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Title:

Modeling Bicycle Crash Costs Using a Grid-Cell-Based Random Parameters Tobit Model

Accession Number:

01664263

Record Type:

Component

Abstract:

Bicyclists are among the most vulnerable road users in the urban transportation systems. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to explore the overall safety patterns of bicyclists in Manhattan. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, sociodemographic and social media data with bicycle crash costs. It is worth to mention that large-scale bicycle ridership data from 2014 to 2016 was obtained from Citi Bike, which is the largest bike sharing program in the United States, and used for model development. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trip, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI lie in: 1) injury severity is considered by being converted into unit costs, and 2) varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolutions. It was found that most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.

Supplemental Notes:

This paper was sponsored by TRB committee ANF20 Standing Committee on Bicycle Transportation.

Report/Paper Numbers:

18-05996

Language:

English

Authors:

Xie, Kun
Ozbay, Kaan
Yang, Di
Xu, Chuan
Yang, Hong

Pagination:

6p

Publication Date:

2018

Conference:

Transportation Research Board 97th Annual Meeting

Location: Washington DC, United States
Date: 2018-1-7 to 2018-1-11
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Maps; References; Tables

Geographic Terms:

Manhattan (New York, New York)

Subject Areas:

Pedestrians and Bicyclists; Safety and Human Factors

Source Data:

Transportation Research Board Annual Meeting 2018 Paper #18-05996

Files:

NTL, TRIS, TRB, ATRI

Created Date:

Jan 8 2018 11:33AM