吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (6): 1771-1778.doi: 10.13229/j.cnki.jdxbgxb201606006

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Panel data models for analysis and prediction of crash count

SUN Lu1,2, XU Jian1,3, CUI Xiang-min4   

  1. 1.School of Transportation, Southeast University, Nanjing 210096, China;
    2.Department of Civil Engineering, Catholic University of America, Washington DC 20064, USA;
    3.Center for Transportation Research, University of Texas at Austin, Austin 78712, USA;
    4.Shandong Provincial Highway Detection Center, Jining 272000, China
  • Received:2013-10-08 Online:2015-11-01 Published:2015-11-01

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Abstract: Spatially disaggregated panel data models using negative binomial process, including pooled model, fixed-effects model and random-effect model, were developed. The contributing factors, such as roadway geometrics, traffic characteristics, land usage and rainfall were collected. Incidence Rate Ratio (IRR) and homogeneous segments were employed for analysis and prediction of crash count, which contains Property-Damage-Only (PDO) crashes, injury crashes, fatal crashes, and total crashes. Meanwhile, F test and Hausman test, log-likelihood value and Deviance Information Criterion (DIC) were used to compare the performance of the three types of models. It is found that the effects and statistical significance of covariates on the four types of crashes are not the same. For example, if the speed limit is increased by 1.609 km/h, the PDO rate, injury crash rate and total crash rate are reduced by 3.89%, 2.24% and 2.79%, respectively; however, the fatal crash rate is increased by 6.38%. Results show that the fixed-effects panel negative binomial model surpasses the pooled model and random-effect model, and the modeling is even better for crash count associated with more severe injuries and fatalities.

Key words: traffic and transportation safety engineering, panel negative binomial model, fixed-effects model, land use, rainfall

CLC Number: 

  • U491.31
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