Influencing factors for right turn lane crash frequency based on geographically and temporally weighted regression models

Lu Ling; Wenbo Zhang; Jie Bao; Satish V Ukkusuri

doi:10.1016/j.jsr.2023.05.010

Influencing factors for right turn lane crash frequency based on geographically and temporally weighted regression models

J Safety Res. 2023 Sep:86:191-208. doi: 10.1016/j.jsr.2023.05.010. Epub 2023 Jun 1.

Authors

Lu Ling¹, Wenbo Zhang², Jie Bao³, Satish V Ukkusuri⁴

Affiliations

¹ Lyles School of Civil Engineering, Purdue University 550 Stadium Mall Drive, West Lafayette, IN 47906, USA. Electronic address: ling58@purdue.edu.
² School of Transportation, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China. Electronic address: wenbozhang@seu.edu.cn.
³ National Key Laboratory of Air Traffic Flow Management, Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Jiangjun Road No. 29, Nanjing 211106, China. Electronic address: jiebao@nuaa.edu.cn.
⁴ Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47906, USA. Electronic address: sukkusur@purdue.edu.

PMID: 37718046
DOI: 10.1016/j.jsr.2023.05.010

Abstract

Introduction: Right-turn lane (RTL) crashes are among the key contributors to intersection crashes in the US. Unfortunately, the lack of deep insights into understanding the effects of RTL geometric design factors on crash frequency impedes improving RTL safety performance.

Method: Taking the crash data in ten counties in Indiana state from 2013 to 2016 as a case study, this study investigates the safety performance of RTL geometric configuration based on multi-sources. We introduce the geographically and temporally weighted negative binomial model (GTWNBR) to capture the space and time instability in crashes.

Results: The results show that the impacts of RTL geometric design factors on crash frequency vary significantly among space and time. Several key insights can be obtained from the state-wide and multi-years crash analysis by associating the estimated parameters with road classes, localities, and counties.

Conclusions: First, the RTL's length, width, turning radius, and the installments of traffic roundabouts present higher spatiotemporal heterogeneity than other factors in modeling the crash frequency. Second, the effects of RTL's geometric factors vary significantly across space and time. The presence of bicycle and pedestrian lanes is more likely to increase crashes in urban areas than in rural ones, especially at nighttime. Third, while exclusive RTLs decrease the crash frequency compared to the shared RTLs, the exclusive RTLs are more likely to increase the crashes for RTLs on the county road than on other road classes. Increasing RTL's turning radius and decreasing RTL's length is more likely to promote crashes for RTLs on county roads than on other road classes.

Practical applications: The insights provide vital guidance to improve the safety performance of geometric configuration for RTLs and intersections.

Keywords: Crash frequency; Geographically temporally weighted negative binomial regression; Geometric design; Right turn lane; Spatiotemporal effects.

MeSH terms

Humans
Indiana
Models, Statistical*
Pedestrians*