Human mobility prediction based on individual and collective geographical preferences

Calabrese, Francesco; Lorenzo, Giusy Di; Ratti, Carlo

doi:10.1109/itsc.2010.5625119

Cited by 87 publications

(42 citation statements)

References 16 publications

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“…Most of recent research has focused on how predictable people's movements are and how long will people stay in the current place. For instance, one of these hot topics is called next location prediction, which benefits a wide range of communication systems [1] [2], from transportation planning and management to viruses spread prediction. At the same time some other research studies the global law of human mobility, for example, Simini et al [3] proposed a radiation model which predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena.…”

Section: Introductionmentioning

confidence: 99%

Link prediction in human mobility networks

Yang

Chawla

Basu³

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-The understanding of how humans move is a longstanding challenge in the natural science. An important question is, to what degree is human behavior predictable? The ability to foresee the mobility of humans is crucial from predicting the spread of human to urban planning. Previous research has focused on predicting individual mobility behavior, such as the next location prediction problem. In this paper we study the human mobility behaviors from the perspective of network science. In the human mobility network, there will be a link between two humans if they are physically proximal to each other. We perform both microscopic and macroscopic explorations on the human mobility patterns. From the microscopic perspective, our objective is to answer whether two humans will be in proximity of each other or not. While from the macroscopic perspective, we are interested in whether we can infer the future topology of the human mobility network. In this paper we explore both problems by using link prediction technology, our methodology is demonstrated to have a greater degree of precision in predicting future mobility topology.

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Section: Introductionmentioning

confidence: 99%

Link prediction in human mobility networks

Yang

Chawla

Basu³

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…To satisfy the requirements, we propose a path prediction algorithm based on Collective Behavior Pattern (CBP) [21]. CBP is a concept that a collective behavior influences personal behavior (e.g., Point of Interest).…”

Section: Collective Behavior Pattern and Weight Measurementmentioning

confidence: 99%

“…CBP is based on that collective behaviors influence personal behaviors, which enables predicting user locations and moves. A CBP-based method can predict paths using the information of people that have visited an area even if there is no history for a specific user [21].…”

Section: Collective Behavior Pattern-based Predictionmentioning

confidence: 99%

Path Prediction Method for Effective Sensor Filtering in Sensor Registry System

Lee

Jeong

Baik

et al. 2015

International Journal of Distributed Sensor Networks

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The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and management, semantic interpretation and processing, and sensor searching and filtering in Wireless Sensor Networks (WSNs). Also, as the number of sensors in an IoT environment increases significantly, sensor filtering becomes more important. Many sensor filtering techniques have been researched. However most of them do not consider real-time searching and efficiency of mobile networks. In this paper, we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS). SRS is a sensor platform to register and manage sensor information for sensor filtering. We also propose a method for learning and predicting user paths based on the Collective Behavior Pattern. To improve prediction accuracy, we consider a time feature to measure weights and predict a path. We implement the method and the implementation and its evaluation confirm the improvement of time and accuracy for processing sensor information.

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“…In this context, several studies have demonstrated a strong influence of the locations of points of interest (e.g., specific businesses) on human mobility [35]. These results have been further leveraged for pedestrian mobility predictions [9]. A similar approach to AntReckoning can be exploited to predict pedestrian movements, where other people, buildings, and shops are treated as points of interest that generate pheromone.…”

Section: Related Workmentioning

confidence: 99%

Interest modeling in games: the case of dead reckoning

2012

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In games, the goals and interests of players are key factors in their behavior. However, techniques used by networked games to cope with infrequent updates and message loss, such as dead reckoning, estimate a player's movements based mainly on previous observations. The estimations are typically made by using dynamics of motion, taking only inertia and some external factors (e.g., gravity, wind) into account while completely ignoring the player's goals (e.g., chasing other players or collecting objects). This paper proposes AntReckoning: a dead reckoning algorithm, inspired from ant colonies, which models the players' interests to predict their movements. AntReckoning incorporates a player's interest in specific locations, objects, and avatars in the equations of motion in the form of attraction forces. In practice, these points of interest generate pheromones, which spread and fade in the game world, and are a source of attraction.To motivate and validate our approach we collected traces from Quake III. We conducted specific experiments that demonstrate the effect of game-related goals, map features, objects, and other players on the mobility of avatars. Our simulations using traces from Quake III

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Human mobility prediction based on individual and collective geographical preferences

Cited by 87 publications

References 16 publications

Link prediction in human mobility networks

Link prediction in human mobility networks

Path Prediction Method for Effective Sensor Filtering in Sensor Registry System

Interest modeling in games: the case of dead reckoning

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