Real-time route planning with stream processing systems: a case study for the city of Lucerne
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
Traffic aware route planning in dynamic road networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Keyword-aware optimal route search
Proceedings of the VLDB Endowment
Group trip planning queries in spatial databases
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
A group based approach for path queries in road networks
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Travel cost inference from sparse, spatio temporally correlated time series using Markov models
Proceedings of the VLDB Endowment
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In this paper, we address the problem of answering continuous route planning queries over a road network, in the presence of updates to the delay (cost) estimates of links. A simple approach to this problem would be to recompute the best path for all queries on arrival of every delay update. However, such a naive approach scales poorly when there are many users who have requested routes in the system. Instead, we propose two new classes of approximate techniques - K-paths and proximity measures to substantially speed up processing of the set of designated routes specified by continuous route planning queries in the face of incoming traffic delay updates. Our techniques work through a combination of pre-computation of likely good paths and by avoiding complete recalculations on every delay update, instead only sending the user new routes when delays change significantly. Based on an experimental evaluation with 7,000 drives from real taxi cabs, we found that the routes delivered by our techniques are within 5% of the best shortest path and have run times an order of magnitude or less compared to a naive approach.