Crash prediction based on random effect negative binomial model considering data heterogeneity

Ying Yan, Ying Zhang, Xiangli Yang, Jin Hu, Jinjun Tang and Zhongyin Guo

Physica A: Statistical Mechanics and its Applications, 2020, vol. 547, issue C

Abstract: In order to improve traffic safety, a large amount of works focusing on crash prediction and identifying factors contributing to crashes. However, the ignorance of data unobserved heterogeneity in some traditional models may lead to biased parameter estimation and erroneous inferences. To investigate the relationship between crash and the potential contributing factors, the crash data occurred in 3-year survey period on Interstate highways in Washington, including 134 fatal crashes, 13936 injury crashes, and 34,084 property damage only (PDO) crashes were collected. A data quality control method based on sensitivity analysis is used to determine the road segments. Then a negative binomial (NB) model and a random negative binomial (RENB) model are constructed to predict crash number. The inverse stepwise procedure was applied to examine the significance of explanatory variable. The horizontal alignment type, speed limit, visibility, road surface condition, and AADT are identified as significant factors on the crash. In the comparison, four standard errors are designed as indicators, and the results show that the errors of RENB model are lower than that of NB model. The comparing results illustrate that the RENB model outperforms the NB model in crash number prediction and safety service level prediction

Keywords: Heterogeneity; Random effect negative binomial model; Negative binomial model; Crash prediction; Traffic safety (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119321430
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:547:y:2020:i:c:s0378437119321430

DOI: 10.1016/j.physa.2019.123858

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().