed
(km/hr)

median speed
(km/hr)

minimum speed
(km/hr)

maximum speed
(km/hr)

2-w

8.07

37.11

38.75

38.57

25.71

56.84

3-w

8.12

33.48

35.16

31.76

22.04

54

4-w

11.31

37.47

40.46

37.91

26.34

63.53

Bus

7.33

35.67

37.07

36.62

26.34

51.43

Truck

3.29

32.65

33.01

32.73

27.69

40.00

Multi Axle Truck

4.79

35.98

36.61

36.00

27.69

46.96

LCV

4.30

34.59

35.09

34.29

27.69

51.43

Spot speed study data on Dakor to Umreth road

standard deviation

Space mean speed
(km/hr)

time mean speed
(km/hr)

median speed
(km/hr)

minimum speed
(km/hr)

maximum speed
(km/hr)

2-w

4.65

38.23

37.70

37.96

30.86

60

3-w

5.83

33.86

34.85

33.75

24

46.96

4-w

7.29

41.47

42.97

41.54

23.48

60

Bus

5.73

34.57

35.49

34.29

23.48

54

Truck

5.14

35.34

36.05

34.84

27.69

49.05

Multi Axle Truck

3.68

36.97

37.32

37.31

30

46.96

LCV

3.10

37.80

38.05

37.96

30.86

45.00

Microprocessor Based Control of Traffic Lights

Abstract:
Due to suitable control measures & strategies which can be countered traffic congestion in urban road & freeway networks leads to degrades the network infrastructure & accordingly reduced throughput. Due to traffic congestion defining the main reasons for infrastructure deterioration is defined, overview of implemented & proposed control strategies is provided for these areas: urban road networks, freeway networks, & route guidance. The impact of various control actions & strategies are illustrated briefly & Selected application results, obtained from either simulation studies or field implementations. Microprocessor based control of traffic light are programmed for automatically run and change their alternatively light automatically. The microprocessor connected to different electronics devices i.e. traffic light controller, a video camera, an electronic display board, compression circuit an I/O interface, a traffic flow detector & connected to the central traffic control computer through the DSL. DSL stands for Digital Subscriber Loop. A traffic light control & information transmission device both are compromised by a microprocessor on the cross road. DLS is used for send the information about the public or news of the central traffic control computer. The control signals, traffic, public information or can go through the DLS to the microprocessor. The microprocessor can control the traffic light & display all the information on the electronic display board. An electronic display board is used for displaying the information that was send by DSP. The traffic flow data of the cross roads can be accessed by the traffic flow detector & the video camera & transmitted back to the central traffic control computer.

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Introduction
Old system works on trigger mechanism. But today many traffic light systems operate on the timing mechanism. Timing mechanism changes the light after a fixed intervals of time. In an intersection of roads the invention that is mainly used to control traffic lights relates to an intelligent traffic light control system. Traffic signal systems will need to address many issues in the next millennium, spanning a broad range of technical, social, & political boundaries. The presence or absence of vehicles within certain range is sensed by the system developed by setting the appropriate duration for the traffic signals to react accordingly. An intelligent traffic light system senses the presence or absence of vehicles & reacts accordingly due to that conditions. A manual input device, an enforced switching device & an intelligent detecting device the invention relates to an intelligent traffic light control system comprising a microprocessor, these three devices are responsible wherein the microprocessor is used for controlling traffic lights. The idea behind intelligent traffic systems is that drivers will not spend unnecessary time waiting for the traffic lights to change. The system to achieve a periodic switching the status of on/off of a traffic light is controlled through a microprocessor. An intelligent traffic system detects traffic in many different way. Trigger mechanism is responsible for older system that means older system are works on this mechanism. for inputting control parameters of traffic light to the microprocessor the manual input device is used, for carrying a preferentially direct operation the enforced switching device is use. The enforced switching device are also used for the direct control of traffic light. Current traffic systems react to motion to trigger the light changes. Once the infrared object detector picks up the presence of a car, a switch causes the lights to change. We need to understate & the function of traffic signals so that we can improve driving habits by controlling the speed in order to reduce the number of associated traffic accidents. To reduce the waiting time of each lane of the cars & also to maximize the total number of cars that can cross an intersection the Intelligent Traffic Signal Simulator is designed and developed. The control parameters cannot be automatically adjusted by the system according to traffic flows in each direction this is the shortcoming of prior technique. The more number of drivers who know about the operation of traffic signals, the less frustrated they are going to be while waiting for the lights to change. They have less frustration while waiting for traffic lights It means that the traffic control in an intersection of roads will be not in a best state at all times.
The Traffic Signal System Consists Of Three Important Parts.
The first part is the controller or we can say that the brain of the traffic system. The selection & timing of traffic movements in accordance to the varying demands of traffic signal that controls by a computer controls as registered to the controller unit by sensors.
The second part is the signal visualization or in another words it is signal face. Controlling traffic in a single direction & consist of one or more signal sections are provided by Signal faces which are part of a signal head. These usually comprise of solid red, yellow, & green lights.
The third part is the detector or sensor. Presence of vehicles is indicated by the sensor or detector. One of the technologies, which are used today, in the pavement at intersections wire loops are placed. Electrical inductance caused by a vehicle passing over or standing over the wire loop is change therefore they are activated
Their Demand
With the increase in urbanization & to operate our roadway systems with maximum efficiency traffic congestion comes a greater demand. New technology, such as traffic-responsive closed loop systems or adaptive traffic signal systems using advanced surveillance & traffic management centers, will become increasingly critical for city, county, & state, organizations to meet transportation needs.
Emergence Of Microprocessor-Based Traffic Signal Control
In early 1960s computers were introduced to traffic signal systems. The first computerized traffic signal control system was installed in Canada in 1963. Hardware & software standardization efforts were first initiated In 1970s, when microprocessors are common
, the developments progressed at a relatively modest pace. The philosophy that controllers would provide a basic set of features & standard connectors the National Electrical Manufacturers Association (NEMA) TS1 standard was based . The concept of the new platform was that traffic signal controllers should not be based on static technology (like the Model170 specification) but on widely used commercial standards, allowing new technology to be adopted rapidly
Technical Challenges
Traffic signal research has been conducted in two distinct areas: roadside equipment & analytical-type operations research. The research on roadside equipment has performed by the Government agencies & vendors virtually. Similarly, the research in analytical-type operations are performed virtually. The roadside equipment & analytical models although significant advancements have been made, neither area has been particularly closely coordinated with the other. Many of the following research issues fall outside the typical DOT, commercial, & university organizational structure, but they show considerable promise for improving the operation of traffic signal systems.
System Integration Research
Due to past research, government agencies & vendors in considered isolation have perfected systems that do an excellent job of meeting today’s needs, but do not provide the building blocks for cost-effectively implementing integrated & systems manufactured by a variety of vendors. It means that the system manufactured by the vendors do not get the basic building block from the government. Similarly, because many of the assumptions made by the universities developing the models do not reflect the technical limitations or traffic engineering conventions imposed by modern controllers many of the promising control algorithms proposed over the years have never been implemented.
Advanced Transportation Controller
Adaptive controller adjust time or re-time every 30 sec. A computer is used to control an operation by monitoring readings from sensors & sending control signals when necessary. The concept of an ATC was initiated in 1989. Caltrans prepared a report documenting some of the deficiencies of the Model 170 controller & recommended a 3U VME-based platform using OS-9 (12). The concept of the new platform was that traffic signal controllers should not be based on static technology (like the Model170 specification) but on widely used commercial standards, allowing new technology to be adopted rapidly. The initial specification developed by Caltrans was called the Model 2070. Ideally, new technology would be incorporated into the Model 2070 traffic signal controller at a rate similar to that observed in the desktop computing market. As interest in the standard development effort broadened, & more public agencies began participating, an ATC standard emerged that is even less dependent on the processor & operating system than the Model 2070. Process control means automatic control of an industrial process…

Characteristics of process control –
sensors are main part of the traffic light based on the microprocessor .It is a real-time operation – input from sensors is processed
It is an example of the use of feedback – if it is out of balance the sensor input is used to adjust the process & control signals are sent back almost immediately.
the timing of each part of the process and the computer usually controls the supply of materials
Some more sophisticated systems allow for ‘learning’ to take place. The computer ‘remembers’ how the best results were obtained & attempts to reproduce those results

Sensors
Sensors are the main part of any traffic signal system, yet are viewed by many as the weakest link in developing better traffic control systems. Sensing needs include so many detection train detection, nonferrous bicycle detection, emergency vehicle detection, transit vehicle detection, pedestrian detection, vehicle detection and queue estimation. Reliability must increase & costs decrease to facilitate widespread use not only must new sensing technology be developed. Furthermore, standards need to emerge for integrating these sensors into traffic signal systems. The standard practice for bringing any sensor information into a traffic signal controller is via discrete logic (contact open/contact closed), which is limiting & needs to improve.
Summary Of The Invention
In order to overcome above shortcomings of the prior technique, the invention provides an intelligent traffic light control system. The control system can automatically adjust the traffic light control parameters according to the changes of traffic flow in different directions, thereby increasing the traffic efficiency of intersection of roads & achieving a best control for traffic.
The technical solution of the invention is that: an intelligent traffic light control system comprises a microprocessor, a manual input device, an enforced switching device & an intelligent detecting device, wherein the microprocessor is used for controlling traffic lights, the manual input device is used for inputting control parameters of traffic light to the microprocessor, the enforced switching device is used for carrying out a preferentially direct operation, the intelligent detecting device includes one or more panoramic cameras & an intelligent controller, wherein the one or more panoramic cameras are used for capturing real-time traffic flow images of each direction, the intelligent is used for receiving the real-time traffic flow images of each direction through a video capture board, identifying vehicles on each lane of each road, identifying status of driving & stopping of each vehicle, counting the length of queue of vehicles in each lane from the status of driving & stopping of each vehicle & sending an instruction for modifying traffic light control parameters to the microprocessor according to a preset program. The microprocessor modifies the traffic light control parameters after receiving the instruction. Provided with an intelligent detecting device, this system can estimate the jamming condition of each road according to the length of queue of driving or stopping vehicles on each road, make a best control mode using a preset program by adjusting switching order & switching time of traffic lights to adapt to the actual traffic condition, thereby increasing traffic efficiency of an intersection of roads, reducing traffic jam of each road in each direction. That is beneficial to the normal traffic on roads, in particular to morning peak & evening peak of traffic, as the main flow directions of the mass & vehicles in morning peak & evening peak are different. Provided with one or more panoramic cameras, the intelligent detecting device can effectively capture images of traffic jam condition in each direction, thereby simplifying the device & ensuring the control effect at the same time. It is therefore a primary object of the invention to provide a traffic light control & information transmission device that applies the existing broadb& network to transmit data between the central traffic control computer & the microprocessors of the cross roads to avoid the installation of the cables & save the construction cost.
In order to achieve the objective set forth, a traffic light control & information transmission device in accordance with the present invention comprises a microprocessor on the cross road, the microprocessor further connects to a traffic light controller, an electronic display board, a video camera, a compression circuitry, an I/O interface, a traffic flow detector & connected to the central traffic control computer through the DSL (Digital Subscriber Loop). The control signals, traffic, public information or news of the central traffic control computer can go through the DSL to the microprocessor; the microprocessor can control the traffic light & display all the information on the electronic display board. The traffic flow data of the cross roads can be accessed by the traffic flow detector & the video camera & transmitted back to the central traffic control computer.
Application Of Traffic Models
Many modeling procedures & techniques have been tried over the years & have achieved varying levels of acceptance & use. These models can be classified as macroscopic or microscopic. Macroscopic models are based on average flow rates & average signal timings. They are particularly useful for signal system timing design software because they provide efficient procedures for formulating objective functions used in optimization logic. In the past decade, many of the macroscopic models have incorporated more detail to account for actuated signals & coordination between them. However, these macroscopic models only provide analytical estimates of average system performance & do not provide insight into the actual signal system operation, particularly during non steady-state conditions such as emergency preemption or timing plan transitions. Microscopic models are based on car-following theory & cycle-by-cycle signal times. These models have significant potential to evaluate & visualize alternative control concepts for traffic signal systems because they consider the car-following dynamics of traffic streams & they can model many of the characteristics of advanced systems such as coordinated actuated controllers. These microscopic simulation procedures can be used to analyze & tune coordinated-actuated systems directly, because they consider a majority of the parameters used in modern, coordinated-actuate signal systems. However, microscopic models.
Traffic Control Concepts
Traffic control concepts for isolated intersections basically fall into two basic categories:
1. Pre-Timed Signal Control
Under these conditions, the signal assigns right-of-way at an intersection according to a predetermined schedule. The sequence of right-of-way (phases), & the length of the time interval for each signal indication in the cycle is fixed, based on historic traffic patterns. No recognition is given to the current traffic demand on the intersection approaches unless detectors are used. The major elements of pre-timed control are fixed cycle length, fixed phase length, & number & sequence of phases
2. Traffic-Actuated Signal Control
Traffic-actuated control of isolated intersections attempts to adjust green time continuously, &, in some cases, the sequence of phasing. These adjustments occur in accordance with real-time measures of traffic demand obtained from vehicle detectors placed on one or more of the approaches to the intersection. The full range of actuated control capabilities depends on the type of equipment employed & the operational requirements.
Conclusion
An intelligent traffic light system had successfully been designed & developed. Increasing the number of sensors to detect the presence of vehicles can further enhance the design of the traffic light system. Another room of improvement is to have the infrared sensors replaced with an imaging system/camera system so that it has a wide range of detection capabilities, which can be enhanced & ventured into a perfect traffic system.
 
 

Road Traffic Accident

SECTION 1
Introduction

The first fatal road collision happened on the 25 of February, 1899, when Mr. Edwin Sewell was driving negligently and after he lost the control of his car and crashed, he and his passenger died from head injuries. Because of the breaking pressure of 20mph the wheels collapsed and this accident was considered to be the first fatal ever recorded, even with the low collection of data (Lavender, 2001).
The Collision Investigation Unit (CIU) (Appendix), is the department of the police force that has to examine the road traffic accident scenes. Collision investigators treat the scene of a road accident in the same way as other serious crimes. As much evidence as possible has to be collected from the collision scene, such as photographs, detailed diagrams, tyre marks and fixtures, in order to establish the cause of the accident and bring the investigation to an agreeable termination (Melling, 2007).

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Year on year, the work of the CIUs is becoming less and less. According to statistics of the Department For Transport (DFT), drivers in Britain are driving more safely year on year, because fatal accidents keep dropping year on year. In the year of 2005 the annual deaths from road traffic accidents were 3,201 in contrast of the year of 2004 that Britain had 3,233 deaths from road collisions (Clarke et. al., 2005).
Another statistic comes up to show that the amount of road accidents is reducing year on year. In the year of 2003, Britain’s annual amount of collisions was 13,908. In the next year 13,879 collisions happened and in the year of 2005, 13,388 road collisions happened. Via the statistics, people drive more safely every year (Clarke et. al., 2005).

This study is going to investigate the current surveying methods that are used in road traffic accidents in Britain by the CIUs of the police force, the types of equipment that being used, the reliability of the equipment and when surveying methods have to be used to survey a collision (The Legal Requirements).
After the inspection of all of the above, in the conclusion there will be a brief review of what this submission was about and the points of high importance will be highlighted.
SECTION 2
Accident Investigation
2.1 The history of mapping the accident scene
The accident investigation courses began for the first time in 1970 in London in order to train Collision Investigators. At first, this system was operating on a low budget and it was easy to teach police officers to use that method. About 1992 Collision Investigators began to use the surveying equipment in their investigations, because this equipment has many advantages. After the accident, distances and angles were obtained and used for the production of a representation model. Moreover, after the examination of the road accident scene, references and summaries had to follow to cover the incident completely (Lavender, 2001).
Despite the fact that today Collision Investigators are able to use devices such as Total Stations or Scanners, the main three steps are still being followed. Firstly, to locate the data such as tyre marks, secondly, to collect as much data as possible and last but not least, to examine the collected data (Devon and Cornwall, 2003).
2.2 General view of an accident:
When a traffic accident occurs, the most known procedure is being carried out. Police have to attend to the accident scene as soon as possible in order to examine it. Police are being informed about the accident by the people who were involved in the accident, or if it is a fatal accident, by a passerby or a witness. If it is reported to the police that it is a fatal collision, automatically the Paramedics and the nearest Fire Brigade are notified. If the Police Force arrives earlier to the accident scene than the Paramedics, the police have to wait for them to come and take care of the injured people at first. The Fire Brigade is involved in the collision scene only if the injured persons are trapped in their vehicles in order to set them free from the debris of their vehicles, or if a fire breaks out from the big impact to put it out quickly for safety reasons and to prevent the evidence from being destroyed (Collision Analysis, 2007).
At the same time that the Paramedics and the Fire Brigade are dealing with the accident, the police force is guiding the traffic. When the Paramedics have done their best and collected the wounded people from the accident, the CIU has a clear runway to do its best too. The Fire Brigade stays at the scene to cooperate with the Investigation Unit if it needs to. The CIU starts to collect any parts of the cars that had been disconnected and at the same time the surveying equipment is being used. The Unit also, takes photographs of the tyre marks and if it is necessary, overview pictures captured from a helicopter to be kept in the record of the accident. CIU is doing the process very analytic, because despite the regular cause of a road accident is exciting the speed limit, it can be founded out that the road was not appropriate or suitable for driving, or any other cause. After the collection of evidence from the accident scene, the vehicles are moved away by a towing vehicle, but the work of the Collision Investigators is not over yet. The work is carried on to the Police Traffic Department and after the examination of the data taken by the surveying equipment, a summary has to be produced so as the Investigators cover the collision totally. In addition to the summary, a 3-D (Dimensional) picture has to be constructed on a computer, to be kept in the records for future examination (Collision Analysis, 2007).
Additionally, sometimes it is possible that the summary of the road accident investigation can be produced in 2-D (Dimensional), because the 2-D scanned area also allows the evidence to be shown clearly. The representation of the road accident with 2-D results or 3-D results depends on the complexity of the road collision (Melling, 2007)
2.3 Legal requirements of an accident:
According to the police officer of the Collision Investigation Unit of Warwickshire Police Department, Robert Melling, every accident scene has to be surveyed and mapped unless the head officer of the Unit feels that the evidence is so obvious that the accident can be classified by just using photographs of the scene. The average attendances and surveys of the Warwickshire CIU to road accidents are approximately 150 investigations per year. In a few words, Collision Investigators examine an accident only if it is complicated, such as pile ups accidents or if the evidence of the scene is not comprehensible to sort out the collision. CIUs might use Total Stations, 3-D (Dimensional) Laser Scanners, or photographic cameras.
SECTION 3
Current Surveying Technologies
3.1 Total Stations:
This section will look at the use of a surveying device called Total Station, in the examination of a road accident. The Total Station (Appendix) is a device that is used in recent surveying and provides measuring distances and angles without using a tape measurement. Everything happens electronically. Total Station integrates an electronic theodolite, an Electronic Distance Measurement (EDM) mechanism and a tripod, (Fialovszky, 1991). This device is connected to an external computer and all the data that is captured from it, is transferred to the computer in order to produce a map of the surveyed area through the software. Mostly, Total Station is based on trigonometry and the actual positions X, Y and Z from the control point (Collins 1972).
Total Station is basically a telescope with a cross in the middle, to mark the target. By the time the target is locked, the angle of rotation and the angle of inclination have to be noted. This telescope offers a digital read out of the angles and it is more accurate and less exposed to faults. Moreover, it is important to add that the read-out is nonstop so the angles can be measured at anytime. The other part of the total station is the EDM (Electronic Distance Measurement), that calculates the distance between the device and its target. The EDM forwards an infrared ray and it is echoed back to the component and the component employs instancing dimensions to analyse the space by the ray (Burnside, 1982). Collision Investigators use a Total Station instead of a tape measurement in their investigation, because of its accuracy and because they are able to review the accident scene at any time, throughout the data that was processed to the external computer.
On the other hand, the Total Station device has some drawbacks such as when the line of the sight is affected from a reflective point the data collection could be erroneous. This can affect the accuracy when an object is between the device and the target, for instance trees. Moreover, the time of the day can affect the collection of the data of a survey, because the Total Station cannot be elaborated at night time (Whyte and Paul, 1982)
Additionally, there are many models available in the market by manufacturers such as Leica, Nikon and Pentax. Leica’s TPS 2000 is the latest series of Total Stations that has very high accuracy at about +/- 2mm and weighs approximately 6 Kg. Also, TPS 2000 sere has an automatic target tracking and a Remote Control System (RCS), (Leica, 2007). Nikon’s latest model is DTM-801 Series has the same accuracy as the previous product with +/- 2mm accuracy. The distance measurement with a single prism is 2400 meters and has a visible range of over 100 meters (Nikon, 2007). Pentax’s latest instrument is R326EX, from the series of R300X, which is compensated on the Dual Axis and the measuring range with a single prism, is 3400 meters. Its battery life is for 6 hours and weights about 5.5 Kg (Pentax, 2007).
3.2 3-D Laser Scanning Systems:
3-D Laser Scanners (Appendix) have been designed with the aim of creating a 3-Dimensional model from the collected data. The laser is being reflected off by any surface and returns back to the instrument, therefore the scanner saves thousands of points per second, which permits a gigantic amount of data to be collected in just a few moments. The axis of the scanner is the values 0, 0, 0 and these points are the distances and the horizontal and vertical angles from the scanner. The 3-D value is being provided to the user when the distances and the vertical and horizontal angles are measured and throughout a computer the user can see the results as they are being downloaded immediately (Rw, 2002)
This mechanism enables the user to scan every single object in a small timing. It is mainly completed from a high definition camera, a laser security system, a target projector, a laboratory calibration and a stand. This device is connected to a laptop computer by a FireWire port and consequently the FireWire port provides faster downloading speed on the external computer. The most important benefit that some 3-D Laser Scanners have is the ability to retain the data without requiring any daylight, because it has its own light provided by the laser and this makes it to work in the same way with the same results even though being day or night. Furthermore, another advantage of the 3-D Laser Scanners is the ability that some devices have to scan the object and download it colorful on the computer even if it is night. Collision Investigators are able to scan the accident scene even though the accident was caused day or night with the same accuracy and other details (VibroDynamics, 2004)
In contrast, there are some drawbacks about the 3-D Laser Scanner. First of all it is very expensive to buy and use and in order to survey and map one scene such as road accident, it is better to do it with a regular product that provides almost the same results as the 3-D Laser Scanner. The second disadvantage is that the user needs many hours of training to learn how to use it properly (Schmiedl, 2007)
3-D Laser Scanners are created by companies like Riegl, Trimble, Leica and more. Riegl’s top product is the LMS-Z420i which it can scan 11000 points per second with fluctuated mirror and 8000 points at a high scanning rate with a rotating mirror. This device is protected from dust and it is water proof, because of a protection glass that is installed on the mechanism. The vertical scanning rate is 80o and the horizontal is 360o, with a distance maximum range of 1000 meters (Riegl, 2007). Trimble’s latest instrument is the Trimble GX 3D Scanner and it uses high-speed laser and video to capture vast amounts of points and data. GX 3D Scanner can collect 5000 point per second and its accuracy is +/-2mm, but that depends on the surface type and the distance. Also, it weighs 13 Kg and it cannot be operated under -20oC and over 50oC (Trimble, 2006). Leica’s HDS4500 (High Definition Survey) is also a high-quality product, because it has a scanning rate from 100,000 up to 500,000 points per second and it is able to scan the horizontal angle at 360o and the vertical angle at 310o. It weighs 26 Kg and it is the most expensive mechanism from all the mentioned above. It costs about £95,000 (Leica, 2007).
3.3 Photogrammetric Systems:
Photogrammetry is the science that obtains reliable measurements from photographs. A digital photogrammetric system can be defined as hardware and software device that produces photographs from digital imagery by using manual or automatic methods. The photogrammetric instrument (Appendix) that is famous and is being used not only from amateurs users, but also from professionals, is the 35mm device. Furthermore, metric and non-metric devices are the main types of cameras, but only the metric cameras can be used for purposes such as accident investigation. (Graham and Koh, 2002).
According to Meyer and Grumstrup (1978), there is a technique for making distance measuring from a single vertical photograph. In order to make a distance measurement from a vertical photograph, the real distance between two points on the picture, has to be known. For instance, a bridge that is being shown on the vertical photograph has real length 500 meters and it covers 0.5cm on the paper, the scale will be 1:1000 for the photograph and all the objects that are printed can be measured and the real dimensions can be calculated throughout some mathematic equations.
Companies for example Canon, Fuji and Nikon are operating the production of these devices and their objective is to make the newer product better that the last, so cameras are developed step by step. Canon’s most recent camera is the EOS 400D, which it offers a high definition analysis in order to capture more details of one object. Basically, Canon EOS 400D has 10.1 megapixels, the zooming system is manually set and it can reach a closer look of 5 times more than the normal view of an object (Canon, 2007). Fuji has a better product than Canon, the Fuji FinePix S5, with 12.3 megapixels, plus, if it is switched to the setting of high continues shooting, it is able to captures up to 3 frames per second and it has object detection technology, which allows the camera to detect one object automatically (Fuji, 2007). Nikon’s camera D200 has 10.2 megapixels and it can capture 5 frames per second. In addition to the D200 model, it has a USB 2.0 (Universal Serial Bus) connection high-speed and it costs approximately £1280 the entire device (Nikon.com, 2007).
Collision Investigation Units are using photographic equipment that is classified in the same category of the products mentioned above, because that photogrammetric technology has the latest improvements than the previous models and CIUs have to examine all the details that they are able to collect so the results of an investigation would be reliable as possible. The better the resolution the camera has, a higher quality image can be captured, but the regular camera resolution for surveys like Collision Investigations, has to be at least 5 megapixels or a little better. Moreover, a downward picture (Aerial Photography) of an area can be captured to make the mapping purposes simpler. This happens if the use of a helicopter is available (Melling, 2007).
SECTION 4
Conclusion
4.1 Review:
While a collision happens, onlookers think that the collision has been covered and the job of the CIUs, Paramedics and Fire Brigade is done by the time the crashed vehicles are collected by a towing vehicle. All that happens in the collision scene was the collection of the evidence and data. Collision Investigators have to examine the data and after the analysis, the results are produced and recorded.
By the time surveying methods are being involved in road collisions, surveys are being completed rapidly in comparison with the traditional style of examining an accident scene. As it was mentioned before, by the year 1992 CIUs have improved the procedure of surveying the road accident by engaging surveying equipment in their work and throughout this decision the examination of the accident is not only quicker than it was, but also it is more accurate and reliable.
4.2 Synopsis:
Briefly, the aims of this project were successfully achieved, by explaining the involvement of the CIUs in road collisions and the importance of the surveying equipment was also evaluated. Total Stations, 3D Laser Scanning Systems and Photogrammetric Systems have been deeply analyzed from the angle of surveying a road traffic accident. These technologies are effective because by using them, accuracy, reliability and quick procedure of any investigation can be brought to the surface.
References:
Burnside, C., D., (1982), ‘Electromagnetic Distance Measurement’, 2nd Edition, Granada Publishing
Canon, (2007), Canon EOS 400D, ‘http://www.canon-europe.com/For_Home/Product_ Finder/Cameras/Digital_SLR/EOS_400D/index.asp’, Last accessed: 28 Apr. 2007
Clarke, D., D., Ward, P., Bartle, C., Truman, W., (2005), ‘An In-depth Study of Work-related Road Traffic Accidents’, London, Department for Transport.
Collins, S., P., (1972), ‘A Handbook of Accurate Surveying Methods’, Pitman and Sons Publishing
Collision Analysis (March 2007), ‘Professional Road Accident Investigation’, ‘http://www.c ollisionanalysis.co.uk/Index.html’, Last accessed: 05 May 2007
Devon and Cornwall Constabulary, ‘Collision Investigation Unit’, ‘http://www.devon-cornwall.police.uk/v3/roadsafe/about/collision.htm’, Last accessed: 03 May 2007
Fialovszky, L., (1991), ‘Surveying Instruments and their Operational Principles’, Elsevier Science Publishers
Fujifilm Corporation, (2007) Fuji FinePix S5 Pro., ‘http://www.fujifilm.com/products/di gital/lineup/s5pro/index.html’, Last accessed: 27 Apr. 2007
Graham, R., Koh, A., (2002), ‘Digital Aerial Survey: Theory and Practice’, Whittles Publishing Services
Lavender, B., (August 2001), ‘Accident Investigation’, ‘http://www.driving.co.uk/3a3.html’, Last accessed: 16 Apr. 2007
Lavender, B., (August 2001), ‘History of BSM’, ‘http://www.driving.co.uk/5a1.html’, Last accessed: 17 Apr. 2007
Leica Geosystems, Leica HDS4500 ‘The ultra high-speed scanner’, ‘http://www.leica-ge osystems.com/corporate/en/ndef/lgs_5572.htm’, Last accessed 29 Apr. 2007
Leica Geosystems, System TPS 2000, ‘http://www.leica-geosystems.com/corporate/en/pr oducts/total_stations/lgs_5253.htm’, Last accessed 25 Apr. 2007
Melling, R., (15/02/2007), Lecture to Newcastle University Upon Tyne, Geomatics Department, ‘A Synopsis of the Traffic Investigation Unit and its work’
Meyer, M., Grumstrup, P., (1978), ‘Operating Manual for the Montana 35mm Aerial Photography System (2nd Revision)’, University of Minnesota.
Nikon Corporation, (2007) Nikon D200, ‘http://nikonimaging.com/global/products/digit alcamera/slr/d200/index.htm’, Last accessed: 25 Apr. 2007
Nikon Trimble co., LTD. 2007, Field Station DTM-801 Series, ‘http://www.ave.nikon.co. jp/survey-e/dtm-801/spec.htm’ Last accessed: 25 Apr. 2007
Pentax U.K. LTD, (2006) Total Station R326EX, ‘http://www.pentax.co.uk/_uk/surv eying/products/index.php?surveying&products&id=1&artikel=8′, Last accessed: 27 Apr. 2007
Riegl Laser Measurement Systems 28/02/2007, Riegl LMS-Z420i 3D Laser Scanner, ‘http://www.riegl.com/terrestrial_scanners/terrestrial_scanner_overview_/terr_scanner_menu_all.htm’, Last accessed: 30 Apr. 2007
Rw, (Aug. 2002), ‘3-D Laser Scanner’, ‘http://ce-gw343.ncl.ac.uk/in_action/ia_scann ing.asp’, Last accessed: 27 Apr. 2007
Schmiedl, E., (2007), ‘3-D Laser Scanner’, ‘www.iop.org/EJ/article/0967-3334/22/3/316/p m1316.pdf’, Last accessed: 02 May 2007
Trimble, (2006) ‘Trimble GX 3D Scanner’, ‘http://trl.trimble.com/docushare/dsweb/Get/ Document-261939/022543-148A_GX_3Dscanner_DS_0206_lr.pdf’, Last accessed: 18 May 2007
VibroDynamics Inc. Copyright (2004), ‘3-D Laser Scanning’, ‘http://www.vibrodynamics. net/3D_Laser_Scanning.htm’, Last accessed: 02 May 2007
Whyte, S., W., Paul, R., E., (1982), ‘Site Surveying and leveling’, 2nd Edition, Butterworth & Co Ltd
 

Effects of Cargo Ship Traffic on Underwater Decibel Levels

IB internal assessment: Analyzing the Effects of Cargo Ship Traffic on Underwater Decibel Levels

 

Identifying the context

Most humans rely on the sense of sight as their primary means of measuring distance. In contrast, cetaceans use biosonar, or echolocation. Echolocation is the process by which cetaceans emit noises to create reflected sound, or echoes, off targets. Cetaceans typically exert low frequency waves (30-60 kHZ), as in optimal conditions, such waves travel great distances[1]. These echoes inform cetaceans of target locations, if they are moving, and the size of the objects[2]. It is believed primal whales evolved 50 million years ago, along with this technique[3].

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Humans have been identified as the point source of the drastic reduction of distance that cetaceans can rely on, when using echolocation. Along with the industrial revolution and other momentous periods of expansion and development, the soundscape of the oceans changed for the worse. Large cargo ships can significantly reduce the maximum echolocation range due to their sizable engines and forceful propellers pushing through the water[4]. Not only do they create noise while treading through the water, but the bubbles produced by the sloshing wake pop and increase the decibel level of the soundscape. The low frequency waves originating from the ships’ engines and propellers travel incredibly well through water, that they may raise the noise levels of the ocean by a large amount[5]. Although the exact distance a whale needs to communicate is presently unknown, this increasingly noisy soundscape could affect their ability to find a mate, feed, navigate, and keep track of their young[6]. In modern times with the increased noise levels, when a whale signals they may only be witnessed from a short distance due to human interference through shipping.

The National Oceanic and Atmospheric Administration (NOAA) is working towards the goal of eliminating noise, caused by human activity, alongside independent non-profits, such as The Oceanic Preservation Society. Strict regulations have been instated, and a timeline has been laid out for the transition of the soundscape to its state prior to the industrial revolution.

Research Question

To what extent have humans impacted the noise levels (measured in decibels) of the Atlantic Ocean, Gulf of Mexico, and Gulf of California through shipping activity when comparing 2002 to 2004, as measured through the use of hydrophones?

Justification

Scientists have noticed a colossal difference in animals’ habits though the past century due to the soundscape in the oceans changing[7]. This change has been attributed, in part, to the more than 17,000 cargo ships that sail daily[8]. Low frequency waves from cetaceans that could have been heard hundreds of miles away are now being drowned out by ships, only to be heard as little as less than a mile away[9]. It is known that ships affect the marine ecosystems by polluting sound, but it is unclear to what extent.

Variables

Independent:

Dependent:

Sound measured from hydrophones (decibels)

Controlled:

Materials

Computer (to analyze the data)

Procedure

Part 1

Obtain maps of decibel levels of the Atlantic Ocean, Gulf of Mexico, and Gulf of California from 2002 and 2004.

Figure 1

Using an Apple computer, use a built-in application, Digital Color Meter, for the observation of values including red green and blue on the screen (Figure 1).

NOTE: This can be done using other computers, yet it was not used, nor has been tested.

Pick twenty points at random from the map. 

Number the points on the map 1 through 20.

Match the value of a point to the red, green, and blue on the scale, as seen in figure 1.

Record the point number and the decibel figure on the data table.

Repeat steps five and six for both years.

Part 2

Obtain shipping route maps from 2002 and 2004 of The Atlantic Ocean, Gulf of Mexico, and Gulf of California.

Use the legends on the shipping maps to gather data about the points on decibel map. The data should be classified a certain degree based on the level of shipping happening in the area.

Relate the qualitative data to quantitative data using the following key, Very low – 1, Low – 2, Mid – 3, High – 4, Very High – 5.

Add the shipping route data to the already existing table that holds the soundscape data from part 1.

Repeat steps 2 – 4 for both 2002 and 2004.

Ethical Considerations

Considerations of used taken data

Disturbances to the ecosystems that were tested

Addition of pollutants in the air from the boat that took the original data.

Consideration of the energy used

Additional sound added to the ecosystems being taken

Analyzation of the data

Consideration of the energy being used in the process of analyzing the data

Results

Figure 2 (Sound in the sea from 2002) [10]

Figure 3 (Sound in the sea from 2004) [11]

Figure 4 (Shipping routes from 2002)[12]

Figure 5 (Shipping routes from 2004) [13]

 

 

Table 1 Data from 2002

Point on the Map

Decibel level (dB)

Level of Shipping Happening in the Area

(qualitative)

Level of shipping

(quantitative)

1

95

High

4

2

66

Very low

1

3

105

Very high

5

4

57

Very low

1

5

97

High

4

6

93

Mid

3

7

92

Mid

3

8

100

Very High

5

9

74

Very low

1

10

77

Low

2

11

79

Low

2

12

84

Low

2

13

95

High

4

14

104

Very high

5

15

62

Very low

1

16

95

High

4

17

61

Very low

1

18

91

High

4

19

94

High

4

20

97

Very high

5

 

Table 2 Data from 2004

Point on the Map

Decibel level (dB)

Level of Shipping Happening in the Area

(qualitative)

Level of shipping

(quantitative)

1

112

Very high

5

2

95

Low

2

3

113

Very high

5

4

98

Very low

1

5

111

Very high

5

6

97

Mid

3

7

100

High

4

8

98

Mid

3

9

103

Very low

1

10

100

Mid

3

11

112

High

4

12

99

Very low

1

13

112

Very high

5

14

114

Very high

5

15

99

Very low

1

16

106

Very high

5

17

113

Very high

5

18

110

High

4

19

94

Mid

3

20

93

Mid

3

 

Processed data

The quantitative data in table one and two were derived from the qualitative data, using the following rules.

Very low – 1

Low – 2

Mid – 3

High – 4

Very High – 5

 

 

 

 

 

Mean

The mean was taken to provide a concise set of numbers to be referenced to aid in the conclusion. The mean was used for the decibel levels for both years along with the levels of shipping for both years. 

Equation for taking the mean:

m = (x1 + x2 + x3 + … + xy) / n

m = mean

y = term number

n = number of terms

x = term

Mean equations for both years of decibel levels:

Mean decibel levels from 2002

m = (95 + 66 + 105+ 57 + 97 + 93 + 92 + 100 + 74 + 77 + 79 + 84 + 95 + 104 + 62 + 95 + 61 + 91 + 94 + 97)

m = 1718 / 20

m = 85.9 dB

Mean decibel levels from 2004

m = (112 + 95 + 113 + 98 + 111 + 97 + 100 + 98 + 103 + 100 + 112 + 99 + 112 + 114 + 99 + 106 + 113 + 110 + 94 + 93) 

m = 2064 / 20

m = 103.95 dB

Table 3 Mean Decibel level calculations for both years

Year

Mean decibel levels (dB)

2002

85.9

2004

103.95

 

Graph 1 Mean decibel level calculation for the two years

Table 4 Mean levels of shipping for the two years

Year

Mean level of shipping

2002

3.05

2004

3.4

 

Graph 2 Mean levels of shipping of 2002 and 2004

Standard deviation

Equation for standard deviation

 

xi = individual values

x̅ = is the mean/expected value

n = the total number of samples

Standard deviation was taken to allow for the understanding of how close the data was to the midpoint.

Table 5 Standard deviation calculations of the mean decibel value

Year

Standard deviation

2002


14.97

2004


6.99

 

Table 6 Standard deviation calculations of the shipping levels (quantitative)

Year

Standard deviation

2002


1.53

2004


1.54

 

Graph 3 Corresponding severity level vs. Decibels for 2002

Graph 4 Corresponding severity level vs. Decibels for 2004

 

 

Conclusion

The data collected exhibits an answer to the question – to what extent have humans impacted the noise levels (measured in decibels) of the Atlantic Ocean, Gulf of Mexico, and Gulf of California through shipping activity when comparing 2002 to 2004, as measured through the use of hydrophones?

Citing table 5, the standard deviation of the mean decibel levels for 2002 was found to be

14.97 and 2004 to be

6.99. The highest decibel levels recorded in 2004 were along the west coast of Europe and Africa. In addition, the decibel levels of the west coast of Mexico and east coast of Canada aggressively grew between the two years. The least affected areas of the two years include point 2 on the map (Gulf of Mexico), and point 19 (off the east coast of Africa).

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When graphing the quantitative value for the level of shipping against the decibel level for both years, graphs 3 and 4, a pattern can be observed, with few exceptions. As decibel levels increase, so does the levels of shipping. The trend can also be observed by looking at the raw data. From 2002 to 2004, the mean level of shipping increased by 0.35, as did the mean sound level in the Atlantic Ocean, Gulf of Mexico, and Gulf of California, increasing by approximately 18.05 decibels, as seen in table 3. Referencing data tables 1 and 2 alongside graph 2, from 2002 to 2004, traffic increased significantly. Thus, showing that as one variable increases (shipping), so does the other (decibel levels).

Discussion

The span of the two years examined had a large effect on the soundscape in the Atlantic Ocean. This can be attributed to the increase in shipping off the coasts of North America, South America, Africa, and Europe. The increase of ship traffic at sea has subsequently led to an increase in underwater noise levels. Shown by graphs 3 and 4, there is direct correlation, with scarce outliers.

A possible reason for the increase may be more frequent transportation of supplies around the world. During the span of 1980 to 2018, container ship capacity grew, starting at about 11 million metric tons to around 253 million metric tons[14], while the number of ships at sea also grew[15]. To accommodate the increase in shipping, logically new ports would have opened, in addition to the size of many ports increasing between the two years. Predicting for the future, based on the current trend, the volume being shipped across the Atlantic Ocean, Gulf of Mexico, and Gulf of California will increase. Consequently, the number of ships will also increase, leading to the creation of an even louder soundscape.

With underwater noise levels increasing, cetaceans trying to find mates, feed, navigate, and keep track of their young are at a disadvantage. A large concern is that even a slender increase in decibels may make these practices more difficult than they already are[16]. Comparing graphs 3 and 4 indicates that cetaceans that would have communicated in 2002, had a different experience than their counterparts in 2004 due to the decreased distance that their communications could reach as a result of human interference.

 

Evaluation

The original concept of analyzing soundscape maps of the Tasman Sea was limited by the absence of soundscape maps available to the general public. The absence is predicted to be filled as soundscape maps continue to be constructed and distributed. Thus, a wider selection will eventually be available allowing for the analyzation of more remote parts of the world.

A clear and resourceful approach to analyzing data was taken, allowing for a precise reading of maps to be recorded in the data tables. The reinforcement of rational data allowed for a clearer answer to the research question. Assigning severity levels was challenging when attempting to quantify the data of the shipping route maps. Various other methods of quantifying data could lead to an improved set of data points of which conclusions could be extracted. Another challenge arose when picking points on the soundscape maps to analyze.

Modifications for the future

Measuring the soundscape at more points to try and achieve a clearer understanding. Instead of randomly picking points, other methods could be utilized in hopes of displaying more trends, such as solely testing on the coasts of continents. Looking into cetacean migration patterns in the tested areas of the ocean to allow for a more in-depth answer to the research question. This could be completed by using two maps of the soundscape of a specific year, but in different seasons, alongside two maps of shipping routes of the same year in different seasons to try and account for the migration patterns.

Solutions

NOAA is leading the way in setting up and maintaining a network of acoustic monitoring devices that would set a strong foundation for any actions going forward[17]. The knowledge and understanding of the severity of past human actions would allow us to move forward with a more informed view of the consequences that follow. These technologies would aid in continuous monitoring of the underwater soundscapes with greater certainty and higher efficiency. As mentioned in the Evaluation, the sparse number of soundscape maps available to the public hinder the analyzing that third parties could accomplish. The increased production would allow for more groups to measure soundscapes. Yet, with a larger production of hydrophones also comes the addition of harmful chemicals in the air (leading to acid deposition), the creation of sound pollution from transporting said materials, use of energy throughout the whole process, and further environmental impacts. The endeavor of engineering noise-reduction technologies that would be affixed to ships is underway[18]. The addition of noise reducing technologies on ships would assist in the lowering of the soundscape decibel levels, while still allowing humans to ship goods across vast bodies of water.

Going further with research

How might the soundscape be different at varying depths? How do local communities interact with the increase of decibels? How quickly might areas recover from the withdrawal of the high levels of decibels? Have cetaceans evolved new approaches to combat the rising decibel levels? Are cetaceans migrating to different parts of the world as a result of the rising decibel levels? How do shipping levels vary from season to season?

 

Word count: Approx. 2100 (not including the bibliography nor footnotes, nor data)

 

References

 

Au, Whitlow W. L., and James A. Simmons. “Echolocation in Dolphins and Bats.” Physics Today, American Institute of Physics, 1 Sept. 2007, physicstoday.scitation.org/doi/10.1063/1.2784683.

“The Evolution of Whales.” Reproductive Isolation, evolution.berkeley.edu/evolibrary/article/evograms_03.

US Department of Commerce, and National Oceanic and Atmospheric Administration. “Listen Up: What You Need to Know About Ocean Noise.” NOAA’s National Ocean Service, 29 Aug. 2018, oceanservice.noaa.gov/podcast/aug18/nop18-ocean-noise.html.

National Marine Mammal Laboratory. “National Marine Mammal Laboratory.” Marine Mammal Education Web, NOAA, 21 Aug. 2006, www.afsc.noaa.gov/nmml/education/cetaceans/cetaceaechol.php.

“Sound Field Data Availability.” Roadmap, cetsound.noaa.gov/sound_data.

“Phase 2 — NOAA’s Ocean Noise Strategy.” Cetacean & Sound Mapping, NOAA, cetsound.noaa.gov/ons.

Raymond Fischer and Kurt Yankaskas. Noise Control on Ships – Enabling Technologies. apps.dtic.mil/dtic/tr/fulltext/u2/a553403.pdf.

IoT Traffic Prediction Using Multi-step Ahead Prediction with Neural Network

IoT traffic prediction using multi-step ahead prediction with neural network

Abstract-The Internet of Things (IoT) is basically a network of interconnected devices, like sensors and smart devices, that have processing, sensing, and communication capabilities and pass on the information to each other and a supreme console via the internet. Network traffic prediction is an operational and management function that is critical for any data network so, it has a significant role for today’s increasingly complex and diverse networks. Also, the network traffic prediction is more important for the IoT networks given the number of connected elements and the real-time nature of many connections. The artificial neural network (ANN) has been successfully applied to traffic prediction. In this paper, we perform the IoT traffic time series prediction using a multistep ahead prediction with Time Series NARX Feedback Neural Networks. The estimation error of a prediction approach has been evaluated using the performance functions MSE, SSE, and MAE, besides, another measure of prediction accuracy the mean absolute percent of error (MAPE).

 

Keywords: Prediction; IoT; Traffic; Artificial neural networks; AI

Introduction

IoT device is any kind of device that has processing, sensing, and communication capabilities. It’s composed of billions of these devices that connected to the Internet and forming dynamically changing ad-hoc connections among them in any possible communication pattern. The range includes devices of any conceivable size, functionality, and applicability.

The Internet of Things (IoT) is one of the priorities of the development of the info-communication system, which construction concept is reflected in the recommendation ITU-T E.800 [1]. The development of IoT is an extremely important step, as it affects almost all areas of human activity. The penetration of the Internet of Things will contribute to the availability of more and more information, the growth of its analysis capabilities, the formation of decisions and actions based on its results.

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Network traffic prediction [2-4] is one of the main application areas of artificial intelligent (AI) to data networking. Traffic volume prediction can be defined as the forecasting of incoming and outgoing bytes-count at different connections levels in the network hierarchy (device, link, routing…). Traffic prediction is a critical element in network operations and management: congestion control, routing, resource allocation and management of service level agreements (SLA), among many other network responsibilities and functions.

According to [5-7], the traffic generated by the Internet of Thing devices can be divided into three characteristic types: deterministic – produced by devices operating on a fixed schedule; deterministic technological – necessary to maintain the functioning of the system; mediated i.e. generated as a reaction to some external events. The traffic generated by the Internet of Things devices can be served together with the traffic of other communication services, for example, base stations, wireless access points, and other network nodes.

In this work we present the multistep ahead prediction with Time Series NARX Feedback Neural Networks for IoT traffic we predict the packet loss rate with time series prediction we use make the prediction in two cases when the number of packets sent 2 pckts/s and 10 pckts/s. The estimation error of a prediction approach is evaluated using MSE, SSE, MAE and MAPE.   Table.1 shows the list of used abbreviations in paper.

The outline of this paper is as follows: section (1) Introduction; section (2) discusses Prediction using neural networks; section (3) discusses recurrent neural networks; section (4) discusses Multi-Step Prediction; section (5) discusses IoT model simulation; section (6) gives our experimental result; section (7) conclusion.

Table. 1 List of abbreviation

MSE

Mean square error

MAE

Mean absolute of error

SSE

Sum square of error

MAPE

Mean absolute percent of error

RNN

Recurrent Neural Network

ANN

Artificial Neural Network

MLP

Multilayer perceptron

NARXNET

Nonlinear auto-associative neural  network with external input

SLA

service level agreements

IoT

The Internet of Things

AI

The artificial intelligent

Prediction using neural networks

Artificial neural networks (ANNs) [8-9] can be applied for the prediction with various levels of success. The advantage of ANNs includes automatic learning only from the measured data dependencies without any need to add more information (such as a kind of dependency like with the regression), in addition they have the ability to learn by examples only and after their learning is finished, they can catch hidden and strongly non-linear dependencies, even when there is significant noise in the training set. 

ANN is trained using the historical data with the hope of discovering hidden dependencies and that it will be able to use them for prediction of the future. In other words, the neural network is not represented with an explicitly given model. Neural network has been described as more a black box that can learn something.

It is possible to predict [10] several types of data with time series. the time series displays the development of value in time and this value can be influenced by other factors than time. Time series represents a discrete history of value and from a continuous function, it can be acquired by sampling.

3.       Recurrent Neural Networks(RNN)

Today’s recurrent neural networks (RNNs) have been proving themselves as powerful predictive engines. It has been successfully applied to time series prediction. In RNN, the temporal relationship of the time series is explicitly modeled using feedback connections to the internal nodes (known as hidden units). Recurrent means the output at the current time step becomes the input to the next time step Fig. 1 shows overview for RNN architecture. At each element of the sequence, the model considers not just the current input, but what it remembers about the preceding elements [11].

An RNN model is trained by presenting the past values of the time series to the input layer and The weights of the network are then adjusted based on the error between the true output and the output predicted by the network until the algorithm converges. Before the network is trained, the user must specify the number of hidden units in the network and the stopping criteria of the learning algorithm.

 time series problem, predicting the future values of a time series y(t) from past values of that time series and past values of a second-time series x(t). This kind of prediction is called nonlinear autoregressive with exogenous (external) input, or NARX Network (narxnet, closed loop), and can be written as follows [12]:

yt=f(yt–1,..,yt–d, xt–1,..,t–d)
     (1)
This model could be used to predict future values of a stock or bond, based on such economic variables as unemployment rates, wireless traffic variables, etc. It could also be used for system identification, where the models are developed to represent dynamic systems, such as chemical processes, manufacturing systems, robotics, aerospace vehicles, etc.

 

Fig.1 Recurrent Neural Networks

Multi-step Prediction

Multi-step prediction predicts the future values of a time series in a step by step manner. We first predict xt+1 using the previous p values, xt+1-p,…, xt-1, xt we then predict xt+2 based on its previous p values, which includes the predicted value for xt+1. The procedure is repeated until the last value, xt+h, has been estimated. In this approach, it is sufficient to construct a single model for making the prediction [11].

IoT model simulation

the model shown in Figure 1 was chosen. The model consists of an IoT traffic generator that simulates the operation of one or a group of IoT devices, Traffic generator of traditional communication services and TI traffic, designated as H2H + TI (H2H – Human to Human, TI – tactile Internet). The produced incoming traffic streams to communication node, the model of which is presented as queuing system with Combined Service Discipline (with delay-basis and failure-basis system). The average service time of a packet (message) is equal to ̅.

The Internet of things traffic arrival rate is denoted by
IoT
, H2H traffic –
h2h
, aggregated stream
=h2h+IoT
. With probability p, a packet arrives at the input of the system where all positions in the queue are occupied and get a service denial (losses occur). The aggregated traffic stream at the system output has a total intensity of λ. The properties of the aggregated traffic stream at the system input are determined by the properties of both streams, therefore, in general, it differs from the properties of both traditional traffic and Internet of Things traffic.

To build the IoT model, the Anylogic simulation system was chosen, which allows creating discrete event simulation models.

To simulate a self-similar stream, a generator of a sequence of independent events was used, the time intervals between which are random and have a Pareto distribution.

Fig.2 Service model of aggregated traffic

Simulation results

In this work we perform IoT traffic prediction approaches using multistep ahead prediction with NARX neural network.   The estimation of error prediction was evaluated using the performance functions MSE, MAE, SSE and another measure is the mean absolute percent of error (MAPE).

Input-output time series problems depend on the prediction of the next value of one time-series given another time-series. The past values of both series (for best accuracy), or only one of the series (for a simpler system) may be used to predict the target series.

 The dataset can be used to demonstrate how a neural network can be trained to make predictions. The datasets are obtained from IoT traffic generator the IoT model was simulated using Anylogic simulator, after collecting and preparing the dataset, it was split randomly into 75%, 15% and 15% for training, validation testing, respectively. The feedback neural network was implemented to predict the performance accuracy of IoT traffic.

Table.2 shows the prediction accuracy for IoT packet loss rate using above mentioned performance functions and the another measure of performance accuracy MAPE.

Table. 2 the accuracy measure for the predicted model validation

Early Predict Performance

No of packets/s

MSE

SSE

MAE

MAPE

2 pckts/s

5.8208e-06

5.2387e-05

0.0015

00.18%

10 pckts/s

1.1145e-05

1.0030e-04

0.0015

6.18%

Table.2 displays the performance prediction of IoT traffic in case of the number of packets 2 pckts/s and 10 pckts/s in order to estimate the error of prediction we use the traditional performance functions MSE, MAE, SSE and another measure for performance accuracy MAPE.

From the tabulate results, the performance predicted based on the MSE performance function has the best performance in the case of 2 pckts/s and 10 pckts/s in comparison to its peers. Also the SSE performance has performance which is approximately equal to MSE performance functions. While performance of prediction using MAE performance function is worse than MSE and SSE.

On other hand, the MAPE has the best prediction accuracy in case of number of packet 2 pckts/s with percent 0.18% while in case of 10 pckts/s it has the least prediction accuracy with percent 6.18%.

Fig. 3 shows two curves the first curve observe the multistep ahead prediction for IoT traffic with time and the prediction time 12 which with the aim of verifying the ability of the ANN in predicting for IoT traffic load in the cases of number of packet sent 2 pckts/s. it illustrates the result, where it is clearly seen that, the packet loss rate with time for the observed and predicted models we notice from that the predicted packet loss rate increase starting from time 1 until time 4 then decrease until the time 10 which give the best prediction accuracy.

The second curve shows plot of the estimated error (difference between desired output and predicted output) of prediction with time

Fig. 3 The response of output element for time series in case of number of packets 2 pckts/s.

Fig. 4 shows two curves the first curve observe the multistep ahead prediction for IoT traffic with time and the prediction time 12 which with the aim of verifying the ability of the ANN in predicting for IoT traffic load in the cases of number of packet sent 10 pckts/s. it illustrates the result, where it is clearly seen that, the packet loss rate with time for the observed and predicted models we notice from that the predicted packet loss rate increase starting from time 1 until time 4 then little bit decrease until time 6   become constant until the time 10 which give the best prediction accuracy.

The second curve shows plot of the estimated error of prediction with time

Fig. 4 The response of output element for time series in case of number of packets 10 pckts/s.

Conclusion

In this paper, we proposed the ANN to predict the IoT traffic, we proposed the prediction approach multistep ahead prediction time series with feedback neural network for prediction IoT packet loss in order to promote IoT traffic prediction accuracy. The prediction accuracy of neural network learning process has been estimated in terms MSE, MAE and SSE in addition, another measure for accuracy is MAPE. Intensive analysis and simulation results show that the MSE performance function has the best prediction accuracy in comparison to its peers and MAPE has the best prediction accuracy in case of number of packets 2pckts/s.

References

[1]      ITU-T, ―E.800: Terms and definitions related to quality of service and network performance including dependability, 2008.

[2]      Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, and Oscar M. Caicedo,” A comprehensive survey on machine learning for networking: evolution, applications and research opportunities” Journal of Internet Services and Applications, https://doi.org/10.1186/s13174-018-0087-2, Jun 2018.

[3]      M.S. Mahdavinejad, et al., Machine learning for internet of things dataanalysis: A survey, Digit. Commun. Netw. 4 (3), pp. 161–175, 2018. https://doi.org/10.1016/j.dcan.2017.10.002.

[4]      Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas “Neural network architecture based on gradient boosting for IoT traffic prediction”, Elsevier, Vol. 100, Nov 2019, pp. 656-673, https://doi.org/10.1016/j.future.2019.05.060.

[5]      A. Muthanna, A. Khakimov, A. Ateya, A. Paramonov, and A. Koucheryavy,” Enabling M2M Communication Through MEC and SDN”, In International Conference on Distributed Computer and Communication Networks, Springer, Cham, pp. 95-105, 2018.

[6]      Paramonov A., Koucheryavy A. “M2M Traffic Models and Flow Types in Case of Mass Event Detection”, NEW2AN/ruSMART, Lecture Notes in Computer Science. Vol. 8638. pp. 294-300, 2014.

[7]      A Muthanna, A.; A. Ateya, A.; Khakimov, A.; Gudkova, I.; Abuarqoub, A.; Samouylov, K.; Koucheryavy, A. “Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain”. J. Sens. Actuator Netw. 2019, 8, 15.

[8]      Ali R. Abdellah, Ammar Muthanna, Andrey Koucheryavy “Robust Estimation of VANET Performance-based Robust Neural Networks Learning”, 19th International Conference on Next Generation Wired/Wireless Advanced Networks and Systems (NEW2AN’2019).

[9]      Ali R. Abdellah, Ammar Muthanna, Andrey Koucheryavy “Energy Estimation for VANET Performance Based Robust Neural Networks Learning”, XXII International Conference on Distributed Computer and Communication Networks: Control, Computation, Communications (DCCN’2019).

[10]   https://www.obitko.com/tutorials/neural-network-prediction/prediction.html

[11]   Haibin Cheng, Pang-Ning Tan, Jing Gao, and Jerry Scripps, “Multistep-Ahead Time Series Prediction”, 10th Pacific-Asia Conference, PAKDD 2006, Singapore, pp. 765 – 774, April 9-12, 2006, Proceedings.

[12]   Zina Boussaada, Octavian Curea, Ahmed Remaci, Haritza Camblong, and Najiba Mrabet Bellaaj,” A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation” Energies, doi:10.3390/en11030620, 10 March 2018.
 

Traffic problems in city centres

Traffic problem has become a major problem in the word,it is obvious from the upsurge of private car use on the roads and the amount of problem they cause.Banning private cars in inner city is a blessing or a curse has sparked spirited debate.
Admittedly, one might have benefited a lot from one’s improved ability to move rapidly from one place to another space.Compared to other model, cars provide carring captivity and privacy,50% of commuters travel to work by car in London(Newman,1996).According to a survey,97,000 cars enter to central area between 7:30 am and 10:00 am.This may explain based on Figure3.3,it emphasize the fact that people spend money on cars reduced(exclude added costs) when travel the same trip,with 6% decreased from 1997 to 2004.However,the price of bus ticket and rail ticket has increased 10% and 4% respectively during the seven years.This may explain why more and more private cars are used.

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However, the massive cars enter to inner city cause some serious problems.The most significant problem is traffic congestion and this is evident in every morning and evening in rush hours(Figure3.1).Moving on a congestion road, speeds of traffic reduced to 6-10 km/h(Newman,1996).As figure3.1 indicates,moving on a congestion road,travel time increased more than 4 minute when drivers travel 1 kilometre.Moreover, sluggish traffic flow leads to high fuel and maintenance costs(Schuitema,G 2007).For instance,the cost of congestion in London is at least €3.5 bn per annum(Bailly).
In addition,the growth in car use decreases the quality of life in urban areas due to exhaust gas and irritating noise,causing actual harm to people health.The WHO reports that in European more than 30% urban dwellers has been disturbed by irritating noise,and 5-15% of all citizens suffer noise disturbance(Bailly).The massive cars enter to central area takes the menace to the bicycle riders and pedestrians.The total number of deaths in Europe per year due to traffic accidents reached 45,000.Inadequate of parking car is another recognize consequence of the upsurge of private car enter to central area that result in many gardens and grasslands give way to construct traffic facilities like highway,avenues(Bailly).
Because of these negative effect some people argued that private car should be banned to enter to inner city.However,if banning car enter to inner city will cause another problems.For instance,in Tokyo,the commute rail system has a over-loaded of 300%of capacity in rush hours so that public transportation fail to cope with the increasing transportation demand(Schuitema,G 2007).So,banning private cars in inner city could reduce the number of transport,it is most unlikely to be an acceptable solution.
Statistic from the London Congestion charge Report are illustrated in Figure 3.1 which shows the problem of traffic delays has improved since implement charging during rush hours in the March of 2003. Figure3.1 shows the travel time saved about 1 minute compared with charging before.However, the development of national economy and the improvement of living standard,people afford to extra fee ,the problem picked up again in 2006.From then onward it fluctuated ,and the general trend was upwards.Therefore,an approach to control the number of private car use is focusing on the sales of the private cars(figure 3)and imposing of road tolls during rush hours which, as figure3.2 indicates,has reduced from 2002 to 2006 the number of cars entering to central area fell dramatically,with a 36% reduction,and vans ,lorries and other charging vehicle decreased by 13% respectively.In contract ,for no-charging vehicle rose sharply,such as the number of bus and coaches increased by 25%.Not only avoid common use of private car entering to center area but solve the under-use and serious wastage of the public transport in peak hours.
To sum up,private cars indeed bring lots of benefit for urban residents,banning private cars in inner city will cause another problem.So government need make some methods control the number of cars,such as to impose charges,to establish bus lane and to subsidy the public transport fares(Newman,P1996) and people should reduce unnecessary daily commuting by car.
 

Traffic Pattern Discovery in Mobile Ad hoc Networks

Abstract
Mobile Ad-hoc Network (MANET) is one of the networks of mobile routers that is self-configuring and connected by wireless links. Anonymity communication is one of the major issues in MANET. Though there are many anonymity enhancing techniques that have been proposed based on packet encryption to protect the communication anonymity of mobile ad hoc networks. There are still passive statistical traffic analysis attacks that can be vulnerable to MANET. The communication anonymity consists of two aspects: source/destination anonymity and end-to-end anonymity. In order to discover the communication pattern without decrypting the captured packets, this proposed system will be designed.

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The proposed system will first search the required node by using a heuristic approach. Then statistical traffic analysis will be performed to find the data transmission of the searched node to its neighboring nodes. After performing the statistical traffic analysis whether the search node is source or destination will be estimated. With the help of this estimation the traffic pattern will be discovered. The utility of this proposed system is basically in military environment.
Keywords: Mobile Ad hoc Network (MANET), anonymity communication, statistical traffic analysis.
Introduction
Mobile ad hoc network (MANET) is a self configuring infrastructure less network of mobile devices connected by wireless network. It is one of the types of ad hoc network. Every device in MANET is independent to move. This results in changes of link of such device. Thus MANET is also known as an infrastructure less network. The devices that are present in the network must forward the traffic to other devices. In MANET each device must act as a router. The basic figure of a mobile ad hoc network can be illustrated in Fig 1. One of the critical issues of MANET is communication anonymity. Anonymity can be defined as the state in which identity of an object that performs the action is hidden. An anonymous communication system can be defined as a technology that hides the object identity. Communication anonymity [1] has two aspects: Source/destination anonymity and End-to-End relationship anonymity. In source/destination anonymity it is difficult to identify the sources or the destinations of the network flows while in end-to-end relationship anonymity it is difficult to identify the end – to- end communication relations.
In MANET communication anonymity has been proposed by anonymous routing protocols such as ANODR (ANonymous On-Demand Routing) [7], OLAR (On-demand Lightweight Anonymous Routing) [6]. All these anonymous routing protocols rely on packet encryption to hide the information from attackers. Still the passive attackers can eavesdrop on the wireless channel, intercept the transmission, and then perform traffic analysis attacks. Traffic analysis [10] is one of the types of passive attack in MANET. Traffic analysis is further subdivided into predecessor attack [8] and disclosure attack [9]. The following are the three nature of MANET due to which above approaches do not work well to analyze traffic in MANET:

Broadcasting nature: In wired networks point to point transmission can be easily applied to only one possible receiver. While in wireless network message is broadcasted to multiple receivers.
Ad hoc nature: Mobile node can be served as both source and destination. This can create confusion to determine the role of the node.
3Mobile nature: Traffic analysis model do not consider the mobility of communication peers. This makes the communication among mobile node more complex.

There is a need of such a technology which can analyze traffic without any interruption of the above three characteristics of MANET. This proposed system fulfills the need. The objective of this paper is to show that passive attackers can perform traffic analysis without the knowledge of the adversaries. This approach is required in military environment. The proposed system will perform statistical traffic analysis to discover the traffic pattern. This system will perform the point to point as well as end-to-end traffic analysis among receivers. Indirectly this calculation will provide probable source and destination of the network that will discover the hidden traffic pattern. Thus the adversaries will not be able to know about the traffic analysis.

Fig. 1. Mobile Ad hoc Network
The remaining paper is organized as follows: Section II describes the previous work. Section III presents the proposed work. Section IV describes the expected outcome of the proposed system. Lastly section V presents the conclusion.
PREVIOUS WORK
Yang Qin, Dijiang Huang and Bing Li [1], proposed that though there are many anonymous routing protocols and anonymous enhancing techniques available still mobile ad hoc network (MANET) is vulnerable to passive statistical traffic analysis attacks. The authors proposed a system called as Statistical Traffic Pattern Discovery System (STARS). A STAR is used to discover the hidden traffic pattern in MANET. The drawback of this proposed system is that no searching algorithm is applied to search the traffic free path.
Douglas Kelly, Richard Raines, Rusty Baldwin, Michael Grimaila, and Barry Mullins [2], investigated on anonymity. For a user anonymity can be defined as using any services while keeping their identity hidden from an adversary. Anonymity help user to protect their data from attacks. Unidentifiability, Unlinkability, and Unobservability are the three properties of anonymity. Unidentifiability means the adversary is unable to determine one’s identity or action among similar ones. Unlinkability means the adversary is unable to relate messages or actions by observing the system. Unobservability means the adversary is unable to observe the presence of messages or action in the system. Since unobservability keeps the identity of messages or action secret it can be implied as anonymity. Unidentifiability is subdivided into sender anonymity (SA), receiver anonymity (RA), mutual anonymity (MA) and group anonymity (GA). Unlinkability is subdivided into location anonymity (LA), communication anonymity (CA) and group communication anonymity (GCA). In order to discover the traffic pattern we have to work on unidentifiability property of anonymity and decrease the sender anonymity (SA) and receiver anonymity (RA).
Lei Liu, Xiaolong Jin, Geyong Min, and Li Xu [3], proposed that in order to detect the attack in a network traffic intensity and packet number are the two important metrics. Lei et al. had designed an anomaly detection system. This anomaly detection system is used to detect the distributed denial of service (DDoS) attack in MANET. When traffic analysis is carried on MANET these two metrics are used to detect the DDoS attack. Similarly when traffic analysis will be carried on our proposed system data transmission will be considered as a parameter. We can conclude that data transmission will be an important factor whenever traffic analysis will be carried out, though the reason may be for detection of attack or for discovery of traffic pattern.
Zhilin Zhang and Yu Zhang [4], introduced that control traffic plays an important role in route discovery in MANET. The characteristic that involve to carry out research on control traffic in MANET when on demand routing protocols are used include distribution of nodes’ control packet traffic, communication of control packets between nodes, rate of RREQ (route request) packets and the ratio of number of RREQ packets originating from one node to all RREQ packets relayed by this node. These characteristics of control traffic are affected by factors such as mobility, node density and data traffic. Thus theoretically we can determine that indirectly one of the factors of control traffic is data traffic. Hence we can conclude that control traffic will also play an important role in route discovery in our proposed system though the situation will be different. In our proposed system we will find traffic free path i.e. control traffic path so that it will be easy to find out the number of data packets transmitted to neighboring nodes. This will help us to discover route in our proposed system.
Y. Liu, R. Zhang, J. Shi, and Y. Zhang [5] designed a novel algorithm called as traffic inference algorithm (TIA) which allows an adversary to infer the traffic pattern in MANET. This algorithm is based on the assumption that difference between data frames, routing frames and MAC control frames is visible to passive adversaries. Through these differences they can identify the point-to-point traffic using the MAC control frames, recognize the end-to-end traffic by tracing the routing frames and then find out the actual traffic pattern using the data frames. This algorithm is not a successful invention as it depends on the deterministic network behaviors.
Stephen Dabideen and J.J. Garcia-Luna-Aceves [6], proposed that routing in MANET using depth first search (DFS) is feasible as well as efficient than breadth first search (BFS). The algorithm introduced is called as ordered walk search algorithm (OSA). The objective of this algorithm is to take advantage of the smaller time complexity of BFS and combine it with the low communication complexity of DFS in order to improve the efficiency of the search through the known path information. In order to demonstrate the effectiveness of OSA, ordered walk with learning (OWL) routing protocol has been presented which uses DFS to establish and repair paths from the source to the destination with minimum signaling overhead and fast convergence. The following are the advantages of DFS over BFS that had been investigated by Stephen et al. in MANET:
(i) DFS require less overhead as compared to BFS. When large number of nodes is performing BFS, the routing
TABLE 1
COMPARISON OF SEARCHING ALGORITHMS

Parameters

Searching Algorithms

Breadth First Search [4]

Depth First Search [4]

Overhead

More

Less

Load in network

More

Less

Packet loss

More

Less

 
 
 

overhead can saturate the network making it difficult to deliver any packets. However DFS use only small network for routing.
(ii) When BFS is used in a network, where there are multiple flows of search packets this situation can lead to increase the load on network and loss of packets. On the other hand as DFS involves only a small part of the network, thus this reduces the load in the network and results in less packet loss.
A comparative study of searching algorithms is shown in TABLE I. From this table we conclude that DFS is better searching algorithm for MANET than BFS.
PROPOSED WORK
One of the characteristic of MANET is that all the nodes are hidden. This proposed system will unhide the nodes by using one of the searching algorithms. The searching algorithm chosen for searching the node will be depth first search (DFS). Source node will use DFS algorithm for traversing or searching the path in the network. Then statistical traffic pattern analysis will be performed on these searched nodes. This analysis will provide an estimation of the data transmitted to all the neighboring nodes of every searched node. We can discover the traffic pattern by using probability distribution. The working of each of the module is explained in detailed below.
Searching node in MANET using depth first search
In this proposed system we are using DFS for routing decisions. When a node receives message for the first time, it sorts all its neighboring nodes according to their distance to destination and then uses that same order in DFS algorithm. It starts its searching from the source node and updates one hop neighbors. This search continues to reach traffic free path between source and destination node. As shown in Fig. 2, depth first search works on tree or graph. The Fig. 2 gives an example of DFS routing path for the following graph:
The searching starts from root node A. It is assumed that the left edges are selected than the right edges. Each node remembers the last visiting nodes which help to backtrack and reach the last node to complete the traversing. From Fig. 2 the path will be: A, B, D, E, C, and F.
Statistical traffic analysis of packets in MANET
For point-to-point (one hop) traffic in a certain period, first build point-to-point traffic matrices such that each traffic

Fig. 2. Depth First Search
matrix only contains independent hop packets. There can be situation in which two packets captured at different time could be the same packet appearing at different location. In order to avoid a single point-to-point traffic matrix form containing two dependent packets time slicing technique is used.
Time slice technique is technique in which the process is allowed to run in a preemptive multitasking system. This is called as the time slice or quantum. The scheduler runs once every time slice to choose the next process to run. In this proposed system a sequence of snapshots during a time interval constructs a slice represented by a traffic matrix. The traffic matrix is denoted by W. This traffic matrix will consists of traffic volume from one node to another. For example,

Here 1 indicates that there is transmission of data (traffic volume) from node 1 to node 2 whereas 0 indicate that there is no transmission of data between the two nodes.
Discovery of system
The traffic matrix tell us the deduce point-to-point and end-to-end traffic volume between each pair of nodes. We need to discover the actual source or destination in order to discover the traffic pattern. Here probability distribution is used. Probability distribution calculates the probability of the data transmitted to neighboring node which provide an accurate estimation of a node as source or destination. This will help to discover the traffic pattern.
The equation required for source probability distribution is
The equation required for destination probability distribution is

Fig.3. System Flow Diagram
Where s’(i) is the source vector, N is number of nodes, r(i,j) is the accumulative traffic volume from node i to node j, d’(i) is the destination vector.
In the Fig. 3, the flow of the proposed system is explained. When the system will start it will form a network. This network will consist of certain number of nodes. All the nodes will be browsed. In order to search the node a heuristic searching algorithm will be applied. If the required node is present then statistical traffic analysis will be performed on it. After performing statistical traffic analysis probability distribution will be applied to discover the traffic pattern. However, if the required node is not found then the system will stop and no further process will be carried out.
EXPECTED OUTCOME
From the idea of the proposed system we are clear with two outcomes. The outcomes will be to discover traffic pattern in MANET and to find probability of point to point transmission among receivers. These two outcomes are discussed below.
Discovery of traffic pattern in MANET
The first step to discover traffic pattern will be searching a node. Then using point-to-point traffic matrix and end-to-end traffic matrix a statistical traffic analysis will be performed. The parameter to be considered for traffic pattern discovery will be number of data transmitted.
Probability of point to point transmission among receivers is estimated
Point-to-Point transmission can be estimated by point-to-point traffic matrix. This matrix will consists of traffic volume between each node at one hop distance. The calculated traffic matrix will be used to determine the probability of point to point transmission among receivers.
CONCLUSION
The proposed system will be an attacking system. As nodes are hidden in MANET a heuristic searching algorithm will be applied. This heuristic searching algorithm will be depth first search (DFS).This system will perform statistical traffic analysis to find the data transmission between one to one and one to many nodes. Probability of point to point transmission among receivers will be estimated by point-to-point traffic matrix. Then by calculating multihop traffic and performing probability distribution the traffic pattern will be discovered. This will provide an approximate traffic pattern with approximate source and destination in the network. The proposed system will reduce the issue of anonymous communication in mobile ad hoc network (MANET).
REFERENCES

Yang Qin, Dijiang Huang and Bing Li “STARS: A Statistical Traffic Pattern Discovery System for MANETs” IEEE Transactions on Dependable and Secure Computing, Vol. 11, No. 2, March/April 2014.
Douglas Kelly, Richard Raines, Rusty Baldwin, Michael Grimaila, and Barry Mullins, “Exploring Extant and Emerging Issues in Anonymous Networks: A Taxonomy and Survey of Protocols and Metrics”, IEEE Communications Surveys & Tutorials, Vol. 14, No. 2, Second Quarter 2012.
Lei Liu, Xiaolong Jin, Geyong Min, and Li Xu, “Real-Time Diagnosis of Network Anomaly based on Statistical Traffic Analysis”, IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, 2012.
Zhilin Zhang and Yu Zhang, “Control Traffic Analysis of On-Demand Routing Protocol in Mobile Ad-hoc Networks”, IEEE Second International Conference on Networking and Distributed Computing, 2011
Y. Liu, R. Zhang, J. Shi, and Y. Zhang, “Traffic Inference in Anonymous MANETs,” Proc. IEEE Seventh Ann. Comm. Soc. Conf. Sensor Mesh and Ad Hoc Comm. and Networks, pp. 1-9, 2010.
Stephen Dabideen and J.J. Garcia-Luna-Aceves, “OWL: Towards Scalable Routing In MANETs Using Depth-First Search On Demand”, IEEE 6th International Conference on Mobile Adhoc and Sensor Systems, Oct 2009.
Y. Qin and D. Huang, “OLAR: On-Demand Lightweight Anonymous Routing in MANETs,” Proc.Fourth Int’l Conf. Mobile Computing and Ubiquitous Networking, pp. 72-79, 2008.
J. Kong, X. Hong, and M. Gerla, “An Identity-Free and On- Demand Routing Scheme against Anonymity Threats in Mobile Ad Hoc Networks,” IEEE Trans. Mobile Computing, vol. 6, no. 8, pp.888-902, Aug. 2007.
M. Wright, M. Adler, B. Levine, and C. Shields, “The Predecessor Attack: An Analysis of a Threat to Anonymous Communications Systems,” ACM Trans. Information and System Security, vol. 7, no. 4, pp. 489-522, 2004.
G. Danezis, “Statistical Disclosure Attacks: Traffic Confirmation in Open Environments,” Proc. Security and Privacy in the Age of Uncertainty, vol. 122, pp. 421-426, 2003.
J. Raymond, “Traffic Analysis: Protocols, Attacks, Design Issues, and Open Problems,” Proc. Int’l Workshop Designing Privacy Enhancing Technologies: Design Issues in Anonymity Unobservability, pp. 10-29, 2001.

 

Reducing Occupational Stress in Air Traffic Control

Recommendations and Conclusions
Introduction
In this chapter, the researcher has formulated a set of recommendations based on data found in the finding and analysis chapter in line with the objectives of the dissertation to help in reducing occupational stress in air traffic control.
Improving job planning and reliability of the work systems
According to Glovanni Coasta (1995), from the past technical means to present support, under full radar coverage of air space, is the key factor which allows a “jump in quality”, not just in terms of work competence, but likewise in terms of stress levels, by decreasing cognitive, memory and communicative loads along with uncertainty and unforeseeability of the situations. The more technological passage to function under “multi-radar” assistance permits an additional rise in levels of reliability and safety as well as a reduction in stress levels.
These improvements allow for well planning of air traffic and, subsequently, a more balanced workload among individual ATCs. These improvements may also subsequently reduce the possibility or the seriousness of many unforeseen situations, by allowing for more reliable information and more time for solving problems and making decisions, while eliminating many stressful and risky traffic peaks.
Reduction of working times and arrangement of working teams and rest pauses in relation to the workload
The mental strength required maintaining the maximum level of attention and vigilance, as well as to securely and efficiently facing the duty in terms of cognitive and memory load that can differ usually in relation to air traffic concentration and connected problems. Therefore, to guarantee the best level of performance efficiency avoiding excessive mental stress and fatigue, particular attention has to be paid to arranging duty periods.
Duty periods:

The length of the duty period should not exceed ten hours (extendable to 12 hours in special circumstances), and should be adjusted according to the workload;
An interval of no less than 12 hours should be scheduled between the conclusion of one period of duty and the commencement of the next period of duty;
Overtime should be an exception.

Breaks during operational duty:

No operational duty shall exceed a period of two hours without there being taken, during or at the end of that period, a break or total break not less than 30 minutes;
During periods of high traffic density, the possibility of having more frequent short breaks (ten minutes) should be provided;
A sufficiently long break for meals should be allowed, providing adequate canteen facilities to assure hot and good quality meals.

Arrangement of shift schedules according to psycho-physiological and social criteria
Shift work, in particular night work, is a stress factor for the ATCs due to its negative effects on various aspects of their lives. This stress can be eliminated by adopting a rapidly-rotating shift system, changing work shifts every one or two days instead of every week. Moreover, reducing the number of consecutive night shifts as much as possible and having a day’s rest after the night-shift period. This prevents accumulation of sleep deficit and fatigue, and allows a quicker recovery. Delaying the beginning of the morning shift (e.g. at 07:00 or later) to allow a normal amount of sleep. Preferring the forward rotation (e.g. morning-afternoon-night) to the backward one (e.g. afternoon-morning-night) to allow a longer period of rest between shifts). Adjusting the length of shifts according to the physical and mental workload that is day shifts should be shorter, whereas night shifts could be longer if the workload is reduced and there are sleeping facilities.
Improving the work environment

Lighting

Taking into consideration that the ATC’s task is performed almost exclusively in front of a visual display unit, particular attention should be paid to providing lighting conditions which favor an optimal visual performance.
Inside the towers, the opposite is the problem. It is necessary to avoid excessive illumination levels due to external bright light using both anti-reflection glass and curtains; it is also important to have the possibility of positioning and shielding the visual display units to avoid indirect glare due to bright reflections on the screen.

Noise

The main sources of noise are represented by conversations, manual operations (e.g. manipulations of strip supports) and office machines (printers, telephones, photocopiers, etc.). Therefore particular attention has to be paid in order to stop background noise from exceeding 45-50 dB by installing quieter office machinery, arranging work sectors in order to have better sound protection from each other, and installing more insulating headsets and more sensitive microphones.
Arranging workplaces according to ergonomic criteria

Workstation design

It is also important to arrange the layout of the workplace in order to avoid glare caused by excessive brightness contrasts between different objects and surfaces; it causes discomfort and hampers the comprehension of the information. The displays should be shaded and the surfaces matte, avoiding the use of reflective materials and bright colors on table-tops and consoles. Data displays containing flight information should preferably be located beside the radar screen.

Sitting postures

A prolonged, constrained sitting posture causes muscular-skeletal discomfort and pain, particularly at the level of the neck, the shoulders and the lumbar tract. In order to avoid or alleviate such disturbances, it is important to use suitable chairs which allow a comfortable sitting posture while working, as well as useful muscle relaxation while on stand-by or resting in front of the screen.
Individual ways of coping with stress
First of all, people should avoid ineffective ways of coping, which can have an apparent short term positive effect but, in the long run, can cause further problems in health and well-being. We refer in particular to smoking. Increasing smoking (for smokers) is sometimes seen as a way of obtaining a sense of relief and calmness. Of course, apart from short-term relief, there are many adverse effects both on performance efficiency, due to interference with the upper nervous system activities, and on health, due to increased risk of lung tumors and chronic bronchitis from smoking.
Secondly, maintaining good physical fitness and emotionally stable psychic conditions are the best aids in fighting and overcoming stress. To stay in satisfactory condition, people should pay particular attention to physical exercise, eating habits, sleeping patterns, relaxation techniques and leisure activities.
Relaxation techniques are becoming more and more popular among people who feel to be under stress. Massage, yoga, meditation and autogenous training are all useful exercises which help to control restlessness, anxiety, muscular tension, inability to concentrate, insomnia and other symptoms of stress.
Training
The aim of training is at teaching occupational and particular coping strategies in order to improve the capacity of event appraisal and problem solving, so that ATCs learn how to cope with emotional effects of stressful events and improve the capacity of control. Air traffic controllers should be trained to develop action-oriented and problem-focused coping abilities. Positive acceptance and reappraisal of stress situations, active coping, seeking to social support for instrumental and emotional reasons must be strengthened, while inclination towards restraint coping, behavioural and mental disengagement should be restricted.

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Conclusion
Air traffic controllers are the working groups having to deal with very stressful and tough job and are widely recognized as an occupational group which has to cope with a highly demanding job that involves a complex series of tasks, requiring high levels of knowledge and expertise, combined with high levels of responsibility. According to this research, it can be seen that most of air traffic controllers rate the level of stress as extreme.
Moreover, this level of stress is caused due to several factors such as duration of break that the controllers have, the shift hours they usually worked and the workload. Stress can be due to conflict arising from workplace and private life also. According to survey, 63% of air traffic controllers have conflict arising from workplace and private life. 50% of controllers agreed that stress is caused due to their nature of the job and responsibilities.
Air traffic controllers must be trained to have high stress resistance and must be able to take best decision in difficult condition and on behalf of the pilot. Training should be given in order to improve the capacity of event appraisal and problem solving, so that ATCs learn how to cope with emotional effects of stressful events and improve the capacity of control. Moreover, it is important to have a stress management system in place in the work place to help controllers deal with suffering a loss of separation incident or accident.

Professor Glovanni Coasta (1995). Occupational stress and prevention in air traffic control. Institute of Occupational Medicine: University of Verona.
 

Traffic Light Controller Using 8085 Microprocessor

Aim
The main aim of this project is to design a Traffic light controller using 8085 microprocessor, interfacing with peripheral device 8085, and program implementing the process.
Introduction
The 8085 Microprocessor is a popular Microprocessor used in Industries for various applications. Such as traffic light control, temperature control, stepper motor control, etc. In this project, the traffic lights are interfaced to Microprocessor system through buffer and ports of programmable peripheral Interface 8255. So the traffic lights can be automatically switched ON/OFF in desired sequence. The Interface board has been designed to work with parallel port of Microprocessor system.
The hardware of the system consists of two parts. The first part is Microprocessor based system with 8085. Microprocessor as CPU and the peripheral devices like EPROM, RAM, Keyboard & Display Controller 8279, Programmable as Peripheral Interface 8255, 26 pin parallel port connector, 21 keys Hexa key pad and six number of seven segment LED’s.
The second part is the traffic light controller interface board, which consist of 36 LED’s in which 20 LED’s are used for vehicle traffic and they are connected to 20 port lines of 8255 through Buffer. Remaining LED’s are used for pedestrian traffic. The traffic light interface board is connected to Main board using 26 core flat cables to 26-pin Port connector. The LED’s can be switched ON/OFF in the specified sequence by the Microprocessor.

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The normal function of traffic lights requires sophisticated control and coordination to ensure that traffic moves as smoothly and safely as possible and that pedestrians are protected when they cross the roads. A variety of different control systems are used to accomplish this, ranging from simple clockwork mechanisms to sophisticated computerized control and coordination systems that self-adjust to minimize delay to people using the road.
Traffic Controller Systems
A traffic signal is typically controlled by a controller inside a cabinet mounted on a concrete pad. Although some electro-mechanical controllers are still in use (New York City still has 4,800), modern traffic controllers are solid state. The cabinet typically contains a power panel, to distribute electrical power in the cabinet; a detector interface panel, to connect to loop detectors and other detectors; detector amplifiers; the controller itself; a conflict monitor unit; flash transfer relays; a police panel, to allow the police to disable the signal; and other components.
Fixed Time Control
The simplest control system uses a timer (fixed-time): each phase of the signal lasts for a specific duration before the next phase occurs; this pattern repeats itself regardless of traffic. Many older traffic light installations still use these, and timer-based signals are effective in one way grids where it is often possible to coordinate the traffic lights to the posted speed limit. They are however disadvantageous when the signal timing of an intersection would profit from being adapted to the dominant flows changing over the time of the day.
Dynamic Control
Dynamic, or actuated, signals are programmed to adjust their timing and phasing to meet changing traffic conditions. The system adjusts signal phasing and timing to minimize the delay of people going through the intersection. It is also commonplace to alter the control strategy of a traffic light based on the time of day and day of the week, or for other special circumstances such as a major event causing unusual demand at an intersection.
The controller uses input from detectors, which are sensors that inform the controller processor whether vehicles or other road users are present, to adjust signal timing and phasing within the limits set by the controllers
programming. It can give more time to an intersection approach that is experiencing heavy traffic, or shorten or even skip a phase that has little or no traffic waiting for a green light. Detectors can be grouped into three classes: in-pavement detectors, non-intrusive detectors, and detection for non-motorized road users.
Working Program
Design of a microprocessor system to control traffic lights. The traffic should be controlled in the following manner.
1) Allow traffic from W to E and E to W transition for 20 seconds. 2) Give transition period of 5 seconds (Yellow bulbs ON) 3) Allow traffic from N to 5 and 5 to N for 20 seconds 4) Give transition period of 5 seconds (Yellow bulbs ON) 5) Repeat the process.
Source Program:
MVI A, 80H: Initialize 8255, port A and port B
OUT 83H (CR): in output mode
START: MVI A, 09H
OUT 80H (PA): Send data on PA to glow R1 and R2
MVI A, 24H
OUT 81H (PB): Send data on PB to glow G3 and G4
MVI C, 28H: Load multiplier count (40ıο) for delay
CALL DELAY: Call delay subroutine
MVI A, 12H
OUT (81H) PA: Send data on Port A to glow Y1 and Y2
OUT (81H) PB: Send data on port B to glow Y3 and Y4
MVI C, 0AH: Load multiplier count (10ıο) for delay
CALL: DELAY: Call delay subroutine
MVI A, 24H
OUT (80H) PA: Send data on port A to glow G1 and G2
MVI A, 09H
OUT (81H) PB: Send data on port B to glow R3 and R4
MVI C, 28H: Load multiplier count (40ıο) for delay
CALL DELAY: Call delay subroutine
MVI A, 12H
OUT PA: Send data on port A to glow Y1 and Y2
OUT PB: Send data on port B to glow Y3 and Y4
MVI C, 0AH: Load multiplier count (10ıο) for delay
CALL DELAY: Call delay subroutine
JMP START
Delay Subroutine:
DELAY: LXI D, Count: Load count to give 0.5 sec delay
BACK: DCX D: Decrement counter
MOV A, D
ORA E: Check whether count is 0
JNZ BACK: If not zero, repeat
DCR C: Check if multiplier zero, otherwise repeat
JNZ DELAY
RET: Return to main program
References
www.rbainnovations.com/…/A%208085/H%20Traffic%20light%20controller-n.doc
www.freshpatents.com/-dt20090702ptan20090167561.php
http://www.8085projects.info/page/free-programs-for-8085-microprocessor.aspx
http://www.8085projects.info/post/Traffic-Light-Control.aspx
U.S.Shah, Microprocessor and its applications, Tech- Max Pulications, Pune.
 

Objectives of Air Traffic Services. (ATS)

First of all I would like to mention about Air Traffic Services.

Air Traffic Control Service ATC.

Aerodrome Control Service
Approach Control Service
Area Control Service

Flight Information Service
Alerting Service
Flight Advisory Service

These are the main ATS services. Then I would like to explain about main objectives of ATS.
a) Prevent collisions between aircraft;
b) Prevent collisions between aircraft on the maneuvering area and obstructions on that area;
c) Expedite and maintain an orderly flow of air traffic;
d) Provide advice and information useful for the safe and efficient conduct of flights;
e) Notify appropriate organizations regarding aircraft in need of search and rescue aid, and assist such organizations as required. (ICAO 2013)

Prevent collisions between aircrafts.

There are two boundaries in the world which is using in the Aviation.

Western Bound
Eastern Bound

Aircrafts which are flying in the Eastern bound, pilots should have to use Odd flight altitude levels. When they are flying to Western bound, Pilots should have to obey Evens flight altitudes. This rule makes sure the safe of aircrafts. Not only that, If the pilots need to descend or climb their aircraft. First of all pilots should have to ask it, from ATC. After ATC unite check the radar and flight altitude, aircraft’s speed, if there are no any aircraft in that rout and they will give the permeation. If there are more aircrafts in that particular area they are asking speeds and estimate time for particular point, then after ATC unite decide the time for pilots to climb or descend. EX. after 15 min. UL302 can climb up to xxx fleet level.
If there are more aircrafts in same route or altitude, ATC check the speeds and change their Flight altitudes.
If there is a busy route, ATC unite change aircrafts speeds and direction. After they help to make sure aircrafts destination route.
Pilots should have to obey ATC instructions while they are flying. Not only that they have to make conversion between Pilots and ATC unite. Because ATC unite always alert about aircrafts speed altitude and their routes. Therefore their instructions will be help to prevent coalition between aircrafts while they are flying.

Prevent collisions between aircraft on the maneuvering area and obstructions on that area.

Maneuvering is pilots flying into Aspen’s Pitkin County Airport (ASE) should be aware of FAA Notice NOTC4835, which addresses two safety issues at the field. The notice attempts to mitigate ongoing safety incidents at the Colorado airport involving aircraft, vehicles and pedestrians on runways and non-movement area. Outside the skiing season, the movement/non-movement area boundary line was repositioned closer to Taxiway. (AIN 2013)
When the aircraft parked at Apron, pilots should have to ask from Air Traffic Control Unite “flight number and can we push back and start the engines.” If there are no any aircraft has pushed back and start their engines at the apron, ATC unite gave the permission to push back and start the engines. If there are any aircraft bushed back and stare ATC will delay the permission. Because ATC avoid the collision between aircraft inside the Apron,

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Before the take off pilots should have to get the permission from ATC unite. They give the flow of orders, ATC check the taxing ways and runway and clear the taxing ways and runway. Later than they give the taxing number for pilots to enter the runway. Behind ATC give the permission to take off and they maintain aircraft’s altitude. ATC unite has to guarantee safe of aircraft, that’s the reason for clear the taxing ways and runway. It stops collision between aircrafts and vehicles.
Pilots should have to follow the orders of instruction before the landing. ATC will clear the runway for landing. They absorb aircrafts and vehicles in the runway. ATC will provide wind directions, and glide path. If there any takeoff or landing on the runway, ATC late their permission and manage both aircrafts routes.
Under the poor visibility situations they delayed aircrafts takeoff and landing. After they manage and help to reach their destinations.
ATC check every nock and comer inside the airport and prevent collision between aircrafts and obstructions on that area.
c.Expedite and maintain an orderly flow of air traffic.
When the busy routes, ATC manage aircraft’s Speed, Altitudes directions. These changes help to prevent from collision between aircrafts and control the traffic among aircrafts.
Before the take off, ATC check and manage aircraft departure times. ATC will manage all aircrafts to takeoff on time.
When if it is a busy periods or busy airports, step by step, ATC will clear the runway, taxing ways and apron. They will manage airport aerospace’s traffic. Because it will be a reason for air collision.
d.Provide advice and information useful for the safe and efficient conduct of flights.
Aircraft’s Altimeter indicates aircraft’s current altitude with using air pressure. Place to place Air pressure and Density can change. Because of this reason pilots have to enter mean see level pressure to the altimeter. Before the landing pilots have to come through correct glide path, therefore pilots have to know correct altitude of aircraft. ATC unit provide ground pressure mean sea level pressure and density of these areas before the landing. Aircrafts use some countries aero spaces, Flight Information Regents FIR of those counties provide pressure density and temperature to maintain aircraft’s altitude.
Under poor visibility situations ATC provide advisory services for aircrafts to prevent collision between aircrafts and Obstructs in the airport area.
Below the connection failure situations between Aircrafts and ATC unite, ATC provide advises using signals. Those signals are useful for the safe landing. Efficiently they check the failures of aircraft and provide some advices within shorter time.
ATC unite always alert about their Flight Information Regent area FIR and provide information for aircrafts. Those advise and Information will help to control traffic of aero space. It will make sure the safe about aircrafts.
Physical appearance of the control tower.

Fig. 01
Control Tower
Building and Terminals
Apron
Ramp Area
Hanger

Taxing way
Runway
This structure shows the basic idea of an airport. Air Traffic Controlling Tower is situated in witch is the place where can get the fully and cleared view point. It is the highest building in that area. ATC members can get the fully 360 degree view of an airport.
Control tower height and location affects airport safety and construction costs, the FAA had no means to measure quantitatively the improvement in air traffic controller visibility that can be gained by changing the tower height and location on the airport surface, and there was no required minimum criterion for tower height.
FAA human factors specialists and Airport Facilities Terminal Integration Laboratory personnel created and conducted tower setting simulations of different existing towers to establish a performance baseline of a controller’s ability to detect and identify aircraft on the airport surface at distance points. Research results were used to determine requirements for future tower construction projects, ensuring safe minimums and constraining costs of the nation’s airport investments. Prior to the simulation, human factors researchers refined and validated an experimental approach and methodology to evaluate the human performance characteristics affecting tower setting decisions. This effort supports the FAA Flight Plan Goals for Increased Safety regarding the FAA Safety Management System (SMS) initiative to update the FAA Order for tower. (FAA 2010)
ATC unit always alerting in the airport and always checking movement and non movement areas of an airport. Its prevent collisions between aircraft on the maneuvering area and obstructions on that area ATC can provide correct advisory service because of the clear view. Then it’s expedited and maintains an orderly flow of air traffic. Easily ATC can provide advice and information useful for the safe and efficient conduct of flights because of 360 clear degrees.
Communication failure procedures.
According to my knowledge an aircraft operated as a controlled flight shall maintain continuous air-ground voice communication watch on the appropriate communication channel of, and establish two-way statement as necessary with, the appropriate air traffic control unit, except as may be prescribed by the appropriate ATS authority in value of aero planes.
If it is a communication failure precludes aircraft shall comply with the communication failure procedures, and with such of the following procedures as are appropriate. The aircraft shall attempt to establish communications with the appropriate air traffic control unit using all other available means. In addition, the aircraft, when forming part of the aerodrome traffic at a controlled aerodrome, shall keep a observe for such instructions as may be issued by visual signals.
If in visual meteorological conditions, aircraft has to:
a. Continue to fly in visual meteorological conditions b. Land at the nearest suitable aerodrome c. Report its arrival by the most expeditious means to the appropriate air traffic control unit. (ICAO 2013)
If in instrument meteorological conditions or when the pilot of an IFR flight considers it inadvisable to complete the flight in peace with the aircraft shall:
In airspace where radar is not used in the provision of air traffic control, maintain the last assigned speed and level, or minimum flight altitude if higher, for a period of 20 minutes next the aircraft’s breakdown to report its position over a compulsory reporting point and thereafter adjust level and speed in accordance with the filed flight plan.
Where radar is used in the provision of air traffic control in airspace, maintain the last assigned speed and level, or least flight altitude if higher, for a period of 7 minutes following.
1) The time the last assigned level or minimum flight altitude is reached
2) The time the transponder is set to Code 7600
3) The aircraft’s failure to report its position over a compulsory reporting point
Whichever is later, and thereafter adjust level and speed in accordance with the filed flight plan
When an aircraft station fails to establish contact with the aeronautical post on the designated frequency, it shall attempt to establish contact on another frequency appropriate to the route. If this attempt fails, the aircraft place shall attempt to establish communication with other aircraft or other aeronautical stations on frequencies appropriate to the route. In addition, an aero plane operating within a network shall supervise the appropriate Very High Frequency for calls from nearby aircraft. If necessary, include the addressee for which the message is intended. Procedures for Air Navigation Services Recommendation — in network operation, a message which is transmitted blind should be transmitted twice on both primary and secondary frequencies. Before changing frequency, the aircraft station should announce about changes. (K.Haroon 2005)
Visual signals and their use in Airport.
In a case of a radio failure, pilots should have to land their aircraft immediately. Therefore air traffic control may use a signal lamp to direct the aircraft. The signal lamp has a focused bright beam and is capable of emitting three different colors red, white and green. These colors may be flashed or steady. Its have different meanings to aircraft in flight or on the ground. Aircraft can acknowledge the instruction by rocking their wings, moving the ailerons if on the ground, or by flashing their landing or navigation lights during in the darkness also.
Lights and Signals from Aerodrome.

Color and type of signal

Aircraft on the ground

Aircraft in flight

Movement of vehicles, equipment and personnel

Steady green

Cleared for takeoff

Cleared to land

Cleared to cross; proceed; go

Flashing green

Cleared to taxi

Return for landing (to be followed by steady green at the proper time)

Not applicable

Steady red

Stop

Give way to other aircraft and continue circling

Stop

Flashing red

Taxi clear of landing area or runway in use

Airport unsafe- Do not land

Clear the taxiway/runway

Flashing white

Return to starting point on airport

Not applicable

Return to starting point on airport

Alternating red and green

General Warning Signal- Exercise Extreme Caution

General Warning Signal- Exercise Extreme Caution

General Warning Signal- Exercise Extreme Caution

Fig.02
(FAA 2014)
Maneuvering Area Marking Signals.
Runway & Taxiway Signs

ILS Critical Area Holding Position Sign
Fig.03

Runway Approach Holding Area Position Sign
Fig.04

Taxiway Location Sign
Fig.05

Runway Holding Position Sign
Fig.06

Destination Signs & Location Sign
Fig.07

Outbound Destination Sign
Fig.08

Inbound Destination Sign
Fig.09

Runway Boundary Sign
Fig.10

Taxiway Ending Marker
Fig.11

Closed Runway and Taxiway Marking
Fig.12

Direction Sign for Runway Exit
Fig.13

ILS Critical Area Boundary Sign
Fig.14

Holding Position and Location Signs
Fig.15

Runway Location Sign
Fig.16

Visual Approaching Navigational Aids.
Any manmade objects with easy to find the way and approach for pilots. VASIS Lights.
Visual Approach Slope Indication System
White/White Too Height Red/White on Glide Path Runway Runway Red/Red Too Low Runway Fig.17
PAPI Lights
Precision Approach Path Indicator
Fig.18 Runway 4 Whites – Too High

3 Whites / 1 Red – Slightly High

2 Whites / 2 Red – On Glide Path

1 Whites / 3 Red – Too Low
 
Relationship among Runway, Taxing and Control Tower.
Runway
A defined area on a land aerodrome prepared for landing and taking off of aircraft called as a runway.
Taxing way
A general idea is path on a Land aerodrome established for taxiing of aircraft and to provide a link between apron and runway.
Control Tower
It is the unite where control aircrafts vehicles and maneuvering area. Air Traffic Controlling Tower is situated in witch is the place where can get the fully and cleared view point. It is the highest building in that area. ATC members can get the fully 360 degree view of an airport.
Runway, Taxiing Ways and Apron have linked with Air Traffic Control Tower. Because it is the most responsible place. ATC tower Control Approaching lights Runway lights, Taxing lights, Runway edge lights, Runway centerline, Runway threshold Runway end. There is a flow of orders, before the landing or take off. Then tower is the place where the guided aircrafts. They always check the airport area and make sure the safe of aircrafts, vehicles, and peoples. When the aircraft pushed back and start their engines and until their take off, ATC provide instructions using taxing and runway signals for safety efficient.
If the aircrafts ready to take off, firstly ATC has to clear taxing and runway from other aircrafts and vehicles. Using lights systems and signals they will remove all the vehicles and aircrafts for safety. Because tower has a rich and clear visibility. If it is an emergency ATC tower will make decisions and Inform those thing for other parties. Therefore they can declare it easily Always they will make sure the safe of an Airport.
Role of tower controller.

BEFORE Transmitting Listen-out carefully to ensure no interference from another station
Be familiar with Good Microphone operating techniques
Use a normal conversation tone, & speak Clearly & Distinctly
Maintain an even rate of speech ï‚£ 100 words per minute, speak even slower when recipient writes-down
Maintain a constant Speaking Volume level
A slight pause before & after numbers assists to make it easier to understand
g) Avoid using hesitation sounds such as “er”
h) A constant distance from ‘mic’ recommended if a modulator with a constant level is not used
i) Suspend speech temporarily if turning your head away from ‘mic’
j) Depress PTT fully before speaking & do not release it until message is complete to ensure transmission of entire message
k) Long messages be interrupted momentarily to permit the transmitting operator to confirm the Frequency in use is Clear, & if necessary to permit the recipient to request repetition of any parts not received. (Exforxys 2012)

Advices for Air Traffic Controllers.

Hold the microphone about one inch from the mouth.
Speak directly into the microphone.
Speak clearly, plan what you intend to say.
Don’t clip transmission.
Listen out before the transmit.
Don’t expect an immediate answer. (wait 10-15 seconds before trying again)
Establish communications. (using the phraseology)
Wait for a reply.

References
Journals
ICAO, 2013, About Annex 11, Air Traffic Services first edition , ICAO, Montreal 01
ICAO, 2013, Annex 10, , 5.2.2.7.1.1, Communication failure procedures 04
ICAO, 2013, Annex 10, , 5.2.2.7.1.1, Communication failure procedures 05

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