Competing risks mixture model for traffic incident duration prediction

Accident Analysis and Prevention

Volumn 75, Issue , 2015, Pages 192-201

  Li, Ruimin ^a,c   Pereira, Francisco C ^b   Ben Akiva, Moshe E ^a  

^a Massachusetts Institute of Technology   (United States)

^b SINGAPORE MIT ALLIANCE FOR RESEARCH AND TECHNOLOGY   (Singapore)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

Competing risks; Mixture model; Traffic incident duration prediction; Traffic incident management

Indexed keywords

FORECASTING; PROBABILITY DENSITY FUNCTION; REGRESSION ANALYSIS; WEIBULL DISTRIBUTION;

ACCELERATED FAILURE TIME MODELS; COMPETING RISKS; MIXTURE MODEL; MULTINOMIAL LOGISTICS; REASONABLE ACCURACY; TEMPORAL CHARACTERISTICS; TRAFFIC INCIDENTS; UNOBSERVED HETEROGENEITY;

MIXTURES;

HUMAN; REGRESSION ANALYSIS; RISK; SINGAPORE; STATISTICS AND NUMERICAL DATA; SURVIVAL; THEORETICAL MODEL; TIME; TRAFFIC ACCIDENT;

ACCIDENTS, TRAFFIC; HUMANS; MODELS, THEORETICAL; REGRESSION ANALYSIS; RISK; SINGAPORE; SURVIVAL ANALYSIS; TIME FACTORS;

EID: 84918781090     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2014.11.023     Document Type: Article

References (60)

1. A.M.S. Alkaabi, D. Dissanayake, and R. Bird Analyzing clearance time of urban traffic accidents in Abu Dhabi, United Arab Emirates, with hazard-based duration modeling method Transport. Res. Rec. 2229 2011 46 54 10.3141/2229-06
2. P.C. Anastasopoulos, J.E. Haddock, M.G. Karlaftis, and F.L. Mannering An analysis of urban travel times: a random parameters hazard-based approach Transport. Res. Rec. 2302 2012 121 129
3. P.C. Anastasopoulos, and F.L. Mannering Analysis of pavement overlay and replacement performance using random parameters hazard-based duration models J. Infrastruct. Syst. 20 2014 10.1061/(ASCE)IS.1943-555X.0000208
4. C.R. Bhat A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute Transport. Res. Part B 30 6 1996 465 480 10.1016/0191-2615(96)00007-0
5. S. Boyles, D. Fajardo, and S.T. Waller A naive Bayesian classifier for incident duration prediction TRB 86th Annual Meeting Compendium of Papers CD-ROM Washington, D.C., United States 2007
6. C. Cox, H. Chu, M.F. Schneider, and A. Muñoz Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution Stat. Med. 26 23 2007 4352 4374
7. Y. Chung Development of an accident duration prediction model on the Korean freeway systems Accid. Anal. Prev. 42 2010 282 289
8. Y. Chung, L.F. Walubita, and K. Choi Modeling accident duration and its mitigation strategies on South Korean freeway systems Transport. Res. Rec. 2178 2010 49 57 10.3141/2178-06
9. Y. Chung, and B.-J. Yoon Analytical method to estimate accident duration using archived speed profile and its statistical analysis KSCE J. Civil Eng. 16 6 2012 1064 1070
10. S. Demiroluk, and K. Ozbay Structure learning for the estimation of non-parametric incident duration prediction models Transportation Research Board 90th Annual Meeting Washington, D.C., United States 2011
11. E.L. Kaplan, and P. Meier Nonparametric estimation from incomplete observations J. Am. Stat. Assoc. 53 1958 457 481
12. D. Ettema, A. Borgers, and H. Timmermans Competing risk hazard model of activity choice, timing, sequencing, and duration Transport. Res. Rec. 1493 1995 101 109
13. J. Fürstová, and Z. Valenta Statistical analysis of competing risks: overall survival in a group of chronic myeloid leukemia patients Eur. J. Biomed. Inform. 7 1 2011 2 10
14. I. Ghosh, P.T. Savolainen, and T.J. Gates Examination of factors affecting freeway incident clearance times: a comparison of the generalized F model and several alternative nested models J. Adv. Transport. 2012 10.1002/atr.1189
15. C.C.S. Gilbert A duration model of automobile ownership Transport. Res. Part B 26 2 1992 97 114 10.1016/0191-2615(92)90001-D
16. K. Haas Benefits of traffic incident management National Traffic Incident Management Coalition 2006
17. B. Haller, G. Schmidt, and K. Ulm Applying competing risks regression models: an overview Lifetime Data Anal. 19 2013 33 58 10.1007/s10985-012-9230-8
18. Q. He, Y. Kamarianakis, K. Jintanakul, and L. Wynter Incident duration prediction with hybrid tree-based quantile regression IBM Res. Rep. 2011
19. D. Hensher The Timing of Change for Automobile Transactions: Competing Risk Multispell Specification 1998 Elsevier Amsterdam
20. A.T. Hojati, L. Ferreira, S. Washington, and P. Charlesa Hazard based models for freeway traffic incident duration Accid. Anal. Prev. 52 2013 171 181 10.1016/j.aap.2012.12.037
21. A.T. Hojati, L. Ferreira, S. Washington, P. Charles, and A. Shobeirinejad Modelling total duration of traffic incidents including incident detection and recovery time Accid. Anal. Prev. 71 2014 296 305 10.1016/j.aap.2014.06.006
22. J. Hu, R. Krishnan, and M.G.H. Bell Incident duration prediction for in-vehicle navigation system Transportation Research Board Annual Meeting Washington, D.C 2011
23. ® 12.3 User's Guide 2013 SAS Institute Inc. Cary, NC
24. B.-b. JiYang, X. Zhang, and L. Sun Traffic incident duration prediction based on the Bayesian decision tree method Proceedings of Transportation and Development Innovative Best Practices Beijing 2008 338 343
25. B. Jones, L. Janssen, and F. Mannering Analysis of the frequency and duration of freeway accidents in Seattle Accid. Anal. Prev. 23 4 1991 239 255 10.1016/0001-4575(91)90003-N
26. G. Kang, and S.-e Fang Applying survival analysis approach to traffic incident duration prediction First International Conference on Transportation Information and Safety (ICTIS) Wuhan, China 2011 1523 1531
27. A. Khattak, X. Wang, and H. Zhang Incident management integration tool: dynamically predicting incident durations, secondary incident occurrence and incident delays IET Intell. Transport Syst. 6 2 2012 204 214
28. A.J. Khattak, J.L. Schofer, and M.-h. Wang A simple time sequential procedure for predicting freeway incident duration IVHS J. 2 2 1995 113 138
29. A.J. Khattak, X. Wang, and H. Zhang Spatial analysis and modeling of traffic incidents for proactive incident management and strategic planning Transport. Res. Rec. 2178 2010 128 137 10.3141/2178-14
30. W. Kim, G.-L. Chang, and S.M. Rochon Analysis of freeway incident duration for ATIS applications 15th World Congress on Intelligent Transport Systems and ITS America's 2008 Annual Meeting New York 2008
31. W. Kim, and G.-L. Chang Development of a hybrid prediction model for freeway incident duration: a case study in Maryland Int. J. Intell. Transport. Syst. Res. 10 1 2012 22 33 10.1007/s13177-011-0039-8
32. W.J.J. Knibbe, T.P. Alkim, J.F.W. Otten, and M.Y. Aidoo Automated estimation of incident duration on Dutch highways Proceedings of the 2006 IEEE Intelligent Transportation Systems Conference Toronto, Canada 2006 870 874
33. M.G. Larson, and G.E. Dinse A mixture model for the regression analysis of competing risks data J. Roy. Stat. Soc. C-APP 34 3 1985 201 211
34. B. Lau, S.R. Cole, R.D. Moore, and S.J. Gange Evaluating competing adverse and beneficial outcomes using a mixture model Stat. Med. 27 21 2008 4313 4327 10.1002/sim.3293
35. B. Lau, S.R. Cole, and S.J. Gange Competing risk regression models for epidemiologic data Am. J. Epidemiol. 170 2 2009 244 256 10.1093/aje/kwp107
36. B. Lau, S.R. Cole, and S.J. Gange Parametric mixture models to evaluate and summarize hazard ratios in the presence of competing risks with time-dependent hazards and delayed entry Stat. Med. 30 6 2011 654 665 10.1002/sim.4123
37. Y. Lee, and C.-H. Wei A computerized feature selection method using genetic algorithms to forecast freeway accident duration times Comput.-Aided Civil Infrastruct. Eng. 25 2010 132 148
38. C.D. Lewis Industrial and Business Forecasting Method 1982 Butterworth-Heinemann London
39. D. Li, and L. Cheng Bayesian network classifiers for incident duration prediction Transportation Research Board 90th Annual Meeting Washington, D.C 2011
40. R. Li Traffic incident duration analysis and prediction models based on the survival analysis approach IET Intell. Transport Syst. 2014 10.1049/iet-its.2014.0036 (accepted)
41. R. Li, and M. Guo Competing risks analysis on traffic accident duration time J. Adv. Transport. 2014 10.1002/atr.1279 (accepted)
42. D. Nam, and F. Mannering An exploratory hazard-based analysis of highway incident duration Transport. Res. Part A 34 1 2000 85 102 10.1016/S0965-8564(98)00065-2
43. N. Owens, A. Armstrong, P. Sullivan, C. Mitchell, D. Newton, R. Brewster, and T. Trego Traffic Incident Management Handbook 2010 Federal Highway Administration, U.S. Department of Transportation
44. S. Peeta, J.L. Ramos, and S. Gedela Providing real-time traffic advisory and route guidance to manage borman incidents on-line using the Hoosier helper program Joint Transportation Research Program Indiana Department of Transportation and Purdue University, West Lafayette, Indiana 2000
45. F.C. Pereira, F. Rodrigues, and M. Ben-Akiva Text analysis in incident duration prediction Transport. Res. Part C 37 2013 177 192 10.1016/j.trc.2013.10.002
46. Y.G. Qi, and H.H. Teng An information-based time sequential approach to online incident duration prediction J. Intell. Transport. Syst.: Technol. Plann. Oper. 12 1 2008 1 12
47. P. Ravani, G. Tripepi, F. Malberti, S. Testa, F. Mallamaci, and C. Zoccal Asymmetrical dimethylarginine predicts progression to dialysis and death in patients with chronic kidney disease: a competing risks modeling approach J. Am. Soc. Nephrol. 16 2005 2449 2455 10.1681/ASN.2005010076
48. J. Shao, and B. He The seasons and countermeasures' analysis of road traffic jams in Beijing Road Traffic Saf. 8 1 2008 39 44 (in Chinese)
49. L. Shen, and M. Huang Data mining method for incident duration prediction Applied Informatics and Communication, Communications in Computer and Information Science, vol. 224, no. 1 2011 484 492
50. F.-Y. Shyr, and M. Ben-Akiva Modeling rail fatigue behavior with multiple hazards J. Infrastruct. Syst. 2 2 1996 73 82 10.1061/(ASCE)1076-0342(1996)2:2(73)
51. K. Smith, and B.L. Smith Forecasting the Clearance Time of Freeway Accidents 2001 Center for Transportation Studies, University of Virginia Charlottesville
52. TRB Highway Capacity Manual (HCM200) 2000 Transportation Research Board, National Research Council Washington, D.C
53. G. Valenti, M. Lelli, and D. Cucina A comparative study of models for the incident duration prediction Eur. Transport Res. Rev. 2 2010 103 111
54. E.I. Vlahogianni, and M.G. Karlaftis Fuzzy-entropy neural network freeway incident duration modeling with single and competing uncertainties Comput.-Aided Civil Infrastruct. Eng. 28 6 2013 420 433 10.1111/mice.12010
55. J. Wang, H. Cong, and S. Qiao Estimating freeway incident duration using accelerated failure time modeling Saf. Sci. 54 2013 43 50 10.1016/j.ssci.2012.11.009
56. S.P. Washington, M.G. Karlaftis, and F.L. Mannering Statistical and Econometric Methods for Transportation Data Analysis second ed. 2011 Chapman & Hall/ LLC, Taylor & Francis Group Boca Raton, FL
57. C.-H. Wei, and Y. Lee Sequential forecast of incident duration using artificial neural network models Accid. Anal. Prev. 39 2007 944 954
58. T. Yamamoto, J.-L. Madre, and R. Kitamura An analysis of the effects of French vehicle inspection program and grant for scrappage on household vehicle transaction Transport. Res. Part B 38 2004 905 926 10.1016/j.trb.2004.02.001
59. C. Zhan, A. Gan, and M. Hadi Prediction of lane clearance time of freeway incidents using the M5P tree algorithm IEEE Trans. Intell. Transport. Syst. 12 4 2011 1549 1557
60. H. Zhang, and A.J. Khattak Analysis of cascading incident event durations on urban freeways Transport. Res. Rec. 2178 2010 30 39