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We developed an energy-efficient mobile recommender system by exploiting the energy-efficient driving patterns extracted from the location traces of Taxi drivers

An energy-efficient mobile recommender system

KDD, pp.899-908, (2010)

被引用379|浏览55
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摘要

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy c...更多

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简介
  • Wireless communication, and information infrastructures such as GPS, WiFi and RFID have enabled them to collect large amounts of location traces of individuals or objects
  • Such a large number of trajectories provide them unprecedented opportunity to automatically discover useful knowledge, which in turn deliver intelligence for real-time decision making in various fields, such as mobile recommendations.
  • A mobile recommender system promises to provide mobile users access to personalized recommendations anytime, anywhere.
  • The development of personalized recommender systems in mobile and pervasive environments is much more challenging than developing recommender systems from traditional domains due to the complexity of spatial data and intrinsic spatiotemporal relationships, the unclear roles of context-aware information, and the increasing availability of environment sensing capabilities
重点内容
  • Advances in sensor, wireless communication, and information infrastructures such as GPS, WiFi and RFID have enabled us to collect large amounts of location traces of individuals or objects
  • We have developed a route recommendation algorithm, named LCP, which exploits the monotone property of the Potential Travel Distance (PTD) function
  • Since the driving distance measured by the Google Map API depends on the driving direction, we use the average to estimate the distance between each pair of pick-up points
  • We developed an energy-efficient mobile recommender system by exploiting the energy-efficient driving patterns extracted from the location traces of Taxi drivers
  • This system has the ability to recommend a sequence of potential pick-up points for a driver in a way such that the potential travel distance before having customer is minimized
  • Based on the monotone property of the PTD function, we proposed a recommendation algorithm, named LCP
结果
  • The authors evaluate the performances of the proposed two algorithms: LCP and SkyRoute.

    5.1 The Experimental Setup

    Real-world Data.
  • The authors obtain 20 cab drivers and their location traces
  • Based on this selected data, the authors generate potential pick-up points and the pick-up probability associated with each pick-up point for different time periods.
  • After calculating the pairwise driving distance of pick-up points with the Google Map API, the authors use Cluto [12] for clustering.
  • The traveling distances between clusters are measured between centroids of clusters with the Google Map API
结论
  • The authors developed an energy-efficient mobile recommender system by exploiting the energy-efficient driving patterns extracted from the location traces of Taxi drivers.
  • This system has the ability to recommend a sequence of potential pick-up points for a driver in a way such that the potential travel distance before having customer is minimized.
  • An advantage of searching an optimal route through skyline computing is that it can save the overall online processing time when the authors try to provide different optimal driving routes defined by different business needs
表格
  • Table1: Some Acronyms
  • Table2: A Comparison of Search Time (Second)
Download tables as Excel
基金
  • This research was partially supported by the National Science Foundation (NSF) via grant number CNS 0831186, the Rutgers CCC Green Computing Initiative, and the National Natural Science Foundation of China (70890080)
研究对象与分析
synthetic data sets with 10: 3
Specifically, we randomly generate potential pick-up points within a specified area and generate the pick-up probability associated with each pick-up point by a standard uniform distribution. In total, we have 3 synthetic data sets with 10, 15 and 20 pickup points respectively. For this synthetic data, we use the Euclidean distance instead of the driving distance to measure the traveling distance between pick-up points

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