Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US
Artikel i vetenskaplig tidskrift, 2024

Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.

Spatial machine learning

Spatial heterogeneity

Interpretability

Traffic crash frequency

Författare

Shuli Wang

Tongji University

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Kun Gao

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Lanfang Zhang

Tongji University

Bo Yu

Tongji University

Said Easa

Ryerson University

Accident Analysis and Prevention

0001-4575 (ISSN)

Vol. 199 107528

Simuleringsbaserade och fälttest för utvärdering av flerdimensionella prestanda hos intelligenta anslutna fordon

VINNOVA (2019-03418), 2020-09-01 -- 2023-08-31.

Ämneskategorier

Transportteknik och logistik

Sannolikhetsteori och statistik

DOI

10.1016/j.aap.2024.107528

PubMed

38447355

Mer information

Senast uppdaterat

2024-03-18