Crash Risks Evaluation of Urban Expressways: A Case Study in Shanghai

Cheng, Zeyang; Yuan, Jinghui; Yu, Bo; Lu, Jian; Zhao, Yi

doi:10.1109/tits.2022.3140345

Title: Crash Risks Evaluation of Urban Expressways: A Case Study in Shanghai

Journal Article · 19 January 2022 · IEEE Transactions on Intelligent Transportation Systems

DOI: https://doi.org/10.1109/tits.2022.3140345 · OSTI ID:1855675

^[1];

^[2]; Yu, Bo ^[3]; Lu, Jian ^[4];

^[5]

Hefei Univ. of Technology (China)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Tongji, Shanghai (China)
Southeast Univ., Nanjing (China)
Nanjing Forestry Univ. (China)

We report that proactive traffic safety management systems can reduce crashes by identifying crash precursors, evaluating real-time crash risks, and implementing suitable interventions. The basic prerequisite for developing such a system is to propose a reliable crash risk evaluation model that takes real-time traffic flow data as input. Previous studies have primarily focused on real-time crash prediction using some statistical or machine-learning methods. However, further quantitative evaluation and classification of crash risks have been ignored. In this study, we conduct a systematic crash risk evaluation workflow, including crash risk prediction, crash risk quantification, and crash risk classification. Specifically, the crash risk prediction using an extended logit model is proposed, from which CAS, CSD, UAS, DAS, DTV are identified to be contributing factors of crash risks. Then a crash risk quantification model based on the parameter evaluation of the extended logit model is developed. The crash risks of urban expressways and their spatial-temporal evolution trends are quantified. Finally, the crash risks are classified into high crash risk level, moderate crash risk level, and low crash risk level by the k-means cluster algorithm. Then the threshold boundaries of different crash risk levels are determined. The research results provide a proactive guidance for traffic safety management of urban expressways.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: Conditional Construction Funds of Hefei University of Technology; National Key R&D Program of China; USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1855675

Journal Information:: IEEE Transactions on Intelligent Transportation Systems, Journal Name: IEEE Transactions on Intelligent Transportation Systems Journal Issue: 1 Vol. 1; ISSN 1524-9050

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (45)

Understanding spatial concentrations of road accidents using frequent item sets Geurts, Karolien; Thomas, Isabelle; Wets, Geert Accident Analysis & Prevention, Vol. 37, Issue 4 https://doi.org/10.1016/j.aap.2005.03.023	journal	July 2005
Evaluation of variable speed limits for real-time freeway safety improvement Abdel-Aty, Mohamed; Dilmore, Jeremy; Dhindsa, Albinder Accident Analysis & Prevention, Vol. 38, Issue 2 https://doi.org/10.1016/j.aap.2005.10.010	journal	March 2006
Impact of traffic oscillations on freeway crash occurrences Zheng, Zuduo; Ahn, Soyoung; Monsere, Christopher M. Accident Analysis & Prevention, Vol. 42, Issue 2 https://doi.org/10.1016/j.aap.2009.10.009	journal	March 2010
Cause, effect and regression in road safety: A case study Hauer, Ezra Accident Analysis & Prevention, Vol. 42, Issue 4 https://doi.org/10.1016/j.aap.2009.12.027	journal	July 2010
Evaluation of traffic safety, based on micro-level behavioural data: Theoretical framework and first implementation Laureshyn, Aliaksei; Svensson, Åse; Hydén, Christer Accident Analysis & Prevention, Vol. 42, Issue 6 https://doi.org/10.1016/j.aap.2010.03.021	journal	November 2010
Evaluation of the impacts of traffic states on crash risks on freeways Xu, Chengcheng; Liu, Pan; Wang, Wei Accident Analysis & Prevention, Vol. 47 https://doi.org/10.1016/j.aap.2012.01.020	journal	July 2012
Utilizing support vector machine in real-time crash risk evaluation Yu, Rongjie; Abdel-Aty, Mohamed Accident Analysis & Prevention, Vol. 51 https://doi.org/10.1016/j.aap.2012.11.027	journal	March 2013
Safety surrogate histograms (SSH): A novel real-time safety assessment of dilemma zone related conflicts at signalized intersections Ghanipoor Machiani, Sahar; Abbas, Montasir Accident Analysis & Prevention, Vol. 96 https://doi.org/10.1016/j.aap.2015.04.024	journal	November 2016
Calibration of crash risk models on freeways with limited real-time traffic data using Bayesian meta-analysis and Bayesian inference approach Xu, Chengcheng; Wang, Wei; Liu, Pan Accident Analysis & Prevention, Vol. 85 https://doi.org/10.1016/j.aap.2015.09.016	journal	December 2015
Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach Yu, Rongjie; Wang, Xuesong; Yang, Kui Accident Analysis & Prevention, Vol. 95 https://doi.org/10.1016/j.aap.2015.11.029	journal	October 2016
Investigation of work zone crash casualty patterns using association rules Weng, Jinxian; Zhu, Jia-Zheng; Yan, Xuedong Accident Analysis & Prevention, Vol. 92 https://doi.org/10.1016/j.aap.2016.03.017	journal	July 2016
Analysis of real-time crash risk for expressway ramps using traffic, geometric, trip generation, and socio-demographic predictors Wang, Ling; Abdel-Aty, Mohamed; Lee, Jaeyoung Accident Analysis & Prevention, Vol. 122 https://doi.org/10.1016/j.aap.2017.06.003	journal	January 2019
Collision risk analysis based train collision early warning strategy Li, Si-hui; Cai, Bai-gen; Liu, Jiang Accident Analysis & Prevention, Vol. 112 https://doi.org/10.1016/j.aap.2017.11.039	journal	March 2018
Time-to-collision analysis of pedestrian and pedal-cycle accidents for the development of autonomous emergency braking systems Lenard, James; Welsh, Ruth; Danton, Russell Accident Analysis & Prevention, Vol. 115 https://doi.org/10.1016/j.aap.2018.02.028	journal	June 2018
How to determine an optimal threshold to classify real-time crash-prone traffic conditions? Yang, Kui; Yu, Rongjie; Wang, Xuesong Accident Analysis & Prevention, Vol. 117 https://doi.org/10.1016/j.aap.2018.04.022	journal	August 2018
Impact of data aggregation approaches on the relationships between operating speed and traffic safety Yu, Rongjie; Quddus, Mohammed; Wang, Xuesong Accident Analysis & Prevention, Vol. 120 https://doi.org/10.1016/j.aap.2018.06.007	journal	November 2018
Freeway crash risks evaluation by variable speed limit strategy using real-world traffic flow data Cheng, Zeyang; Lu, Jian; Li, Yunxuan Accident Analysis & Prevention, Vol. 119 https://doi.org/10.1016/j.aap.2018.07.009	journal	October 2018
A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data Bao, Jie; Liu, Pan; Ukkusuri, Satish V. Accident Analysis & Prevention, Vol. 122 https://doi.org/10.1016/j.aap.2018.10.015	journal	January 2019
Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements Hossain, Moinul; Abdel-Aty, Mohamed; Quddus, Mohammed A. Accident Analysis & Prevention, Vol. 124 https://doi.org/10.1016/j.aap.2018.12.022	journal	March 2019
Trajectory data based freeway high-risk events prediction and its influencing factors analyses Yu, Rongjie; Han, Lei; Zhang, Hui Accident Analysis & Prevention, Vol. 154 https://doi.org/10.1016/j.aap.2021.106085	journal	May 2021
Predicting reduced visibility related crashes on freeways using real-time traffic flow data Hassan, Hany M.; Abdel-Aty, Mohamed A. Journal of Safety Research, Vol. 45 https://doi.org/10.1016/j.jsr.2012.12.004	journal	June 2013
Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data Yu, Rongjie; Abdel-Aty, Mohamed Safety Science, Vol. 63 https://doi.org/10.1016/j.ssci.2013.10.012	journal	March 2014
Classifying the traffic state of urban expressways: A machine-learning approach Cheng, Zeyang; Wang, Wei; Lu, Jian Transportation Research Part A: Policy and Practice, Vol. 137 https://doi.org/10.1016/j.tra.2018.10.035	journal	July 2020
Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making Wu, Jiaming; Kulcsár, Balázs; Ahn, Soyoung Transportation Research Part B: Methodological, Vol. 141 https://doi.org/10.1016/j.trb.2020.09.011	journal	November 2020
Real-time prediction of visibility related crashes Abdel-Aty, Mohamed A.; Hassan, Hany M.; Ahmed, Mohamed Transportation Research Part C: Emerging Technologies, Vol. 24 https://doi.org/10.1016/j.trc.2012.04.001	journal	October 2012
A data fusion framework for real-time risk assessment on freeways Ahmed, Mohamed; Abdel-Aty, Mohamed Transportation Research Part C: Emerging Technologies, Vol. 26 https://doi.org/10.1016/j.trc.2012.09.002	journal	January 2013
Assessing the safety of routes in a regional network Dijkstra, Atze Transportation Research Part C: Emerging Technologies, Vol. 32 https://doi.org/10.1016/j.trc.2012.10.008	journal	July 2013
Dynamic data-driven local traffic state estimation and prediction Antoniou, Constantinos; Koutsopoulos, Haris N.; Yannis, George Transportation Research Part C: Emerging Technologies, Vol. 34 https://doi.org/10.1016/j.trc.2013.05.012	journal	September 2013
A probabilistic approach towards a crash risk assessment of urban segments Ferreira, Sara; Couto, António Transportation Research Part C: Emerging Technologies, Vol. 50 https://doi.org/10.1016/j.trc.2014.09.012	journal	January 2015
Quasi-vehicle-trajectory-based real-time safety analysis for expressways Wang, Ling; Abdel-Aty, Mohamed; Ma, Wanjing Transportation Research Part C: Emerging Technologies, Vol. 103 https://doi.org/10.1016/j.trc.2019.04.003	journal	June 2019
Development of a Real-Time Crash Risk Prediction Model Incorporating the Various Crash Mechanisms Across Different Traffic States Xu, Chengcheng; Wang, Wei; Liu, Pan Traffic Injury Prevention, Vol. 16, Issue 1 https://doi.org/10.1080/15389588.2014.909036	journal	September 2014
A Freeway Safety Strategy for Advanced Proactive Traffic Management Pande, Anurag; Abdel-Aty, Mohamed Journal of Intelligent Transportation Systems, Vol. 9, Issue 3 https://doi.org/10.1080/15472450500183789	journal	July 2005
A hybrid clustering and classification approach for predicting crash injury severity on rural roads Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein International Journal of Injury Control and Safety Promotion, Vol. 25, Issue 1 https://doi.org/10.1080/17457300.2017.1341933	journal	July 2017
Defining Time-to-Collision Thresholds by the Type of Lead Vehicle in Non-Lane-Based Traffic Environments Das, Sanhita; Maurya, Akhilesh Kumar IEEE Transactions on Intelligent Transportation Systems, Vol. 21, Issue 12 https://doi.org/10.1109/TITS.2019.2946001	journal	December 2020
Variable Speed Limit Signs: Control and Setting Locations in Freeway Work Zones Wang, Wei; Cheng, Zeyang Journal of Advanced Transportation, Vol. 2017 https://doi.org/10.1155/2017/4390630	journal	January 2017
Split Models for Predicting Multivehicle Crashes during High-Speed and Low-Speed Operating Conditions on Freeways Abdel-Aty, Mohamed; Uddin, Nizam; Pande, Anurag Transportation Research Record: Journal of the Transportation Research Board, Vol. 1908, Issue 1 https://doi.org/10.1177/0361198105190800107	journal	January 2005
Real-Time Detection of Crash-Prone Conditions at Freeway High-Crash Locations Hourdos, John N.; Garg, Vishnu; Michalopoulos, Panos G. Transportation Research Record: Journal of the Transportation Research Board, Vol. 1968, Issue 1 https://doi.org/10.1177/0361198106196800110	journal	January 2006
Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering Celikoglu, Hilmi Berk; Silgu, Mehmet Ali Transportation Science, Vol. 50, Issue 3 https://doi.org/10.1287/trsc.2015.0653	journal	August 2016
Analysis of Crash Precursors on Instrumented Freeways Lee, Chris; Saccomanno, Frank; Hellinga, Bruce Transportation Research Record: Journal of the Transportation Research Board, Vol. 1784, Issue 1 https://doi.org/10.3141/1784-01	journal	January 2002
Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic Lee, Chris; Hellinga, Bruce; Saccomanno, Frank Transportation Research Record: Journal of the Transportation Research Board, Vol. 1840, Issue 1 https://doi.org/10.3141/1840-08	journal	January 2003
Operational Criteria of Causality for Observational Road Safety Evaluation Studies Elvik, Rune Transportation Research Record: Journal of the Transportation Research Board, Vol. 2019, Issue 1 https://doi.org/10.3141/2019-10	journal	January 2007
Do Calculated Conflicts in Microsimulation Model Predict Number of Crashes? Dijkstra, Atze; Marchesini, Paula; Bijleveld, Frits Transportation Research Record: Journal of the Transportation Research Board, Vol. 2147, Issue 1 https://doi.org/10.3141/2147-13	journal	January 2010
Estimation of Real-Time Crash Risk Pande, Anurag; Das, Abhishek; Abdel-Aty, Mohamed Transportation Research Record: Journal of the Transportation Research Board, Vol. 2237, Issue 1 https://doi.org/10.3141/2237-07	journal	January 2011
Traffic Congestion Evaluation Method for Urban Arterials Sun, Daniel J.; Liu, Xiaofeng; Ni, Anning Transportation Research Record: Journal of the Transportation Research Board, Vol. 2461, Issue 1 https://doi.org/10.3141/2461-02	journal	January 2014
K-Means Clustering Method to Classify Freeway Traffic Flow Patterns Silgu, Mehmet Ali; Çelikoğlu, Hilmi Berk Pamukkale University Journal of Engineering Sciences, Vol. 20, Issue 6 https://doi.org/10.5505/pajes.2014.36449	journal	January 2014

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Title: Crash Risks Evaluation of Urban Expressways: A Case Study in Shanghai

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