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traffic signal optimization to Improve Sustainability: A Literature Review

ENERGIES 2022年第22期15卷 8452页

作者： Alshayeb, Suhaib Stevanovic, Aleksandar Mitrovic, Nikola Espino, Elio CHA Consulting 8935 NW 35th Ln Doral FL 33172 USA Univ Pittsburgh Swanson Sch Engn Dept Civil & Environm Engn 341A Benedum Hall3700 OHara St Pittsburgh Pittsburgh PA 15261 USA

Optimizing traffic signals to improve traffic progression relies on minimizing mobility performance measures (e.g., delays and stops). However, delay and stop minimizations do not necessarily lead to minimal sustainability measures (e.g., fuel consumption and emissions). For that reason, researchers have focused, for decades, on integrating traffic models, signal optimization models, and fuel consumption and emissions models to minimize sustainability metrics while keeping acceptable levels of mobility metrics. Therefore, this paper reviews, classifies, and analyzes studies found in the literature regarding optimizing sustainable traffic signals. This paper provides researchers with a good starting point to further develop solutions which can address sustainable traffic control. To achieve that, this study details the most notable sustainable signal timing optimization studies from six perspectives: traffic models, fuel consumption and emissions models, optimization methods, objective functions, operating conditions, and reported sustainability savings. Outcomes of this research show that the previous studies deployed many combinations of elements from the six-perspective mentioned above, leading to a wide range of fuel consumption and emissions savings. The study also concludes that the available fuel consumption and emissions models are relatively old. Hence, future research is needed to develop new fuel consumption and emissions models based on recently collected data.

关键词： traffic signal optimization fuel consumption emissions objective function traffic simulation operating conditions sustainability saving

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traffic signal optimization using Real-Time Pedestrian and Vehicle Counts

Traffic Signal Optimization using Real-Time Pedestrian and V...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Gomathi, S. Sharan Sampath Kumar, V.B. Rithish Kumar, G. Thiruvenkadakrishnan, S. Sri Sairam Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798331512088

Urban traffic congestion is a challenge for cities around the globe, prompting the need for the development of more effective and responsive traffic signal systems. This review aims to discuss recent progress in traffic signal optimization, particularly in the integration of real-time data analysis, machine learning, and adaptive algorithms. We discuss several strategies, including Dynamic traffic Light Scheduling (DTLS), Q-learning, and intelligent traffic light signaling measures, which exploit real-time vehicle detection and adaptive signal control for enhancing traffic flow and minimizing intersection congestion. Additionally, the review emphasizes the promise of enabling emergency vehicle prioritization and the application of real-time video processing for dynamic signal adaptation. Although giving promising results, there are still many areas of research left open, such as the requirement for more integrated optimization algorithms, increased scalability for large cities, and a clearer understanding of the economic and environmental impacts of these systems. This work also addresses a suggested solution that integrates vehicle and pedestrian detection, dynamic traffic signal optimization, and real-time video processing for improved traffic flow, pedestrian safety, and environmental viability. The performance of such systems is measured against important metrics, such as mean wait times, traffic flow efficiency, and environmental cost, demonstrating the potential of smart traffic management systems to solve the challenges of contemporary urban mobility. © 2025 IEEE.

关键词： CCTV surveillance object detection pedestrian counting real-time processing TensorFlow traffic signal optimization vehicle counting

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traffic signal optimization by Integrating Reinforcement Learning and Digital Twins 9

Traffic Signal Optimization by Integrating Reinforcement Lea...

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9th IEEE Smart World Congress, SWC 2023

作者： Kumarasamy, Vijayalakshmi K Saroj, Abhilasha Jairam Liang, Yu Wu, Dalei Hunter, Michael P. Guin, Angshuman Sartipi, Mina University of Tennessee at Chattanooga Department of Computer Science and Engineering United States Oak Ridge National Laboratory Applied Research for Mobility Systems United States Georgia Institute of Technology School of Civil and Environmental Engineering United States

ISBN: (纸本)9798350319804

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario. © 2023 IEEE.

关键词： Digital Twin Fuel Consumption Graph Neural Network Multi-Agent Reinforcement Learning traffic signal optimization

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Real-time application of grey system theory in intelligent traffic signal optimization

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第4-5期24卷 3137-3153页

作者： Zhang, Shu Univ Chinese Acad Sci Sch Engn Sci Beijing Peoples R China

In order to solve these problems, this paper introduced the grey system theory (GST) method in the real-time application of intelligent traffic signal optimization (ITSO). In this paper, the deep Q-network (DQN) algorithm was used to realize the dynamic signal light setting of real-time traffic conditions, which can improve the overall operating efficiency of the traffic system, and the PPO (Proximal Policy optimization) algorithm was used to solve the problem of the lack of real-time performance of the traditional traffic signal optimization methods. By comparing the traffic congestion index of S city before and after the application of the GST method, the paper found that the average one week before the application was 60.1%, but it dropped to 26.6% after the application. In the experimental test of average speed comparison, the speed after applying the GST method was generally higher than the value before application, and the overall speed increase was about 20 km/h. This paper emphasizes the importance of evaluating the robustness of the GST method, particularly in its ability to manage unexpected scenarios. The research concentrates on assessing four critical indicators: outlier handling, noise tolerance, handling missing data, and nonlinear coping ability.

关键词： Grey system theory intelligent transportation traffic signal optimization real-time data optimization algorithm

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Decentralized arterial traffic signal optimization with connected vehicle information

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JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2023年第2期27卷 145-160页

作者： Liang, Xiao (Joyce) Guler, S. Ilgin Gayah, Vikash V. Penn State Univ Dept Civil & Environm Engn 231L Sackett Bldg University Pk PA 16802 USA

This paper proposes a decentralized signal control algorithm that leverages connected vehicle information to improve traffic operations along arterials. The proposed algorithm obtains real-time vehicle locations and speeds, as well as information on pedestrians waiting to cross individual intersections, to optimize signal phasing and timing plans. signal timing is optimized at individual intersections;however, information about vehicle platoons passing a given intersection is shared with neighboring intersections to facilitate natural coordination between adjacent intersections. The decentralized algorithm is compared to a centralized algorithm that optimizes signal timing at all intersections simultaneously, as well as a traditional coordination strategy. The proposed algorithm is shown both to be more computationally efficient than the centralized approach and provide better operational performance (in terms of person, vehicle and pedestrian delay) than both the centralized algorithm and the traditional strategy. The algorithm is robust to a range of demand patterns and can be applied under scenarios in which not all vehicles are connected or full information about pedestrian arrivals is not available.

关键词： Arterial coordination connected vehicles decentralized traffic signal optimization traffic signal optimization

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Integrating traffic signal optimization with vehicle microscopic control to reduce energy consumption in a connected and automated vehicles environment

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JOURNAL OF CLEANER PRODUCTION 2022年 371卷

作者： Jiang, Zhongtai Yu, Dexin Luan, Siliang Zhou, Huxing Meng, Fanyun Jilin Univ Sch Transportat Changchun 130022 Jilin Peoples R China Jilin Engn Res Ctr Intelligent Transportat Changchun 130022 Jilin Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266520 Peoples R China Jimei Univ Nav Inst Xiamen 361021 Fujian Peoples R China

Transportation systems face a variety of problems, especially in the aspects of traffic efficiency and energy consumption. The emerging of connected and automated vehicles (CAVs) technologies offers promising solutions to tackle these challenges. Integrating traffic signal control with vehicle trajectory optimization has significant potential to improve transportation sustainability and traffic efficiency. This paper presents an integrated traffic control framework for traffic signal optimization and vehicle microscopic control of CAVs at an isolated signalized intersection to reduce fuel consumption and improve transportation sustainability. Firstly, considering the higher controllability of CAVs, the Minimizing Overall Braking Induced by Lane Changes Model and the Intelligent Driver Model are redesigned to control the microscopic longitudinal behavior and lateral behavior of vehicles. Secondly, an optimal control model is established to optimize the trajectories of vehicles considering fuel consumption and driving comfort. The analytical, closed-form solutions are derived through Pontryagin's Maximum Principle. Thirdly, to alleviate vehicle delay, the Controlled optimization of Phase model is applied to determine the optimal intersection signal timing plan, including signal cycle length and duration of green and red phases. Finally, a receding horizon framework is built to integrate these control strategies and conduct the optimization. Experimental results suggest that the proposed integrated traffic control framework can improve both traffic efficiency and energy efficiency. The reduced traffic delay, energy consumption, and pollutant emission can be as much as 33.51-44.25%, 18.44-22.14%, and 13.36-55.20%, respectively depending on the demand scenario.

关键词： Energy consumption reduction traffic signal optimization Connected and automated vehicles Vehicle microscopic control

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traffic signal optimization with Estimation of Distribution Algorithms and VISSIM-MATLAB Integrated Simulation Platform

Traffic Signal Optimization with Estimation of Distribution ...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Liang, Yongsheng Ren, Zhigang Chen, An Guo, Daofu Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China

ISBN: (纸本)9781728169293

traffic signal optimization plays a crucial role in improving the service ability of traffic networks in urban areas. With the traffic network getting more and more complex, there have been increasing research interests in employing intelligent algorithms to find proper settings for traffic signals. As a special type of evolutionary algorithms, estimation of distribution algorithms (EDAs) possess strong optimization ability but have seldom been used in traffic signal optimization. In this paper, two efficient variants of continuous EDAs, namely EDA with variance enlargement strategy (EDA(ve)) and EDA with variable-width histogram model (EDA-VWH), are modified and adopted as optimizers to find proper traffic signal cycles in an actual urban area with multiple intersections. The performances of the two resultant algorithms, i.e. modified EDAve (mEDA(ve)) and modified EDA-VWH (mEDA-VWH), are comprehensively studied through a VISSIM-MATLAB integrated simulation platform, which could provide a convenient and close-to-reality simulation environment. The simulation results showed that mEDAve and mEDA-VWH could effectively reduce the mean delay time of all vehicles under different traffic conditions. In comparison with four other algorithms, including genetic algorithm, particle swarm optimization, differential evolution and random search method, the two modified EDAs also achieved competitive results.

关键词： urban traffic network traffic signal optimization estimation of distribution algorithms VISSIM

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traffic signal optimization: Combining Static and Dynamic Models

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TRANSPORTATION SCIENCE 2019年第1期53卷 21-41页

作者： Koehler, Ekkehard Strehler, Martin Brandenburg Tech Univ Cottbus D-03046 Cottbus Germany

In this paper, we present a cyclically time-expanded network model for simultaneous optimization of traffic assignment and traffic signal parameters, in particular, offsets, split times, and phase orders. Since travel times are of great importance for developing realistic solutions for traffic assignment and traffic signal coordination in urban road networks, we perform an extensive analysis of the model. We give an example showing that a linear time-expanded model can reproduce realistic nonconvex travel times especially for use with traffic signals and we verify this by simulation. Furthermore, we show how exact mathematical programming techniques-namely, mixed-integer linear programming-can be used for optimizing the control of traffic signals. We provide computational results for real-world instances and demonstrate the capabilities of the cyclically time-expanded model by simulation results obtained with state-of-the-art traffic simulation tools.

关键词： traffic signal optimization traffic assignment cyclically time-expanded network flow over time link performance mixed-integer programming

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Deep Reinforcement Learning based traffic signal optimization for Multiple Intersections in ITS

Deep Reinforcement Learning based Traffic Signal Optimizatio...

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IEEE International Conference on Advanced Networks and Telecommunications Systems (IEEE ANTS)

作者： Paul, Ananya Mitra, Sulata IIEST Dept Comp Sci & Technol Sibpur Howrah India

ISBN: (纸本)9781728192901

The number of vehicles is drastically increasing worldwide, especially in large cities. Thus there is a need to model and enhance the traffic management to help meet this rising requirement. The primary purpose of traffic management is to reduce traffic congestion by optimizing traffic signal, which is currently one of the main concerns. Reinforcement Learning (RL) approaches in Intelligent Transportation System (ITS) are infeasible for traffic management of large road networks. However, Deep Reinforcement Learning (DRL) is capable of handling this enlarged problem. In order to manage the traffic flow of a large road network, there is a strong need for coordination between traffic signals of the intersections, enabling vehicles to pass through intersections more easily. In this paper, a single DRL agent manages the traffic signal of multiple intersections using policy gradient algorithm. In particular, the agent is trained with spatio-temporal data of the environment that allows it to perform action in different deep neural network models. The simulation experiment is studied in terms of three different simulation metrics. The proposed system outperforms while comparing it with the baseline i.e. fixed signal duration systems.

关键词： Reinforcement Learning Deep Learning Policy Gradient traffic signal optimization

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traffic signal timing optimization for isolated urban intersections considering environmental problems and non-motorized vehicles by using constrained optimization solutions

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2022年第5期7卷 1-12页

作者： Quang Hoc Tran Van Manh Do Tuan Hai Dinh Univ Transport & Commun Hanoi Vietnam Hanoi Architectural Univ Fac Civil Engn Km 10Nguyen Trai Rd Hanoi Vietnam

In optimizing traffic signal timing schemes, various researchers provided their own suggested model to optimize timing plans at isolated urban intersections. Those model formulations were reputed and qualified for their case studies and could apply in different particular circumstances;however, most of the mentioned studies underestimated improving the current traffic control system by advancing traffic efficiency, enhancing traffic safety, and protecting the urban environment simultaneously. Moreover, several stochastic solutions demonstrated taking advantage of searching global optimization results and overcoming the deterministic methods in multi-objective traffic optimization in various pieces of research. In addition, the environmental factor in optimizing traffic lights is also eliminated because current studies mainly focus on traffic efficiency and traffic safety. To limit the shortcomings of the above studies, this study aims to provide an adequate and flexible model formulation to handle the optimal traffic signal timing issue at an isolated signalized intersection in urban areas considering the vehicle exhaust emissions and the other effective objective functions simultaneously by applying the enhanced stochastic solutions. The provided model formulations followed some steps. First, a fitness function based on the simultaneous optimization condition of traffic efficiency functions, traffic safety functions, and protected environment functions was established. Then, the constrained function according to the reliable theoretical basis was generated to improve the search capacity of the suggested model formulation. Next to several enhanced stochastic methods are used to analyze the fitness function constrained genetic algorithm (GA) and constrained particle swarm optimization (PSO). Henceforward, some effective comparisons were made among stochastic solutions, between existing timing plans and suggested timing plans, and between a traditional well-known meth

关键词： traffic signal optimization Sustainable development Sustainability Vehicle exhaust emissions Non-motorized vehicles and environment protection Intelligent transport system

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