Citation

Yu C, Hua W, Yang C, Fang S, Li Y, Yuan Q. Accid. Anal. Prev. 2024; 200: e107491.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.aap.2024.107491

PMID

38489941

Abstract

Freight truck-related crashes in urban contexts have caused significant economic losses and casualties, making it increasingly essential to understand the spatial patterns of such crashes. Limitations regarding data availability have greatly undermined the generalizability and applicability of certain prior research findings. This study explores the potential of emerging geospatial data to delve deeply into the determinants of these incidents with a more generalizable research design. By synergizing high-resolution satellite imagery with refined GIS map data and geospatial tabular data, a rich tapestry of the road environment and freight truck operations emerges. To navigate the challenges of zero-inflated issues of the crash datasets, the Tweedie Gradient Boosting model is adopted.

RESULTS reveal a pronounced spatial heterogeneity between highway and urban non-highway road networks in crash determinants. Factors such as freight truck activity, intricate road network patterns, and vehicular densities rise to prominence, albeit with varying degrees of influence across highways and urban non-highway terrains.

RESULTS emphasize the need for context-specific interventions for policymakers, encompassing optimized urban planning, infrastructural overhauls, and refined traffic management protocols. This endeavor may not only elevate the academic discourse around freight truck-related crashes but also champion a data-driven approach towards safer road ecosystems for all.

Language: en

Keywords

Emerging geospatial data; Freight truck-related crash; Geospatial big data analysis; Influencing factors analysis; Satellite imagery