01846766

Component

https://doi.org/10.1177/03611981221090943

This study presents a framework to employ naturalistic driving study (NDS) data to understand and predict crash risk at a disaggregate trip level accommodating for the influence of trip characteristics (such as trip distance, trip proportion by speed limit, trip proportion on urban/rural facilities) in addition to the traditional crash factors. Recognizing the rarity of crash occurrence in NDS data, the research employs a matched case-control approach for preparing the estimation sample. The study also conducts an extensive comparison of different case-to-control ratios including 1:4, 1:9, 1:14, 1:19, and 1:29. The model parameters estimated with these control ratios are reasonably similar (except for the constant). Employing the 1:9 sample, a multi-level random parameters binary logit model is estimated where multiple forms of unobserved variables are tested including (a) common unobserved effects for each case-control panel, (b) common unobserved factors affecting the error margin in the trip distance variable, and (c) random effects for all independent variables. The estimated model is calibrated by modifying the constant parameter to generate a population conforming crash risk model. The calibrated model is employed to predict crash risk of trips not considered in model estimation. This study is a proof of concept that NDS data can be used to predict trip-level crash risk and can be used by future researchers to develop crash risk models.

Tanmoy Bhowmik https://orcid.org/0000-0002-0258-1692 © National Academy of Sciences: Transportation Research Board 2022.

English

0000-0002-0258-1692

0000-0001-7856-5376

0000-0003-1221-4113

pp 737-745

2022-10

Transportation Research Record: Journal of the Transportation Research Board

Web

References (37)

Crash risk forecasting; Logits

Naturalistic Driving Study Database

Highways; Safety and Human Factors

TRIS, TRB, ATRI

May 20 2022 3:06PM