Increased Traffic Flow Through Node-Based Bottleneck Prediction and V2X Communication

Backfrieder, Christian; Ostermayer, Gerald; Mecklenbräuker, Christoph F.

doi:10.1109/tits.2016.2573292

Cited by 74 publications

(40 citation statements)

References 32 publications

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“…The vehicles with higher urgency are assigned to the optimal routes first. Using the BSMs from each vehicle via V2I communication, the roadway bottleneck effects can be predicted based on the calculation of traffic density [148]. In combination with the A* searching algorithm, which is an classical method to find the general route with the lowest cost, e.g., shortest travel distance [149], it is shown that the presented route planning algorithm can efficiently avoid the roadway bottleneck.…”

Section: Route Planning Layermentioning

confidence: 99%

A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles

Sarker

Shen

Rahman

et al. 2020

IEEE Trans. Intell. Transport. Syst.

104

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Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to its potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies, human factors, and information-aware controller design. First, different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factor issues, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.Automated vehicle (AV) field testing began in 1986 in the United States when the Partners for Advanced Transit and Highways (PATH) program at the University of California Berkley developed a platooning application of six AVs in specially guided highway sections [1]. Since, the most significant AV development was prompted by the Defense Advanced Research Projects Agency (DARPA) Urban Challenge 2007, which accelerated private sector AV research and development. Since then, major automobile companies including internet giant Google have developed the prototypes of AVs that need no special highway infrastructure to operate in mixed traffic scenarios [2,3]. In a research by Bhavsar et al. concluded that although there is a considerable risk of AV sensor failure, future innovations in computation and communication technologies as well as backup sensors can significantly reduce the failure probability of AVs in a mixed traffic stream (which includes AVs and non-AVs) [4]. To facilitate the development of AV technologies, several US states issued special permits to AV technology manufactures conducting pilot testing, most notably in California, automated vehicle laws was issued on February 26, 2018 [5]. This interest in AV technology from both the automotive industry and the public sector will advance the development of fully automated (i.e., autonomous or level 5 automation) vehicle development in the next decade.The Society of Automotive Engineers (SAE) has a classification scheme for automated vehicles with six levels from no-automation (level 0) to full automation (level 5) [6]. Full vehicle automation enables maximum benefit in terms of traffic safety, efficiency, and environmental impacts. According to a r...

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Section: Route Planning Layermentioning

confidence: 99%

A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles

Sarker

Shen

Rahman

et al. 2020

IEEE Trans. Intell. Transport. Syst.

104

View full text Add to dashboard Cite

show abstract

“…However, their approach did not consider the problem of protecting information security or reliable transmission of incident flow. e approaches in [19][20][21][22] achieve a reasonable level of privacy protection. In these studies, vehicles are issued pseudonym certificates, and certificate generation and provisioning are divided among multiple organisations.…”

Section: Related Workmentioning

confidence: 99%

STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

Luo

Bao

et al. 2020

Mobile Information Systems

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e Internet of Vehicles (IoV) is an important artificial intelligence research field for intelligent transportation applications. Complex event interactions are important methods for data flow processing in a Vehicle to Everything (V2X) environment. Unlike the classic Internet of ings (IoT) systems, data streams in V2X include both temporal information and spatial information. us, effectively expressing and addressing spatiotemporal data interactions in the IoV is an urgent problem. To solve this problem, we propose a spatiotemporal event interaction model (STEIM). STEIM uses a time period and a raster map for its temporal model and spatial model, respectively. In this paper, first, we provide a spatiotemporal operator and a complete STEIM grammar that effectively expresses the spatiotemporal information of the spatiotemporal event flow in the V2X environment. Second, we describe the design of the operational semantics of the STEIM from the formal semantics. In addition, we provide a spatiotemporal event-stream processing algorithm that is based on the Petri net model. e STEIM establishes a mechanism for V2X event-stream temporal and spatial processing. Finally, the effectiveness of the STEIM-based system is demonstrated experimentally.

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“…Recent research efforts have shown the ability to model congestion prediction and control of road transportation [1][2][3][4][5] and the metro [6][7][8][9][10]. Sun et al [11] explored the hazardous materials route problem in the road-rail multimodal transportation network with a hub-and-spoke structure.…”

Section: Literature Reviewmentioning

confidence: 99%

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

2019

Symmetry

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Timely predicting and controlling the traffic congestion in a station caused by an emergency is an important task in railway emergency management. However, traffic forecasting in an emergency is subject to a dynamic service network, with uncertainty surrounding elements such as the capacity of the transport network, schedules, and plans. Accurate traffic forecasting is difficult. This paper proposes a practical time-space network model based on a train diagram for predicting and controlling the traffic congestion in a station caused by an emergency. Based on the train diagram, we constructed a symmetric time-space network for the first time by considering the transition of the railcar state. On this basis, an improved A* algorithm based on the railcar flow route was proposed to generate feasible path sets and a dynamic railcar flow distribution model was built to simulate the railcar flow distribution process in an emergency. In our numerical studies, these output results of our proposed model can be used to control traffic congestion.

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Increased Traffic Flow Through Node-Based Bottleneck Prediction and V2X Communication

Cited by 74 publications

References 32 publications

A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles

A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles

STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

A Time-Space Network Model Based on a Train Diagram for Predicting and Controlling the Traffic Congestion in a Station Caused by an Emergency

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