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Assessment Method for Driving Risk of Heavy‐Duty Trucks at Interchange Ramps

Xiaomin Yan et al.

Journal of Advanced Transportation2026https://doi.org/10.1155/atr/7003248article
ABDC B
Weight
0.50

Abstract

The accident rate of interchange ramps based on investigated Chinese cases is approximately two times higher than that of mainline sections, where losses in human lives and economic costs caused by heavy‐duty truck (HDT) accidents are far greater than those of sedans. Nevertheless, existing risk assessments overlook the coupled effects of human–vehicle–road–environment factors, primarily focusing on the single‐directional driving risk assessment of HDT longitudinal braking or lateral skidding. This study proposes a visual assessment method to evaluate the comprehensive lateral and longitudinal driving risk of HDTs on interchange ramps, utilizing floating vehicle data that incorporate the coupling effects of multiple factors. Based on 800 floating vehicle data samples of HDTs from 11 types of ramps, this study integrates driver experience, moderate adverse environments, and lateral/longitudinal acceleration distribution into the G–G diagram (longitudinal acceleration plotted versus lateral acceleration) to define safety thresholds. A mathematical model was fitted in Table Curve 2D to establish the basis for proposing the Driving Risk Index (DRI) and driving risk grading (DRG). Furthermore, precise geospatial matching and visualization of driving risks are achieved using a geographic information system (GIS). The method is validated from multiple dimensions, including statistical methods, surrogate safety measures, and comparison with existing models. Both empirical and statistical analyses demonstrated a strong correlation between the visualized distribution of DRI and route alignment. Moreover, validated by the coefficient of variation (CV), the model achieves an accuracy rate of 85.9%, exhibiting 28.2% and 15.5% higher performance than the two groups of comparative methods, respectively. This integrated approach from data processing to visualization overcomes traditional limitations and supports ramp optimization and intelligent early‐warning systems.

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https://doi.org/https://doi.org/10.1155/atr/7003248

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@article{xiaomin2026,
  title        = {{Assessment Method for Driving Risk of Heavy‐Duty Trucks at Interchange Ramps}},
  author       = {Xiaomin Yan et al.},
  journal      = {Journal of Advanced Transportation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1155/atr/7003248},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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