Exploring factors affecting pedestrians’ red-light running behaviors at intersections in China

Zhang, Weihua; Wang, Kun; Wang, Lei; Feng, Zhongxiang; Du, Yingjie

doi:10.1016/j.aap.2016.07.038

Cited by 56 publications

(32 citation statements)

References 32 publications

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“…Except for the researches mentioned above, logistic regression method is widely used for analyzing the redlight violations of pedestrians and bicyclists in a lot of other previous studies [12][13][14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The logistic regression is used to identify fitting, defensible models that describe the relationship between a binary dependent variable and explanatory variables [29]. It has been applied to model a wide variety of transportation data: accident-injury severities modelling [30,31]; commuters' valuation of travel time analysis [32]; airport risk and accidents models development [33]; examination of risk factors associated with RLR behaviour at signalized intersections [34,35].…”

Section: Logit Modelmentioning

confidence: 99%

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A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections

Wang

Yuan

Liu

et al. 2019

Journal of Advanced Transportation

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It is a dangerous behaviour for pedestrians and nonmotorized vehicles to cross intersections without waiting when they arrive at intersections during the red-light period. This paper investigates three typical signalized major-major intersections in the center of Beijing, by collecting and analyzing 1368 samples of pedestrians and nonmotorized vehicles. A random parameter logit model (RPLM) is established, with immediate red-light running (IRLR) behaviour as the dependent variable. The results show that the number of people waiting upon arrival, number of people crossing upon arrival, traffic mode, motor vehicle phase upon arrival, and speed change upon arrival have significant effects on IRLR behaviour. Accordingly, we suggest enforcing education administration on cyclists to reduce cyclists' IRLR behaviour. Thus, people's red-light running (RLR) behaviour will further decrease with fewer cyclists' IRLR behaviour. Accordingly, the safety of pedestrians and nonmotorized vehicles at signalized intersections has attracted the

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Section: Literature Reviewmentioning

confidence: 99%

Section: Logit Modelmentioning

confidence: 99%

A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections

Wang

Yuan

Liu

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Logistics regression has been widely applied to analyze the risk behaviour of road users in previous researches, especially to examine the factors contributing to RLR and YLR illegal behaviour [4,20,28,32,33,45]. In this study, we developed two logit models, namely, a base logit model and a random parameter logit model, to analyze the e-bikers' behaviour with respect to the stop-go decision when they were facing the signal change from green to yellow.…”

Section: Methodsmentioning

confidence: 99%

Can I Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

Cai

Zhao

Xiang

et al. 2020

Journal of Advanced Transportation

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Electric bike (e-bike) riders’ inappropriate go-decision, yellow-light running (YLR), could lead to accidents at intersection during the signal change interval. Given the high YLR rate and casualties in accidents, this paper aims to investigate the factors influencing the e-bikers’ go-decision of running against the amber signal. Based on 297 cases who made stop-go decisions in the signal change interval, two analytical models, namely, a base logit model and a random parameter logit model, were established to estimate the effects of contributing factors associated with e-bikers’ YLR behaviours. Besides the well-known factors, we recommend adding approaching speed, critical crossing distance, and the number of acceleration rate changes as predictor factors for e-bikers’ YLR behaviours. The results illustrate that the e-bikers’ operational characteristics (i.e., approaching speed, critical crossing distance, and the number of acceleration rate change) and individuals’ characteristics (i.e., gender and age) are significant predictors for their YLR behaviours. Moreover, taking effects of unobserved heterogeneities associated with e-bikers into consideration, the proposed random parameter logit model outperforms the base logit model to predict e-bikers’ YLR behaviours. Providing remarkable perspectives on understanding e-bikers’ YLR behaviours, the predicting probability of e-bikers’ YLR violation could improve traffic safety under mixed traffic and fully autonomous driving condition in the future.

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“…Onelcin et al [9] investigates the pedestrians' delays and gap perceptions at various signalized intersections. Zhang et al [10] used a binomial logistic model to investigate the factors affecting pedestrians' red-light running behaviors at intersection areas. Zhuang et al [8] analyzed pedestrian choices after arrival, evaluated safety of the choices, and built a model to identify the predictors of pedestrian choices.…”

Section: Pedestrian's Red-light Violation At Signalized Intersectionsmentioning

confidence: 99%

Analysis of Red-Light Violation Behavior of Pedestrian Two-Stage Crossing at a Signalized Intersection

et al. 2019

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Studying pedestrians’ twice-crossing behavior is of great significance to enhance safety and efficiency for pedestrians at signalized intersections. However, researchers have paid little attention to analyze and model pedestrians’ red-light running behavior on a two-stage crossing at signalized intersections. This paper focuses on analyzing the characteristics of pedestrian red-light violation behavior at the two stages, including the time distribution of violation behavior, the consistency of violation behavior, and the violation behavior in group. A goal-oriented and time-driven red-light violation behavior model was proposed for pedestrian two-stage crossing. A video-recording method was used to collect field data, and the results show that pedestrians in the two directions present different red-light violation behaviors in time selection and violation count, as well as, pedestrians in the two stages of a direction present different red-light violation behaviors in time selection. The main reasons leading to the phenomena were analyzed, regarding from people’s cognitive psychology and visual perception. The results also show that the proposed model is effective in simulating pedestrian red-light violation behavior of twice crossing. This research provides a theoretical basis for optimizing signal timing, improving pedestrian safety and developing user-friendly transportation system.

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Exploring factors affecting pedestrians’ red-light running behaviors at intersections in China

Cited by 56 publications

References 32 publications

A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections

A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections

Can I Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

Analysis of Red-Light Violation Behavior of Pedestrian Two-Stage Crossing at a Signalized Intersection

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