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Popular route planning with travel cost estimation from trajectories

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Abstract

With the increasing number of GPS-equipped vehicles, more and more trajectories are generated continuously, based on which some urban applications become feasible, such as route planning. In general, popular route that has been travelled frequently is a good choice, especially for people who are not familiar with the road networks. Moreover, accurate estimation of the travel cost (such as travel time, travel fee and fuel consumption) will benefit a well scheduled trip plan. In this paper, we address this issue by finding the popular route with travel cost estimation. To this end, we design a system consists of three main components. First, we propose a novel structure, called popular traverse graph where each node is a popular location and each edge is a popular route between locations, to summarize historical trajectories without road network information. Second, we propose a self-adaptive method to model the travel cost on each popular route at different time interval, so that each time interval has a stable travel cost. Finally, based on the graph, given a query consists of source, destination and leaving time, we devise an efficient route planning algorithm which considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost estimation. Moreover, we conduct comprehensive experiments and implement our system by a mobile App, the results show that our method is both effective and efficient.

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Acknowledgements

Our research was supported by the National Key Research and Development Program of China (2017YFC0803700 and 2016YFB1000905), the National Natural Science Foundation of China (Grant Nos. 61370101, 61532021, U1501252, 61702423, and 61772327).

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Authors and Affiliations

  1. School of Data Science and Engineering, School of Computer Science and Software Engineering, East China Normal University, Shanghai, 200062, China

    Huiping Liu, Cheqing Jin & Aoying Zhou

Authors
  1. Huiping Liu
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  2. Cheqing Jin
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  3. Aoying Zhou
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Corresponding author

Correspondence to Cheqing Jin.

Additional information

Huiping Liu, received the BS degree in Software Engineering from East China Normal University, China in 2013. Currently, he is a PhD student supervised by Professor Cheqing Jin. His research mainly focuses on location-based services, massive data mining and processing and data quality.

Cheqing Jin is a professor on computer science at East China Normal University, China. He received Excellent Young Teacher Award by Fok Ying Tung Education Foundation. His main research interests include: streaming data management, location-based services, and uncertain data management.

Aoying Zhou is a professor on computer science at East China Normal University, China. His research interests include Web data management, data management for data-intensive computing, in-memory cluster computing, benchmarking for big data and performance.

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Liu, H., Jin, C. & Zhou, A. Popular route planning with travel cost estimation from trajectories. Front. Comput. Sci. 14, 191–207 (2020). https://doi.org/10.1007/s11704-018-7249-z

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