Effects of Connected Autonomous Vehicles on the Energy Performance of Signal-Controlled Junctions

Wen, Yiqing; Wang, Yibing; Zhang, Zhao; Wu, Jiaxin; Zhong, Liangxia; Papageorgiou, Markos; Zheng, Pengjun

doi:10.3390/su15075672

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…In article [24], an eco-approach is proposed to achieve efficiency, as well as overtaking slow-moving vehicles for trucks in fixed timing control signalized intersections. A micro-simulation-based optimization technique is discussed in [25]. Different saturation rates and green phase ratios are simulated using MATLAB and SUMO.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Bhat

Chen

2023

Future Transportation

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Traffic intersections throughout the United States combine fixed, semi-actuated, and fully actuated intersections. In the case of the semi-actuated and actuated intersections, uncertainties are considered in phase duration. These uncertainties are due to car waiting queues and pedestrian crossing. Intelligent transportation systems deployed in traffic infrastructure can communicate Signal and Phase Timing messages (SPaT) to vehicles approaching intersections. In the connected and automated vehicle ecosystem, the fuel savings potential has been explored. Prior studies have predominantly focused on fixed time control for the driver. However, in the case of actuated signals, there is a different and significant challenge due to the randomness caused by uncertainties. We have developed a predictive control using the SPaT information communicated from the actuated traffic intersections. The developed MPC-based algorithm was validated using model-based design platforms such as AMBER®, Autonomie®, MATLAB®, and SIMULINK®. It was observed that the proposed algorithm can save energy in a single phase, in multiple phase scenarios, and in compelled stopping at stop signs when employed considering communications.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Bhat

Chen

2023

Future Transportation

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“…Li et al [17] proposed an optimized speed-trajectory control method based on braking energy recovery efficiency to reduce energy consumption caused by frequent braking at signalized intersections. Wen et al [18] proposed an optimal control method for connected and automated vehicles to pass through signalized intersections, aiming to reduce energy consumption in mixed traffic scenarios with conventional vehicles and connected and automated vehicles. Yan et al [19] optimized traffic signal-control strategies according to the spatio-temporal characteristics of different traffic scenarios to alleviate urban traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

Li,

Gong,

Peng

et al. 2023

Energies

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With the development of the economy, urban road transportation has been continuously improved, and the number of motor vehicles has also increased significantly, leading to serious energy consumption issues. As critical nodes in the urban road transportation network, intersections have become a focal point of research on vehicle energy consumption. To investigate whether traffic signal lights affect fuel consumption and emissions, this study analyzed the operating characteristics, fuel consumption, and emissions of intersections with and without traffic signal lights using real-world vehicle data. The data from the INTERACTION dataset for both signalized intersection VA and unsignalized intersection MA are used in the study, with a time duration of 3200 s. The VT-micro energy consumption and emissions model was applied to calculate and comprehensively analyze the vehicle flow, fuel consumption, and emissions. Additionally, the study compared the fuel consumption and emissions for different driving scenarios, including straight through, left turn, right turn, and U-turn, within a single traffic signal cycle. The results revealed that at signalized intersections, the average fuel consumption per vehicle was 26.54 L/100 km, NOx emissions were 68.76 g/100 km, and CO2 emissions were 61.07 g/100 km. In contrast, at unsignalized intersections, the average fuel consumption per vehicle was 46.88 L/100 km, NOx emissions were 149.26 g/100 km, and CO2 emissions were 107.16 g/100 km. The study indicated that for traffic volumes between 50 and 103 vehicles per 100 s, signalized intersections demonstrated better fuel consumption and emission performance than unsignalized intersections. Signalized intersections could accommodate larger traffic volumes and provide enhanced traffic safety. In conclusion, the findings of this study are important for urban traffic planning and environmental policies. They provide a scientific basis for reducing fuel consumption and emissions and improving road traffic efficiency. Due to the advantages of signalized intersections in terms of energy consumption and emissions, future urban traffic planning should consider more signal light controls to achieve energy savings, emission reduction, and improved traffic operation efficiency.

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A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation

Zakeri,

Konstantas,

Sorooshian

et al. 2024

Artif Intell Rev

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This paper proposes a novel decision-support system (DSS) to assist decision-makers in the ULTIMO project with integrating Autonomous Vehicles (AVs) in Geneva, Switzerland. Specifically, it aids in selecting the best scenario for incorporating AVs into Geneva’s public transportation system. The proposed DSS is architected on a combined integrated framework that includes a machine learning (ML) algorithm, random forest (RF) algorithm, and three novel multi-criteria decision-making (MCDM) algorithms: (1) Modified E-ARWEN (ME-ARWEN) for selecting the best scenario with high sensitivity; (2) Compromiser—Positive, Neutral, Negative (Compromiser-PNN) for extracting weights from stakeholders, considering their preferences and potential conflicts; and (3) Collective Weight Processor (CWP) for deriving weights from expert opinions. Besides the main objective, this article also aims to: (1) Address the gap in practical DSS software within AV-related studies by providing Python codes of the DSS; (2) Develop a highly sensitive and comprehensive MCDM framework to address the project’s needs; and (3) Employ Artificial Intelligence within the DSS to optimize outputs. By the application of the proposed DSS, four scenarios were evaluated: (1) Full integration of AVs; (2) Partial integration; (3) Pilot project in limited areas; and (4) Delayed integration. The analysis identified partial integration as the best scenario for integrating AVs. Furthermore, comprehensive analyses conducted to validate the DSS outputs demonstrated the reliability of the results.

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Effects of Connected Autonomous Vehicles on the Energy Performance of Signal-Controlled Junctions

Cited by 4 publications

References 34 publications

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation

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