Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation

Fu, Chuanyun; Sayed, Tarek

doi:10.1016/j.aap.2021.106051

Cited by 49 publications

(23 citation statements)

References 24 publications

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“…The same is the case with the variable “vehicles > 10 years”, which is significant with a positive coefficient in the three models, indicating that the age of the vehicle affects the number of victims per accident. This result coincides with that obtained by Anderson and Searson [ 60 ] who observed that the risk of accident seemed to be the lowest when vehicles were two years old, and increased as they aged. In an analysis carried out in Australia and New Zealand, Smith [ 81 ] observed that the oldest vehicles were over-represented in the deaths of their occupants during the period 2012–2016.…”

Section: Resultssupporting

confidence: 92%

“…Some recent studies have attempted to account for unobserved heterogeneity using random effect (RE) terms representing between-site variances, which are consistent for extremes in the same site but vary between different sites [ 55 , 56 , 57 , 58 , 59 , 60 ]. This approach, while representing an improvement over models that do not account for unobserved heterogeneity, still has limitations.…”

Section: Methodsmentioning

confidence: 99%

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Does the Implementation of Ride-Hailing Services Affect Urban Road Safety? The Experience of Madrid

Flor

Ortuño

Guirao

2022

IJERPH

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In recent years, changes have occurred in consumption, ownership, and social relations, giving rise to new economic models in which technology enables new ways of connecting, creating, and sharing value. The nature of transport has transformed with the emergence of mobile applications, such as Uber and Cabify, which offer an alternative to the services traditionally provided by the taxi and chauffeur-driven hire vehicle (CDV) sectors. These services have developed within a context of market regulation of the taxi and CDV which are subject to considerable unjustified restrictions for entering and operating in the market, including the numerus clausus of licenses, the limited geographical scope of the license and, in the case of taxis, the regulation of prices as inflexible public rates. Bearing in mind the latest legislative changes affecting mostly the provision of the services of these platforms, this study analyzes whether the number of traffic accident victims has fallen since the introduction of these services in the city of Madrid using a Random Effects Negative Binominal model. The results show that the deployment of these platforms is associated with a reduction of 25% in the number of serious injuries and deaths.

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Section: Resultssupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Does the Implementation of Ride-Hailing Services Affect Urban Road Safety? The Experience of Madrid

Flor

Ortuño

Guirao

2022

IJERPH

View full text Add to dashboard Cite

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“…Generally, some limitations in this study merit further study. Firstly, the psychological characteristics that affect pedestrian crossing violations should be further explored to refine the influencing factors; secondly, the model assumes that the indicator variables can be obtained by actual observation, a limitation that can be handled by a Hidden Markov Model; thirdly, the research results can be extended for a traffic flow assessment [28,29] to improve road safety analysis [30,31]. Finally, more pedestrian crossing data from multiple types of intersections are needed to validate the proposed model in this paper.…”

Section: Discussionmentioning

confidence: 99%

Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

et al. 2021

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Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

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“…00 ( , ) xy is the coordinate of vehicle center position. is the angle between the speed direction and the connecting line, which connects a point and midpoint on the equal field intensity circle. Bring the water drop line model into (4) to obtain the kinetic energy field model shown in (6).…”

Section: ) Driving Risk Field Modelmentioning

confidence: 99%

“…Deterministic method usually used the conflict indicators of time, distance, and deceleration to distinguish whether the vehicle collision occur. Many scholars used the single conflict indicator to evaluate vehicle collision risk [3], [4], [5], [6]. These researches showed that using different indicator has different evaluation result [7].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

2022

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In order to take more active measures to prevent and control secondary accidents, it is necessary to describe risk propagation process after the accident. To this end, this paper deeply analyzed the risk propagation mechanism between vehicles and proposed a novel secondary rear-end collision accident risk propagation model, which could real-time evaluate vehicle rear-end collision risk. The research scene of the paper is a single-lane road rear-end collision scene, so a driving risk field model suitable for this scene is first established. Based on this, the vehicle operation interaction risk field force is calculated. Then, the risk field force is converted into risk probability through the hyperbolic tangent function, and the vehicle operation interaction risk model is obtained. In addition, a risk propagation framework based on dynamic Bayesian network is constructed to describe the propagation process of rear-end collision risk from the accident vehicle to following vehicles. Finally, according to the probabilistic reasoning process of the framework, combined with the accident vehicle risk, a secondary rearend collision accident risk propagation model is established. Simulation experiments show that the paper model can describe the evolution trend of rear-end risk and the risk assessment results are more accurate. And after the accident, the rear-end collision risk propagation speed will increase with the increase of traffic flow, and the number of secondary rear-end vehicles will also increase with the increase of traffic speed. These conclusions are of great significance in formulating vehicle anti-collision strategies and deploying risk management and control facilities.INDEX TERMS Secondary rear-end collision; Risk propagation; Driving risk field; Dynamic Bayesian network

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Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation

Cited by 49 publications

References 24 publications

Does the Implementation of Ride-Hailing Services Affect Urban Road Safety? The Experience of Madrid

Does the Implementation of Ride-Hailing Services Affect Urban Road Safety? The Experience of Madrid

Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field

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