Real‐time traffic signal control for intersections based on dynamic O–D estimation and multi‐objective optimisation: combined model and algorithm

Jiao, Pengpeng; Sun, Tuo; Dong-yue, LI; Guo, Han; Li, Ruimin; Hou, Zenghao

doi:10.1049/iet-its.2018.5308

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Later, with the development of wireless sensors, the control method began to become intelligent. Heuristic signal control methods appeared; the signal control scheme can be formulated according to the traffic information at the intersection [3], such as Jiao et al [4] switched phase sequence to achieve congested phase priority traffic. In actual application scenarios, this method greatly reduces the traffic congestion problem compared to the fixed mode control method.…”

Section: Related Workmentioning

confidence: 99%

Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game

Zhang

Jin

Fang

et al. 2021

Wireless Communications and Mobile Computing

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In the adaptive traffic signal control (ATSC), reinforcement learning (RL) is a frontier research hotspot, combined with deep neural networks to further enhance its learning ability. The distributed multiagent RL (MARL) can avoid this kind of problem by observing some areas of each local RL in the complex plane traffic area. However, due to the limited communication capabilities between each agent, the environment becomes partially visible. This paper proposes multiagent reinforcement learning based on cooperative game (CG-MARL) to design the intersection as an agent structure. The method considers not only the communication and coordination between agents but also the game between agents. Each agent observes its own area to learn the RL strategy and value function, then concentrates the Q function from different agents through a hybrid network, and finally forms its own final Q function in the entire large-scale transportation network. The results show that the proposed method is superior to the traditional control method.

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Section: Related Workmentioning

confidence: 99%

Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game

Zhang

Jin

Fang

et al. 2021

Wireless Communications and Mobile Computing

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“…In this paper, the fitness function of TSC is defined as the average travel time of all vehicles that are generated according to the flow demands of the transportation network.Definition The flow demands of a network

boldF

consist of the trips of all Origin‐Destination (OD) pairs [26], as expressed by

F = {F_{o, d} | o, d \in N} .

Without further explanations, this paper follows Stabler [25] to describe the flow demands with the hourly flow.…”

Section: Problem Formulationmentioning

confidence: 99%

“…). The flow demands of a network F consist of the trips of all Origin-Destination (OD) pairs [26], as expressed by…”

Section: Definition 8 (Flow Demandmentioning

confidence: 99%

An agile vehicle‐based dynamic user equilibrium scheme for urban traffic signal control

Liu

Gong

Chen

et al. 2021

IET Intelligent Trans Sys

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Traffic Signal Control (TSC) is a fundamental task in modern intelligent transport systems. TSC is often formulated as a bi-level optimization problem, comprised by the signal timing at the upper level and the Dynamic User Equilibrium (DUE) traffic assignment at the lower level. Since DUE is non-convex, existing methods either formulate approximation models or adopt traffic simulators. However, approximation models may oversimplify the practical situations, while traffic simulators are usually time-consuming. This paper formulates a vehicle-based DUE (vDUE) model and proposes an agile method that can simultaneously maintain the computational simplicity and the traffic dynamics for the traffic assignment. Further, an agile TSC system is built by combining the vDUE at the lower level for the traffic assignment with an adaptive differential evolution algorithm at the upper level for the signal timing optimization. To enhance the effectiveness of optimization, the TSC problem formulation is also improved to make it better characterize the practical requirements. In the experiments undertaken, comparisons of different TSC methods are carried out on both real-world and synthetic transportation networks. The experimental results validate the effectiveness of the proposed agile TSC system in various traffic situations.

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Network-level signal predictive control with real-time routing information

Lin

Dai

2023

Transportation Research Part C: Emerging Technologies

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Real‐time traffic signal control for intersections based on dynamic O–D estimation and multi‐objective optimisation: combined model and algorithm

Cited by 5 publications

References 20 publications

Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game

Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game

An agile vehicle‐based dynamic user equilibrium scheme for urban traffic signal control

Network-level signal predictive control with real-time routing information

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