A Multinomial Logit Model of Pedestrian–Vehicle Crash Severity

Tay, Richard; Choi, Jaisung; Kattan, Lina; Khan, Amjad

doi:10.1080/15568318.2010.497547

Cited by 213 publications

(140 citation statements)

References 45 publications

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“…Commercial vehicles' adherence to traffic laws is more closely monitored by road traffic police as more people are affected in such vehicle crashes. Drivers of passenger-carrying vehicles (buses and coaches) are responsible for many people's safety, and heavy vehicle crashes are fatal (29,30). Therefore, such drivers are monitored at police checkpoints and also by passengers for any traffic offences, and drastic penalties are imposed for them.…”

Section: Discussionmentioning

confidence: 99%

Seatbelt Use and Related Factors Among Drivers Involved in Road Crashes in Southeast Iran

et al. 2015

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Background: Seatbelts are a relatively low-cost safety device that provides easy basic protection for occupants of 4-wheeled vehicles. Objectives: This study investigates frequency of seatbelt use and its related factors among drivers involved in a vehicle crash. Materials and Methods:In this cross-sectional study, all crash profiles recorded in a province from March 2010 to March 2011 were reviewed. Necessary information was extracted from crash reports in which at least one 4-wheeled vehicle was involved. Data were analyzed using binary and multinomial logistic regression. Results: Of a total of 1427 motor vehicle crashes, a seatbelt was used by 58.2% of drivers. In the univariate analysis, the following were significantly associated with seatbelt use: driver age, education, and occupation along with front seat passenger's sex and seatbelt use, type and make of vehicle, speed, road surface condition, and type of road. In the multivariate model, the following remained significant: driver education, seatbelt use by front seat passenger, and type of road. Furthermore, a restraining seatbelt protected drivers from severe injury and death. Unbelted drivers were 7 and 17.4 times more likely to experience injury and death respectively than belted drivers. Conclusions: The seatbelt wearing rate among the study participants was much lower than the 90% rate reported among Iranian drivers in 2010. Mandating seatbelt use, as in most countries, will be more effective if a combination of factors such as changes in vehicle design, road safety, and driver and passenger behavior are taken into account.

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Section: Discussionmentioning

confidence: 99%

Seatbelt Use and Related Factors Among Drivers Involved in Road Crashes in Southeast Iran

et al. 2015

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“…The multinomial model is more flexible and allows for estimating the effect of independent variables in each severity category relative to the base outcome case (6).In other words, any contributing factor may be significant in one category but not significant in other categories or in the whole data, thus the interpretation of the results can be easier. MNL model is used for the Montreal dataset.…”

Section: Injury Severity Modelsmentioning

confidence: 99%

“…There are several statistical methods that can be used for analyzing the crash severity such as ordered logit or probit models (2), (4), generalized logit model (3), multinomial logit model (6), binary logit model (5). Data mining has been used for data exploration and analysis in many scientific areas for years.…”

Section: Introductionmentioning

confidence: 99%

A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada

et al. 2013

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Understanding the underlying relationship between pedestrian injury severity outcomes and factors leading to more severe injuries is very important in dealing with the problem of pedestrian safety. To investigate injury severity outcomes, many previous works relied on statistical regression models. There has also been some interest for data mining techniques, in particular for clustering techniques which segment the data into more homogeneous subsets. This research combines these two approaches (data mining and statistical regression methods) to identify the main contributing factors associated with the levels of pedestrian injury severity outcomes. This work relies on the analysis of two unique pedestrian injury severity datasets from the City of New York, US (2002US ( -2006 and the City of Montreal, Canada (2003-2006. General injury severity models were estimated for the whole datasets and for subpopulations obtained through clustering analysis. This paper shows how the segmentation of the accident datasets help to better understand the complex relationship between the injury severity outcomes and the contributing geometric, built environment and socio-demographic factors. While using the same methodology for the two datasets, different techniques were tested. For instance, for New York, latent class with ordered probit method provides the best results. However, for Montreal, the K-means with multinomial logit model is identified as the most appropriate technique. The results show the power of using clustering with regression to provide a complementary and more detailed analysis. Among other results, it was found that pedestrian age, location at intersection, actions prior to accident, driver age, vehicle type, vehicle movement, driver alcohol involvement and lighting conditions have an influence on the likelihood of a fatal crash. Moreover, several features within the built environment are shown to have an effect. Finally, the research provides recommendations for policy makers, traffic engineers, and law enforcement to reduce the severity of pedestrian-vehicle collisions. KEYWORDS:Pedestrian safety, regression, latent class, clustering, severity, built environmental, land use variables Mohamed, Saunier, Miranda-Moreno, Ukkusuri 3 INTRODUCTIONRoad user safety is a primary concern, not only for traffic safety specialists and traffic engineers, but for educators and law enforcement as well. Most importantly, pedestrian safety is a vital traffic issue as all road users are pedestrians at one point or another. Since pedestrians are vulnerable road users and suffer more in road crashes, it is important to understand the factors affecting pedestrian injury severity levels. In this way, traffic engineers, planners, decision makers and law enforcement will be able to precisely target these factors through various counter-measures, such as improvements to motorized vehicles, roadway and pedestrian facility design, control strategies at conflict locations, and driver and pedestrian education programs.This pape...

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“…The weather condition effects simplified to a simple binary representation of cloudy condition. The result indicates that if collisions occur during cloudy weather (relative to those during other weather conditions) the drivers are less likely to evade early death, perhaps because of the reduced visibility, which presumably results in reduced perception-reaction and reduced ability to take evasive actions at the crash incident (Tay et al, 2011). The effect of cloudy weather condition on the threshold also indicates increased likelihood of death within 2nd-5 days of crash.…”

Section: Environmental Factorsmentioning

confidence: 79%