Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks

Analytic Methods in Accident Research

Volumn 10, Issue , 2016, Pages 12-25

  Zeng, Qiang ^a,b   Huang, Helai ^b   Pei, Xin ^c   Wong, S C ^d  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b CENTRAL SOUTH UNIVERSITY   (China)

^c TSINGHUA UNIVERSITY   (China)

^d UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Crash frequency by severity; Neural network; Over fitting; Rule extraction; Structure optimization

Indexed keywords

ABILITY TESTING; CODES (SYMBOLS); EXTRACTION; HEALTH RISKS; MOTOR TRANSPORTATION; NEURAL NETWORKS; ROADS AND STREETS; SHAPE OPTIMIZATION;

CRASH FREQUENCY; MODIFIED NEURAL NETWORKS; NON-LINEAR RELATIONSHIPS; OVERFITTING; PREDICTIVE PERFORMANCE; RULE EXTRACTION; RULE EXTRACTION METHOD; STRUCTURE OPTIMIZATION;

STRUCTURAL OPTIMIZATION;

EID: 84964078450     PISSN: 22136657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amar.2016.03.002     Document Type: Article

References (37)

1. H.T. Abdelwahab, and M.A. Abdel-Aty Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections Transportation Research Record 1746 2001 6 13
2. J. Aguero-Valverde, and P.P. Jovanis Bayesian multivariate Poisson lognormal models for crash severity modeling and site ranking Transportation Research Record 2136 2009 82 91
3. P.C. Anastasopoulos, V.N. Shankar, J.E. Haddock, and F.L. Mannering A multivariate tobit analysis of highway accident-injury-severity rates Accident Analysis and Prevention 45 2012 110 119
4. S. Barua, K. El-Basyouny, and M.T. Islam A full Bayesian multivariate count data model of collision severity with spatial correlation Analytic Methods in Accident Research 3 2014 28 43
5. S. Barua, K. El-Basyouny, and M.T. Islam Multivariate random parameters collision count data models with spatial heterogeneity Analytic Methods in Accident Research 9 2016 1 15
6. F.D. Bijleveld The covariance between the number of accidents and the number of victims in multivariate analysis of accident related outcomes Accident Analysis and Prevention 37 4 2005 591 600
7. L. Chang Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network Safety Science 43 8 2005 541 557
8. Y.C. Chiou, and C. Fu Modeling crash frequency and severity using multinomial-generalized Poisson model with error components Accident Analysis and Prevention 50 2013 73 82
9. Y.C. Chiou, and C. Fu Modeling crash frequency and severity with spatiotemporal dependence Analytic Methods in Accident Research 5 2015 43 58
10. Y.C. Chiou, C. Fu, and H. Chih-Wei Incorporating spatial dependence in simultaneously modeling crash frequency and severity Analytic Methods in Accident Research 2 2014 1 11
11. E.T. Donnell, and J.M. Mason Jr. Predicting the frequency of median barrier crashes on Pennsylvania interstate highways Accident Analysis and Prevention 38 3 2006 590 599
12. K. El-Basyouny, and T. Sayed Collision prediction models using multivariate Poisson-lognormal regression Accident Analysis and Prevention 41 4 2009 820 828
13. K. El-Basyouny, S. Barua, and M.T. Islam Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models Accident Analysis and Prevention 73 2014 91 99
14. S.S. Haykin Neural Networks and Learning Machines 3rd edition 2009 Prentice Hall New York
15. Highway Safety Manual, 1st edition, 2010. AASHTO, Washington D.C.
16. H. Huang, Q. Zeng, X. Pei, S.C. Wong, and P. Xu Predicting crash frequency using an optimized radial basis function neural network model Transportmetrica A 12 4 2016 330 345
17. M.G. Karlaftis, and E.I. Vlahogianni Statistical methods versus neural networks in transportation research: Differences, similarities and some insights Transportation Research Part C 19 3 2011 387 399
18. B. Lan, and B. Persaud Evaluation of multivariate Poisson log normal Bayesian methods for before-after road safety evaluations Journal of Transportation Safety and Security 4 3 2012 193 210
19. X. Li, D. Lord, Y. Zhang, and Y. Xie Predicting motor vehicle crashes using support vector machine models Accident Analysis and Prevention 40 4 2008 1611 1618
20. J. Ma, and K.M. Kockelman Bayesian multivariate Poisson regression for models of injury count, by severity Transportation Research Record 1950 2006 24 34
21. J. Ma, K.M. Kockelman, and P. Damien A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods Accident Analysis and Prevention 40 3 2008 964 975
22. S.P. Miaou, and J.J. Song Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence Accident Analysis and Prevention 37 4 2005 699 720
23. J. Milton, and F. Mannering The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies Transportation 25 4 1998 395 413
24. E.S. Park, and D. Lord Multivariate Poisson-lognormal models for jointly modeling crash frequency by severity Transportation Research Record 2019 2007 1 6
25. X. Pei, S.C. Wong, and N.N. Sze A joint-probability approach to crash prediction models Accident Analysis and Prevention 43 3 2011 1160 1166
26. X. Pei, S.C. Wong, and N.N. Sze The roles of exposure and speed in road safety analysis Accident Analysis and Prevention 48 2012 464 471
27. R. Poli, J. Kennedy, and T. Blackwell Particle swarm optimization Swarm Intelligence 1 2007 33 57
28. R. Setiono, and W.K. Leow Pruned Neural Networks for Regression. PRICAI 2000 Topics in Artificial Intelligence 2000 Springer Berlin, Heidelberg 500 509
29. R. Setiono, and J.Y. Thong An approach to generate rules from neural networks for regression problems European Journal of Operational Research 155 2004 239 250
30. N. Venkataraman, G.F. Ulfarsson, and V.N. Shankar Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type Accident Analysis and Prevention 59 2013 309 318
31. C. Wang, M.A. Quddus, and S.G. Ison Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model Accident Analysis and Prevention 43 6 2011 1979 1990
32. Y. Xie, D. Lord, and Y. Zhang Predicting motor vehicle collisions using Bayesian neural networks: an empirical analysis Accident Analysis and Prevention 39 5 2007 922 933
33. X. Xu, S.C. Wong, and K. Choi A two-stage bivariate logistic-Tobit model for the safety analysis of signalized intersections Analytic Methods in Accident Research 3-4 2014 1 10
34. X. Ye, R.M. Pendyala, V. Shankar, and K.C. Konduri A simultaneous equations model of crash frequency by severity level for freeway sections Accident Analysis and Prevention 57 2013 140 149
35. X. Ye, R.M. Pendyala, S.P. Washington, K. Konduri, and J. Oh A simultaneous equations model of crash frequency by collision type for rural intersections Safety Science 47 3 2009 443 452
36. Q. Zeng, and H. Huang Bayesian spatial joint modeling of traffic crashes on an urban road network Accident Analysis and Prevention 67 2014 105 112
37. Q. Zeng, and H. Huang A stable and optimized neural network model for crash injury severity prediction Accident Analysis and Prevention 73 2014 351 358