Tobit analysis of vehicle accident rates on interstate highways

Anastasopoulos, Panagiotis Ch.; Tarko, Andrew P.; Mannering, Fred

doi:10.1016/j.aap.2007.09.006

Cited by 174 publications

(85 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…5 Sample selection regression models have been widely applied in the road safety literature (Goldstein, 1986;Anastasopoulos et al, 2008;Tarko andAzam, 2011, Farah et al, 2009 (Mashtare Jr. and Hutson, 2011). Consequently, the hypothesis of a normal distribution of errors must be tested when the Tobit regression model is applied.…”

Section: Institut De Recerca En Economia Aplicada Regional I Pública mentioning

confidence: 99%

Factors affecting hospital admission and recovery stay duration of in-patient motor victims in Spain

Santolino

Bolancé

Alcañiz

2012

Accident Analysis & Prevention

View full text Add to dashboard Cite

Section: Institut De Recerca En Economia Aplicada Regional I Pública mentioning

confidence: 99%

Factors affecting hospital admission and recovery stay duration of in-patient motor victims in Spain

Santolino

Bolancé

Alcañiz

2012

Accident Analysis & Prevention

View full text Add to dashboard Cite

“…Censored data are different from truncated data because only non-limited values are available in truncated data while limited data information is provided as well in censored data [3]. As for the crash rate, the data can be considered as left-censored at zero (zero crash per million vehicle miles travelled) in that not all the roadway segments include crashes during the observation period.…”

Section: Methodsmentioning

confidence: 99%

“…More details about the model's expected value calculation and estimation are referred to in literature [3].…”

Section: Methodsmentioning

confidence: 99%

“…a a a f = + + + (3) where Zi represents the factors influencing the crash rate, vector of exogenous variables, 2 a is the coefficient of the endogenous variables, 3 a is the coefficient of the exogenous variables, 1 a is a constant, and i f is a real number and denotes the error terms.…”

Section: Methodsmentioning

confidence: 99%

“…The way in which the crash rates are used is because crashes per 100-million vehicle miles travelled is a continuous variable instead of the non-negative integer used for discrete crash event over some period, but there might be no-crash-occurred for some roadway segments during the observation period, so that the data will be left-censored at zero. Literature [3] explored the application of the tobit regression on the censoring problem, and the results suggested the potentiality that tobit regression has in analyzing crash rates on interstate highways, but the study ignored the unobserved heterogeneity across observations, which may lead to biased estimation and incorrect inferences [4]. In order to deal with the heterogeneity across observations, literature [2] employed a random-parameters tobit regression model in the study of motor vehicle crash rates, and the results showed that the random-parameters tobit model had the potential to analyze the factors determining crash rates, while addressing the unobserved heterogeneity problem across observations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis on Influencing Factors Identification of Crash Rates Using Tobit Model with Endogenous Variable

Kouhpanejade

Šarić

2013

PROMET

View full text Add to dashboard Cite

show abstract

Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

Chan¹,

Huang²,

Yan³

et al. 2010

J of Advced Transportation

109

View full text Add to dashboard Cite

SUMMARYPavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness-of-fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system.

show abstract

Tobit analysis of vehicle accident rates on interstate highways

Cited by 174 publications

References 33 publications

Factors affecting hospital admission and recovery stay duration of in-patient motor victims in Spain

Factors affecting hospital admission and recovery stay duration of in-patient motor victims in Spain

Analysis on Influencing Factors Identification of Crash Rates Using Tobit Model with Endogenous Variable

Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

Contact Info

Product

Resources

About