Go slow to go fast: minimal on-road time route scheduling with parking facilities using historical trajectory

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In: The VLDB Journal, 27(2018), 3, S. 321 - 345
Format: E-Article
Sprache: Englisch
veröffentlicht: Springer Berlin Heidelberg
Schlagworte:
ISSN: 1066-8888
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finc.format ElectronicArticle
finc.mega_collection Springer Journals
finc.id dswarm-105-MTAuMTAwNy9zMDA3NzgtMDE4LTA0OTktNA
finc.record_id 10.1007/s00778-018-0499-4
finc.source_id 105
ris.type EJOUR
rft.atitle Go slow to go fast: minimal on-road time route scheduling with parking facilities using historical trajectory
rft.eissn 0949-877X
rft.epage 345
rft.genre article
rft.issn 1066-8888
rft.issue 3
rft.jtitle The VLDB Journal
rft.pages 321-345
rft.place Berlin/Heidelberg
rft.pub Springer Berlin Heidelberg
rft.date 2018
x.date 2018-06-01T00:00:00Z
rft.stitle The VLDB Journal
rft.spage 321
rft.volume 27
abstract For thousands of years, people have been innovating new technologies to make their travel faster, the latest of which is GPS technology that is used by millions of drivers every day. The routes recommended by a GPS device are computed by path planning algorithms (e.g., fastest path algorithm), which aim to minimize a certain objective function (e.g., travel time) under the current traffic condition. When the objective is to arrive the destination as early as possible, waiting during travel is not an option as it will only increase the total travel time due to the <Emphasis Type="Italic">First-In-First-Out</Emphasis> property of most road networks. However, some businesses such as logistics companies are more interested in optimizing the actual on-road time of their vehicles (i.e., while the engine is running) since it is directly related to the operational cost. At the same time, the drivers’ trajectories, which can reveal the traffic conditions on the roads, are also collected by various service providers. Compared to the existing speed profile generation methods, which mainly rely on traffic monitor systems, the trajectory-based method can cover a much larger space and is much cheaper and flexible to obtain. This paper proposes a system, which has an online component and an offline component, to solve the minimal on-road time problem using the trajectories. The online query answering component studies how parking facilities along the route can be leveraged to avoid predicted traffic jam and eventually reduce the drivers’ on-road time, while the offline component solves how to generate speed profiles of a road network from historical trajectories. The challenging part of the routing problem of the online component lies in the computational complexity when determining if it is beneficial to wait on specific parking places and the time of waiting to maximize the benefit. To cope with this challenging problem, we propose two efficient algorithms using <Emphasis Type="Italic">minimum on-road travel cost function</Emphasis> to answer the query. We further introduce several approximation methods to speed up the query answering, with an error bound guaranteed. The offline speed profile generation component makes use of historical trajectories to provide the traveling time for the online component. Extensive experiments show that our method is more efficient and accurate than baseline approaches extended from the existing path planning algorithms, and our speed profile is accurate and space efficient.
authors Li Lei
doi 10.1007/s00778-018-0499-4
languages eng
url https://doi.org/10.1007/s00778-018-0499-4
x.subjects Road network
Shortest path
Trajectory