Developing a safety heatmap of uncontrolled intersections using both conflict probability and severity

Ma, Yingying; Qin, Xiaoran; Grembek, Offer; Chen, Zhiwei

doi:10.1016/j.aap.2018.01.038

Cited by 13 publications

(7 citation statements)

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“…Considering the rear-end collision and the front or side collision from different angles, a conflict probability model of the vehicle for any cell at signalized intersections is established. Let k denote the vehicle trajectory, and there are at most three vehicle trajectories passing through a cell simultaneously (k ≤ 3) [28]. en, the probability of traffic conflict in cell Rec(i, j) is…”

Section: Conflict Probability Models Considering Micro Operationmentioning

confidence: 99%

“…is study takes the T-shaped intersection of Wushan road and Yuehan road in Guangzhou, China, as a case study. In this case, β j is set as 1, 3, 5, and 7 according to the risk level based on previous research experience [28,31].…”

Section: Case Studymentioning

confidence: 99%

“…To facilitate the extension and application of the definition of traffic conflict in practice, scholars have proposed a variety of traffic conflict indicators to quantify the proximity and interaction between two or more road users in time or space. e most commonly used conflict indicators include the time to collision (TTC) [24,25], the postencroachment time (PET) [26][27][28], and the stopping distance index (SDI) [29][30][31]. For example, Hayward [25] proposed TTC as a measurement index of traffic conflict, which is defined as the time to collision when two vehicles maintain their existing speed and direction unchanged, to evaluate the traffic safety level of intersections.…”

Section: Introductionmentioning

confidence: 99%

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Conflict Probability Prediction and Safety Assessment of Straight-Left Traffic Flow at Signalized Intersections

Zhang

2022

Journal of Advanced Transportation

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The safety of signalized intersections is of great concern. To allow for an effective evaluation measure on the safety level of intersections, traffic conflict analysis methods are commonly used. However, the existing literature has mainly focused on the statistical prediction of conflicts by using surrogate measurements, among which the spatial-temporal characteristics of the potential conflicts have been less addressed. In addition, most of the relevant studies rely on precise trajectory data, and the results could be limited to engineering applications when real-time/comprehensive trajectory data are not available. To address these issues, this study proposes a SICP (signalized intersection conflict probability) model to predict a straight-left traffic flow conflict with a spatial-temporal distribution in the heat map, which could effectively evaluate the traffic safety of the existing or prebuilt signalized intersections on urban roads. Firstly, the impact of vehicle movement characteristics on traffic conflict at signalized intersections was considered by incorporating the vehicle movement trajectory. Secondly, the signal phase was categorized in several stages (each phase contains switching and nonswitching stages); then, a vehicle-vehicle conflicts probability prediction model was established by integrating both horizontal and vertical arrival probability. Finally, to validate the performance of the proposed model, the measured data were collected from the intersection of Wushan road and Yuehan road in Tianhe District, Guangzhou, China. SSAM（Surrogate Safety Assessment Model）traffic conflict simulation was used to analyze the traffic conflict in the actual data and compared to the SICP model. A case study was conducted to reveal the evolution mechanism of the conflict risk coefficient at the signalized intersection and to estimate the safety status under the various security optimization strategies. The experimental results verified the effectiveness of the SICP model, indicating that the proposed model is effective in evaluating the safety level of existing or prebuilt signalized intersections.

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Section: Conflict Probability Models Considering Micro Operationmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Conflict Probability Prediction and Safety Assessment of Straight-Left Traffic Flow at Signalized Intersections

Zhang

2022

Journal of Advanced Transportation

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“…Thus, it is one of the most dangerous and congested road facilities due to the complexity of traffic conflict movement. Statistics showed half of the crashes occurred at intersections in San Francisco, California [38] and Victoria, Australia [39]. Because the majority of crashes occur at an intersection, many transportation researchers have investigated intersection safety [40].…”

Section: A Questionnaire Designmentioning

confidence: 99%

“…Unsignalized intersections are common in rural areas of China. Meanwhile, there are no effective control measures to reduce traffic conflict points, so the probability of crashes at unsignalized intersections is higher than that at signalized intersections [38]. How to improve the safety and efficiency of unsignalized intersections has become an urgent problem to be solved worldwide.…”

Section: A Questionnaire Designmentioning

confidence: 99%

Intention of Risk-Taking Behavior at Unsignalized Intersections Under the Connected Vehicle Environment

et al. 2021

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With the rapid emergence of connected vehicle (CV) technologies, there is a shortage of research to understand CV technology's effect on drivers' risk-taking behavioral intentions. This paper aims to analyze driver responses to the real-time information by comparing their reactions to driving intentions between the CV and non-CV environments. A multi-group structural equation model (SEM) is employed to explore the heterogeneity in the relationships between behavioral intentions, attitudes, subjective norms, perceived behavioral control, and risk perceptions under the two different environments. This study reveals two key findings: 1) regarding driver responses to the theory of planned behavior (TPB) model, there are significant differences in attitudes and risk perceptions between the CV and non-CV environments; 2) irrespective of driving environments, risk-taking behavioral intentions are directly related to perceived behavioral control and risk perceptions. While intentions are directly related to attitudes but not associated with subjective norms under the non-CV environment. In contrast, intentions are directly related to subjective norms but not associated with attitude under the CV environment. The findings provide a theoretical basis for using TPB to evaluate CV technology's effects and understanding the differences between the CV and non-CV environments. INDEX TERMS Connected vehicles, Risky driving intentions, Structural equation model, Theory of planned behavior, Unsignalized intersections.

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A Traffic Conflict Identification Model Considering Heterogeneity of Traffic Participants

Zhang,

Peng

2024

Lecture Notes in Civil Engineering

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Developing a safety heatmap of uncontrolled intersections using both conflict probability and severity

Cited by 13 publications

References 13 publications

Conflict Probability Prediction and Safety Assessment of Straight-Left Traffic Flow at Signalized Intersections

Conflict Probability Prediction and Safety Assessment of Straight-Left Traffic Flow at Signalized Intersections

Intention of Risk-Taking Behavior at Unsignalized Intersections Under the Connected Vehicle Environment

A Traffic Conflict Identification Model Considering Heterogeneity of Traffic Participants

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