A bi-level dynamic emergency route planning system considering signal preemption control using CV technology
Yulu Dai,
Liang Hu,
Shutong Zhou,
Yanbin Liu and
Aixi Yang
PLOS ONE, 2025, vol. 20, issue 5, 1-25
Abstract:
Emergency Vehicles (EVs) are of considerable significance in saving human lives and property damages. To promote the efficiency of emergency operation, signal preemption control could give priority to EVs heading toward the incident location. On the other hand, providing dynamic and precise route planning for EVs plays an important role in emergency rescue since traffic changes constantly. Furthermore, connected vehicle (CV) technology that incorporates advanced wireless communication technologies, offers a huge potential to promote the efficiency of EVs and maintain smooth traffic flow via collaborative optimization of routes and signals. This study presents a bi-level dynamic emergency route planning system considering signal preemption control, which builds on traffic flow combined with hierarchical bi-layer model predictive control (MPC), for more than one EV under partial CV environment. In this approach, the mobility of EVs is prioritized before decreasing the impact of EVs operation on normal traffic. In the upper layer, a road-level emergency route would be dynamically planned and updated after each time horizon, according to the network-wide traffic flow estimation under diverse CV market penetration ratios through loop detectors and Cellular-Vehicle-to-Everything (C-V2X) communication. In the lower layer, a lane-level emergency route that combined with signal preemption control would be planned to ensure the efficiency of EVs and reduce the adverse impact of signal preemption on normal traffic. In the end, a microscopic simulation environment for a real traffic network is carried out to test the effectiveness of the proposed system. The simulation results indicate that the proposed system provides reliable and practical emergency route planning and signal control services for EVs under different traffic flow conditions.
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0323209
DOI: 10.1371/journal.pone.0323209
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