Intersection Traffic Control Based on Multi-Objective Optimization

Mou, Jinbao

doi:10.1109/access.2020.2983422

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…In their model, a PSO-based method enhanced with a difference operator and dynamic relaxation strategy is presented, and the effectiveness of the algorithm is verified by numerical experiments for a single intersection. Similar studies are also available in [17]. For the ISTP, there are mutual conflicts among the objectives to be optimized.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network

Zhang

Fan

et al. 2023

Sensors

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Urban intersections are one of the most common sources of traffic congestion. Especially for multiple intersections, an appropriate control method should be able to regulate the traffic flow within the control area. The intersection signal-timing problem is crucial for ensuring efficient traffic operations, with the key issues being the determination of a traffic model and the design of an optimization algorithm. So, an optimization method for signalized intersections integrating a multi-objective model and an NSGAIII-DAE algorithm is established in this paper. Firstly, the multi-objective model is constructed including the usual signal control delay and traffic capacity indices. In addition, the conflict delay caused by right-turning vehicles crossing straight-going non-motor vehicles is considered and combined with the proposed algorithm, enabling the traffic model to better balance the traffic efficiency of intersections without adding infrastructure. Secondly, to address the challenges of diversity and convergence faced by the classic NSGA-III algorithm in solving traffic models with high-dimensional search spaces, a denoising autoencoder (DAE) is adopted to learn the compact representation of the original high-dimensional search space. Some genetic operations are performed in the compressed space and then mapped back to the original search space through the DAE. As a result, an appropriate balance between the local and global searching in an iteration can be achieved. To validate the proposed method, numerical experiments were conducted using actual traffic data from intersections in Jinzhou, China. The numerical results show that the signal control delay and conflict delay are significantly reduced compared with the existing algorithm, and the optimal reduction is 33.7% and 31.3%, respectively. The capacity value obtained by the proposed method in this paper is lower than that of the compared algorithm, but it is also 11.5% higher than that of the current scheme in this case. The comparisons and discussions demonstrate the effectiveness of the proposed method designed for improving the efficiency of signalized intersections.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network

Zhang

Fan

et al. 2023

Sensors

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“…In multi-objective optimization problems, the Pareto optimal solution is used to select according to the practical problem [44]. e conception of the Pareto optimal solution set is introduced as below.…”

Section: Multi-objective Discrete Evolutionary Algorithmmentioning

confidence: 99%

A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning

Jiang

Zhang

et al. 2022

Journal of Advanced Transportation

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The development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack of a general framework for dispatching CAVs in these bottlenecks, which is expected to ensure safety, traffic efficiency, and energy consumption in real time. This study aimed to fill the technique gap, and a comprehensive cooperative intelligent driving framework is put forward to study the problem, which can be used in both on-ramp and intersection scenarios. Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. The simulation results show that the proposed framework can get satisfying performance with low time and energy consumption. It can reduce time consumption by up to 16.51% for the on-ramp scenario and 9.8% for the intersection scenario, while reducing energy consumption by up to 16.39% and 11.39% for the two scenarios. Meanwhile, an analysis of computation time is carried out, illuminating the flexibility and controllability of the new strategy.

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“…Traffic control, management and optimization (Costa et al 2017;Kaffash et al 2021;Kim et al 2017;Lv et al 2015;Mehmood et al 2017;Mou 2020), route optimization, and travel time (Chen, 2020;Hess et al 2015) Statistical analysis (Costa et al 2017;Mehmood et al 2017;Yves R. et al, 2018), simulation (Naumov et al 2022;Perrotta et al 2019) and optimization (Basso et al 2021;Chen, 2020;Mou 2020) Demand Demand forecast (Lee 2017;Yves R. et al, 2018) and supply chain (Chen, 2020;Giusti et It answers the question of what should be done to achieve specific goals (Souza 2014;Wang et al 2016). Predictive and prescriptive analytics are vital in helping freight-related entities make effective decisions on the organization's strategic direction (Munizaga 2019).…”

Section: Trafficmentioning

confidence: 99%