Traffic signal control by leveraging Cooperative Adaptive Cruise Control (CACC) vehicle platooning capabilities

Liu, Hao; Lu, Xiao‐Yun; Shladover, Steven E

doi:10.1016/j.trc.2019.05.027

Cited by 59 publications

(30 citation statements)

References 32 publications

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“…In those studies, different numerical and microscopic traffic simulation results showed that greater environmental benefit and/or mobility benefit of the signalized intersections can be gained than from the aforementioned approaches, where only cooperation among multiple CAVs is considered. However, such studies may not be applicable to our current traffic environment, since many of them made the assumption that all vehicles in the system are CAVs [88], or all traffic signals have fixed cycle lengths [89].…”

Section: ) Theoretical Research and Simulationmentioning

confidence: 99%

A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles

Wang

Bian

Shladover

et al. 2020

IEEE Intell. Transport. Syst. Mag.

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234

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Connected and automated vehicles (CAVs) have the potential to address a number of safety, mobility, and sustainability issues of our current transportation systems. Cooperative longitudinal motion control is one of the key CAV technologies that allows vehicles to be driven in a cooperative manner to achieve system-wide benefits. In this paper, we provide a literature survey on the progress accomplished by researchers worldwide regarding cooperative longitudinal motion control systems of multiple CAVs. Specifically, the architecture of various cooperative CAV systems is reviewed to answer how cooperative longitudinal motion control can work with the help of multiple system modules. Next, different operational concepts of cooperative longitudinal motion control applications are reviewed to answer where they can be implemented in today's transportation systems. Different cooperative longitudinal motion control methodologies and their major characteristics are then described to answer what the critical design issues are. This paper concludes by describing an overall landscape of cooperative longitudinal motion control of CAVs, as well as pointing out opportunities and challenges in the future research and experimental implementations.

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Section: ) Theoretical Research and Simulationmentioning

confidence: 99%

A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles

Wang

Bian

Shladover

et al. 2020

IEEE Intell. Transport. Syst. Mag.

Self Cite

234

View full text Add to dashboard Cite

show abstract

“…where is the sum of resistance, i.e., rolling resistance, gravity resistance, and aerodynamic drag, ( ) = + + 0.5 2 ( ). Combining the (19), (25), and (26), the collision avoidance constraint is converted to…”

Section: ) Constraints Conversionmentioning

confidence: 99%

“…Therefore, this paper will focus on the EV's EAD strategy design at the signalized intersection with consideration of vehicle queue, where the key is the queue discharge prediction. In the respect of vehicle speed optimization, there are a variety of queue discharge prediction methods available, including deterministic kinematic based method (DKM) [25,26] and traffic flow based method [27,28]. These methods mostly assume that all vehicles in the queue are simplified as a pointmass, and moving as a whole.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Eco-Approach Control of Connected Electric Vehicles at Signalized Intersection With Queue Discharge Prediction

Dong

Zhuang

Chen

et al. 2021

IEEE Trans. Veh. Technol.

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“…Such technological capability is a cornerstone for developing the proposed user-based traffic control strategy. Other CVs capabilities, such as driving assistance technology and cooperation with other CVs, can improve signal controller performance [57,58]. In this study, we focus on data transmission ability of CVs that can be used instead of conventional loop detectors or cameras.…”

Section: Aims and Contributionsmentioning

confidence: 99%

Signalised intersection control in a connected vehicle environment: User throughput maximisation strategy

Mohammadi

Roncoli

Mladenović

2021

IET Intelligent Trans Sys

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Connected vehicle (CV) technology is expected to bring additional opportunities to share, collect, and exploit various information on vehicles and their occupants. Assuming that CVs are able to transmit on-board users and vehicle data, a user-based signal timing optimisation (UBSTO) strategy is proposed, designed to optimise user throughput for signalised intersections. In the CV environment, the inputs of the proposed algorithm consist of position and speed of CVs, as well as the number of passengers travelling in each vehicle, while the output is the optimum green time duration for each signal phase. In addition, authors' proposed strategy is able to adapt the cycle length to the traffic volume condition. In case of missing users data, the same strategy can also operate in vehicle-based mode, where the objective is vehicle-throughput maximization. The performance of the proposed strategy is compared with a fully actuated controller (FAC) in microscopic simulation, for several scenarios, including different CV penetration rates. Authors' findings show that UBSTO can effectively increase user throughput and decrease average user delay in comparison with FAC, while also prioritising vehicles with higher number of users on-board. These findings have implications for further development of prioritization strategies for public transport and ride-sharing vehicles.

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Traffic signal control by leveraging Cooperative Adaptive Cruise Control (CACC) vehicle platooning capabilities

Cited by 59 publications

References 32 publications

A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles

A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles

Enhanced Eco-Approach Control of Connected Electric Vehicles at Signalized Intersection With Queue Discharge Prediction

Signalised intersection control in a connected vehicle environment: User throughput maximisation strategy

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