A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles

Wang, Jian; Gong, Siyuan; Peeta, Srinivas; Lu, Lili

doi:10.1016/j.trb.2019.08.002

Cited by 78 publications

(34 citation statements)

References 32 publications

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“…In the future, studies of these relationships need to consider the autonomous vehicles environment [ 37 , 38 , 39 ]. Cellular automaton models could be used to further refine the interactions of e-bikes and bicycles [ 40 ].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…In the future, the relationship between traffic congestion and cruising for parking is worth studying. At the same time, studying the relationship needs consider autonomous vehicle environments in the future [26][27][28]. Consider using cellular automaton model to further refine the influence of cruising for parking [29].…”

Section: Discussionmentioning

confidence: 99%

Impact of Cruising for Parking on Travel Time of Traffic Flow

Zhu

Chen

et al. 2020

Sustainability

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Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed in with normal cars that are actually going somewhere. In order to mitigate the influence of cruising for parking on the normal cars, the park-and-visit cruising tests with GPS and cameras was applied to collect the behavior of the cruisers, and the videotapes of traffic flows were used to measure the volume of cruising cars and the traffic status of normal cars, simultaneously. On this basis, a parking time model based on proportional hazard-based duration model was proposed, and the factors affecting cruise for parking were analyzed, including the volume, search time, speed, acceleration, lane-change frequency, and distracted time of the cruising car. The multiple linear regression model was also established to compare with proportional hazard-based duration model results. The results indicated that between 9 and 56 percent of the traffic was cruising for parking, and the average search time was about 6.03 min. The low-speed, volume, high acceleration frequency, and lane-change times of cruising cars have a negative effect on shortening travel time of the normal traffic flow. Conversely, high-speed of cruising cars has a positive effect on shortening travel time of traffic flow. Moreover, travel time changes in varying degrees due to various factors. Under postulated conditions, the model can be used to estimate the travel time. It is hoped that this study will contribute to improve the planning and management of cruising for parking.

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“…Eq. (6) depicts that when < 0 ( ) , the acceleration is positive, in the next moment vehicle is in an accelerated state; when > 0 ( ) , the acceleration is negative, in the next moment vehicle is in a decelerated state.…”

Section: Figure 2 the Distribution Of Safety Potential Field With DImentioning

confidence: 99%

A Macroscopic Model of Heterogeneous Traffic Flow Based on the Safety Potential Field Theory

Gan

et al. 2021

IEEE Access

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A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles

Cited by 78 publications

References 32 publications

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

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

Impact of Cruising for Parking on Travel Time of Traffic Flow

A Macroscopic Model of Heterogeneous Traffic Flow Based on the Safety Potential Field Theory

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