Learning to route with sparse trajectory sets

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45 Citationer (Scopus)

Abstrakt

Motivated by the increasing availability of vehicle trajectory data, we propose learn-To-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, %and although the introduction of regions serves to consolidate the sparse data, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality.

OriginalsprogEngelsk
TitelProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Antal sider12
ForlagIEEE
Publikationsdato24 okt. 2018
Sider1073-1084
Artikelnummer8509321
ISBN (Elektronisk)9781538655207
DOI
StatusUdgivet - 24 okt. 2018
Begivenhed34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, Frankrig
Varighed: 16 apr. 201819 apr. 2018

Konference

Konference34th IEEE International Conference on Data Engineering, ICDE 2018
Land/OmrådeFrankrig
ByParis
Periode16/04/201819/04/2018
NavnProceedings of the International Conference on Data Engineering
ISSN1063-6382

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