Development of Crash Prediction Models Involving Heavy-duty Trucks over Long Downhill Segments Considering Multi-mode Failure Probability

YIN Yanna; WEN Huiying

doi:10.3963/j.jssn.1674-4861.2022.03.001

Volume 40 Issue 3

Jun. 2022

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YIN Yanna, WEN Huiying. Development of Crash Prediction Models Involving Heavy-duty Trucks over Long Downhill Segments Considering Multi-mode Failure Probability[J]. Journal of Transport Information and Safety, 2022, 40(3): 1-9. doi: 10.3963/j.jssn.1674-4861.2022.03.001

Citation:

YIN Yanna, WEN Huiying. Development of Crash Prediction Models Involving Heavy-duty Trucks over Long Downhill Segments Considering Multi-mode Failure Probability[J]. Journal of Transport Information and Safety, 2022, 40(3): 1-9. doi: 10.3963/j.jssn.1674-4861.2022.03.001

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Development of Crash Prediction Models Involving Heavy-duty Trucks over Long Downhill Segments Considering Multi-mode Failure Probability

doi: 10.3963/j.jssn.1674-4861.2022.03.001

YIN Yanna,
WEN Huiying^,

School of civil engineering and transportation, South China University of Technology, Guangzhou 510641, China

Received Date: 2022-01-01
Available Online: 2022-07-25

Abstract

Abstract

A crash prediction model is developed, in order to explore the relationship between multi-mode failure probability and heavy-duty truck crashes over long downhill road sections. A model for multi-mode failure probability prediction is developed to study the probability of different types of failures associated with heavy-duty trucks, such as skidding, rollover, insufficient sight distance, and braking failure, on the long downhill sections. The single-mode failure probability is simulated using a Monte Carlo method and the multi-mode failure probability of the system is studied by a wide bound method. Three crash prediction models including a Poisson model, a random-effect Poisson model, and a random-parameter Poisson model are developed, considering multi-mode failure probability as one of the explanatory variables together with other impact factors. The models are used to link the multi-mode failure probability with the crashes of heavy-duty trucks. The optimal crash prediction model is selected through the goodness-of-fit for accurately modeling the relationship between crashes of the trucks and their multi-mode failure probability. The method is verified by a 10-year data of heavy-duty truck crash and road design of 71 long downhill sections in the Washington State, the United States. The results show that there is little difference in the goodness of fit between the random-effect Poisson model and random-parameter Poisson model, and both of them are better than the Poisson model. It is found that radius of the horizontal curves, grades and superelevation rates are not significant in leading to the crashes, when compared with the multi-mode failure probability. Study results show that, the elasticity of multi-mode failure probability (0.239) is much greater than that of the radius of horizontal curve and superelevation (0.097 and 0.002) respectively; heavy-duty truck crashes and multi-mode failure probability are approximately linearly correlated, and the intercept of the model is found to be other than "0". The above results indicate that the multi-mode failure probability can be used for road safety analysis, but it is not equivalent to the crash probability, which may be used as a basis for improving road design.
- traffic safety,
- long downhill segments,
- multi-mode failure probability,
- random effects Poisson model,
- random parameters Poisson model,
- heavy-duty truck

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References

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Development of Crash Prediction Models Involving Heavy-duty Trucks over Long Downhill Segments Considering Multi-mode Failure Probability

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