Collision risk analysis in the merging area of interchanges using Monte Carlo traffic simulation

Dejing Liu, Faxing Li, Zhen Yang, Guilong Xu, Xin Zheng, Song Yue and Yingjun Gao

PLOS ONE, 2025, vol. 20, issue 9, 1-16

Abstract: Traffic simulation has gained significant attention due to its ability to quickly evaluate traffic efficiency and safety in the merging areas of interchanges. However, most of the existing studies mainly investigate how to achieve great traffic efficiency, few studies focus on simulation precision and collision risk under traffic uncertainty in merging areas of interchanges. Therefore, the purpose of this paper is to assess potential collision risk in the merging area of interchanges considering uncertainty of traffic flow. A random traffic simulation is established by Monte Carlo method to simulate the high-field traffic flow in the merging area of interchanges. Then the safe braking deceleration (SBD) as a safety measure index is proposed to identify the vehicle’s collision risk in merging. Several risk variables including relative distance, relative speed and merging angular velocity (MAV) between vehicles on the ramp and main line are extracted from the simulation scenario. The results show that the relative speed and distance between the vehicle on the ramp and the one in front on the mainline have little impact on SBD, while MAV significantly indicates collision risk in the merging area of the interchange. Additionally, SBD increases as MAV rises. This study introduces a new traffic simulation platform that accounts for parameter uncertainty to simulate high-fidelity traffic situations, enabling the design of more reliable ramp merging control strategies. Moreover, MAV, which effectively represents SBD, can significantly identify collision risks in the merging area of interchanges.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0330224

DOI: 10.1371/journal.pone.0330224

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