04Mar 2018

CRASH PREDICTION MODELLING OF STATE HIGHWAYS IN ARKANSAS.

  • University of Alaska Fairbanks, AK 99775.
  • Agile Assets Inc. Austin, TX 78749.
  • Verizon, Washington, DC 20006.
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This paper presents the development of crash prediction models for State highways in Arkansas. Based on over-dispersion, Negative Binomial models were developed, with additional features for dealing with more complex and correlated count data. Six models are presented based on the location and type of the highway, i.e., urban and rural; divided and undivided highways, and junction (intersection and intersection related), and non-junction. Statistical analysis of crash data identified variables associated with crashes. Results indicate that in general Annual Average Daily Traffic (AADT) and number of lanes increased crash frequency.

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[Ghulam H. Bham, Uday R. R. Manepalli and Bhanu S. Javvadi. (2018); CRASH PREDICTION MODELLING OF STATE HIGHWAYS IN ARKANSAS. Int. J. of Adv. Res. 6 (Mar). 403-414] (ISSN 2320-5407). www.journalijar.com

Uday Manepalli
Sr. Consultant

DOI:

Article DOI: 10.21474/IJAR01/6694      
DOI URL: https://dx.doi.org/10.21474/IJAR01/6694

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