Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

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A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates

Accident Analysis & Prevention ◽

10.1016/j.aap.2019.105326 ◽

2020 ◽

Vol 134 ◽

pp. 105326 ◽

Cited By ~ 5

Author(s):

Qinzhong Hou ◽

Xiaoyan Huo ◽

Junqiang Leng

Keyword(s):

Tobit Model ◽

Random Parameters ◽

Factors Affecting ◽

Crash Rates ◽

Temporal Instability

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Analysis of Crash Rates at Freeway Diverge Areas using Bayesian Tobit Modeling Framework

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119837219 ◽

2019 ◽

Vol 2673 (4) ◽

pp. 652-662 ◽

Cited By ~ 5

Author(s):

Yanyong Guo ◽

Zhibin Li ◽

Tarek Sayed

Keyword(s):

Risk Factors ◽

Tobit Model ◽

Information Criteria ◽

Random Parameters ◽

Modeling Framework ◽

Crash Rates ◽

Tobit Models ◽

Heterogeneous Effects ◽

The Impact ◽

Diverge Areas

The goal of this study is to evaluate the impact of various risk factors on crash rates at freeway diverge areas. Crash rates data for a three-year period from 367 freeway diverge areas were used for analysis. Four candidate Tobit models were developed and compared under the Bayesian framework: a traditional Tobit model; a random parameters Tobit (RP-Tobit) model; a grouped random parameters Tobit (GRP-Tobit) model; and a random intercept Tobit (RI-Tobit). The results showed that the RP-Tobit model performs best with highest value of Rd2 as well as lowest Mean Absolute Deviance (MAD) and Deviance Information Criteria (DIC), indicating the importance of accounting for unobserved heterogeneity to improve the model fit. Both the GRP-Tobit and the RI-Tobit models provide better performance than the traditional Tobit model. The model results showed that crash rates at freeway diverge areas were positively associated with mainline annual average daily traffic (AADT) and negatively associated with ramp AADT, indicating the different mechanisms of the impact of traffic volume on crash rates at freeway diverge areas. Lane-balanced design and high speed limits at freeway diverge areas have a negative effect on crash rates. The number of lanes on mainline and ramp length have significant heterogeneous effects on crash rates across observations. The RP-Tobit model provides a more comprehensive understanding of the heterogeneous effects of risk factors on crash rates across observations.

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