Hazard-based duration models have gained popularity in predicting traffic incident durations. However, most studies analyze congestion duration as a whole, overlooking the varying levels of congestion (minor, moderate, and severe), which may be interrelated and influenced by different factors. This study proposes multivariate joint survival analysis models to examine the relationships across these congestion levels using traffic incident data from New York State (2017–2019), treating minor and moderate congestion as recurrent events, with severe congestion as a terminal event. By incorporating a frailty term, unobserved heterogeneity among road segments is accounted for. The results show that real-time weather factors, such as temperature, wind speed, visibility, and precipitation (rain/snowfall), exhibit varying effects on the duration of different congestion levels, with these effects fluctuating over time. For example, in 2017–2019, low temperatures increase the duration of minor congestion by 40.88 %, 26.66 %, and 52.69 %, respectively. Conversely, for severe congestion, low temperatures also show stable temporal effects but reduce congestion duration by 70.81 %, 60.07 %, and 70.81 %, respectively. Rainy weather increases the duration of moderate congestion by 54.10 %, 31.94 %, and 54.10 %, respectively, while snowy weather reduces it by 41.38 %, 37.19 %, and 27.48 %. More importantly, a significant correlation is found between minor or moderate congestion, which are recurrent events, and severe congestion, the terminal event. Furthermore, a positive correlation between minor and moderate congestion suggests that unobserved factors jointly influence the duration of both. The study confirms the superiority of the proposed joint model for analyzing traffic incident duration and provides practical insights for transportation policymakers to massively ease congestion more effectively.
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号