Following Safely on Curved Segments: A Measure with Discontinuous Line Markings to Increase the Time Headways

Ding, Naikan; Zhu, Shunying; Wang, Hong; Jiao, Nisha

doi:10.1007/s40996-017-0072-1

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…Actually, the influence of visual perception on driving behaviors and collision avoidance was originally noticed by some cognitive psychologists, who verified in virtual-reality scenes that the existence of certain visual information (which was termed as "visual cues") on ground surface could lead to drivers' overestimation of speed [42,44,48,62,63], underestimation of distance [44,[64][65][66][67][68][69][70], and untimely and inadequate brake [42,43]. Inspired by those cognitive psychologists, Ding et al [14,21,24,71,72] conducted a series of studies focusing on associating, evaluating, and explaining the effects and mechanism of drivers' speed perception and distance perception on car-following behaviors and crash risk, by introducing and testing several specially designed perceptual markings with on-road experiments and observations. In addition, the inherent connection between speed/distance perception, driving behaviors, and crash risk was comprehensively accounted for by two latent variables, i.e., "speed risk perception (SRP)" and "distance risk perception (DRP)," which differentiated the risk perception originating from variations in speed and distance [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, most of the experimental sites, designs, and processes of data collection and data treatment had already been demonstrated in previous studies [14,71,72], which will be briefly mentioned here. erefore, the variable selection and description are of primary focus to be explained in this study.…”

Section: Experiments and Data Collectionmentioning

confidence: 99%

“…It is important to mention here that there was no tunnel, entrance/exit, or any signs within 300 m area that could possibly impact driver behaviors, nor any exposed surveillance device for violation capture. e design of the perceptual markings, as introduced in Ding et al [14,71,72], was also followed in the present study. at is, three forms of perceptual markings were adopted, which were specified by the units of the perceptual markings, i.e., λ � 2 m, λ � 4 m, and λ � 8 m (see Figure 2).…”

Section: Experimental Site and Designmentioning

confidence: 99%

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Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Ding

Jiao

2021

Discrete Dynamics in Nature and Society

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Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Experiments and Data Collectionmentioning

confidence: 99%

Section: Experimental Site and Designmentioning

confidence: 99%

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Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Ding

Jiao

2021

Discrete Dynamics in Nature and Society

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“…Liu et al (2013) studied the influence of the plane layout of the edge rate lines on the speed reduction effect, and found that the double-row edge rate lines had better speed reduction effect. Furthermore, through road experiments, Ding et al (2017aDing et al ( , 2017bDing et al ( , 2019 found that edge rate marking can not only reduce the speed, but also increase the headway time, the cycle length of edge rate lines selected by the experiment were 2 m.…”

Section: Introductionmentioning

confidence: 99%

Influence Mechanism of Visual Perception of Edge Rate Lines Cycle Length on Driver’s Speed

Liu

Hong

et al. 2020

Transport

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It is known that the installation of edge rate lines can help reduce driving speed. Theoretically, higher edge rate density leads to higher perception speed, so as the effect of speed reduction. However, there has not been a successful evaluation of appropriate design cycle length requirements. A series of experiments were taken on the straight sections on Hangrui highway in China. The cycle length was separately set in different values of 16, 8, 4, 2 and 1 m. The results showed that cycle length had significantly influence on the speed reduction effect. When the length of spatial edge rate line in each cycle λ equalled to the value of 16, 8, 4, 2 m, the effect of speed reduction was significantly enhanced as λ decreased. Percent of average speed reduction was 0.8, 3.0, 5.8 and 7.4%, respectively. However, when λ = 1 m, speed reduction effect was weaker than λ = 2 m, reduction percent of average speed was 5.2%. Then, relations between acceleration and average edge rate was analysed. When temporal frequency of edge rate lines fell in (10 Hz, 19 Hz], the braking deceleration of drivers increased as the temporal frequency increased, which conformed to the relation between temporal frequency and perception speed; when temporal frequency was lower than 10 Hz, some drivers will speed up. It may be related to the threshold of perception speed difference; when temporal frequency was higher than 19 Hz, some drivers will speed up. It may be related to flicker fusion phenomenon. According to the experiments results, edge rate lines cycle length for future implementations should be determined by the speed distribution of the target road.

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Car Following Prediction Based on Support Vector Regression and Multi-adaptive Regression Spline by Considering Instantaneous Reaction Time

Pahlavani

Moghadam

Bigdeli

2018

Iran J Sci Technol Trans Civ Eng

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Following Safely on Curved Segments: A Measure with Discontinuous Line Markings to Increase the Time Headways

Cited by 11 publications

References 21 publications

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Influence Mechanism of Visual Perception of Edge Rate Lines Cycle Length on Driver’s Speed

Car Following Prediction Based on Support Vector Regression and Multi-adaptive Regression Spline by Considering Instantaneous Reaction Time

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