Yunlong Zhang

Summary

Affiliation: Texas A and M University
Country: USA

Publications

  1. doi request reprint Crash frequency analysis of different types of urban roadway segments using generalized additive model
    Yunlong Zhang
    Zachry Department of Civil Engineering, Texas A and M University, 3136 TAMU, College Station, TX 77843 3136, USA
    J Safety Res 43:107-14. 2012
  2. doi request reprint Application of finite mixture of negative binomial regression models with varying weight parameters for vehicle crash data analysis
    Yajie Zou
    Zachry Department of Civil Engineering, Texas A and M University, 3136 TAMU, College Station, TX 77843 3136, United States
    Accid Anal Prev 50:1042-51. 2013
  3. doi request reprint Predicting motor vehicle crashes using Support Vector Machine models
    Xiugang Li
    Zachry Department of Civil Engineering, Texas A and M University, College Station, TX 77843 3136, USA
    Accid Anal Prev 40:1611-8. 2008

Detail Information

Publications3

  1. doi request reprint Crash frequency analysis of different types of urban roadway segments using generalized additive model
    Yunlong Zhang
    Zachry Department of Civil Engineering, Texas A and M University, 3136 TAMU, College Station, TX 77843 3136, USA
    J Safety Res 43:107-14. 2012
    ..This paper utilizes generalized additive model to explore the potential non-linear relationship between crash frequency and exposure on different types of urban roadway segments...
  2. doi request reprint Application of finite mixture of negative binomial regression models with varying weight parameters for vehicle crash data analysis
    Yajie Zou
    Zachry Department of Civil Engineering, Texas A and M University, 3136 TAMU, College Station, TX 77843 3136, United States
    Accid Anal Prev 50:1042-51. 2013
    ..Therefore, it is concluded that in many cases the GFMNB-2 models may be a better alternative to the FMNB-2 models for explaining the heterogeneity and the nature of the dispersion in the crash data...
  3. doi request reprint Predicting motor vehicle crashes using Support Vector Machine models
    Xiugang Li
    Zachry Department of Civil Engineering, Texas A and M University, College Station, TX 77843 3136, USA
    Accid Anal Prev 40:1611-8. 2008
    ..Given this characteristic and the fact that SVM models are faster to implement than BPNN models, it is suggested to use these models if the sole purpose of the study consists of predicting motor vehicle crashes...