1,808 research outputs found

    Intersection control with connected and automated vehicles: a review

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    Purpose: This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs). Design/methodology/approach: The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control. Findings: It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies. Originality/value: In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions

    Field Experiments on Anchoring of Economic Valuations

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    A pillar of behavioral research is the view that preferences are constructed during the value elicitation process, but it is unclear whether, and to what extent, such biases influence real market equilibria. This paper examines the โ€œanchoringโ€ phenomenon in the field. The first experiment produces evidence that inexperienced consumers can be anchored in the value elicitation process, yet there is little evidence that experienced agents are influenced by anchors. The second experiment finds that anchors have only transient effects on prices and quantities traded: aggregate market outcomes converge to the intersection of supply and demand after a few market periods.field experiment, anchoring, valuation, experience

    Intelligent Transportation Systems: Fusing Computer Vision and Sensor Networks for Traffic Management

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    Intelligent Transportation Systems (ITS) represent a pivotal approach to addressing the complex challenges posed by modern-day urban mobility. By seamlessly integrating computer vision and sensor networks, ITS offer a comprehensive solution for traffic management, safety enhancement, and environmental sustainability. This paper delves into the synergistic fusion of computer vision and sensor networks within the framework of ITS, emphasizing their collective role in optimizing traffic flow, mitigating congestion, and enhancing overall road safety. Leveraging cutting-edge technologies such as machine learning, image processing, and Internet of Things (IoT), ITS harness real-time data acquisition and analytics capabilities to facilitate informed decision-making by transportation authorities. Through a comprehensive review of recent advancements, challenges, and opportunities, this paper illuminates the transformative potential of integrating computer vision and sensor networks in ITS. Furthermore, it presents compelling case studies and exemplary applications, showcasing the tangible benefits of this fusion across diverse traffic management scenarios. Ultimately, this paper advocates for the widespread adoption of integrated ITS solutions as a means to usher in a new era of smarter, safer, and more sustainable urban transportation systems

    Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks

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    The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community

    2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

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    The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

    ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ด๋™์„ฑ ์ธ์‹ ๋ถ„์„ ํ”„๋ ˆ์ž„์›Œํฌ ๋ฐ ๋„คํŠธ์›Œํฌ ๊ด€๋ฆฌ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021. 2. ๋ฐ•์„ธ์›….Millimeter wave (mmWave) communication enables high rate transmission, but its network performance may be degraded significantly due to blockages between the transmitter and receiver. There have been two approaches to overcome the blockage effect and enhance link reliability: multi-connectivity and ultra-dense network (UDN). Particularly, multi-connectivity under a UDN environment facilitates user-centric communication. It requires dynamic configuration of serving base station groups so that each user experiences high quality services. This dissertation studies a mathematical framework and network manament schemes for user-centric mmWave communication systems. First, we models user mobility and mobility-aware performance in user-centric mmWave communication systems with multi-connectivity, and proposes a new analytical framework based on the stochastic geometry. To this end, we derive compact mathematical expressions for state transitions and probabilities of various events that each user experiences. Then we investigate mobility-aware performance in terms of network overhead and downlink throughput. This helps us to understand network operation in depth, and impacts of network density and multi-connection capability on the probability of handover related events. Numerical results verify the accuracy of our analysis and illustrate the correlation between mobility-aware performance and user speed. Next, we propose user-oriented configuration rules and price based association algorithms for user-centric mmWave networks with fully/partially wired backhauls. We develop a fair association algorithm by solving the optimization problem that we formulate for mmWave UDNs. The algorithm includes an access price based per-user request decision method and a price adjustment rule for load balancing. Based on insights from the algorithm, we develop path-aware access pricing policy for mmWave integrated access and backhaul networks. Numerical evaluations show that our proposed methods are superior to other comparative schemes. Our findings from analysis and optimization provide useful insights into the design of user-centric mmWave communication systems.๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹ ์€ ๊ณ ์† ์ „์†ก์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์ง€๋งŒ ์†ก์‹ ๊ธฐ์™€ ์ˆ˜์‹ ๊ธฐ ์‚ฌ์ด์˜ ์žฅ์• ๋ฌผ๋กœ ์ธํ•ด ๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ์ €ํ•˜๋  ์ˆ˜ ์žˆ๋‹ค. ์žฅ์• ๋ฌผ ํšจ๊ณผ๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ๋งํฌ ์•ˆ์ •์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋‹ค์ค‘ ์—ฐ๊ฒฐ ๋ฐ ๋„คํŠธ์›Œํฌ ์ดˆ๊ณ ๋ฐ€ํ™” ๋‘๊ฐ€์ง€ ์ ‘๊ทผ๋ฒ•์ด ์žˆ๋‹ค. ํŠนํžˆ ๊ฐ ์‚ฌ์šฉ์ž๊ฐ€ ๊ณ ํ’ˆ์งˆ์˜ ์„œ๋น„์Šค๋ฅผ ๊ฒฝํ—˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ์„œ๋น™ ๊ธฐ์ง€๊ตญ ๊ทธ๋ฃน์˜ ๋™์  ๊ตฌ์„ฑ์ด ํ•„์š”ํ•˜๋ฏ€๋กœ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์—์„œ ๋‹ค์ค‘ ์—ฐ๊ฒฐ์€ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ํ†ต์‹ ์„ ์šฉ์ดํ•˜๊ฒŒ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆ˜ํ•™์  ํ”„๋ ˆ์ž„์›Œํฌ์™€ ๋„คํŠธ์›Œํฌ ๊ด€๋ฆฌ ์ฒด๊ณ„๋ฅผ ์—ฐ๊ตฌํ•œ๋‹ค. ๋จผ์ € ๋‹ค์ค‘ ์—ฐ๊ฒฐ์„ ์‚ฌ์šฉํ•˜์—ฌ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ์—์„œ ์‚ฌ์šฉ์ž ์ด๋™์„ฑ๊ณผ ์ด๋™์„ฑ ์ธ์‹ ์„ฑ๋Šฅ ์ง€ํ‘œ๋ฅผ ๋ชจ๋ธ๋งํ•˜๊ณ  ํ™•๋ฅ ๊ธฐํ•˜๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ถ„์„ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ฐ ์‚ฌ์šฉ์ž๊ฐ€ ๊ฒฝํ—˜ํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ด๋ฒคํŠธ์˜ ์ƒํƒœ ์ „์ด ํ™•๋ฅ ์— ๋Œ€ํ•œ ์ˆ˜ํ•™์  ํ‘œํ˜„์„ ๋„์ถœํ•œ๋‹ค. ๊ทธ๋Ÿฐ ๋‹ค์Œ ๋„คํŠธ์›Œํฌ ์˜ค๋ฒ„ํ—ค๋“œ ๋ฐ ๋‹ค์šด ๋งํฌ ์ˆ˜์œจ ์ธก๋ฉด์—์„œ ์ด๋™์„ฑ ์ธ์‹ ์„ฑ๋Šฅ์„ ์—ฐ๊ตฌํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ ์šด์˜์— ๋Œ€ํ•œ ๊นŠ์ด์žˆ๋Š” ์ดํ•ด์™€ ๋„คํŠธ์›Œํฌ ๋ฐ€๋„ ๋ฐ ๋‹ค์ค‘ ์—ฐ๊ฒฐ ๊ธฐ๋Šฅ์ด ํ•ธ๋“œ ์˜ค๋ฒ„์™€ ๊ด€๋ จ๋œ ์ด๋ฒคํŠธ์˜ ํ™•๋ฅ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ๋ถ„์„์˜ ์ •ํ™•์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ  ์ด๋™์„ฑ ์ธ์‹ ์„ฑ๋Šฅ๊ณผ ์‚ฌ์šฉ์ž ์†๋„ ๊ฐ„์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๋ณด์—ฌ์ค€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์™„์ „ ๋˜๋Š” ๋ถ€๋ถ„ ์œ ์„  ๋ฐฑํ™€์ด ์žˆ๋Š” ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ๊ตฌ์„ฑ ๊ทœ์น™ ๋ฐ ์ ‘์† ๊ฐ€๊ฒฉ ๊ธฐ๋ฐ˜ ์—ฐ๊ฒฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ์— ๋Œ€ํ•œ ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์—ฌ ๊ณต์ •ํ•œ ์—ฐ๊ฒฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•œ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์—๋Š” ์ ‘์† ๊ฐ€๊ฒฉ ๊ธฐ๋ฐ˜ ์‚ฌ์šฉ์ž ๋ณ„ ์š”์ฒญ ๊ฒฐ์ • ๋ฐฉ๋ฒ•๊ณผ ๋กœ๋“œ ๋ฐธ๋Ÿฐ์‹ฑ์„ ์œ„ํ•œ ๊ฐ€๊ฒฉ ์กฐ์ • ๊ทœ์น™์ด ํฌํ•จ๋œ๋‹ค. ์œ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ์„ ํ†ตํ•ด ์–ป์€ ํ†ต์ฐฐ๋ ฅ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ตํ•ฉ ์•ก์„ธ์Šค ๋ฐ ๋ฐฑํ™€ ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ๊ฒฝ๋กœ ์ธ์‹ ์ ‘์† ์š”๊ธˆ ์ •์ฑ…์„ ๊ฐœ๋ฐœํ•œ๋‹ค. ์ˆ˜์น˜ ํ‰๊ฐ€์— ๋”ฐ๋ฅด๋ฉด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ๋‹ค๋ฅธ ๋น„๊ต ๊ธฐ๋ฒ•๋ณด๋‹ค ์šฐ์ˆ˜ํ•˜๋‹ค. ๋ถ„์„ ๋ฐ ์ตœ์ ํ™” ๊ฒฐ๊ณผ๋Š” ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ ์„ค๊ณ„์— ๋Œ€ํ•œ ์œ ์šฉํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•  ๊ฒƒ ์ด๋‹ค.Abstract i Contents iii List of Tables vi List of Figures vii 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Outline and Contributions . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Mobility-Aware Analysis of MillimeterWave Communication Systems with Blockages 5 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 Connectivity Model . . . . . . . . . . . . . . . . . . . . . . 10 2.2.3 Mobility Model . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Mobility-Aware Analysis . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 Analytical Framework . . . . . . . . . . . . . . . . . . . . . 13 2.3.2 Urban Scenario with Ultra-Densely Deployed BSs . . . . . . 18 2.3.3 Handover Analysis for Macrodiversity . . . . . . . . . . . . . 22 2.3.4 Normalized Network Overhead and Mobility-Aware Downlink Throughput with Greedy User Association . . . . . . . . 24 2.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 Association Control for User-Centric Millimeter Wave Communication Systems 34 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.2 Channel Model and Achievable Rate . . . . . . . . . . . . . . 39 3.2.3 User Centric mmWave Communication Framework . . . . . . 39 3.3 Traffic Load Management . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.1 Optimal Association and Admission Control . . . . . . . . . 45 3.3.2 Outage Analysis . . . . . . . . . . . . . . . . . . . . . . . . 51 3.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.1 Evaluation Environments . . . . . . . . . . . . . . . . . . . . 53 3.4.2 Performance Comparison . . . . . . . . . . . . . . . . . . . . 55 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4 Path Selection and Path-Aware Access Pricing Policy in Millimeter Wave IAB Networks 60 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2.1 Geographic and Pathloss Models . . . . . . . . . . . . . . . . 62 4.2.2 IAB Network Model . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Path Selection Strategies . . . . . . . . . . . . . . . . . . . . . . . . 66 4.4 Path-Aware Access Pricing Policy . . . . . . . . . . . . . . . . . . . 69 4.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5 Conclusion 80 5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2 Limitations and Future Work . . . . . . . . . . . . . . . . . . . . . . 82 Abstract (In Korean) 90Docto
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