JTTsJournal of Transportation Technologies2160-0473Scientific Research Publishing10.4236/jtts.2017.71002JTTs-72831ArticlesEngineering Modulating Traffic Signal Phases to Realize Real-Time Traffic Control System RajendraS. Parmar1BhushanH. Trivedi2GLS Institute of Computer Technology, Ahmedabad, IndiaGujarat Technology University (GTU), Ahmedabad, India1212201607012635October 14, 2016Accepted: December 16, December 19, 2016© Copyright 2014 by authors and Scientific Research Publishing Inc. 2014This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

This paper proposes innovations to address challenges emanating from road traffic congestion. Improving economies create more car owners resulting in increased automobile manufacturing, increased vehicle population giving rise to higher emission of CO 2 resulting in traffic congestion. Congested traffic has idling vehicles which emit higher CO 2 and pollution. Besides, traffic congestion increases turnaround time, delivery time, commuting time and related logistical aspects. Commuting time negatively impacts working hours. Unless the traffic congestion is mitigated, the economy will take a beating creating a vicious ecology cycle. Building new roads, bridges or reconditioning of infrastructure is not always the best possible solutions. Efficient traffic management is a key to country’s economic growth. Various analytical models are employed to study, appreciate traffic congestion. The paper studies these models to infer that real time approach is the only solution. Several approaches are being worked on and few commercial systems too are available. These systems provide traffic information for course correction. However, it has latency and hence deviates from real time environment. Traffic congestion being highly dynamic in nature, it necessitates real time solution with real time inputs. It is proposed to integrate Real time traffic data with the traffic signal thus modulating the cycle timings at every junction. Deviation from static asymmetric cycle timing is implemented by assigning green phases based on density of vehicles. With minimalistic infrastructure and negligible incremental cost, the paper not only proposes to address traffic congestion but also paves the way for capturing traffic offenses, vehicle tracking and toll collection. The research is imminently realizable and makes a strong case for a PPP (Public Private Partnership) project.

Vehicular Traffic Vehicular Congestion Detection System Vehicular Congestion Detection System Vehicle Mobility Sensors Traffic Signals Intelligent Traffic Signals DynamicTraffic Assignment1. Introduction

The location accuracy from Global Positioning System (GPS) device is continuously improving; from 15 meters we have accuracy of 3 meters on systems that deploy WAAS (Wide Area Augmentation System). GPS uses at least three satellite data and communicates with GPS enabled receivers. WAAS uses a network of ground-based reference stations. Measurements from ground based stations along with GPS are sent to the geostationary WAAS satellites. WAAS satellites then sent the corrected data which is received by the WAAS enabled GPS devices.

PNDs (Personal Navigation Devices) are best suited for traffic application. The advantages are large displays, keyboards, arms length viewing and usability while driving. Besides, PND have room to accommodate speed, upcoming street, time of arrival and other parameters. However, in spite of these factors smart phones are preferred over PND. The mapping assistance is available through mapping giants like TeleNav, NavTeq (Navigation Technologies), Google, AT & T. The PND based navigation systems is available at USD $70 with a yearly update cost of USD $70 approx whereas the smart phone besides the device cost & data connectivity charges is available for USD $50 for the navigation app. Analog traffic receivers have an update frequency of 300 seconds. Garmin live traffic updates 1000 messages every 2 minutes on smart phones. Garmin devices with High Definition (HD) digital offer navigation services with live traffic updates for approx. more than USD $300. The traffic update frequency is 30 seconds.

Motion X GS drive offers community contributed road alerts and reporting including accidents, hazards, police location, weather, and more. Tom-Tom pro- vides traffic information by integrating variety of traditional data sources like governmental or third party data through road sensors or through mobile phone users. The system merges this information, analyses it and sends the data to users every three minutes.

In short, data come from millions of Garmin device owners, millions of cellular phone owners, incident reports, live information through radio feeds, news stations, NAVTEQ Traffic Supply and historical traffic data from each of these agencies. Some of the systems require downloading with periodic refreshing and work without the need for continuous connectivity.

2. Related Work

Proposed efficient Intelligent Traffic Control System (ITCS) system [1] . The digital-logic based principle being “a car can only move ahead if there is space for it” and “the signal remains green until the present cars have passed”. Sensors are placed entry and exit of a road segment or a road leading to a traffic junction. These sensors provide information about the vehicles entering the junction and leaving the junction. This information is sufficient to know the number of vehicles on the road segment. Based on the vehicle population, the traffic signal phases are computed. The challenge here is fixed sensors. Fixed sensors have higher capital costs, installation cost and maintenance cost. It requires 24 × 7 power supply. Broadly speaking is prohibitively costly as all the traffic signals have to be equipped with the sensors.

Defines guidelines for Anthropocentric design for self-driving vehicles [2] . Presented intelligent Traffic Light Controllers (ITLC) based on microcontroller and microprocessor [3] . It has communication interface through which cycle times can be changed dynamically. Traffic signal related performance criterions can be logged and stored on a central server.

Google traffic displays traffic in real time by acquiring data from its users and calculating the speed [4] . On the map, Google shows green if traffic is moving along; yellow, if some traffic; red, if appreciable congestion.

GNSS-1 is the first generation system, which integrates existing GPS with other technologies. In USA, the satellite based component is the Wide Area Augmentation System (WAAS), in Europe it is the European Geostationary Navigation Overlay Service (EGNOS), and in Japan it is the Multi-Functional Satellite Augmentation System (MSAS). GNSS-2 is the second generation of system. These systems collate vehicle velocities. Commercial system like Tom Tom & NavTeq capture PVT (Position, Velocity & Time) to provide traffic information.

Academicians, researchers and corporations have been working on traffic control systems with a view to optimize the traffic signal phases, reduce signal idle time and address traffic congestion. The technology is based on ATCS (Ada- ptive Traffic Control System). The systems are evolving and continuously improvising to mitigate the traffic congestion problems. Before considering Adaptive Traffic Control Systems (ATCS) deployment, it is important to understand the reasons an ATCS is required.

Contemporary Systems

The software is based on Windows and OS and all of them have application software of their own. The traffic controllers used are Siemens M 50 series or 2070, Peek 3000E, Model 170, SITRAFFIC C8xx, C9xx, Econolite ASC/2 and NEMA AWA Delta 3N. The installation cost of ATCS per junction is approx. USD $65,000.

SCOOT (Split Cycle Offset Optimization Technique) is a real time application for controlling traffic signals based on ATCS (Adaptive Traffic Control System) [5] . All traffic signals are connected and coordinated to allow smooth traffic flow. It deploys infrastructure information with the in-road sensors to compute traffic flow and traffic density. It offers all the information required to device vehicle routing thereby minimizing vehicle delays.

SCAT (Sydney Coordinated Adaptive Traffic System) is an ATCS using complex algorithms to generate traffic signal phases so as to minimize delays [6] . SCAT is a real time solution adjusting signal phases based on traffic area rather than vehicle count. The data is gathered through in-road loop sensors, which provides computing traffic density. SCAT in turn determines the duration of signal phases. SCAT provides operations and maintenance monitoring including; real time alarm monitoring, space and time diagrams as well as support from historical data which can help future planning.

Urban Traffic Optimization by Integrated Automation (UTOPIA) is integrated with SPOT to account to changes at the network level (Mauro and DiTaranto 1990). The parameters considered are vehicle detection, vehicle flow rate, traffic intensity, speed, road occupancy, density and type of vehicle. FHWA (Federal High-Way Administration), USA, developed ACS Lite which is widely deployable, at much reduced cost than traditional installation, operations and maintenance [7] . The system has CORSIM simulator and employs signal controllers from Eagle, Econolite, McCain and Peek controllers. The installations began around 2005. RHODES is a ATCS which began its implementations around 1990 [8] . RHODES predicts traffic levels taking sensors inputs from inductive loop sensors, RADAR, SONAR or video inputs. It then converts into lanes data and computes signal phases and communicates with server. RHODES also computes dynamic shortest real time to divert traffic from congested area to lean traffic area.

The comparison of the two systems with largest market share is tabulated in Table 1.

 3. Analytical Models

Dynamic traffic assignment (DTA) is a subject of interest for the last decade and is yet maturing. The traffic networks are probabilistic and uncertain, various models were proposed. Broadly these models, model travelers’ decision making, probabilistic travel time and random perception errors of individual travelers. Lighthill, Whitham, Richard introduced the continuum model (LWR model) based on fluid dynamics [9] . It proposes a function between traffic density and speed to capture the characteristics of traffic congestion formation. A congestion factor is evolved based on traffic data and principles of traffic congestion formation. The first-order continuum flow model, on dense traffic with equilibrium where k and q are density and flow respectively which depend on time t and position x

The model suggests preferred speed for vehicles. It is flawed, because, when passing is allowed, the preferred speed for each vehicle varies over time. Also the desired speeds among a group of vehicles vary.

PW Model: Payne-Whitham higher model introduces additional terms in the equation of motion proposed by LRW model, Navier-Stokes equation of motion for one-dimensional compressible flow with pressure and a relaxation term [9] [10] :

 ReferencesBilal, J.M. and Jacob, D. (2007) Intelligent Traffic Control System. IEEE International Conference on Signal Processing and Communications (ICSPC 2007), Dubai, 24-27 November 2007.Mladenovic, M.N., Abbas, M. and McPherson, T. (2014) Development of Socially Sustainable Traffic-Control Principles for Self-Driving Vehicles: The Ethics of Anthropocentric Design. IEEE International Symposium on Ethics in Science, Technology and Engineering.Chavan, S.S., Deshpande, R.S. and Rana, J.G. (2009) Design of Intelligent Traffic Light Controller Using Embedded System. Second International Conference on Emerging Trends in Engineering and Technology, ICETET.http://www.theconnectivist.com/2013/07/how-google-tracks-traffic/http://www.scoot-utc.com/http://www.scats.com.au/Shelby, S.G., Bullock, D.M., Gettman, D., Ghaman, R.S., Sabra, Z.A. and Soyke, N. (2008) An Overview and Performance Evaluation of ACS Lite—A Low Cost Adaptive Signal Control System. 87th TRB Annual Meeting, Washington DC, January 2008.RHODES—Real Time Hierarchical Optimized Distributed Effective System. http://ocw.nctu.edu.tw/course/sc011/2012-08-23-1.pdfZhang, W., Tan, G.Z., Ding, N. and Wang, G.Y. (2012) Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms. School of Computer Science and Technology, Dalian University of Technology, Dalian.http://people.umass.edu/ndh/TFT/Ch15%20High.pdfHerrera, J.C. and Bayen, A.M. Departamento de Ingeniería de Transporte y Logí- stica, Pontificia Universidad Católica de Chile, Chile, Systems Engineering, Depart- ment of Civil and Environmental Engineering, University of California, Berkeley, United States; Incorporation of Lagrangian Measurements in Freeway Traffic State Estimation.Ou, G.Y., Gao, Y. and Liu, Y. (2012) Real-Time Vehicular Traffic Violation Detection in Traffic Monitoring Stream. IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.Parmar, R.S. and Trivedi, B. (2014) Real Time Computation of Optimal Signal Timing to Maximize Vehicular Throughput for a Traffic Junction. 3rd International Conference on Eco-Friendly Computing and Communication Systems (ICECCS), ICECCS 2014, Mangalore, 18-21 December 2014.