Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates

Fountas, Grigorios; Anastasopoulos, Panagiotis Ch.; Abdel-Aty, Mohamed

doi:10.1016/j.amar.2018.04.003

Cited by 136 publications

(71 citation statements)

References 62 publications

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“…Other forms such as the mixed generalized linear model with multiple link functions have been widely used as well. Fountas et al analyzed the injury severities using a correlated random parameter ordered probit approach with time-variant covariates [17]. Yang et al conducted a two-step identification of the method of secondary crashes on the freeway by using random effect logit regression model [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

Wang

2019

PLoS ONE

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Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

Wang

2019

PLoS ONE

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“…Considering the cyclist database for Lisbon, oversampling is the best resampling technique for both classifiers. Besides, this Road Conditions (19) Month (17) Age (14) Month (18) Road Markings (14) Age (14) Road Markings (42) Time (21) Age (19) Age (43) Gender (30) Road Markings (20) explore and compare the results of two supervised classification techniques in order to entify which variables can significantly affect pedestrian and cyclist injury severity when olved in a motor vehicle crash. e three resampling techniques were applied to the datasets, resulting in six different datasets city and the overall perspective.…”

Section: Datasetsmentioning

confidence: 99%

“…On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.Predicting road crashes and finding patterns from crash registrations is an important step to develop safety measures. Among a lot of research into the evaluation of crash likelihood and frequency, several studies have focused on factors affecting the injury severity [9][10][11][12][13][14][15][16][17][18]. Notwithstanding the personal injury, the economic and societal cost of crashes varies substantially based on the severity level of injury.…”

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confidence: 99%

A Rare Event Modelling Approach to Assess Injury Severity Risk of Vulnerable Road Users

Vilaça

Macedo

Coelho

2019

Safety

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Vulnerable road users (VRUs) represent a large portion of fatalities and injuries occurring on European Union roads. It is therefore important to address the safety of VRUs, particularly in urban areas, by identifying which factors may affect the injury severity level that can be used to develop countermeasures. This paper aims to identify the risk factors that affect the severity of a VRU injured when involved in a motor vehicle crash. For that purpose, a comparative evaluation of two machine learning classifiers—decision tree and logistic regression—considering three different resampling techniques (under-, over- and synthetic oversampling) is presented, comparing both imbalanced and balanced datasets. Crash data records were analyzed involving VRUs from three different cities in Portugal and six years (2012–2017). The main conclusion that can be drawn from this study is that oversampling techniques improve the ability of the classifiers to identify risk factors. On the one hand, this analysis revealed that road markings, road conditions and luminosity affect the injury severity of a pedestrian. On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.

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“…However, regardless of the superior transportation capabilities likely to be offered by this technology, the widespread adoption of flying cars will be predominantly shaped by public perception. Evaluation and statistical analysis of public perception toward a forthcoming transportation technology pose significant methodological challenges in terms of unobserved heterogeneity and temporal instability (Mannering and Bhat, 2014;Mannering et al, 2016;Fountas et al, 2018;Mannering, 2018). A number of recent studies have demonstrated that people's perception toward potential benefits and concerns from the future use of flying cars, as well as the associated safety and security issues are multifaceted, and influenced by a broad range of socio-demographic factors (Eker et al, 2019(Eker et al, , 2020a.…”

Section: Introductionmentioning

confidence: 99%

The Flying Car—Challenges and Strategies Toward Future Adoption

Ahmed

Hulme²,

Fountas

et al. 2020

Front. Built Environ.

Self Cite

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In recent years, our surface transportation infrastructure is suffering from overuse, extreme traffic congestion, and roadway disrepair. Instead of following the traditional infrastructure expansion policy, current transportation research focuses on developing innovative and novel solutions to the aforementioned issues. Current pathways to overcoming these issues include the gradual transition toward a number of emerging transportation technologies, such as, autonomous motor vehicles for human transport, as well as unmanned aerial vehicles (UAV's) and "drone" technologies for surveillance, and package deliveries. However, as a long-term solution, transportation scientists are also investigating the once-seemingly futuristic notion of flying car technology-a convergent form of ground/air vehicle transportation, and assessing associated regulations. In this paper, an extensive review of current literature is conducted to explore the technological capabilities of flying cars-each requiring appropriate regulations and governance-to become fully sustainable. Specifically, issues pertinent to training, safety, environment, navigation, infrastructure, logistics/sustainability, and cybersecurity and human factors are explored. This paper concludes with a preliminary quantitative analysis exploring the public perceptions associated with flying cars-including anticipated benefits, concerns, and willingness to both hire and acquire the technology once available to consumers. Insights offered by this data will help inform next-generation policies and standards associated with the gradual advancement of flying cars.

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Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates

Cited by 136 publications

References 62 publications

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

A Rare Event Modelling Approach to Assess Injury Severity Risk of Vulnerable Road Users

The Flying Car—Challenges and Strategies Toward Future Adoption

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