A joint control model for connected vehicle platoon and arterial signal coordination

Wang, Pangwei; Jiang, Yilun; Lin, Xiao; Zhao, Yulin; Li, Yinghong

doi:10.1080/15472450.2019.1579093

Cited by 46 publications

(16 citation statements)

References 26 publications

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“…e goal of coordinated control is to achieve the global optimum in an arterial or a network, through considering the coordination of all the controllers. Wang et al [78] developed a joint control model which optimizes the speeds of the connected vehicles and coordinating signals along an arterial simultaneously. is control model forms connected vehicles into platoons so that the vehicles can pass through intersections together with no stops or the least stop time.…”

Section: Isolated Control Versus Coordinated Controlmentioning

confidence: 99%

Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

Wang

Jiang

Qiu

et al. 2021

Journal of Advanced Transportation

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Traffic signal optimization is a significant means for smoothing urban traffic flow. However, the operation of traffic signals is currently seriously constrained by the data available from traditional point detectors. In recent years, an emerging technology, connected vehicle (CV), which can percept the overall traffic environment in real time, has drawn researchers’ attention. With the new data source, traffic controllers should be able to make smarter decisions. A lot of work has been done to develop a new traffic signal control pattern under connected-vehicle environment. This paper provides a comprehensive review of these studies, aiming at sketching out the state of the arts in this research field. Several basic control problems, communication, control input, and objectives, are briefly introduced. The commonly used optimization models for this problem are summarized into three types: rule-based models, mathematical programming-based models, and artificial intelligence-based models. Then some major technical issues are discussed in detail. Finally, we raise the limitation of the existing studies and give our perspectives of the future research directions.

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Section: Isolated Control Versus Coordinated Controlmentioning

confidence: 99%

Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

Wang

Jiang

Qiu

et al. 2021

Journal of Advanced Transportation

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“…Furthermore, some researchers used the platoon control model to improve the intersection efficiency under CAV conditions ( 24 , 25 ). For example, Sun et al proposed dynamic lane assignment and green duration optimization based on automated vehicles ( 15 ).…”

Section: Background Of the Studymentioning

confidence: 99%

Application of Connected and Automated Vehicles in a Large-Scale Network by Considering Vehicle-to-Vehicle and Vehicle-to-Infrastructure Technology

Rahman

Abdel-Aty

2020

Transportation Research Record

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Application of connected and automated vehicles (CAVs) is expected to have a significant impact on traffic safety and mobility. Although several studies evaluated the effectiveness of CAVs in a small roadway segment, there is a lack of studies analyzing the impact of CAVs in a large-scale network by considering both freeways and arterials. Therefore, the objective of this study is to analyze the effectiveness of CAVs at the network level by utilizing both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. Also, the study proposed a new signal control algorithm through V2I technology to elevate the performance of CAVs at intersections. A car-following model named cooperative adaptive cruise control was utilized to approximate the driving behavior of CAVs in the Aimsun Next microsimulation environment. For the testbed, the research team selected Orlando central business district area in Florida, U.S. To this end, the impacts of CAVs were evaluated based on traffic efficiency (e.g., travel time rate [TTR], speed, and average approach delay, etc.) and safety surrogates (e.g., standard deviation of speed, real-time crash-risk models for freeways and arterials, time exposed time-to-collision). The results showed that the application of CAVs reduced TTR significantly compared with the base condition even with the low market penetration level. Also, the proposed signal control algorithm reduced the approach delay for 94% of the total intersections present in the network. Moreover, safety evaluation results showed a significant improvement of traffic safety in the freeways and arterials under CAV conditions with different market penetration rates.

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“…In another study, a joint algorithm was proposed that optimizes both traffic light signal timing and speed for different ratios of AVs and human-driven speed guidance-enabled vehicles (SGV) to minimize the number of stops [20]. Similarly, Wang et al developed a joint control model; however, in their model, the vehicles form a platoon to cross the intersection with no stop or minimum stop time [21]. Another study investigated optimal platoon formation to maximize the platooning benefit with a focus on drivers' economic consideration [22].…”

Section: Introductionmentioning

confidence: 99%

Traffic flow and emissions improvement via Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication for an intelligent intersection

Namazi

Taghavipour

2021

Asian Journal of Control

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As transportation becomes more demanding in modern communities, the challenges involved such as congestion and emissions grow more concerns. Intelligent transportation system (ITS) is one of the most recognizable solution to resolve the problem. One of the components of an ITS is intelligent intersections that use vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to reduce the waiting time and consequently the idling time and emission of the vehicles. To implement different control strategies to reach the aforementioned objectives, a validated platform is required to simulate the real-world traffic flow. To this end, Simulation of Urban Mobility (SUMO) software along with real data are utilized for developing two control strategies: Single Vehicle Speed Advisory Algorithm (SVSAA) and Platoon Speed Advisory Algorithm (PSAA). The results of real-world simulation demonstrate that the proposed schemes are able to reduce the waiting time, fuel consumption, and emissions significantly compared with the baseline strategy. Moreover, it turns out that PSAA scheme implementation via model predictive controller may totally eliminate the congestion at the intersection.

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A joint control model for connected vehicle platoon and arterial signal coordination

Cited by 46 publications

References 26 publications

Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

Application of Connected and Automated Vehicles in a Large-Scale Network by Considering Vehicle-to-Vehicle and Vehicle-to-Infrastructure Technology

Traffic flow and emissions improvement via Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication for an intelligent intersection

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