Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2013

Abstract

Nowadays, there are many taxis traversing around the city searching for available passengers, but their hunts of passengers are not always efficient. To the dynamics of traffic and biased passenger distributions, current offline recommendations based on place of interests may not work well. In this paper, we define a new problem, global-optimal trajectory retrieving (GOTR), as finding a connected trajectory of high profit and high probability to pick up a passenger within a given time period in real-time. To tackle this challenging problem, we present a system, called HUNTS, based on the knowledge from both historical and online GPS data and business data. To achieve above objectives, first, we propose a dynamic scoring system to evaluate each road segment in different time periods by considering both picking-up rate and profit factors. Second, we introduce a novel method, called trajectory sewing, based on a heuristic method and the Skyline technique, to produce an approximate optimal trajectory in real-time. Our method produces a connected trajectory rather than several place of interests to avoid frequent next-hop queries. Third, to avoid congestion and other real-time traffic situations, we update the score of each road segment constantly via an online handler. Finally, we validate our system using a large-scale data of around 15,000 taxis in a large city in China, and compare the results with regular taxis’ hunts and the state-of-the-art.

Discipline

Databases and Information Systems

Publication

MDM '13 Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management

Volume

1

First Page

107

Last Page

116

ISBN

978-0-7695-4973-6

Identifier

10.1109/MDM.2013.21

Publisher

IEEE

Copyright Owner and License

LARC

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