Efficient evaluation of shortest travel-time path queries through spatial mashups

Detian Zhang, Chi-Yin Chow, An Liu, Xiangliang Zhang, Qingzhu Ding, Qing Li

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


In the real world, the route/path with the shortest travel time in a road network is more meaningful than that with the shortest network distance for location-based services (LBS). However, not every LBS provider has adequate resources to compute/estimate travel time for routes by themselves. A cost-effective way for LBS providers to estimate travel time for routes is to issue external route requests to Web mapping services (e.g., Google Maps, Bing Maps, and MapQuest Maps). Due to the high cost of processing such external route requests and the usage limits of Web mapping services, we take the advantage of direction sharing, parallel requesting and waypoints supported by Web mapping services to reduce the number of external route requests and the query response time for shortest travel-time route queries in this paper. We first give the definition of sharing ability to reflect the possibility of sharing the direction information of a route with others, and find out the queries that their query routes are independent with each other for parallel processing. Then, we model the problem of selecting the optimal waypoints for an external route request as finding the longest simple path in a weighted complete digraph. As it is a MAX SNP-hard problem, we propose a greedy algorithm with performance guarantee to find the best set of waypoints in an external route request. We evaluate the performance of our approach using a real Web mapping service, a real road network, real and synthetic data sets. Experimental results show the efficiency, scalability, and applicability of our approach.
Original languageEnglish (US)
Pages (from-to)3-28
Number of pages26
Issue number1
StatePublished - Jan 7 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Research reported in this publication was partially supported by King Abdullah University of Science and Technology (KAUST), the Fundamental Research Funds for the Central Universities in China (Project No. JUSRP11557), the National Natural Science Foundation of China (Project No. 61572336 and 61472337), and a Strategic Research Grant from City University of Hong Kong (Project No. 7004420).


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