scholarly journals Urban Traffic Signal Control Based on Multiobjective Joint Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yongrong Wu ◽  
Yijie Zhou ◽  
Yanming Feng ◽  
Yutian Xiao ◽  
Shaojie He ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Urban Traffic ◽  
Joint Optimization ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Timing Optimization ◽  
Average Delay ◽  
Annealing Algorithm

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.

Enhanced Genetic Algorithm for Signal-Timing Optimization of Oversaturated Intersections

2000 ◽  
Vol 1727 (1) ◽  
pp. 32-41 ◽  
Author(s):  
Byungkyu “Brian” Park ◽  
Carroll J. Messer ◽  
Thomas Urbanik
Keyword(s):  
Genetic Algorithm ◽  
Operating Conditions ◽  
Throughput Maximization ◽  
Signal Timing ◽  
Timing Optimization ◽  
Average Delay ◽  
Delay Minimization ◽  
Traffic Conditions ◽  
Cycle Lengths ◽  
Oversaturated Intersections

Enhancements were provided to a previously developed genetic algorithm (GA) for traffic signal optimization for oversaturated traffic conditions. A broader range of optimization strategies was provided to include modified delay minimization with a penalty function and throughput maximization. These were added to the initial delay minimization strategy and were further extended to cover all operating conditions. The enhanced program was evaluated at different intersection spacings. The optimization strategies were evaluated and compared with their counterpart from TRANSYT-7F, version 8.1. A microscopic stochastic simulation program, CORSIM, was used as the unbiased evaluator. Hypothesis testing indicated that the GA-based program with average delay minimization produced a superior signal-timing plan compared with those produced by other GA strategies and the TRANSYT-7F program in terms of queue time. It was also found from the experiments that TRANSYT-7F tended to select longer cycle lengths than the GA program to reduce random plus oversaturation delay.

Real-Time Coordinated Signal Control Through Use of Agents with Online Reinforcement Learning

2003 ◽  
Vol 1836 (1) ◽  
pp. 64-75 ◽  
Author(s):  
Min Chee Choy ◽  
Ruey Long Cheu ◽  
Dipti Srinivasan ◽  
Filippo Logi
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Multiagent System ◽  
Fuzzy Rule ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Rule Base ◽  
Traffic Network ◽  
Average Delay

A multiagent architecture for real-time coordinated signal control in an urban traffic network is introduced. The multiagent architecture consists of three hierarchical layers of controller agents: intersection, zone, and regional controllers. Each controller agent is implemented by applying artificial intelligence concepts, namely, fuzzy logic, neural network, and evolutionary algorithm. From the fuzzy rule base, each individual controller agent recommends an appropriate signal policy at the end of each signal phase. These policies are later processed in a policy repository before being selected and implemented into the traffic network. To handle the changing dynamics of the complex traffic processes within the network, an online reinforcement learning module is used to update the knowledge base and inference rules of the agents. This concept of a multiagent system with online reinforcement learning was implemented in a network consisting of 25 signalized intersections in a microscopic traffic simulator. Initial test results showed that the multiagent system improved average delay and total vehicle stoppage time, compared with the effects of fixed-time traffic signal control.

scholarly journals A MULTI-INTERSECTION MODEL AND SIGNAL TIMING PLAN ALGORITHM FOR URBAN TRAFFIC SIGNAL CONTROL

Transport ◽  
2014 ◽  
Vol 32 (4) ◽  
pp. 368-378 ◽  
Author(s):  
Wenbin Hu ◽  
Huan Wang ◽  
Bo Du ◽  
Liping Yan
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Delay Time ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Traffic Network ◽  
Intersection Model ◽  
Volume Algorithm

The urban traffic signal control system is complex, non-linear and non-equilibrium in real conditions. The existing methods could not satisfy the requirement of real-time and dynamic control. In order to solve these difficulties and challenges, this paper proposes a novel Multi-Intersection Model (MIM) based on Cellular Automata (CA) and a Multi-Intersection Signal Timing Plan Algorithm (MISTPA), which can reduce the delay time at each intersection and effectively alleviate the traffic pressure on each intersection in the urban traffic network. Our work is divided into several parts: (1) a multi-intersection model based on CA is defined to build the dynamic urban traffic network; (2) MISTPA is proposed, which truly reflects the real-time demand degree to green time of the traffic flow at each intersection. The MISTPA is composed Single Intersection Volume Algorithm (SIVA), Single-Lane Volume Algorithm (SLVA) and single intersection signal timing plan algorithm (SISTPA). Extensive experiments show that when the saturation is greater than 0.3, the MIM and the MISTPA achieve good performance, and can significantly reduce the vehicle delay time at each intersection. The average delay time of the traffic flow at each intersection can obviously be reduced. Finally, a practical case study demonstrates that the proposed model and the corresponding algorithm are correct and effective.

Research of Traffic Signal Control Strategy Based on the Fuzzy Control

2014 ◽  
Vol 651-653 ◽  
pp. 486-490 ◽  
Author(s):  
Xue Bo Yan
Keyword(s):  
Control Strategy ◽  
Traffic Congestion ◽  
Dynamic Change ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Time Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Control Effect

To ease the traffic pressure on urban traffic signal control strategy research started. Dynamic change prediction analysis of traffic flow through the flow of information as a basis for fuzzy reasoning, automatically adjust the signal cycle, green ratio and phase control parameters, real-time signal timing to generate optimal solutions for optimal control effect. The results show that this method can effectively alleviate traffic congestion, meet the design expectations.

scholarly journals Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Li ◽  
Lijie Yu ◽  
Siran Tao ◽  
Kuanmin Chen
Keyword(s):  
Simulation Software ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Simulation Environment ◽  
Multi Objective Optimization ◽  
Good Efficiency ◽  
Multi Objective ◽  
Traffic Signal Timing

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition.

Extremum-Seeking for Adaptation of Urban Traffic Signal Control

2014 ◽  
Vol 47 (3) ◽  
pp. 5067-5072 ◽  
Author(s):  
Ronny Kutadinata ◽  
Will Moase ◽  
Chris Manzie ◽  
Lele Zhang ◽  
Tim Garoni
Keyword(s):  
Traffic Signal ◽  
Urban Traffic ◽  
Extremum Seeking ◽  
Signal Control ◽  
Traffic Signal Control

Combining SysML with Petri Nets for the Design of an Urban Traffic Signal Control

2021 ◽  
pp. 115-126
Author(s):  
Layse Santos Souza ◽  
Michel S. Soares
Keyword(s):  
Petri Nets ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control

Optimal Training of Artificial Neural Networks to Forecast Power System State Variables

2014 ◽  
Vol 3 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Victor Kurbatsky ◽  
Denis Sidorov ◽  
Nikita Tomin ◽  
Vadim Spiryaev
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Simulated Annealing ◽  
Power System ◽  
Simulated Annealing Algorithm ◽  
Active Power ◽  
Support Vector ◽  
State Variables ◽  
Annealing Algorithm ◽  
Artificial Neural

The problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The proposed method has two stages. At the first stage the input signal is decomposed into orthogonal basis functions based on the Hilbert-Huang transform. The genetic algorithm and simulated annealing algorithm are applied to optimal training of the artificial neural network and support vector machine at the second stage. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm.

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