A Human Mobility Model in Malls

Duong, Dat Van Anh; Yoon, Seokhoon

doi:10.1109/ictc49870.2020.9289315

Cited by 1 publication

(2 citation statements)

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“…Real mobility traces at Dartmouth College [35] and Disney World theme park in Orlando [36] have been analyzed in [37] to obtain movement characteristics. Consequently, they obtained a visiting probability of people, distribution of movement speed, and pause time from the traces.…”

Section: Related Workmentioning

confidence: 99%

“…Next, we run an agglomerative clustering technique [45] using a single linkage method, sometimes called connectedness or minimum method and created location clusters based on the similarity of the closest pair of locations. Features [20] (2012) [19] (2008) [7] (2011) [9] RWP [37] (2020) [34] (2009) [34] (2017 e more we know about the movement cluster properties such as a travel distance and direction of movement, the more we can predict the behavior of a pedestrian movement pattern for accurate design against possible crowd congestion or emergency scenarios.…”

Section: Characterization Of Human Mobilitymentioning

confidence: 99%

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Escape Path Obstacle-Based Mobility Model (EPOM) for Campus Delay-Tolerant Network

Bakura

Lambert

Nowak

2021

Journal of Advanced Transportation

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In Delay-Tolerant Networks (DTNs), humans are the main carriers of mobile devices, signifying that human mobility can be exploited by extracting nodes’ interests, social behavior, and spatiotemporal features for the performance evaluation of DTNs protocols. This paper presents a new mobility model that describes students’ daily activities in a campus environment. Unlike the conventional random walk models, which use a free space environment, our model includes a collision-avoidance technique that generates an escape path upon encountering obstacles of different shapes and sizes that obstruct pedestrian movement. We evaluate the model’s usefulness by comparing the distributions of its synthetic traces with realistic traces in terms of spatial, temporal, and connectivity features of human mobility. Similarly, we analyze the concept of dynamic movement clusters observed on the location-based trajectories of the studied real traces. The model synthetically generates traces with the distribution of the intercluster travel distance, intracluster travel distance, direction of movement, contact duration, intercontact time, and pause time similar to the distribution of real traces.

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Section: Related Workmentioning

confidence: 99%