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Related Experiment Video

Updated: Jun 17, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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

Zehao Wang¹, Wei Fan¹

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.

Accident; Analysis and Prevention

|June 15, 2026

Summary

This summary is machine-generated.

Pedestrian crash factors differ between weekdays and weekends, with infrastructure being key. Sociodemographic factors like poverty disproportionately impact weekend pedestrian crashes, requiring tailored safety interventions.

Keywords:

Context-dependent effects Geospatial explainable artificial intelligence Nonlinear effects Pedestrian crash density Spatial heterogeneity

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Last Updated: Jun 17, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Transportation Safety
Geospatial Analysis
Machine Learning in Public Health

Background:

Weekday and weekend travel patterns significantly influence pedestrian exposure and crash risks.
Understanding context-dependent interactions among crash factors is crucial for effective safety policies.

Purpose of the Study:

To investigate weekday-weekend variations in factors affecting pedestrian crash density.
To account for nonlinear threshold effects, context-dependent effects, and spatial heterogeneity in crash analysis.

Main Methods:

Utilized four years of pedestrian-vehicle crash data from Mecklenburg County, North Carolina (2021-2024).
Employed a geospatial explainable machine learning framework with an XGBoost-Tweedie model and SHAP/GeoShapley for analysis.
Stratified crash data into weekday and weekend periods at the census tract level.

Main Results:

The XGBoost-Tweedie model demonstrated superior performance over other machine learning models.
Infrastructure characteristics are dominant factors in pedestrian crashes on both weekdays and weekends.
Sociodemographic factors, such as Black and poverty ratios, have a greater impact on weekend pedestrian crashes.

Conclusions:

Findings highlight significant variations in crash factor effects across different thresholds, locations, and contexts.
Period-specific and location-specific countermeasures are essential for enhancing pedestrian safety.
Addressing spatial inequities in crash risk is critical, particularly during weekends.