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Multi-scale modeling of electric vehicle fatal crash risk: uncovering spatial heterogeneity and infrastructure-land use coupling mechanisms.

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Τίτλος: Multi-scale modeling of electric vehicle fatal crash risk: uncovering spatial heterogeneity and infrastructure-land use coupling mechanisms.
Συγγραφείς: Zhao Y; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China., Wang S; Research Institute of Highway, Ministry of Transport, Beijing 100088, China., Dong H; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China., Xie K; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China., Dong C; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China., Wang M; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Πηγή: Accident; analysis and prevention [Accid Anal Prev] 2026 Oct; Vol. 236, pp. 108647. Date of Electronic Publication: 2026 Jul 01.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Pergamon Press Country of Publication: England NLM ID: 1254476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2057 (Electronic) Linking ISSN: 00014575 NLM ISO Abbreviation: Accid Anal Prev Subsets: MEDLINE
Imprint Name(s): Publication: Oxford : Pergamon Press
Original Publication: [New York, Pergamon Press]
Ιατρικοί όροι (MeSH): Accidents, Traffic*/mortality , Accidents, Traffic*/statistics & numerical data , Environment Design* , Automobiles*, Humans ; United States ; Spatial Analysis
Περίληψη: Understanding the spatial heterogeneity of electric vehicle (EV) crash risks is critical for safeguarding electrified mobility systems, yet existing methods cannot simultaneously capture spatial dynamics while maintaining model interpretability. By accounting for spatially varying macro-level spatial non-stationarity and complex interaction, a Multi-Scale Adaptive Bandwidth Geographically Weighted Random Forest (MSAB-GWRF) method is developed to uncover the spatial heterogeneity and infrastructure-land use coupling mechanisms of EV fatal crash risk at the regional level. Other than traffic and weather factors, this study investigates the effects of charging infrastructure density, the Social Vulnerability Index (SVI), and land-use characteristics. Meanwhile, a cost-based EV crash risk indicator is formulated by incorporating crash-associated economic costs. Based on monthly state-level observations across the U.S. from 2020 to 2022 (N = 1836), the modelling results indicate that the MSAB-GWRF method outperforms the fixed-bandwidth GWRF, improving average R2 by 18.2%, particularly in the high-risk regions. More importantly, SHAP-based explanations further reveal pronounced spatial non-stationarity in the drivers of EV fatal crash risk. Land-use and EV-related indicators dominate in most states, outweighing conventional traffic exposure, while social vulnerability dominates in the South and Appalachia. SHAP analysis further identifies a capacity-dependent threshold effect. In high-risk regions, charging infrastructure and land-use intensity act as risk amplifiers once regional exposure exceeds critical levels, whereas low-risk regions absorb comparable expansion without safety deterioration. These findings indicate that uniform safety policies are insufficient, calling for regionally differentiated strategies coordinating charging deployment, land-use planning, and equity-oriented interventions.
(Copyright © 2026 Elsevier Ltd. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: EV fatal crash risk; MSAB-GWRF framework; Multidimensional interaction effects; Spatial heterogeneity
Entry Date(s): Date Created: 20260701 Date Completed: 20260814 Latest Revision: 20260814
Update Code: 20260815
DOI: 10.1016/j.aap.2026.108647
PMID: 42385521
Βάση Δεδομένων: MEDLINE
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  Data: Understanding the spatial heterogeneity of electric vehicle (EV) crash risks is critical for safeguarding electrified mobility systems, yet existing methods cannot simultaneously capture spatial dynamics while maintaining model interpretability. By accounting for spatially varying macro-level spatial non-stationarity and complex interaction, a Multi-Scale Adaptive Bandwidth Geographically Weighted Random Forest (MSAB-GWRF) method is developed to uncover the spatial heterogeneity and infrastructure-land use coupling mechanisms of EV fatal crash risk at the regional level. Other than traffic and weather factors, this study investigates the effects of charging infrastructure density, the Social Vulnerability Index (SVI), and land-use characteristics. Meanwhile, a cost-based EV crash risk indicator is formulated by incorporating crash-associated economic costs. Based on monthly state-level observations across the U.S. from 2020 to 2022 (N = 1836), the modelling results indicate that the MSAB-GWRF method outperforms the fixed-bandwidth GWRF, improving average R<superscript>2</superscript> by 18.2%, particularly in the high-risk regions. More importantly, SHAP-based explanations further reveal pronounced spatial non-stationarity in the drivers of EV fatal crash risk. Land-use and EV-related indicators dominate in most states, outweighing conventional traffic exposure, while social vulnerability dominates in the South and Appalachia. SHAP analysis further identifies a capacity-dependent threshold effect. In high-risk regions, charging infrastructure and land-use intensity act as risk amplifiers once regional exposure exceeds critical levels, whereas low-risk regions absorb comparable expansion without safety deterioration. These findings indicate that uniform safety policies are insufficient, calling for regionally differentiated strategies coordinating charging deployment, land-use planning, and equity-oriented interventions.<br /> (Copyright © 2026 Elsevier Ltd. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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