Citation

Saxena A. J. Transp. Health 2023; 28: e101541.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.jth.2022.101541

PMID

unavailable

Abstract

Walking is the primary means of transportation. For assessing individual's health, travel behaviour and benchmarking service levels of pedestrian infrastructure, walking/crossing speed acts as a relevant indicator. In spite of this, there is a paucity of literature that has focused on the evaluation of the individual's perceived crossing speed and the factors that influence it. Based on the responses collected from 616 pedestrians, the present study adopts an exploratory approach to identify the factors that influence an individual's perception of crossing speed. Five different types of classification-based machine learning methods (decision trees, boosting, K-nearest neighbour, random forest, and support vector machine) are employed to study the influence of selected factors of an individual's perceived crossing speed at mid-block crossings. Furthermore, using mediation analysis, it was analyzed if the crash risk perception (CRP) of an individual had a significant effect on their perceived crossing speed (PCS) directly and indirectly through their discrete crossing pattern (CP). Study findings indicate that crossing pattern (frequency of crossing/walking for recreational trips per week), crash risk perception, and age were the three most factors influencing the respondent's perceived crossing speed (PCS). In comparison to other four classification-based machine learning methods, random forest proved to be the most accurate method. Mediation analysis indicated that crash risk perception (CRP) directly affects the perceived crossing speed of individuals (individuals tend to cross slowly if they perceive a high risk of crashes) as well as indirectly influences crossing speed through their crossing pattern (CP). This study contributes to the growing body of research in investigating relationship between crossing pattern, perceived safety, and walking speed of pedestrians. The results of the present study will be the basis for advocating the need to pay more attention to pedestrian safety perception, since perceived crossing speed and crossing pattern are directly related to it.

Language: en

Keywords

Crash risk perception (CRP); Crossing pattern (CP); Machine learning classification methods; Mediation analysis; Perceived crossing speed (PCS)