A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics

• Published in: Accident analysis and prevention 2021-02, Vol.150, p.105898, Article 105898
• Main Authors: Su, Junbiao, Sze, N.N., Bai, Lu
• Format: Article
• Language: English
• Subjects: accessibility; crash prediction model; joint probability model; Pedestrian safety; walkability
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2020.105898

•A joint probability approach is proposed to simultaneously estimate total and pedestrian crashes.•Walking frequency is used as a proxy for the exposure of pedestrian involvement in crashes.•Traffic flow, trip generation, road characteristics, and points of interest are correlated with crash occurrence.•Pedestrian involvement is correlated with the characteristics of residents, and access to public transport. Road safety is a major public health issue, with road crashes accounting for one-fourth of all documented injuries. In these crashes, pedestrians are more vulnerable to fatal and/or severe injuries than car occupants. Therefore, it is necessary to have a better understanding of the relationship between pedestrian crashes and possible influencing factors, including road environment, traffic conditions, and population characteristics. In conventional studies, separate prediction models were established for pedestrian crashes and other crash types, which could have ignored possible correlations among the different crash types. Additionally, these influencing factors can contribute to pedestrian crashes in two manners, i.e., contributing to crash occurrence and propensity of pedestrian involvement. Furthermore, extensive pedestrian count data were generally not available, affecting the estimation of pedestrian crash exposure. In this study, a joint probability model is adopted for the simultaneous modeling of crash occurrence and pedestrian involvement in crashes; effects of possible influencing factors, including land use, road networks, traffic flow, population demographics and socioeconomics, public transport facilities, and trip attraction attributes, are considered. Additionally, trip generation and pedestrian activity data, based on a comprehensive household travel survey, are used to determine pedestrian crash exposure. Markov chain Monte Carlo full Bayesian approach is then applied to estimate the parameters. Results indicate that crash occurrence is correlated to traffic flow, number of non-signalized intersections, and points of interest such as restaurants and hotels. By contrast, population age, ethnicity, education, household size, road density, and number of public transit stations could affect the propensity of pedestrian involvement in crashes. These findings indicate that better design and planning of built environments are necessary for safe and efficient access for pedestrians and for the long-term improvement of walkability in a high-d