Genetic algorithm-based traffic lights timing optimization and routes definition using Petri net model of urban traffic flow

This paper proposes two algorithms for signal timing optimization of single intersections, namely, microbial genetic algorithm and simulated annealing algorithm. The basis of the optimization of these two algorithms is the original timing scheme of the SCATS, and the optimized parameters are the average delay of vehicles and the capacity. Experiments verify that these two algorithms are, respectively, improved by 67.47% and 46.88%, based on the original timing scheme.

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Development and Application of Dynamic Timing Optimization Platform for Big Data Intelligent Traffic Signals

E3S Web of Conferences ◽

10.1051/e3sconf/201913601008 ◽

2019 ◽

Vol 136 ◽

pp. 01008

Author(s):

Zhao Wang ◽

Mengjie Wang ◽

Wenqiang Bao

Keyword(s):

Big Data ◽

Travel Time ◽

Traffic Flow ◽

Delay Time ◽

Traffic Signals ◽

Traffic Signal ◽

Total Delay ◽

Timing Optimization ◽

Traffic Lights ◽

Signal Dynamic

As the number of car ownership increases, road traffic flow continues to increase. At the same time, traffic pressure at intersections is increasing as well. At present, most of the traffic lights in China are fixed cycle control. This timing control algorithm obviously cannot make timely adjustments according to changes in traffic flow. In this case, a large number of transportation resources would be wasted. It is very necessary to establish a dynamic timing system for Big data intelligent traffic signals. In this research, the video recognition method was used to acquire the number of vehicles at the intersection in real time, and the obtained data was processed by the optimization algorithm to make a reasonable dynamic timing of the traffic signals. The test results show that after using the big data intelligent traffic signal dynamic timing optimization control platform, in the experimental area, the overall total delay time was reduced by 23%, and the travel time was reduced by 15%. During the off-peak period, the overall total delay time in the experimental region was reduced by 17% and travel time was reduced by 10%. The big data intelligent traffic signal dynamic timing optimization platform would improve the operational efficiency and traffic supply capacity of the existing transportation infrastructure, and could provide real convenience for citizens.

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Two Dimensional Cellular Automaton Model of the Mixed Traffic Flow for Urban Traffic

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.2082 ◽

2012 ◽

Vol 241-244 ◽

pp. 2082-2087

Author(s):

Li Yang ◽

Jun Hui Hu ◽

Ling Jiang Kong

Keyword(s):

Boundary Conditions ◽

Cellular Automaton ◽

Traffic Flow ◽

Flow Model ◽

Urban Traffic ◽

Mixed Traffic ◽

Traffic Flow Model ◽

Traffic Lights ◽

Mixed Traffic Flow ◽

Transit Traffic

Based on the two-dimension cellular automaton traffic flow model (BML model), a mixed traffic flow model for urban traffic considering the transit traffic is established in this paper. Under the don't block the box rules and the opening boundary conditions, the impacts of transit traffic, the central station, traffic lights cycle, the vehicles length on the mixed traffic flow is studied by computer simulation. Some important characters appearing in the new model are also elucidated. It shows that traffic flow is closely related to traffic lights cycle, the geometric structure of transport network and boundary conditions. Under certain traffic light cycle time, the traffic flow has a periodical oscillation change. The comparison to practical measured data shows that our stimulation results are accordant with the changes of real traffic flow, which confirms the accuracy and rationality of our model.

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Management of urban traffic flow based on traffic lights scheduling optimization

IEEE Latin America Transactions ◽

10.1109/tla.2019.8826701 ◽

2019 ◽

Vol 17 (01) ◽

pp. 102-110 ◽

Cited By ~ 1

Author(s):

Pablo Javier Vidal ◽

Ana Carolina Olivera

Keyword(s):

Traffic Flow ◽

Urban Traffic ◽

Scheduling Optimization ◽

Traffic Lights

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Real-Time Traffic Signal Control Based on Kalman Filtering Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.915 ◽

2015 ◽

Vol 713-715 ◽

pp. 915-918

Author(s):

Yuan Xin Xu ◽

Wan Ying Yang ◽

Wen Shi

Keyword(s):

Kalman Filter ◽

Traffic Flow ◽

Control Method ◽

Urban Traffic ◽

Traffic Signal Control ◽

Signal Timing ◽

Traffic Flows ◽

Traffic Lights ◽

Real Time Traffic ◽

Stable Signal

Aiming at the problem that individual control of urban traffic lights and stable signal timing. This paper proposed a real timing control method of traffic lights which based on Kalman filter. This method use Kalman filter to predict the next time traffic flows and then update the signal timing. By field researching the traffic flow of intersection in peak hour and predicting the traffic flow. Then update the signal timing. Meanwhile using the VISSIM to simulate the intersection. The result of the simulation shows that the length of vehicle queue decreased significantly and the number of stops dropped. The efficiency of access has been greatly improved.

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Real-Time Monitoring Unit of Traffic Flow

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.588-589.1058 ◽

2012 ◽

Vol 588-589 ◽

pp. 1058-1061

Author(s):

Ting Zhang ◽

Zhan Wei Song

Keyword(s):

Real Time ◽

Traffic Flow ◽

Traffic Congestion ◽

Urban Traffic ◽

Traffic Information ◽

Real Time Monitoring ◽

Traffic Lights ◽

Real Time Traffic ◽

Transit Use ◽

Control Traffic

With the sustained growth of vehicle ownerships, traffic congestion has become obstacle of urban development. In addition to developing public transport and accelerating the construction of rail transit, use scientific managing and controlling method in real-time monitoring traffic flow to divert the traffic stream is an effective way to solve urban traffic problems. In this paper, cross-correlation algorithm is used to obtain real-time traffic information, such as capacity and occupancy of a lane, so as to control traffic lights intelligently.

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Cellular Automata Intersection Model

Collective Dynamics ◽

10.17815/cd.2020.80 ◽

2020 ◽

Vol 5 ◽

Cited By ~ 1

Author(s):

Tim Peter Erich Vranken ◽

Michael Schreckenberg

Keyword(s):

Cellular Automata ◽

Traffic Flow ◽

Variable Number ◽

Urban Traffic ◽

Traffic Data ◽

Traffic Lights ◽

Car Following ◽

Time Value ◽

Intersection Model ◽

Flow Through

This paper introduces a cellular automaton design of intersections and defines rules to model traffic flow through them, so that urban traffic can be simulated. The model is able to simulate an intersection of up to four streets crossing. Each street can have a variable number of lanes. Furthermore, each lane can serve multiple purposes at the same time, like allowing vehicles to keep going straight or turn left and/or right. The model also allows the simulation of intersections with or without traffic lights and slip lanes. A comparison to multiple empirical intersection traffic data shows that the model is able to realistically reproduce traffic flow through an intersection. In particular, car following times in free flow and the required time value for drivers that turn within the intersection or go straight through it are reproduced. At the same time, important empirical jam characteristics are retained.

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Traffic Urban Control Using an Intelligent PSO Algorithm Based on Integrated Approach

Algerian Journal of Signals and Systems ◽

10.51485/ajss.v5i1.89 ◽

2020 ◽

Vol 5 (1) ◽

pp. 1-9

Author(s):

Radja DIAF ◽

Cherif TOLBA ◽

Ahmed Nait Sidi Moh

Keyword(s):

Traffic Flow ◽

Evaluation Criteria ◽

Particle Swarm ◽

Integrated Approach ◽

Pso Algorithm ◽

Urban Traffic ◽

Pollutant Emissions ◽

Particle Swarm Optimizer ◽

Traffic Lights ◽

Vehicle Delay

In this paper, we intend to contribute to the improvement of urban traffic mobility using a learning method of traffic lights controllers. We proposed a Particle Swarm Optimization (PSO) method in which the intelligent swarm acts as the cycle time of the traffic signal. The best swarm (solution found) meets the evaluation criteria selected to describe desired objectives. The main measures of traffic lights efficiency are to maximize flow-rate at which vehicles can cross a road junction and minimize the additional travel time of the driver called vehicle delay. Particle Swarm Optimizer was coupled with the traffic flow model based on Continuous Petri nets (PN). One potential advantage of CPN model is to provide insights regarding a behavior of the platoon of vehicles on the target road network. The result obtained from this study has tested with various scenarios related to intersections in different situations. The developed self-scheduling of the optimal signal timing ensures safety and continuous traffic flow, thus increasing the mobility and reducing fuel consumption and pollutant emissions.

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