Modeling railroad trespassing crash frequency using a mixed-effects negative binomial model

被引:9
|
作者
Kang, Y. [1 ,2 ]
Iranitalab, A. [1 ,2 ]
Khattak, A. [1 ,2 ]
机构
[1] Univ Nebraska, Dept Civil Engn, Lincoln, NE 68588 USA
[2] Univ Nebraska, Nebraska Transportat Ctr, Lincoln, NE 68588 USA
关键词
Rail safety; trespassing; mixed-effects negative binomial; PREVENTION; FATALITIES; RAILWAYS; SUICIDE;
D O I
10.1080/23248378.2018.1550626
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
A better understanding of rail trespass crashes is needed as more than 400 trespassing related fatalities occur along rail tracks each year in the United States (U.S.). The objective of this research was to investigate factors associated with the occurrence of rail trespass crashes. Yearly crash frequency for counties in the U.S. with train tracks was modeled using a Mixed-effects Negative Binomial Model based on 2012-2016 datasets from the Federal Railroad Administration, the U.S. Census Bureau and National Historical Geographic Information System. Results revealed that key factors affecting rail trespassing crashes include county population density, length of rail tracks in a county, median age and male proportion of the county population, and average train traffic within a county. The findings provided useful information on improving public safety along railroad tracks.
引用
收藏
页码:208 / 218
页数:11
相关论文
共 50 条
  • [1] Model crash frequency at highway-railroad grade crossings using negative binomial regression
    Hu, Shou-Ren
    Li, Chin-Shang
    Lee, Chi-Kang
    JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2012, 35 (07) : 841 - 852
  • [2] Examples of mixed-effects modeling with crossed random effects and with binomial data
    Quene, Hugo
    van den Bergh, Huub
    JOURNAL OF MEMORY AND LANGUAGE, 2008, 59 (04) : 413 - 425
  • [3] Application of a random effects negative binomial model to examine crash frequency for freeways in China
    Hou, Qinzhong
    Meng, Xianghai
    Leng, Junqiang
    Yu, Lu
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 509 : 937 - 944
  • [4] A Mixed-Effects Heterogeneous Negative Binomial Model for Postfire Conifer Regeneration in Northeastern California, USA
    Crotteau, Justin S.
    Ritchie, Martin W.
    Varner, J. Morgan
    FOREST SCIENCE, 2014, 60 (02) : 275 - 287
  • [5] Assessing the safety impacts of raising the speed limit on Michigan freeways using the multilevel mixed-effects negative binomial model
    Kwayu, Keneth Morgan
    Kwigizile, Valerian
    Oh, Jun-Seok
    TRAFFIC INJURY PREVENTION, 2020, 21 (06) : 401 - 406
  • [6] A negative binomial Lindley approach considering spatiotemporal effects for modeling traffic crash frequency with excess zeros
    Wang, Wencheng
    Yang, Yang
    Yang, Xiaobao
    Gayah, Vikash V.
    Wang, Yunpeng
    Tang, Jinjun
    Yuan, Zhenzhou
    ACCIDENT ANALYSIS AND PREVENTION, 2024, 207
  • [7] Crash frequency modeling using negative binomial models: An application of generalized estimating equation to longitudinal data
    Mohammadi, Mojtaba A.
    Samaranayake, V. A.
    Bham, Ghulam H.
    ANALYTIC METHODS IN ACCIDENT RESEARCH, 2014, 2 : 52 - 69
  • [8] Modeling stand mortality of Chinese fir plantations in subtropical China using mixed-effects zero-inflated negative binomial models
    Liu, Jun
    Ouyang, Xunzhi
    Pan, Ping
    Ning, Jinkui
    Guo, Yang
    FOREST ECOLOGY AND MANAGEMENT, 2024, 565
  • [9] Conspicuous spatial frequency features in mammograms using a mixed-effects model
    Perconti, P
    Loew, MH
    MEDICAL IMAGING 2005: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, 2005, 5749 : 103 - 113
  • [10] A Bayesian approach to functional mixed-effects modeling for longitudinal data with binomial outcomes
    Kliethermes, Stephanie
    Oleson, Jacob
    STATISTICS IN MEDICINE, 2014, 33 (18) : 3130 - 3146