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Yunpeng Wang

Researcher at Beihang University

Publications -  152
Citations -  6832

Yunpeng Wang is an academic researcher from Beihang University. The author has contributed to research in topics: Traffic flow & Vehicular ad hoc network. The author has an hindex of 31, co-authored 144 publications receiving 4499 citations. Previous affiliations of Yunpeng Wang include Chinese Ministry of Public Security.

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Effect of the Uncertainty Level of Vehicle-Position Information on the Stability and Safety of the Car-Following Process

TL;DR: In this article, the authors employed the desired safety margin (DSM) model, an adaptive cruise control (ACC) control strategy, to investigate the influence of the uncertainty level of vehicle-position information on string stability and car-following safety.
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Analytical Model of Spread of Epidemics in Open Finite Regions

TL;DR: A susceptible–exposed–infected–recovered model with a variable contact rate is proposed to depict the dynamic spread processes of epidemics among heterogeneous individuals in open finite regions to demonstrate the effectiveness of the proposed model.
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A time-varying parameters vector auto-regression model to disentangle the time varying effects between drivers’ responses and tolling on high occupancy toll facilities

TL;DR: The existence of time-varying effects between drivers’ responses and toll rates is identified, and the evolving interactions amongst HOT demand, general purpose demand and tolling via time-Varying impulse responses are quantified.
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Fine-tuning time-of-day partitions for signal timing plan development: revisiting clustering approaches

TL;DR: This study investigated several classical clustering approaches for determining TOD breakpoints by revisiting K-means, hierarchical and Fisher ordinal clustering and examined the following factors that may have large impact on the partition results of TOD, namely data collection duration, multi-day and multi-phase choices, and time-dimension in the dataset.
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Comprehensive Review on Misbehavior Detection for Vehicular Ad Hoc Networks

TL;DR: A novel taxonomy of misbehavior detection is proposed, which considers the interaction between vehicles and which is refined by emphasizing the detection modes and participants, and is presented.