Adaptive Coordination Based on Connected Vehicle Technology

Beak, Byungho; Head, Larry; Feng, Yiheng

doi:10.3141/2619-01

Cited by 69 publications

(75 citation statements)

References 14 publications

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“…More recent traffic signal control algorithms have begun to consider both connected and unconnected data sources. Beak et al [20] used stop bar detectors to supplement an adaptive phase optimisation strategy using CV data at CV penetrations as low as 25% using a perfect communication system. Ilgin Guler et al [21] proposed an algorithm to enumerate and optimise discharge sequences to reduce delay and tested it at CV penetrations from 0-100%.…”

Section: A Related Workmentioning

confidence: 99%

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

Rafter

Anvari

Box

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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The increase in traffic volumes in urban areas makes network delay and capacity optimisation challenging. However, the introduction of connected vehicles in intelligent transport systems presents unique opportunities for improving traffic flow and reducing delays in urban areas. This paper proposes a novel traffic signal control algorithm called Multimode Adaptive Traffic Signals (MATS) which combines position information from connected vehicles with data obtained from existing inductive loops and signal timing plans in the network to perform decentralised traffic signal control at urban intersections. The MATS algorithm is capable of adapting to scenarios with low numbers of connected vehicles, an area where existing traffic signal control strategies for connected environments are limited. Additionally, a framework for testing connected traffic signal controllers based on a large urban road network in the city of Birmingham (UK) is presented. The MATS algorithm is compared with MOVA on a single intersection, and a calibrated TRANSYT plan on the proposed testing framework. The results show that the MATS algorithm offers reductions in mean delay up to 28% over MOVA, and reductions in mean delay and mean numbers of stops of up to 96% and 33% respectively over TRANSYT, for networks with 0-100% connected vehicle presence. The MATS algorithm is also shown to be robust under non-ideal communication channel conditions, and when heavy traffic demand prevails on the road network.

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Section: A Related Workmentioning

confidence: 99%

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

Rafter

Anvari

Box

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…As described previously, a network of five intersections was simulated using Vissim [42]. The volume on the network varied across 20 simulation runs by increasing the vehicle input rate at each source node over a range of ±5% [24], [48]. The optimization logic was utilized to minimize total delay.…”

Section: Model Applicationmentioning

confidence: 99%

A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

Liu

Wang²,

Yao

2020

IEEE Access

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With the development of connected vehicles (CVs) technology, it has become a new research topic to capture the dynamic traffic system by using CVs data. The traditional vehicle arrival prediction models are limited to the fixed detectors, which can only recognize the passing information of vehicles, but cannot identify the state of vehicles. This paper proposed a new vehicle arrival prediction model of traffic signal control in a connected vehicle environment. First, the vehicle's identification number (ID), position, velocity, and acceleration were obtained in a CV environment. Second, a new vehicle arrival prediction model was developed by the joint probability distribution calibrated based on CV data. Then, the simulation experiment was designed for analyzing influence factors on model performance. The results show that the average relative error of each factor is less than 10%. Finally, the reliability of the proposed model was verified based on an adaptive signal control algorithm. Therefore, the proposed model can be used in the adaptive signal control system.

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“…Day et al (2017) Detector-free approach to signal coordination by optimizing offsets using CV data ''Virtual Detection'' generates arrival profiles of vehicles using CV data that can be used to optimize offsets for signal coordination. Beak, Head, and Feng (2017) Adaptive coordination-based signal control to provide progression to vehicles Bi-level optimization approach. Upper level is the intersection level, which allocates green phase using dynamic programming based on coordination constraints.…”

Section: Simulation Framework To Evaluate Benefitsmentioning

confidence: 99%

Strategic and Tactical Guidance for the Connected and Autonomous Vehicle Future

Ukkusuri¹,

Sagir²,

Mahajan³

et al. 2019

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JOINT TRANSPORTATION RESEARCH PROGRAMThe Joint Transportation Research Program serves as a vehicle for INDOT collaboration with higher education institutions and industry in Indiana to facilitate innovation that results in continuous improvement in the planning, design, construction, operation, management and economic efficiency of the Indiana transportation infrastructure. Abstract Autonomous vehicle (AV) and connected vehicle (CV) technologies are rapidly maturing and the timeline for their wider deployment is currently uncertain. These technologies are expected to have a number of significant societal benefits: traffic safety, improved mobility, improved road efficiency, reduced cost of congestion, reduced energy use, and reduced fuel emissions. State and local transportation agencies need to understand what this means for them and what they need to do now and in the next few years to prepare for the AV/CV future. In this context, the objectives of this research are as follows:1. Synthesize the existing state of practice and how other state agencies are addressing the pending transition to AV/CV environment 2. Estimate the impacts of AV/CV environment within the context of (a) traffic operations-impact of headway distribution and traffic signal coordination; (b) traffic control devices; (c) roadway safety in terms of intersection crashes 3. Provide a strategic roadmap for INDOT in preparing for and responding to potential issues This research is divided into two parts. The first part is a synthesis study of existing state of practice in the AV/CV context by conducting an extensive literature review and interviews with other transportation agencies. Based on this, we develop a roadmap for INDOT and similar agencies clearly delineating how they should invest in AV/CV technologies in the short, medium, and long term. The second part assesses the impacts of AV/CVs on mobility and safety via modeling in microsimulation software Vissim. 17.

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Adaptive Coordination Based on Connected Vehicle Technology

Cited by 69 publications

References 14 publications

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

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

A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

Strategic and Tactical Guidance for the Connected and Autonomous Vehicle Future

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