Augmenting Traffic Signal Control Systems for Urban Road Networks With Connected Vehicles

Rafter, Craig B.; Anvari, Bani; Box, Simon; Cherrett, Tom

doi:10.1109/tits.2020.2971540

Cited by 46 publications

(18 citation statements)

References 23 publications

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“…Rafter et al [74] proposed a novel traffic signal control algorithm called Multimode Adaptive Traffic Signals (MATS) which can offer reductions in mean delay for networks with 0-100% connected-vehicle presence.…”

Section: Different Stages Of CV Technologiesmentioning

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: Different Stages Of CV Technologiesmentioning

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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“…The authors in [31] propose the concept of Mobile-Generated Content to enhance the production efficiency of intelligent connected vehicles in sustainable cities. With the increasingly mature of IoVs, a large amount of literature adopts this technique to address the dynamic reconfiguration of TSC and VRG [32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…For the IoVs-based dynamic TSC, the study in [32] combines the real-time position data from connected vehicles with intersection information from inductive loops to produce dynamic signal timing at isolated intersections. The work in [33] introduces the joint passing rates between adjacent intersections to present an adaptive multiple intersections TSC model based on the IoVs technique that enables vehicles to communicate with each other.…”

Section: Introductionmentioning

confidence: 99%

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Zhang

Khalgui

2021

Sensors

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Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.

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“…CV capabilities are functional to deploy DICs since real-time and accurate data of vehicles are transmitted via V2I (vehicle-to-infrastructure) communication systems. Improved detection and communication capabilities offered by CVs provide various opportunities for controlling signalized intersections, such as improved signal arterial coordination, transit signal priority or signal-vehicle coupled control [3], [14]- [16]. The aim of these controllers is usually to minimize vehicle delay or maximize vehicle throughput by using CV accurate and real-time data to estimate the arrival time of each vehicle to the intersection.…”

Section: Introductionmentioning

confidence: 99%

Assessment of connected vehicle information quality for signalised traffic control

Verona

Mohammadi

Roncoli

2021

2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Connected vehicles (CVs) present a great opportunity to smooth and improve traffic flows at intersections thanks to their communication capabilities, which may allow a realtime flow of information with the controllers operating traffic signals. Therefore, it is reasonable to envision that, in the near future, CV data may complement or replace spot detector data that is currently used to operate traffic signals. However, CV data may be affected by errors, such as positioning error, which may depend on the technology that is employed. In this paper, we investigate the performances of different control strategies, namely a strategy that employs only aggregated information, such as queue lengths, and a strategy using disaggregated vehiclebased information, when they are operated with CV data, considering various realistic measurement accuracy settings. Our experiments, conducted via microscopic simulations, show that the disaggregated strategy features better performance and robustness in most of the tested scenarios.Index Terms-traffic control, connected vehicles, signal timing, GPS This research is partly funded by Henry Ford Foundation Finland (Henry Fordin Säätiö) and the FINEST Twins Center of Excellence (H2020) under Grant 856602.

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Augmenting Traffic Signal Control Systems for Urban Road Networks With Connected Vehicles

Cited by 46 publications

References 23 publications

Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Assessment of connected vehicle information quality for signalised traffic control

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