Recommended Widths for Separated Bicycle Lanes Considering Abreast Riding and Overtaking

Yan, Xingchen; Wang, Tao; Ye, Xiaofei; Yang, Zaili; Bai, Hua

doi:10.3390/su10093127

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…However, Cheng's study only used 70 questionnaires to determine the lateral offset at different speeds, which may lack powerful data support. Although previous research has also proposed suggested values for lane width [34,35], their subjects and scenarios were inconsistent with this study.…”

Section: Discussioncontrasting

confidence: 89%

Lateral Oscillation Characteristics of Vehicle Trajectories on the Straight Sections of Freeways

et al. 2022

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The lateral oscillations of vehicle trajectories are a significant cause of collisions. There is a dearth of research, however, on the oscillatory behaviors of vehicles driving on straight sections of freeways. This study aimed to investigate the effects of vehicle type, lane position, and speed on oscillation behavior and to propose quantitative indicators to explain lateral oscillation characteristics. Based on these characteristics, a more appropriate lane width can be determined. First, the k-means algorithm was performed to cluster the vehicles into three categories: passenger cars, medium-large cars, and extra-large trucks. Then, statistical methods such as analysis of variance (ANOVA) and regression analysis were employed to elaborate on the speed distribution, lateral amplitude (LA), and distance traveled within the oscillation cycle (DTOC) for various vehicle types. The results show that different types of vehicles have different lateral oscillation tendencies. The LA and DTOC for passenger cars are generally more extensive than for medium-large cars and extra-large trucks, and their oscillation patterns are the most complicated. The vehicle trajectory oscillation pattern varies significantly for different lane positions and speeds, but speed is the dominant influencing factor. The naturalistic driving dataset from German freeways served as the foundation for this study. These results can assist road engineers in better understanding the behavioral characteristics of vehicle trajectory oscillations and designing safer freeways.

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Section: Discussioncontrasting

confidence: 89%

Lateral Oscillation Characteristics of Vehicle Trajectories on the Straight Sections of Freeways

et al. 2022

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“…Sukor et al [ 15 ] found that attitude and perceived behavioral control are significantly related to motorcyclist safety. For e-bikes, the literature has focused on external characteristics related to e-bike riding behaviors, including demographic variables, riding skills, riding distraction, traffic characteristics, and management measures [ 16 , 17 , 18 ]. In a comparison study of e-bikes and conventional bicycles, e-bike riders were found to commit more violations and experience more hazards than conventional cyclists [ 19 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model

Wang

Xie

et al. 2020

IJERPH

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To identify and quantify the factors that influence the risky riding behaviors of electric bike riders, we designed an e-bike rider behavior questionnaire (ERBQ) and obtained 573 valid samples through tracking surveys and random surveys. An exploratory factor analysis was then conducted to extract four scales: riding confidence, safety attitude, risk perception, and risky riding behavior. Based on the exploratory factor analysis, a structural equation model (SEM) of electric bike riding behaviors was constructed to explore the intrinsic causal relationships among the variables that affect the risky e-bike riding behavior. The results show that the relationship between riding confidence and risky riding behavior is mediated by risk perception and safety attitudes. Safety attitude was found to be significantly associated with risky riding behaviors. Specifically, herd mentality is most closely related to safety attitudes, which means that those engaged in e-bike traffic management and safety education should pay special attention to riders’ psychological management and education. Risk perception has a direct path to risky riding behaviors. Specifically, stochastic evaluation and concern degree are significantly related to e-bike riders’ risk perception. The findings of this study provide an empirical basis for the creation of safety interventions for e-bike riders in China.

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“…The inertia weight is adjusted in Equations (7) and (8), and a constriction factor is introduced [ 32 , 33 , 34 , 35 ]. All particles in PSO iteratively update their speed and location according to Equations (7) and (8).…”

Section: Model Solutionmentioning

confidence: 99%

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

Zhang

Hu²,

Zhang

et al. 2022

IJERPH

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Western China is a sparsely populated area with dispersed transportation infrastructure, making it challenging to meet people’s accessibility and mobility requirements. Rescue efficiency in sparse networks is severely hampered by the difficulty rescue teams have in getting to the scene of abrupt traffic accidents. This paper develops a layout optimization model for multiple rescue points to address this issue, suggests an improved particle swarm algorithm by including a variation that can reduce optimization time and avoid the disadvantage of precocity, and designs a MATLAB program using an adaptive variation algorithm. The proposed approach increases the effectiveness of rescue in a sparse network and optimizes the distribution of emergency resources.

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Recommended Widths for Separated Bicycle Lanes Considering Abreast Riding and Overtaking

Cited by 11 publications

References 7 publications

Lateral Oscillation Characteristics of Vehicle Trajectories on the Straight Sections of Freeways

Lateral Oscillation Characteristics of Vehicle Trajectories on the Straight Sections of Freeways

Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

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