Examining weekday-weekend variations in factors affecting pedestrian crashes: A geospatial explainable machine learning framework.

Zehao Wang¹, Wei Fan²

¹USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States. Electronic address: zwang54@uncc.edu.

²USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States. Electronic address: wfan7@uncc.edu.

PubMed DOI

Abstract

Temporal shifts in travel demand and activity patterns between weekdays and weekends substantially alter pedestrian exposure and crash occurrence mechanisms, implying that the contributing factors differ accordingly. In addition, although nonlinear effects and spatial heterogeneity have been widely investigated, context-dependent interactions among multiple factors in pedestrian crashes remain ins…

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