Modeling Checkpoint-Based Movement with the Earth Mover’s Distance

Duckham, Matt; Kreveld, Marc van; Purves, Ross S.; Speckmann, Bettina; Tao, Yaguang; Verbeek, Kevin; Wood, Jo

doi:10.1007/978-3-319-45738-3_15

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…We are aware of only a single geometric approach to reconstruct flow from checkpoint data. Duckham et al [10] use the Earth Mover's distance to estimate the movement of couriers from checkpoint data. Given the limited data, the results are quite accurate, but a full reconstruction of the movement is clearly out of reach.…”

Section: Related Workmentioning

confidence: 99%

Route Reconstruction from Traffic Flow via Representative Trajectories

Custers¹,

Meulemans²,

Speckmann³

et al. 2020

Preprint

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Understanding human mobility patterns is an important aspect of traffic analysis and urban planning. Trajectory data provide detailed views on specific routes, but typically do not capture all traffic. On the other hand, loop detectors built into the road network capture all traffic flow at specific locations, but provide no information on the individual routes. Given a set of loop-detector measurements as well as a (small) set of representative trajectories, our goal is to investigate how one can effectively combine these two partial data sources to create a more complete picture of the underlying mobility patterns. Specifically, we want to reconstruct a realistic set of routes from the loop-detector data, using the given trajectories as representatives of typical behavior.We model the loop-detector data as a network flow that needs to be covered by the reconstructed routes and we capture the realism of the routes via the strong Fréchet distance to the representative trajectories. We prove that several forms of the resulting algorithmic problem are NP-hard. Hence we explore heuristic approaches which decompose the flow well while following the representative trajectories to varying degrees. First of all, we propose an iterative Fréchet Routes (FR) heuristic which generates candidates routes which have bounded Fréchet distance to the representative trajectories. Second we describe a variant of multi-commodity min-cost flow (MCMCF) which is only loosely coupled to the trajectories. Lastly we also consider global min-cost flow (GMCF) which is essentially agnostic to the representative trajectories.We perform an extensive experimental evaluation of our two proposed approaches in comparison to the min-cost flow baseline, both on synthetic and on real-world trajectory data. To create a ground truth for our experiments, we extract the flow information from map-matched trajectories. We find that GMCF explains the flow best, but produces a large number of routes (significantly more than the ground truth); these routes are often nonsensical. Also MCMCF produces a large number of routes which explain the flow reasonably well, however, the routes are mostly realistic. In contrast, FR produces significantly (orders of magnitude) smaller sets of realistic routes which still explain the flow well, albeit at the cost of a higher running time.

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Section: Related Workmentioning

confidence: 99%

Route Reconstruction from Traffic Flow via Representative Trajectories

Custers¹,

Meulemans²,

Speckmann³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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Stepping stone graph for public movement analysis

Kannangara

Tanin

Harwood

et al. 2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Stepping Stone Graph

Kannangara

Tanin

Harwood

et al. 2019

ACM Trans. Spatial Algorithms Syst.

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There are many real world applications that require identifying public movements such as identifying movement corridors in cities and most popular paths. If one is not given user trajectories but rather sporadic location data, such as location-based social network data, finding movement related information becomes difficult. Rather than processing all points in a dataset given a query, a clever approach is to construct a graph, based on user locations, and query this graph to answer questions such as shortest paths, most popular paths, and movement corridors. Shortest path graph is one of the popular graphs. However, the shortest path graph can be inefficient and ineffective for analysing movement data, as it calculates the graph edges considering the shortest paths over all the points in a dataset. Therefore, edge sets resulting from shortest path graphs are usually very restrictive and not suitable for movement analysis because of its global view of the dataset. We propose the stepping stone graph, which calculates the graph considering point pairs rather than all points; the stepping stone graph focuses on possible local movements, making it both efficient and effective for location-based social network related data. We demonstrate its capabilities by applying it in the Location-Based Social Network domain and comparing with the shortest path graph. We also compare its properties to a range of other graphs and demonstrate how stepping stone graph relates to Gabriel graph, relative neighbourhood graph, and Delaunay triangulation.

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Modeling Checkpoint-Based Movement with the Earth Mover’s Distance

Cited by 4 publications

References 26 publications

Route Reconstruction from Traffic Flow via Representative Trajectories

Route Reconstruction from Traffic Flow via Representative Trajectories

Stepping stone graph for public movement analysis

Stepping Stone Graph

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