research-article

TOP: Optimizing Vehicle Driving Speed with Vehicle Trajectories for Travel Time Minimization and Road Congestion Avoidance

Authors:

Haiying ShenAuthors Info & Claims

ACM Transactions on Cyber-Physical Systems, Volume 4, Issue 2

Article No.: 17, Pages 1 - 25

https://doi.org/10.1145/3362162

Published: 16 November 2019 Publication History

Abstract

Traffic congestion control is pivotal for intelligent transportation systems. Previous works optimize vehicle speed for different objectives such as minimizing fuel consumption and minimizing travel time. However, they overlook the possible congestion generation in the future (e.g., in 5 minutes), which may degrade the performance of achieving the objectives. In this article, we propose a vehicle <u>T</u>rajectory–based driving speed <u>OP</u>timization strategy (TOP) to minimize vehicle travel time and meanwhile avoid generating congestion. Its basic idea is to adjust vehicles’ mobility to alleviate road congestion globally. TOP has a framework for collecting vehicles’ information to a central server, which calculates the parameters depicting the future road condition (e.g., driving time, vehicle density, and probability of accident). Based on the collected information, the central server also measures the friendship among the vehicles and considers the delay caused by red traffic signals to help estimating the vehicle density of the road segments. The server then formulates a non-cooperative Stackelberg game considering these parameters, in which when each vehicle aims to minimize its travel time, the road congestion is also proactively avoided. After the Stackelberg equilibrium is reached, the optimal driving speed for each vehicle and the expected vehicle density that maximizes the utilization of the road network are determined. Our real trace analysis confirms some characteristics of vehicle mobility to support the design of TOP. Extensive trace-driven experiments show the effectiveness and superior performance of TOP in comparison with other driving speed optimization methods.

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Karmakar PMeena ANatani KChakraborty S(2024)Multimodal Sensing for Predicting Real-time Biking Behavior based on Contextual Information2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503501(441-444)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503501
Ye JZhao JYe KXu C(2022)How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304325023:5(3904-3924)Online publication date: May-2022
https://doi.org/10.1109/TITS.2020.3043250
Rossi LAjmar APaolanti MPierdicca R(2021)Vehicle trajectory prediction and generation using LSTM models and GANsPLOS ONE10.1371/journal.pone.025386816:7(e0253868)Online publication date: 1-Jul-2021
https://doi.org/10.1371/journal.pone.0253868
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Index Terms

TOP: Optimizing Vehicle Driving Speed with Vehicle Trajectories for Travel Time Minimization and Road Congestion Avoidance
1. Human-centered computing
  1. Ubiquitous and mobile computing

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Published In

cover image ACM Transactions on Cyber-Physical Systems

ACM Transactions on Cyber-Physical Systems Volume 4, Issue 2

April 2020

266 pages

ISSN:2378-962X

EISSN:2378-9638

DOI:10.1145/3372402

Editor:
Tei-Wei Kuo
City University of Hong Kong and National Taiwan University, Taiwan

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Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 16 November 2019

Accepted: 01 September 2019

Revised: 01 August 2019

Received: 01 October 2018

Published in TCPS Volume 4, Issue 2

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Cited By

Karmakar PMeena ANatani KChakraborty S(2024)Multimodal Sensing for Predicting Real-time Biking Behavior based on Contextual Information2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503501(441-444)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503501
Ye JZhao JYe KXu C(2022)How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304325023:5(3904-3924)Online publication date: May-2022
https://doi.org/10.1109/TITS.2020.3043250
Rossi LAjmar APaolanti MPierdicca R(2021)Vehicle trajectory prediction and generation using LSTM models and GANsPLOS ONE10.1371/journal.pone.025386816:7(e0253868)Online publication date: 1-Jul-2021
https://doi.org/10.1371/journal.pone.0253868
Chen JLi KLi KYu PZeng Z(2021)Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement LearningACM Transactions on Cyber-Physical Systems10.1145/34476235:4(1-24)Online publication date: 22-Sep-2021
https://dl.acm.org/doi/10.1145/3447623
Zhao JYe JXu MXu C(2020)Practical model with strong interpretability and predictability: An explanatory model for individuals' destination prediction considering personal and crowd travel behaviorConcurrency and Computation: Practice and Experience10.1002/cpe.615135:18Online publication date: 23-Dec-2020
https://doi.org/10.1002/cpe.6151

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