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Journal ArticleDOI

Traffic Lights Management Using Optimization Tool

01 Jan 2018-Procedia - Social and Behavioral Sciences (Elsevier)-Vol. 238, pp 323-330
TL;DR: The purpose of this paper is to present an approach of optimization tool using in order to decrease the traffic congestion and the crossing time of a road network.
About: This article is published in Procedia - Social and Behavioral Sciences.The article was published on 2018-01-01 and is currently open access. It has received 13 citations till now. The article focuses on the topics: Traffic congestion & Intelligent transportation system.
Citations
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Journal ArticleDOI
TL;DR: Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities.
Abstract: The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.

29 citations

Journal ArticleDOI
12 Apr 2019
TL;DR: A three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.
Abstract: A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.

13 citations

Journal ArticleDOI
TL;DR: A distributed security architecture scheme based on blockchain technology for the existing intelligent traffic light system, where the smart contract is improved to achieve redundant cutting of ledger data in the process of block consensus, which greatly reduces the pressure of blockchain ledger data transmission.
Abstract: In recent years, under the background that the rapid development of traffic volume makes the current traffic lights far from meeting the urban traffic demand, intelligent traffic lights based on the centralized architecture began to appear. However, in the traffic network with complex structure and private data flow, there are many malicious attacks against the centralized architecture, such as Sybil and ghost car attacks, which undoubtedly brings great security risks to the traditional intelligent traffic lights. Blockchain technology is a popular security framework nowadays. Based on its outstanding characteristics in the distributed architecture and the development of Edge Intelligence (EI) technology, this paper proposes a distributed security architecture scheme based on blockchain technology for the existing intelligent traffic light system. At the same time, based on the model cutting technology proposed by EI, the smart contract is improved to achieve redundant cutting of ledger data in the process of block consensus, which greatly reduces the pressure of blockchain ledger data transmission. In the end of this paper, the superiority of this scheme compared with the traditional intelligent traffic light scheme in communication cost and time cost is demonstrated by simulation experiment.

12 citations

Journal ArticleDOI
TL;DR: A more dynamic green time setting system by using Fuzzy Inference System type Mamdani and it can be deduced that the output results from the implementation of this methods show a more dynamic value.

10 citations

References
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Book
11 Oct 2012
TL;DR: This instructional guide describes the use of simulation and mathematical models in determining traffic flow dynamics and presents various mathematical models including: continuity equations, the Lighthill–Whitham–Richards Model, macroscopic models, car-following models, lane-changing models, stability analysis, and phase diagrams.
Abstract: Introduction.- Part I Traffic Data: Trajectroy and Floating-Car Data.- Cross-Sectional Data.- Representations of Cross-Sectional Data.- Spatiotemporal Reconstruction of the Traffic State.- Part II Traffic Flow Modeling: General Aspects.- Continuity Equation.- The Lighthill-Whitham-Richards Model.- Macroscopic Models with Dynamic Velocity.- Elementary Car-Following Models.- Car-Following Models based on Driving Strategies.- Modeling Human Aspects of Driving Behavior.- Cellular Automata.- Lane-Changing and other Discrete-Choice Situations.- Stability Analysis.- Calibration and Validation.- The Phase Diagram of Congested Traffic States.- Part III Applications of Traffic Flow Theory: Traffic Flow Breakdown and Traffic-State Recognition.- Travel Time Estimation.- Fuel Consumption and Emissions.- Model-Based Traffic-Flow Optimization.- Solutions to the Problems.

590 citations

BookDOI
TL;DR: Models, Traffic Models, Simulation, and Traffic Simulation - Microscopic Traffic Flow Simulator VISSIM.
Abstract: Models, Traffic Models, Simulation, and Traffic Simulation.- Microscopic Traffic Flow Simulator VISSIM.- Traffic Simulation with AVENUE.- Traffic Simulation with Paramics.- Traffic Simulation with Aimsun.- Traffic Simulation with MITSIMLab.- Traffic Simulation with SUMO - Simulation of Urban Mobility.- Traffic Simulation with DRACULA.- Traffic Simulation with Dynameq.- Traffic Simulation with DynaMIT.- Traffic Simulation with METANET.

381 citations

Journal ArticleDOI
TL;DR: The results show that the adaptive traffic signal control scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.
Abstract: In this paper, we present an adaptive signal control scheme to prevent intersection traffic blockage resulted from vehicle queue spillover. A method to identify vehicle queue spillover condition through simplified shockwave analysis is developed. Instead of measuring the vehicle queue length or locating the end of queue directly, this method relies on the vehicle speed which is more feasible to measure in practice. The adaptive traffic signal control scheme is designed to prevent potential intersection traffic blockage, and adaptively allocates green time to appropriate signal phases. At the end, a simulation study is carried out to evaluate the proposed adaptive control scheme. The results show that the scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.

50 citations

01 Jan 2009
TL;DR: The genetic algorithm technology in the traffic control system and pedestrian crossing is applied to provide intelligent green interval responses based on dynamic traffic load inputs, thereby overcoming the inefficiencies of the conventional traffic controllers.
Abstract: Summary The increase in urban traffic has resulted in traffic congestions, long travel times and increase hazards to pedestrians due to inefficient traffic light controls. These scenarios necessitate the use of new methods in the design of traffic light control for vehicles and pedestrian crossings. In a conventional traffic light controller, the traffic lights change at constant cycle times which are clearly not optimal. The preset cycle time regardless of the dynamic traffic load only adds to the problem. It would be more feasible and sensible to pass more vehicles at the green interval if there are fewer vehicles waiting behind the red lights or vice versa. We apply the genetic algorithm technology in the traffic control system and pedestrian crossing to provide intelligent green interval responses based on dynamic traffic load inputs, thereby overcoming the inefficiencies of the conventional traffic controllers. We apply such technology to a four-way, two-lane junction based on two sets of parameters: vehicles and pedestrians queues behind a red light and number of vehicles and pedestrians that passes through a green light. The algorithms dynamically optimize the red and green times to control the flow of both the vehicles and the pedestrians. To represent a typical traffic flow system, we use the Cellular Automata for modeling vehicular motion behind the traffic lights. We developed an algorithm to model the situation of a four-way two-lane junction based on this technology. We compare the performance between the genetic algorithms controller and a conventional fixed time controller and the results show that the genetic algorithms controller performs better than the fixed-time controller.

40 citations