Exploring the association of rear-end crash propensity and micro-scale driver behavior

Kim, Sangkey; Song, Tae-Ho; Rouphail, Nagui M.; Aghdashi, Seyedbehzad; Amaro, Ana M.; Gonçalves, Gonçalo

doi:10.1016/j.ssci.2016.05.016

Cited by 39 publications

(23 citation statements)

References 9 publications

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“…When evaluating the fit of a model, the Akaike information criterion (AIC) and Bayesian information criterion (BIC) are usually used to analyze the model. The calculation formulas are shown in Equations (13) and (14). The AIC and the BIC are proposed to prevent overfitting problems when training the model.…”

Section: Discussionmentioning

confidence: 99%

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Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

Hao

Cui

et al. 2020

Sustainability

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The focus of this paper is the crash risk assessment of off-ramps in Xi’an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi’an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov–Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

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Section: Discussionmentioning

confidence: 99%

“…It is found that ramp AADT, the number of exit ramp lanes, ramp alignment [5], speed-changing lane length [6][7][8], vehicle characteristics (steering, acceleration, and speed) [9,10], the number of lanes of ramps [11,12], driving behaviors [13,14], etc., are closely related to ramp crashes.…”

Section: Introductionmentioning

confidence: 99%

Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

Hao

Cui

et al. 2020

Sustainability

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“…The system consists of an on-board unit (OBU), a mobile communications network (via M2M protocols), and a secure cloud database. The OBU connects to the vehicle's OBD-II interface, and includes a GPS sensor along with a 3D accelerometer and a barometric altimeter (Kim et al, 2016). Multiple engine and vehicle dynamics measures are acquired from the OBU at high resolution (1 Hz) and transmitted to the cloud database using mobile communications every 23 seconds.…”

Section: Data Sourcementioning

confidence: 99%

Development and analysis of eco-driving metrics for naturalistic instrumented vehicles

Tanvir

Chase

Roupahil

2019

Journal of Intelligent Transportation Systems

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This article is concerned with the development of eco-driving metrics for instrumented vehicles in a longitudinal study environment. Motivations for developing such metrics include an ability to distill driving style effects on fuel use from other confounding factors, to contrast and benchmark driving styles for a cohort of drivers and to ascertain the effects of information and/or incentives on fuel use both in the short and long term. High resolution (1 Hz) trip data were collected for a local sample of 35 drivers over a period of 2 years, yielding over 20 million second by second observations. To account for the difference in vehicle type choice, a standard vehicle was used to model fuel consumption based on instantaneous vehicle activity. Difference in route choice was accounted for using speed-bin dependent metrics. Two metrics were developed: a trip-based measure called the fuel efficiency score (FES), and a difference in fuel use metric that uses the second by second observations called the fuel use difference (FUD). FES varies from 20 to 100 while FUD covers positive and negative percentage differences from a speed-bin dependent mean value. Both measures passed the test of consistency so that, at the driver level, both revealed no temporal trend in the scores from month to month across a period of 2 years. Moreover, the FES metric passed the heterogeneity test. It was able to identify four distinct clusters of driving styles.

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“…Conversely, incident response time may be the most effective strategy for mitigating the impacts of crashes during congested periods. However, crash records in a crash analysis database do not indicate congestion conditions at the crash scene but only environmental, crash, roadway, and driver characteristics [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Crash Classification by Congestion Type for Highways

Song

Kim²,

Williams

et al. 2020

Applied Sciences

Self Cite

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Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.

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Exploring the association of rear-end crash propensity and micro-scale driver behavior

Cited by 39 publications

References 9 publications

Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

Development and analysis of eco-driving metrics for naturalistic instrumented vehicles

Crash Classification by Congestion Type for Highways

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