Developing crash prediction models using parametric and nonparametric approaches for rural mountainous freeways: A case study on Wyoming Interstate 80

Interstate 80 in Wyoming is one of the busiest freight corridors that is characterized with harsh winter conditions and challenging mountainous roadway sections. The fatality rates in Wyoming are always typically higher than the national level. The 402-mile I-80 corridor in Wyoming was selected by the USDOT FHWA for piloting connected vehicle technology to improve the safety and mobility of heavy trucks. To accurately quantify the effectiveness of the pilot, evaluation of the pre-deployment safety performance is essential. Unlike other studies, the full 402-mile of I-80 corridor passing through Wyoming was investigated as a requirement of the USDOT. Homogeneous segmentation was used to divide the corridor based on horizontal and vertical roadway characteristics. A transferability analysis was conducted to investigate whether a short portion of the corridor would be representative of the whole 402-miles of I-80. Results showed that the whole 402 miles should be considered in the analysis due to the radical changes throughout the corridor. Several SPFs were developed using three models; negative binomial (NB) model, spatial autoregressive (SAR) model, and non-parametric multivariate adaptive regression splines (MARS). Comparisons were performed for the developed models. Crash prediction models for total crashes and Fatal and Injury (F + I) crashes in addition to truck crashes were calibrated utilizing five years of crash data from 2012 to 2016. The results obtained from the three statistical approaches showed that MARS model provided a better model fit compared to NB and SAR models, given the lower AIC values for the developed models. Yet, SAR models showed the significant spatial dependency between the neighbor roadway segments. Additionally, the NB model showed its superiority on SAR when the spatial correlation was not significant. Parametric and non-parametric techniques should be interchangeably used in developing SPFs according to the modeling needs.

• Record URL:
  • https://doi.org/10.1016/j.aap.2018.10.011
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457518308352
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Gaweesh, Sherif M
  • Ahmed, Mohamed M
  • Piccorelli, Annalisa V
• Publication Date: 2019-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 176-189
• Serial:
  • Accident Analysis & Prevention
  • Volume: 123
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Case studies; Crash risk forecasting; Freeways; Highway safety; Mobile communication systems; Mountain roads; Nonparametric analysis; Parametric analysis; Rural areas; Traffic crashes
• Identifier Terms: Safety Performance Functions
• Uncontrolled Terms: Crash prediction models
• Geographic Terms: Wyoming
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01691540
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 28 2019 10:13AM