A Markov Jump Process Model for Urban Vehicular Mobility: Modeling and Applications

Li, Yong; Jin, Depeng; Hanzo, Lajos; Hui, Pan; Zeng, Lieguang; Chen, Sheng

doi:10.1109/tmc.2013.159

Cited by 34 publications

(16 citation statements)

References 39 publications

(52 reference statements)

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“…11) Beijing taxi [137]: This dataset collects the mobility track logs of 27000 participating taxis in Beijing, China, for the duration of one month. This large vehicular mobility trace is widely utilized to study large-scale vehicular systems in large urban environments [29], [153]- [156].…”

Section: )mentioning

confidence: 99%

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

Waqas

Niu

et al. 2020

IEEE Commun. Surv. Tutorials

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114

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Device-to-device (D2D) communication proposes a new epitome in mobile networking to avail data exchange between physically proximate devices. The exploitation of D2D communication enables mobile operators to harvest short range communications for improving network performance and corroborating proximity-based services. In this article, we investigate mobility aspects of D2D communication, which are indispensable for the adoption and implementation of D2D communication technology. We present an extensive review of the state-of-theart problems and the corresponding solutions for encouraging the exploitation of mobility to assist D2D communication. Specifically, by identifying the mobility models, traces, problems, requirements, and features of different proposals, we discuss the lessons learned and summarize the advantages of mobility-aware D2D communication. We also present open problems and highlight future research directions concerning D2D communication applications in real-life scenarios. To the best of our knowledge, this is the first comprehensive survey to address mobility-aware D2D communication, which offers insight to the underlying problems and provides the potential solutions. Index Terms-Device-to-device communication, mobility, mobile communication, mobile data traffic. I. INTRODUCTION D EVICE-to-device (D2D) communication takes the advantage of opportunistic encounters by mobile users with each other [1]. These opportunistic encounters' information between users are highly related to their movement. By exploiting users' movement, D2D enabled applications and services visualize highly opportunistic and unpredictable human mobility. Therefore, the challenges of exploiting mobility resides in the inherent complexity of users' mobility. It is mainly concerns with predicting the establishment of communication links among D2D users. For instance, two mobile users can establish a link at the time when they are in close proximity to each other. However, there are two key questions have to M.

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Section: )mentioning

confidence: 99%

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

Waqas

Niu

et al. 2020

IEEE Commun. Surv. Tutorials

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114

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“…1 and marked as v1...v6), while each vulnerable user is assigned to one of either ends of the pedestrian lane (p1 or p2). Following [14], vehicle arrivals are modeled as a Poisson process with parameter λ v ; similarly, we model pedestrian arrivals with a different Poisson process with rate λ p .…”

Section: A Populating the Scenariomentioning

confidence: 99%

Performance Analysis of C-V2I-Based Automotive Collision Avoidance

Malinverno

Avino

Casetti

et al. 2018

2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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The purpose of this paper is to evaluate the performance of a system for vehicle-with-vehicle and vehicle-with-pedestrian collision detection when cellular vehicle-to-infrastructure (C-V2I) is adopted as a communication technology. In particular, we are mainly interested in the number of collisions that could be avoided and in the number of false positive alerts (i.e., alert messages referring to situations of low or no danger, that the system delivers to the users). Indeed, a low number of false positive alerts is essential in establishing user confidence in the reliability of alerts received through the system.The remainder of this paper is organized as follows: Section II reviews the research related to the automotive collision avoidance application. Our reference scenario is introduced in Section III, while Section IV presents the design of the automotive collision avoidance system, along with the detection algorithm. The description of the methodology for our simulations and the output analysis technique are in Section V. Section VI contains the results obtained; the paper closes with our conclusions and future research directions in Section VII. II. RELATED WORKThere are several works in the literature that are related to safety applications in the automotive domain (e.g., [5]). Many of these works, such as [6] and [7], propose collision avoidance and collision detection applications that do not leverage any mobile network infrastructure. In particular [6] focuses on collisions between vehicles and pedestrians in industrial plants. In this case, positioning is achieved using a combination of GPS, MEMS and smart sensors, while the type of wireless communication to the control center is not specified. In [7], White et al. propose a way to automatically detect a collision after it has occurred, using smartphone accelerometers to reduce the time gap between the actual collision and the first aid dispatch.Our solution proposes a trajectory-based collision detection system based on a state-of-the art algorithm that we enhanced to match our needs. The same base-algorithm has been used, in different flavors, in [8] and [9].[8] offers a top-down and specification driven design of an adaptive, peer-to-peer based collision warning system, while [9] proposes a V2V-like approach. However, those two works offer little simulation results. In particular, [8] only focuses on the collision avoidance algorithm, with little attention paid to implementation and network infrastructure.[9] provides some simulation results, Abstract-One of the key applications envisioned for C-V2I (Cellular Vehicle-to-Infrastructure) networks pertains to safety on the road. Thanks to the exchange of Cooperative Awareness Messages (CAMs), vehicles and other road users (e.g., pedestrians) can advertise their position, heading and speed and sophisticated algorithms can detect potentially dangerous situations leading to a crash. In this paper, we focus on the safety application for automotive collision avoidance at intersections, and study the effect...

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“…Markov jump process (MJP) is a continuous extension of discrete-time Markov chain where the timing information is also modeled, which has been adopted to model the transitions among states of dynamic systems. For example, MJP was used in [45] to model vehicular mobility among urban areas divided by the intersections of roads.…”

Section: Markov Jump Processmentioning

confidence: 99%

Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

Cheung

Liu

et al. 2018

ACM Trans. Intell. Syst. Technol.

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The elderly living in smart homes can have their daily movement recorded and analyzed. Given the fact that different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this thesis research, we focus on developing data mining algorithms for automatic detection of behavioral patterns from the trajectory data of an individual for activity identification, daily routine discovery, and activity prediction.

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A Markov Jump Process Model for Urban Vehicular Mobility: Modeling and Applications

Cited by 34 publications

References 39 publications

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

Performance Analysis of C-V2I-Based Automotive Collision Avoidance

Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

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