1,808 research outputs found
Intersection control with connected and automated vehicles: a review
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
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 โanchoringโ 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
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
Recommended from our members
Modeling and optimizing network infrastructure for autonomous vehicles
Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naรฏve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin
Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks
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
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
Distributed and dynamic traffic congestion controls without requiring demand forecasting: Tradable network permits and its implementation mechanisms
Tohoku University่ตคๆพ้่ชฒ
์ฌ์ฉ์ ์ค์ฌ์ ๋ฐ๋ฆฌ๋ฏธํฐํ ํต์ ์์คํ ์ ์ํ ์ด๋์ฑ ์ธ์ ๋ถ์ ํ๋ ์์ํฌ ๋ฐ ๋คํธ์ํฌ ๊ด๋ฆฌ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2021. 2. ๋ฐ์ธ์
.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.๋ฐ๋ฆฌ๋ฏธํฐํ ํต์ ์ ๊ณ ์ ์ ์ก์ ๊ฐ๋ฅํ๊ฒ ํ์ง๋ง ์ก์ ๊ธฐ์ ์์ ๊ธฐ ์ฌ์ด์ ์ฅ์ ๋ฌผ๋ก ์ธํด ๋คํธ์ํฌ ์ฑ๋ฅ์ด ํฌ๊ฒ ์ ํ๋ ์ ์๋ค. ์ฅ์ ๋ฌผ ํจ๊ณผ๋ฅผ ๊ทน๋ณตํ๊ณ ๋งํฌ ์์ ์ฑ์ ํฅ์์ํค๋ ๋ค์ค ์ฐ๊ฒฐ ๋ฐ ๋คํธ์ํฌ ์ด๊ณ ๋ฐํ ๋๊ฐ์ง ์ ๊ทผ๋ฒ์ด ์๋ค. ํนํ ๊ฐ ์ฌ์ฉ์๊ฐ ๊ณ ํ์ง์ ์๋น์ค๋ฅผ ๊ฒฝํํ ์ ์๋๋ก ์๋น ๊ธฐ์ง๊ตญ ๊ทธ๋ฃน์ ๋์ ๊ตฌ์ฑ์ด ํ์ํ๋ฏ๋ก ์ด๊ณ ๋ฐ๋ ๋คํธ์ํฌ ํ๊ฒฝ์์ ๋ค์ค ์ฐ๊ฒฐ์ ์ฌ์ฉ์ ์ค์ฌ ํต์ ์ ์ฉ์ดํ๊ฒ ํ๋ค. ๋ณธ ๋
ผ๋ฌธ์ ์ฌ์ฉ์ ์ค์ฌ์ ๋ฐ๋ฆฌ๋ฏธํฐํ ํต์ ์์คํ
์ ์ํ ์ํ์ ํ๋ ์์ํฌ์ ๋คํธ์ํฌ ๊ด๋ฆฌ ์ฒด๊ณ๋ฅผ ์ฐ๊ตฌํ๋ค. ๋จผ์ ๋ค์ค ์ฐ๊ฒฐ์ ์ฌ์ฉํ์ฌ ์ฌ์ฉ์ ์ค์ฌ์ ๋ฐ๋ฆฌ๋ฏธํฐํ ํต์ ์์คํ
์์ ์ฌ์ฉ์ ์ด๋์ฑ๊ณผ ์ด๋์ฑ ์ธ์ ์ฑ๋ฅ ์งํ๋ฅผ ๋ชจ๋ธ๋งํ๊ณ ํ๋ฅ ๊ธฐํ๋ถ์์ ๊ธฐ๋ฐ์ผ๋ก ํ๋ ์๋ก์ด ๋ถ์ ํ๋ ์์ํฌ๋ฅผ ์ ์ํ๋ค. ์ด๋ฅผ ์ํด ๊ฐ ์ฌ์ฉ์๊ฐ ๊ฒฝํํ๋ ๋ค์ํ ์ด๋ฒคํธ์ ์ํ ์ ์ด ํ๋ฅ ์ ๋ํ ์ํ์ ํํ์ ๋์ถํ๋ค. ๊ทธ๋ฐ ๋ค์ ๋คํธ์ํฌ ์ค๋ฒํค๋ ๋ฐ ๋ค์ด ๋งํฌ ์์จ ์ธก๋ฉด์์ ์ด๋์ฑ ์ธ์ ์ฑ๋ฅ์ ์ฐ๊ตฌํ๋ค. ์ด๋ฅผ ํตํด ๋คํธ์ํฌ ์ด์์ ๋ํ ๊น์ด์๋ ์ดํด์ ๋คํธ์ํฌ ๋ฐ๋ ๋ฐ ๋ค์ค ์ฐ๊ฒฐ ๊ธฐ๋ฅ์ด ํธ๋ ์ค๋ฒ์ ๊ด๋ จ๋ ์ด๋ฒคํธ์ ํ๋ฅ ์ ๋ฏธ์น๋ ์ํฅ์ ์ดํดํ ์ ์๋ค. ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ๋ ๋ถ์์ ์ ํ์ฑ์ ๊ฒ์ฆํ๊ณ ์ด๋์ฑ ์ธ์ ์ฑ๋ฅ๊ณผ ์ฌ์ฉ์ ์๋ ๊ฐ์ ์๊ด ๊ด๊ณ๋ฅผ ๋ณด์ฌ์ค๋ค.
๋ค์์ผ๋ก ์์ ๋๋ ๋ถ๋ถ ์ ์ ๋ฐฑํ์ด ์๋ ์ฌ์ฉ์ ์ค์ฌ ๋ฐ๋ฆฌ๋ฏธํฐํ ๋คํธ์ํฌ๋ฅผ ์ํ ์ฌ์ฉ์ ์ค์ฌ ๊ตฌ์ฑ ๊ท์น ๋ฐ ์ ์ ๊ฐ๊ฒฉ ๊ธฐ๋ฐ ์ฐ๊ฒฐ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๋ฐ๋ฆฌ๋ฏธํฐํ ์ด๊ณ ๋ฐ๋ ๋คํธ์ํฌ์ ๋ํ ์ต์ ํ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ์ฌ ๊ณต์ ํ ์ฐ๊ฒฐ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ๋ค. ์ด ์๊ณ ๋ฆฌ์ฆ์๋ ์ ์ ๊ฐ๊ฒฉ ๊ธฐ๋ฐ ์ฌ์ฉ์ ๋ณ ์์ฒญ ๊ฒฐ์ ๋ฐฉ๋ฒ๊ณผ ๋ก๋ ๋ฐธ๋ฐ์ฑ์ ์ํ ๊ฐ๊ฒฉ ์กฐ์ ๊ท์น์ด ํฌํจ๋๋ค. ์ ์๊ณ ๋ฆฌ์ฆ ๊ฐ๋ฐ์ ํตํด ์ป์ ํต์ฐฐ๋ ฅ์ ๊ธฐ๋ฐ์ผ๋ก ๋ฐ๋ฆฌ๋ฏธํฐํ ํตํฉ ์ก์ธ์ค ๋ฐ ๋ฐฑํ ๋คํธ์ํฌ๋ฅผ ์ํ ๊ฒฝ๋ก ์ธ์ ์ ์ ์๊ธ ์ ์ฑ
์ ๊ฐ๋ฐํ๋ค. ์์น ํ๊ฐ์ ๋ฐ๋ฅด๋ฉด ์ ์๋ ๋ฐฉ๋ฒ์ด ๋ค๋ฅธ ๋น๊ต ๊ธฐ๋ฒ๋ณด๋ค ์ฐ์ํ๋ค. ๋ถ์ ๋ฐ ์ต์ ํ ๊ฒฐ๊ณผ๋ ์ฌ์ฉ์ ์ค์ฌ์ ๋ฐ๋ฆฌ๋ฏธํฐํ ํต์ ์์คํ
์ค๊ณ์ ๋ํ ์ ์ฉํ ํต์ฐฐ๋ ฅ์ ์ ๊ณตํ ๊ฒ ์ด๋ค.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
- โฆ