title: Modeling work zone crash frequency by quantifying measurement errors in work zone length.
abstract: Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence.
authors.affiliation: Rutgers Intelligent Transportation Systems (RITS) Laboratory, Department of Civil and Environmental Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08854, United States. yanghong@rci.rutgers.edu
authors.forename: Hong
authors.initials: H
authors.lastname: Yang
authors.forename: Kaan
authors.initials: K
authors.lastname: Ozbay
authors.forename: Ozgur
authors.initials: O
authors.lastname: Ozturk
authors.forename: Mehmet
authors.initials: M
authors.lastname: Yildirimoglu
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D004777:Environment; D006801:Humans; D015233:Models, Statistical; D009515:New Jersey; D012044:Regression Analysis; D012307:Risk Factors