A count data model with endogenous covariates: Formulation and application to roadway crash frequency at intersections

Bhat, Chandra R.; Born, Kathryn; Sidharthan, Raghuprasad; Bhat, Prerna C.

doi:10.1016/j.amar.2013.10.001

Cited by 53 publications

(24 citation statements)

References 32 publications

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“…They proposed a two-stage mixed multivariate model and showed how disaggregated data at the level of individual accident could be used to predict a certain type of low-frequency accident. Bhat et al (2014) formulated a count outcome model with multinomial probit selection that accommodates unobserved heterogeneity and endogeneity issues at intersections. Their results showed that the model can be used for intersection crash analysis.…”

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confidence: 99%

A two-stage bivariate logistic-Tobit model for the safety analysis of signalized intersections

Wong

Choi

2014

Analytic Methods in Accident Research

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a b s t r a c tCrash frequency and crash severity models have explored the factors that influence intersection safety. However, most of these models address the frequency and severity independently, and miss the correlations between crash frequency models at different crash severity levels. We develop a two-stage bivariate logistic-Tobit model of the crash severity and crash risk at different severity levels. The first stage uses a binary logistic model to determine the overall crash severity level. The second stage develops a bivariate Tobit model to simultaneously evaluate the risk of a crash resulting in a slight injury and the risk of a crash resulting in a kill or serious injury (KSI). The model uses 420 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during 2002 and 2003. The results obtained from the first-stage binary logistic model indicate that the overall crash severity level is significantly influenced by the annual average daily traffic and number of pedestrian crossings. The results obtained from the second-stage bivariate Tobit model indicate that the factor that significantly influences the numbers of both slight injury and KSI crashes is the proportion of commercial vehicles. The existence of four or more approaches, the reciprocal of the average turning radius and the presence of a turning pocket increase the likelihood of slight injury crashes. The average lane width and cycle time affect the likelihood of KSI crashes. A comparison with existing approaches suggests that the bivariate logistic-Tobit model provides a good statistical fit and offers an effective alternative method for evaluating the safety performance at signalized intersections.

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confidence: 99%

A two-stage bivariate logistic-Tobit model for the safety analysis of signalized intersections

Wong

Choi

2014

Analytic Methods in Accident Research

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“…The model was cast in an ordered probit setting and estimated by a composite marginal likelihood approach. Bhat et al (2014) enhanced the model by permitting multivariate correlations through a multinomial probit (MNP) kernel. A MNP model is traditionally used in consumer choice or decision science to anticipate the influences of external variables on a person's choices (e.g., voting decision, vehicle purchase choice, etc.).…”

Section: Generalized Ordered-response Modelsmentioning

confidence: 99%

A Synthesis of Spatial Models for Multivariate Count Responses

Wang

Kockelman

Jamali

2017

Regional Research Frontiers - Vol. 2

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“…In this approach, the number of accidents which occurred during a specified period determines a road segment's perilousness. Estimations for Crash Frequency typically utilise count data models at selected locations (Anastasopoulos and Mannering, 2009;Bhat et al, 2014). Moreover, other examples of this approach have examined how the frequency of highway accidents is impacted by roadway geometries (e.g.…”

Section: Accident Analysismentioning

confidence: 99%

Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

Ryder

Dahlinger

Gahr

et al. 2019

Transportation Research Part A: Policy and Practice

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Despite the fact that semi-autonomous vehicles will become more and more prevalent in the coming decades, recent studies have highlighted that traffic accidents will persist as a core issue for road users, insurers, and policy makers alike. Researchers and industry players see potential in the technology embedded in semi-autonomous vehicles to combat this challenge by reliably predicting locations with a high likelihood of traffic accidents. This technology can be leveraged to detect accidents and 'near miss incidents', such as heavy braking and evasive manoeuvres, otherwise known as Critical Driving Events (CDEs). The locations of CDEs could identify areas of high accident exposure, offering automotive insurers a unique opportunity to reduce traffic accidents through the adoption of active loss prevention business models, such as providing saferouting services and in-vehicle warnings. To date, there is limited empirical evidence on whether the Crash Frequency and Crash Rate of locations can be accurately identified through CDEs. To address this research gap, an 18-week naturalistic driving field study of 72 vehicles was conducted in Switzerland, covering over 690,000 km. Data collected from the CAN Bus of these vehicles indicate that there is a proportional relationship between the CDEs of the fleet, and the Crash Frequency and Crash Rate of a location. Furthermore, a nationwide spatial regression analysis was applied to determine Crash Frequency across the majority of the Swiss road network. We identify the relationship between Crash Frequency, and the CDEs and Trip Frequency of the fleet, along with additional explanatory variables for urban and highway locations. These insights provide first evidence that insurance companies and other industry players with access to a nationwide semi-autonomous fleet can determine existing and emerging locations of high accident probability, enabling more proactive business models and safety focused services.

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A count data model with endogenous covariates: Formulation and application to roadway crash frequency at intersections

Cited by 53 publications

References 32 publications

A two-stage bivariate logistic-Tobit model for the safety analysis of signalized intersections

A two-stage bivariate logistic-Tobit model for the safety analysis of signalized intersections

A Synthesis of Spatial Models for Multivariate Count Responses

Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

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