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

Xie, Kun; Ozbay, Kaan; Yang, Di; Xu, Chuan; Yang, Hong; Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)

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

2017-08-01
By Xie, Kun ; Ozbay, Kaan ; Yang, Di ; ...

[PDF-968.36 KB]

English

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Creators:

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

Xie, Kun ; Ozbay, Kaan ; Yang, Di ; Xu, Chuan ; Yang, Hong Less -
Corporate Creators:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Subject/TRT Terms:

[+]

Bicycle Crashes Big Data Costs High Risk Locations Highway Safety Hotspot Identification Random Parameters Tobit Model Safety Analysis Vehicle Sharing
Publication/ Report Number:

18-05996
Resource Type:

Manuscript
Geographical Coverage:

United States
Corporate Publisher:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
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.
Format:

PDF
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

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urn:sha256:4de575485d5dfcbb3943ef6a8ecd374b84beaad2f1c4741fc9c1510f3dccc28d
Download URL:

https://rosap.ntl.bts.gov/view/dot/49469/dot_49469_DS1.pdf
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Modeling Bicycle Crash Costs Using a Grid-Cell-Based Random Parameters Tobit Model

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