Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service |
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| Institution: | 1. School of Transportation Engineering, Tongji University, 4800 Cao''an Road, 201804, Shanghai, China;2. Key Laboratory of Road and Traffic Engineering of Ministry of Education, 4800 Cao’an Road, 201804, Shanghai, China;3. School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom;1. Department of Civil Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet District 10, Ho Chi Minh City, Vietnam;2. Department of Civil Engineering, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand;3. Department of Urban Management, Kyoto University, C1-2, Katsura Campus, Kyoto, Japan;1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China;3. College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China;4. Department of Civil, Environmental and Construction Engineering, University of Central Florida Orlando, FL 32826-2450, United States |
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| Abstract: | The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model. |
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| Keywords: | Crash risk Level of service Bayesian inference Markov Chain Monte Carlo (MCMC) Random forest |
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