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Urban expressway diverging areas face higher conflict risks due to complex weaving movements. This study aims to explore the determinants influencing conflict risk in diverging areas. High-resolution trajectory data totaling 32.1 hours from six expressway diverging areas in Wuhan were collected using unmanned aerial vehicles. By incorporating vehicle kinematic information, a new method for identifying traffic conflicts in a two-dimensional plane was defined. Based on this, a correlated random parameters multinomial logit model with heterogeneity in means was developed by relaxing fixed constraints on the means of random parameters, and allowing potential correlations among random parameters. The results show that exit configuration, speed difference between lanes, acceleration, and vehicle in exit significantly influence conflict risk, with their estimates exhibiting random effects. The influencing factors for lateral and longitudinal conflict risks differ. The findings provide insights for improving traffic safety in expressway diverging areas.
Wen et al. (Thu,) studied this question.
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