A new network equilibrium flow model: User-equilibrium with quantity adjustment

Huang, Ruqing; Lee, Han; Huang, Zhongxiang

doi:10.1016/j.tre.2022.102719

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…Coordinated strategies for guidance and signal control aiming to improve system performance by providing information via variable message signs (VMS) and favored traffic signal operations. Previous attempts had predicted travel behavior through the concept of user equilibrium or dynamic user equilibrium [21,22]. Some major assumptions in these studies were rational thinking and complete knowledge of network-level performance.…”

Section: Introductionmentioning

confidence: 99%

Proactive Coordination of Traffic Guidance and Signal Control for a Divergent Network

Guo,

Zhang,

Chen

et al. 2023

Mathematics

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In the realm of transportation system optimization, enhancing overall performance through the proactive coordination of traffic guidance and signal control in a divergent network can tackle the challenges posed by traffic congestion and inefficiency. Thus, we propose an innovative approach to first allow the information on variable message signs (VMS) that deviates from estimated travel times. This proactive approach guides drivers towards optimal routes from a system-wide perspective, such as minimizing vehicle hours traveled. The deviation is constrained both by the lower bound of drivers’ long-term compliance rate and the upper bound of the favored traffic signal operation. The proposed approach coordinates the traffic guidance system with the signal control system. The traffic signal control system sets the upper limit for information deviation in the traffic guidance system, while the traffic guidance system provides demand predictions for the traffic signal control system. Overall, the objective function of the approach is the network-level performance of all users. We gauge traveler satisfaction as a measure of system credibility, using both a route choice module and a satisfaction degree module established through stated preference surveys. Numerical results demonstrate that proactive-coordinated (PC) strategies outperform reactive-coordinated (RC), proactive-independent (PI), and reactive-independent (RI) strategies by improving the system performance, meanwhile keeping the system trustworthy. Under the normal traffic scenario, the PC strategy reduces total travel time by approximately 10%. Driver satisfaction with the PC strategy increases from a baseline of 76% to 95%. Moreover, in scenarios with sudden changes in either traffic demand or supply, e.g., accidents or large events, the proactive guidance strategy is more flexible and can potentially improve more from the system perspective.

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Section: Introductionmentioning

confidence: 99%