Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study.

ABSTRACT: Predicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks. In this paper, we present a comprehensive framework for accurately predicting incident duration, with a particular emphasis on the critical role of street conditions and locations as major incident triggers. To demonstrate the effectiveness of our framework, we performed an in-depth case study using a dataset from San

SUBMITTER: Salehi A 

PROVIDER: S-EPMC11694969 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature