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IPOD Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings

CAV Impacts

1677250 - Impact of Automated Driving under Virtual Traffic Signals on Resilience of Urban Road Networks

Tuesday, June 18, 2024

10:50 AM - 11:15 AM ET

Room: 219

Primary Author(s)

Yankai Zhang

Beihang University
Beihang University
Beijing, Beijing, China (People's Republic)

Co-Author(s)

JC

Jiajing Chen, n/a

Beihang University
Beihang University
Beijing, Beijing, China (People's Republic)
BL

Bo Liang, n/a

Beihang University
Beihang University
Beijing, Beijing, China (People's Republic)
ZX

Zihuan Xu, n/a

Beihang University
Beihang University
Beijing, Beijing, China (People's Republic)
LM

Lei Mo, n/a

Beihang University
Beihang University
Beijing, Beijing, China (People's Republic)
Zhao Zhang, Assistant Professor

Assistant Professor
Beihang University
Beijing, Beijing, China (People's Republic)

Submitter(s)

YZ

Yankai Zhang

Beihang University
Beijing, Beijing, China (People's Republic)

Co-Author(s)

RB

Ruijie Bian, PhD (she/her/hers)

Research Assistant Professor
Louisiana Transportation Research Center, Louisiana State University
Baton Rouge, LA, United States

Abstract: With the rapid development of autonomous driving technology, it is imperative to study the impact of this technology on the resilience of urban road transportation networks. This study focuses on Virtual Traffic Signal Control (VTS) as a representative of autonomous driving technology and proposes a multi-dimensional resilience evaluation system to compare the impacts of three different signal-timing strategies on the resilience of urban traffic networks under various scenarios. Simulation results demonstrate that the average vehicle speed under VTS control is 38.1% and 40.1% higher than the other two control modes, respectively. In addition, the VTS approach significantly reduces delays, and minimizes fuel consumption compared to the other two approaches. Moreover, this paper delves into the intrinsic mechanisms by which VTS enhances traffic resilience. It highlights the importance of integrating autonomous driving technology into traffic strategies to achieve carbon emission reduction and promote sustainable transportation. The network resilience enhanced by VTS enables cities to effectively respond to unexpected events, thereby ensuring smooth traffic flow and safety. This research provides valuable insights into existing knowledge and promotes further research in the field of traffic resilience.

Learning Objectives:

Attendees can expect to learn the following from this session:

Identify and describe the specific impacts of Virtual Traffic Signal Control (VTS) on the resilience of urban road transportation networks.
Evaluate and compare the resilience of urban traffic networks under various scenarios by implementing three different signal-timing strategies, including VTS.
Quantify the percentage change in relevant urban road network resilience evaluation indicators achieved by implementing VTS compared to two alternative control modes.
Measure and compare the reduction in delays and fuel consumption resulting from the implementation of VTS versus the other two approaches.
Analyze and explain the specific mechanisms by which VTS enhances traffic resilience, emphasizing its contribution to carbon emission reduction and sustainable transportation.