A Hybrid Latent Class Analysis Modeling Approach to Analyze Urban Expressway Crash Risk

Accident Analysis and Prevention

Volumn 101, Issue , 2017, Pages 37-43

  Yu, Rongjie ^a,b   Wang, Xuesong ^a,b   Abdel Aty, Mohamed ^c  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b TONGJI UNIVERSITY   (China)

^c UNIVERSITY OF CENTRAL FLORIDA   (United States)

Author keywords

Bayesian random parameter model; Crash risk analysis; Latent class analysis; Unobserved heterogeneity

Indexed keywords

ADVANCED TRAFFIC MANAGEMENT SYSTEMS; CRASHWORTHINESS; LIFE CYCLE; RISK ASSESSMENT; RISK PERCEPTION; TRAFFIC CONTROL;

ACTIVE TRAFFIC MANAGEMENTS; GEOMETRIC CHARACTERISTICS; HETEROGENEOUS EFFECTS; LATENT CLASS ANALYSIS; LIKELIHOOD RATIO TESTS; RANDOM PARAMETER MODEL; TRAFFIC FLOW CHARACTERISTICS; UNOBSERVED HETEROGENEITY;

RISK ANALYSIS;

BAYES THEOREM; CHINA; ENVIRONMENTAL PLANNING; HUMAN; PROCEDURES; RISK ASSESSMENT; RISK FACTOR; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TRAFFIC ACCIDENT; URBAN POPULATION;

ACCIDENTS, TRAFFIC; BAYES THEOREM; CHINA; ENVIRONMENT DESIGN; HUMANS; LOGISTIC MODELS; RISK ASSESSMENT; RISK FACTORS; URBAN POPULATION;

EID: 85011879617     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2017.02.002     Document Type: Article

References (28)

1. Abdel-Aty, M., Uddin, N., Pande, A., Abdalla, F., Hsia, L., Predicting freeway crashes from loop detector data by matched case-control logistic regression. Transportation Research Record: Journal of the Transportation Research Board 1897 (2004), 88–95.
2. Ahmed, M., Abdel-Aty, M., The Viability of Using Automatic Vehicle Identification Data for Real-Time Crash Prediction. IEEE Transactions on Intelligent Transportation Systems 13:2 (2012), 459–468.
3. Bhat, C., An endogenous segmentation mode choice model with an application to intercity travel. Transportation Science 31:1 (1997), 34–48.
4. Chen, C., Zhang, G., Tarefder, R., Ma, J., Wei, H., Guan, H., A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accident Analysis and Prevention 80 (2015), 76–88.
5. Chen, C., Zhang, G., Yang, J., Milton, J.C., Alcántara, A. Dely, An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier. Accident Analysis and Prevention 90 (2016), 95–107.
6. Depaire, B., Wets, G., Vanhoof, K., Traffic accident segmentation by means of latent class clustering. Accident Analysis and Prevention 40:4 (2008), 1257–1266.
7. de Oña, J., Mujalli, R.O., Calvo, F.J., Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accident Analysis and Prevention. 43 (2011), 402–411.
8. de Oña, J., López, G., Mujalli, R., Calvo, F., Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis and Prevention 51 (2013), 1–10.
9. Dziak, J.J., Lanza, S.T., Xu, S., LCA Bootstrap SAS macro users’ guide (version 1.1.0). 2011, University Park: The Methodology Center, Penn State http://methodology.psu.edu.
10. Eluru, N., Bagheri, M., Miranda-Moreno, L., Fu, L., A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings. Accident Analysis and Prevention 47 (2012), 119–127.
11. Gelman, A., Rubin, D., Inference from Iterative Simulation Using Multiple Sequences. Statistical Science 7 (1992), 457–511.
12. Hole, A., Fitting mixed logit models by using maximum simulated likelihood. The Stata Journal 7 (2007), 388–401.
13. Lanza, S., Collins, L., Lemmon, D., Schafer, J., PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling 14:34 (2007), 671–694.
14. Liang, Y., Lee, J., A hybrid Bayesian Network approach to detect driver cognitive distraction. Transportation Research Part C: Emerging Technologies 28 (2014), 146–155.
15. Lunn, D., Thomas, A., Best, N., Spiegelhalter, D., WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10:4 (2000), 325–337.
16. Ma, J., Kockelman, K., Crash frequency and severity modeling using clustered data from Washington State. Intelligent Transportation Systems Conference, 2006, 1621–1626.
17. Mannering, F., Bhat, C., Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research 1 (2013), 1–22.
18. Ng, K., Hung, W., Wong, W., An algorithm for assessing the risk of traffic accident. Journal of Safety Research 33:3 (2002), 387–410.
19. Oh, C., Oh, J., Ritchie, S., Chang, M., Real-time estimation of freeway accident likelihood. 80th Annual Meeting of Transportation Research Board, Washington, DC, 2001.
20. Pande, A., Abdel-Aty, M., Comprehensive analysis of the relationship between real-time traffic surveillance data and rear-end crashes on freeways. Transportation Research Record: Journal of the Transportation Research Board. 1953 (2006), 31–40.
21. Roshandel, S., Zheng, Z., Washington, S., Impact of real-time traffic characteristics on freeway crash occurrence: systematic review and meta-analysis. Accident Analysis and Prevention 79 (2015), 198–211.
22. Savolainen, P., Mannering, F., Lord, D., Quddus, M., The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accident Analysis and Prevention 43 (2011), 1666–1676.
23. Shaheed, M., Gkritza, K., A latent class analysis of single-vehicle motorcycle crash severity outcomes. Analytic Methods in Accident Research 2 (2014), 30–38.
24. Smith, B., Boa: An r package for MCMC output convergence assessment and posterior inference. Journal of Statistical Software 21 (2007), 1–37.
25. Train, K., Discrete Choice Methods with Simulation. 2009, Cambridge University Press, New York, NY.
26. Xu, C., Liu, P., Wang, W., Li, Z., Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service. Transportation Research Part A: Policy and Practice 69 (2014), 58–70.
27. Yu, R., Abdel-Aty, M., Investigating the different characteristics of weekday and weekend crashes. Journal of Safety Research 46 (2013), 91–97.
28. Yu, R., Abdel-Aty, M., Multi-level Bayesian analyses for single-and multi-vehicle freeway crashes. Accident Analysis and Prevention 58 (2013), 97–105.