Hermoupolis: A Trajectory Generator for Simulating Generalized Mobility Patterns

Pelekis, Nikos; Ntrigkogias, Christos; Tampakis, Panagiotis; Sideridis, Stylianos; Theodoridis, Yannis

doi:10.1007/978-3-642-40994-3_49

Cited by 25 publications

(12 citation statements)

References 17 publications

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“…Using users' call locations stored on cellphones, their trajectory patterns are extracted, with which new routes will be calculated and added to reduce users' waiting and travel time. Pelekis et al [5] develop a tool called Hermoupolis to generate trajectories by simulating given trajectory patterns. This tool provides a method to synthesise trajectory datasets for researchers when reallife datasets are unavailable or not sufficiently large in experimental validation.…”

Section: Related Workmentioning

confidence: 99%

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MinUS: Mining User Similarity with Trajectory Patterns

Chen

Kordy

et al. 2014

Machine Learning and Knowledge Discovery in Databases

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The development of positioning systems and wireless connectivity has made it possible to collect users' fine-grained movement data. This availability of movement data can be applied in a broad range of services. In this paper, we present a novel tool for calculating users' similarity based on their movements. This tool, MinUS, integrates the technologies of trajectory pattern mining with the state-of-the-art research on discovering user similarity with trajectory patterns. Specifically, with MinUS, we provide a platform to manage movement datasets, and construct and compare users' trajectory patterns. Tool users can compare results given by a series of user similarity metrics, which allows them to learn the importance and limitations of different similarity metrics and promotes studies in related areas, e.g., location privacy. Additionally, MinUS can also be used by researchers as a tool for preliminary process of movement data and parameter tuning in trajectory pattern mining.

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Section: Related Workmentioning

confidence: 99%

“…Trajectory patterns can be explored in many ways [4,5,6], one of which is friend recommendation in social networks. This is inspired by the fact that users' movement reflects their interest.…”

Section: Introductionmentioning

confidence: 99%

MinUS: Mining User Similarity with Trajectory Patterns

Chen

Kordy

et al. 2014

Machine Learning and Knowledge Discovery in Databases

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“…Even when available, some are significantly distorted or heavily aggregated, resulting in very limited usefulness. On the other hand, several attempts have been made [3,4,11,19,21] to develop synthetic datasets or data generators to realistically approximate real-world human mobility. However, many of these generators or datasets fall short on one or more aspects of simulating realistic human mobility.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is reasonable to argue that synthetic trajectories generated with contextual information will be a more natural and realistic representation of mobility behaviors. Traditionally, a trajectory is a sequence of recorded locations together with timestamps when the location was recorded [21]. A trajectory generated with contextual information, i.e., a contextual trajectory, is a labeled trajectory that is generated through a set of pre-defined contextual constraints or rules.…”

Section: Introductionmentioning

confidence: 99%

Generating contextual trajectories from user profiles

Yang

Poellabauer

Mitra

et al. 2020

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation

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The trajectory of traffic participants is an essential source for pattern mining and knowledge discovery in urban mobility. However, real-world trajectory data are often not publicly available due to privacy concerns or intellectual property constraints. Although some simulators or synthetic trajectory datasets have been proposed, many of them only consider the spatial-temporal aspects of the trajectory data, but ignore other contextual information that could impact trajectories. On one hand, trajectories are usually associated with and affected by user profiles (e.g., a person's daily routines and preferred modes of transportation). On the other hand, an individual's movements are also affected by environmental conditions and interactions with other traffic participants, particularly in urban scenarios (e.g., routing choices due to congestion or road conditions). Such contextual trajectories provide a more realistic representation of the mobility patterns of traffic participants. Due to the lack of such datasets or trace generators, this work presents ConTraSim (Contextual Trajectory Simulation), a novel approach for generating contextual trajectories based on the Simulation of Urban MObility (SUMO) traffic simulator. More specifically, the proposed approach is designed to produce GPS traces annotated by contextual information that mimic the movements of multiple types of traffic participants in urban areas. As a case study, we also generate a sample dataset using the proposed method and compare it to real-world data to demonstrate how well the synthetic data reflects real-world data characteristics. CCS CONCEPTS • Information systems → Spatial-temporal systems.

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“…Despite these unprecedented opportunities to generate large quantities of real trajectory data, there are several applications where the simulation of trajectories remains a necessity. Examples include completing missing data in recorded trajectories (Wentz et al 2003); the generation of synthetic patterns in trajectories as a basis for performance testing of spatiotemporal database management systems (Brinkhoff 2002) or pattern recognition algorithms (Pelekis et al 2013); and the simulation of particular moving objects such as cars (Joubert et al 2010) or pedestrians (Torrens et al 2012).…”

Section: Introductionmentioning

confidence: 99%

From A to B, randomly: a point-to-point random trajectory generator for animal movement

Technitis

Othman

Safi

et al. 2015

International Journal of Geographical Information Science

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For applications in animal movement, we propose a random trajectory generator (RTG) algorithm that combines the concepts of random walks, space-time prisms, and the Brownian bridge movement model and is capable of efficiently generating random trajectories between a given origin and a destination point, with the least directional bias possible. Since we provide both a planar and a spherical version of the algorithm, it is suitable for simulating trajectories ranging from the local scale up to the (inter-) continental scale, as exemplified by the movement of migrating birds. The algorithm accounts for physical limitations, including maximum speed and maximum movement time, and provides the user with either single or multiple trajectories as a result. Single trajectories generated by the RTG algorithm can be used as a null model to test hypotheses about movement stimuli, while the multiple trajectories can be used to create a probability density surface akin to Brownian bridges. For applications in animal movement, we propose a random trajectory generator (RTG) algorithm that combines concepts of random walks, space-time prisms, and the Brownian bridges movement model and is capable of efficiently generating random trajectories between a given origin and a destination point, with the least directional bias possible. Since we provide both a planar and a spherical version of the algorithm, it is suitable for simulating trajectories ranging from the local scale up to the (inter-)continental scale, as exemplified by the movement of migrating birds. The algorithm accounts for physical limitations, including maximum speed and maximum movement time, and provides the user with either single or multiple trajectories as a result. Single trajectories generated by the RTG algorithm can be used as a null model to test hypotheses about movement stimuli, while the multiple trajectories can be used to create a probability density surface akin to Brownian bridges.

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Hermoupolis: A Trajectory Generator for Simulating Generalized Mobility Patterns

Cited by 25 publications

References 17 publications

MinUS: Mining User Similarity with Trajectory Patterns

MinUS: Mining User Similarity with Trajectory Patterns

Generating contextual trajectories from user profiles

From A to B, randomly: a point-to-point random trajectory generator for animal movement

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