Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions

Pathivada, Bharat Kumar; Vedagiri, P.

doi:10.1016/j.trf.2018.10.010

Cited by 49 publications

(40 citation statements)

References 14 publications

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“…Similar conclusions were found by Papaioannou, Bharat, Hurwitz, and Ding et al [12,[16][17][18]. Elmitiny et al [19] and Pathivada, and Perumal [16] focused on the exposures affecting the violation behavior and they found that vehicle type had a statistically influence on the YLR behavior. e existing signal violation research studies have similarities and differences in the contributing factors that are explained in terms of the following aspects: (a) General summarization: drivers' characteristics including gender and age were proved to have significant impact on the e-bike riders' red-light running and vehicle drivers' yellow-light running violation behaviors, and in both violations, vehicle type is a significant exposure.…”

Section: Introductionsupporting

confidence: 81%

“…Köll et al concluded that drivers were likely to pass through intersection rather than stop with a higher approaching speed and shorter distance [15]. Similar conclusions were found by Papaioannou, Bharat, Hurwitz, and Ding et al [12,[16][17][18]. Elmitiny et al [19] and Pathivada, and Perumal [16] focused on the exposures affecting the violation behavior and they found that vehicle type had a statistically influence on the YLR behavior.…”

Section: Introductionmentioning

confidence: 72%

“…Modeling techniques were explored by many researchers to explain the e-bike rider's and vehicle driver's signal violation behavior at the signalized intersections. e logistic regression model was the most commonly used model to explain the signal violation behavior in studies conducted by Wu, Zhang, Tang, and Bharat et al [7,8,11,16]. Yan et al used the Poisson model to describe the e-bike cyclist's and bicyclist's RLR behavior [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Cai

Zhao

Liu

et al. 2020

Journal of Advanced Transportation

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Electric bikes play an important role in the urban transportation system in China. Yellow-light running behavior of riders is one of the most critical factors for e-bike riders involved in traffic crashes at intersection. The main purpose of this study is to explore how a variety of factors affect e-bike riders’ yellow-light running behaviors at intersection by a field observation conducted in Xi’an, China. Based on 396 e-bike riders who faced yellow-light samples, two analytical methods, the principle component analysis logistics model and a base logistics model, were employed to evaluate the impacts of contributing factors on e-bike riders’ yellow-light running behavior. The modeling results showed that seven variables significantly affect the e-bike riders’ yellow-light running behavior, which were the approaching speed of e-bike, the distance to stop line, riders’ age and gender attributes, type of e-bike, and the characteristics of intersection including the width of intersection and the existence of physical barriers. This study can provide valuable insights into understanding e-bike riders’ yellow-light running behavior and may also help decision makers propose countermeasures to reduce e-bike rider-related crashes at intersection.

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Section: Introductionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

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Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Cai

Zhao

Liu

et al. 2020

Journal of Advanced Transportation

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“…erefore, it is extremely crucial to study distracted driving considering mixed traffic environments [8]. e National Highway Traffic Safety Administration roughly divides distraction into visual distraction [9], operational distraction [10], and cognitive distraction [11].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Distraction State Recognition of Drivers at a Nonsignalized Intersection in a Mixed Traffic Environment

Hua

Jin

Jiang

et al. 2021

Advances in Civil Engineering

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Distracted driving has become a growing traffic safety concern. With advances in autonomous driving and connected vehicle technology, a mixture of various types of intelligent vehicles will become normal in the near future, while more factors that may cause driver cognitive distraction are emerging. However, there are rarely studies on distracted driving in mixed traffic environments. To fill this gap, we conducted a natural driving experiment with three representative events at a nonsignalized intersection in a mixed traffic environment and proposed a novel method of identifying cognitive distraction based on bidirectional long short-term memory (Bi-LSTM) with attention mechanism. Forty participants were recruited for each event, who completed three different cognitive distraction experiments induced by three different secondary tasks in contrast with a normal driving process when passing a nonsignalized intersection. Related driving performance and eye movement data were collected to train and test the Bi-LSTM with attention mechanism model. Compared with the support vector machine (SVM) model, its recognition accuracy rate is 94.33%, which is 3.83% higher than that of the SVM in the total event, which has reasonable applicability for distraction recognition in a mixed traffic environment. Potential applications of this model include distraction alarm and autonomous driving assistance systems, which could avoid road traffic accidents.

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“…As the essential nodes in the road network, intersection connects traffic streams from different directions [7], while the frequent occurrences of acceleration, deceleration and idling conditions of vehicles at intersections cause severe traffic pollution problems. In fact, the efficiency of the overall road network depends a lot on the signal timing strategy at intersections.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Environmental Impact of Bus Signal Priority at Intersections under Hybrid Energy Consumption Conditions

Huan

Yao

Fan³

et al. 2019

Energies

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The acceleration of the motorization process creates severe environmental problems by affecting the energy consumption of urban traffic. As a major source of traffic pollution, vehicle exhaust deserves more attention when making traffic policy. Actually, the acceleration, deceleration, and idling conditions of vehicles cause more pollution than usual, which mainly happens at intersections of the road network. Besides, in the context of giving priority on public transport development, bus signal priority (BSP) at intersections becomes a quite prevalent measure to reduce average capita delay for travelers, while long-term practice also indicates that the unreasonable setting of bus lane further worsens the running conditions for other vehicles by occupying excessive traffic capacity, which highlights the indirect environmental effects of BSP. This paper provides a simulation-based method for evaluating the adaptability of BSP to find an optimum balance between efficient and environmental care. Specifically, the traffic volume, bus mixed rate of the intersection and energy types of vehicles consist of hybrid energy consumption conditions collectively. A VSP (vehicle specific power)-based exhaust emission models for both buses and other vehicles are employed to estimate the environmental cost of the entire intersection. Moreover, the overall efficiency of gasoline and electric vehicles is further evaluated to offer more implications for traffic control practice.

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Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions

Cited by 49 publications

References 14 publications

Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Cognitive Distraction State Recognition of Drivers at a Nonsignalized Intersection in a Mixed Traffic Environment

Evaluating the Environmental Impact of Bus Signal Priority at Intersections under Hybrid Energy Consumption Conditions

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