Determination of Optimal Spatial Sample Sizes for Fitting Negative Binomial-Based Crash Prediction Models with Consideration of Statistical Modeling Assumptions

Mohammadreza Koloushani (), Seyed Reza Abazari, Omer Arda Vanli, Eren Erman Ozguven, Ren Moses, Rupert Giroux and Benjamin Jacobs
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Mohammadreza Koloushani: Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA
Seyed Reza Abazari: Department of Industrial and Manufacturing Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA
Omer Arda Vanli: Department of Industrial and Manufacturing Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA
Eren Erman Ozguven: Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA
Ren Moses: Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, Tallahassee, FL 32310, USA
Rupert Giroux: Florida Department of Transportation, State Safety Office, Central Office, Tallahassee, FL 32399, USA
Benjamin Jacobs: Florida Department of Transportation, State Safety Office, Central Office, Tallahassee, FL 32399, USA

Sustainability, 2023, vol. 15, issue 20, 1-16

Abstract: Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on negative binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating the assumption of violation identification. This study, part of an FDOT-sponsored project, investigates the established statistical diagnostic tests to identify model violations and proposes a novel approach to determine the optimal spatial regions for empirical Bayes adjustment. This adjustment aligns HSM SPFs with regression assumptions. This study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe-injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM SPFs with intended assumptions and determining appropriate region sizes.

Keywords: crash prediction model; safety performance function; Highway Safety Manual; negative binomial regression; model diagnostic; context classification system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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