Paramics-Based Microscopic Simulation Evaluation for Urban Traffic Signal Two-Stage Controller

ICTE 2013 ◽  
2013 ◽  
Author(s):  
Wenchen Yang ◽  
Lun Zhang ◽  
Zhaocheng He ◽  
Qian Rao
Keyword(s):  
Traffic Signal ◽  
Urban Traffic ◽  
Microscopic Simulation ◽  
Two Stage ◽  
Simulation Evaluation

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

scholarly journals Signal Preemption Control of Emergency Vehicles Based on Timed Colored Petri Nets

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haibo Mu ◽  
Linzhong Liu ◽  
Xiaojing Li
Keyword(s):  
Petri Nets ◽  
Control Strategies ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Colored Petri Nets ◽  
Current Phase ◽  
Emergency Vehicle ◽  
Phase Switching ◽  
Emergency Vehicles ◽  
Timed Colored Petri Nets

This paper focuses on the use of timed colored Petri nets (TCPN) to study emergency vehicle (EV) preemption control problem. TCPN is adopted to establish an urban traffic network model composed of three submodels, namely, traffic flow model, traffic signal display and phase switch model, and traffic signal switch control model. An EV preemption optimization control system, consisting of monitoring subsystem, phase time determination subsystem, and phase switching control subsystem, is designed. The calculation method of the travelling speed of EV on road sections is presented, and the methods of determining the actual green time of current phase and the other phase are given. Some computational comparisons are performed to verify the signal preemption control strategies, and simulation results indicate that the proposed approach can provide efficient and safe running environments for emergency vehicles and minimize EV’s interference to social vehicles simultaneously.

scholarly journals Multi-Agent Reinforcement Learning for Optimizing Traffic Signal Timing

2021 ◽  
Author(s):  
Areej Salaymeh ◽  
Loren Schwiebert ◽  
Stephen Remias
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signals ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Model Free ◽  
Proposed Model ◽  
Multi Agent ◽  
Traffic Signal Timing

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network. In this paper, a model-free approach is used to optimize signal timing for complicated multiple four-phase signalized intersections. We propose a multi-agent deep reinforcement learning framework that aims to optimize traffic flow using data within traffic signal intersections and data coming from other intersections in a Multi-Agent Environment in what is called Multi-Agent Reinforcement Learning (MARL). The proposed model consists of state-of-art techniques such as Double Deep Q-Network and Hindsight Experience Replay (HER). This research uses HER to allow our framework to quickly learn on sparse reward settings. We tested and evaluated our proposed model via a Simulation of Urban MObility simulation (SUMO). Our results show that the proposed method is effective in reducing congestion in both peak and off-peak times.

scholarly journals Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1402 ◽  
Author(s):  
Haibo Zhang ◽  
Xiaoming Liu ◽  
Honghai Ji ◽  
Zhongsheng Hou ◽  
Lingling Fan
Keyword(s):  
Cooperative Control ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Data Driven ◽  
Experimental Comparison ◽  
Signal Control ◽  
Signal Timing ◽  
Agent Based ◽  
Multi Agent ◽  
Traffic Signal Timing

Data-driven intelligent transportation systems (D2ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

scholarly journals Software-defined Architecture for Urban Regional Traffic Signal Control

2020 ◽  
Vol 32 (2) ◽  
pp. 229-236
Author(s):  
Songhang Chen ◽  
Dan Zhang ◽  
Fenghua Zhu
Keyword(s):  
Traffic Congestion ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
The Novel ◽  
Simulation Experiments ◽  
Microscopic Traffic Simulation ◽  
Control Devices ◽  
Innovation Platform

Regional Traffic Signal Control (RTSC) is believed to be a promising approach to alleviate urban traffic congestion. However, the current ecology of RTSC platforms is too closed to meet the needs of urban development, which has also seriously affected their own development. Therefore, the paper proposes virtualizing the traffic signal control devices to create software-defined RTSC systems, which can provide a better innovation platform for coordinated control of urban transportation. The novel architecture for RTSC is presented in detail, and microscopic traffic simulation experiments are designed and conducted to verify the feasibility.

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