Competing risks analysis on traffic accident duration time

Li, Ruimin; Guo, Min

doi:10.1002/atr.1279

Cited by 17 publications

(7 citation statements)

References 39 publications

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“…On the other hand, specific deterrence occurs when a driver directly experiences detection and punishment [10]. Some scholars posit that general deterrence can be enhanced by strategically targeting high-risk periods and locations through a combination of visible and non-visible enforcement strategies.…”

Section: Mpe Operationmentioning

confidence: 99%

Linking Deployment Outcomes to Program Impacts for Mobile Photo Enforcement

Agina,

Momeni Rad,

El-Basyouny

2023

Safety

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Previous research has delved into the effectiveness of Mobile Photo Enforcement (MPE) in curbing speed violations and enhancing road safety. This present study extends this investigation to explore the potential influence of MPE deployment efforts on subsequent collision occurrences. Specifically, the research team applied survival analysis techniques to examine the connection between MPE deployment variables and the duration between collisions. K-M survival estimates were employed to assess the survivability of classified groups, categorized based on deployment hours, visits, the ratio of hours to visits, and traffic count. The findings illuminated that the ratio of hours to visits emerged as the most impactful variable in prolonging the time interval between collisions and diminishing the likelihood of collisions. Notably, the anticipated reduction in collision hazards, signifying the occurrence of collisions, exhibited variations ranging from 22% to 52%. The most substantial decreases were observed when deployment occurred in high-traffic locations. These outcomes reinforce the positive impact of deployed MPE hours and visits in extending the duration between consecutive collisions, thus reducing the risk of collision occurrences.

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Section: Mpe Operationmentioning

confidence: 99%

Linking Deployment Outcomes to Program Impacts for Mobile Photo Enforcement

Agina,

Momeni Rad,

El-Basyouny

2023

Safety

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“…In the field of transportation, the concept of competing risks has been utilized by experts to investigate traffic choice models and vehicle transaction behaviors. Additionally, other transportation infrastructure performance assessments have been analyzed using the dependent failure assumption [26,[32][33][34]. Other researchers have employed machine-learning models and artificial intelligence frameworks to evaluate traffic signal communications, hybrid electric vehicles' performance costs and other transportation infrastructure operations [10,11,35,36].…”

Section: Literature Reviewmentioning

confidence: 99%

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

2020

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Intelligent transportation system (ITS) has become a crucial section of transportation and traffic management systems in the past decades. As a result, transportation agencies keep improving the quality of transportation infrastructure management information for accessibility and security of transportation networks. The goal of this paper is to evaluate the impact of two competing risks: “natural deterioration” of ITS devices and hurricane-induced failure of the same components. The major devices employed in the architecture of this paper include closed circuit television (CCTV) cameras, automatic vehicle identification (AVI) systems, dynamic message signals (DMS), wireless communication systems and DMS towers. From the findings, it was evident that as ITS infrastructure devices age, the contribution of Hurricane Category 3 as a competing failure risk is higher and significant compared to the natural deterioration of devices. Hurricane Category 3 failure vs. natural deterioration indicated an average hazard ratio of 1.5 for CCTV, AVI and wireless communications systems and an average hazard ratio of 2.3 for DMS, DMS towers and portable DMS. The proportional hazard ratios of the Hurricane Category 1 compared to the devices was estimated as <0.001 and that of Hurricane Category 2 < 0.5, demonstrating the lesser impact of the Hurricane Categories 1 and 2. It is expedient to envisage and forecast the impact of hurricanes on the failure of wireless communication networks, vehicle detection systems and other message signals, in order to prevent vehicle to infrastructure connection disruption, especially for autonomous and connected vehicle systems.

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“…Qiu et al [7] put forward a novel multi-objective particle swarm optimization-based partial classification method to identify the contributing factors that influence accident severity. Li and Guo [8] developed a sub-distribution hazard regression model for competing risks analysis on traffic accident duration time. Yuan and Chen [9] established a logistic regression model to analyze the significance of main contributing factors of vehicle to vulnerable road user crash.…”

Section: Literature Reviewmentioning

confidence: 99%

Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones: The Empirical Analysis of Beijing

Sun

Wang

Chen

et al. 2018

IJERPH

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The objective of this study was to identify influence factors on injury severity of traffic accidents and discuss the differences in urban functional zones in Beijing. A total of 3982 sets of accident data in Beijing were analyzed from the perspective of whole city and different urban functional zones. From the aspects of accident attribute, occurrence time, infrastructure, management status, and environmental condition, the influence factors set of injury severity of traffic accidents in Beijing are set up in this paper, which include 17 influence factors. Based on Pearson’s chi-squared test, factors are preselected. On the basis of binary logistic regression analysis, the impact of the value of influence factors on injury severity of traffic accidents is calibrated. Based on classification and regression tree analysis, the impact of influence factors is analyzed. Through Pearson’s chi-squared test and binary logistic regression analysis, it is found that there are similarities and differences among different urban functional zones. There are two common influence factors, including accident type and cross-section position, and six personalized influence factors, including lighting conditions, visibility, signal control, road physical isolation facility, occurrence period and road type, and the other nine weak influence factors. The results of binary logistic regression analysis and classification and regression tree analysis are basically the same. The factors that should be paid attention to in different urban functional zones and the value of the factors that need special attention are determined by synthesizing two methods.

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Competing risks analysis on traffic accident duration time

Cited by 17 publications

References 39 publications

Linking Deployment Outcomes to Program Impacts for Mobile Photo Enforcement

Linking Deployment Outcomes to Program Impacts for Mobile Photo Enforcement

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones: The Empirical Analysis of Beijing

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