标题:TTDM: A Travel Time Difference Model for Next Location Prediction
作者:Liu, Qingjie; Zuo, Yixuan; Yu, Xiaohui; Chen, Meng
通讯作者:Chen, M
作者机构:[Liu, Qingjie] Shandong Univ, Sch Software, Jinan, Shandong, Peoples R China.; [Zuo, Yixuan] Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan, S 更多
会议名称:20th IEEE International Conference on Mobile Data Management (IEEE MDM)
会议日期:JUN 10-13, 2019
来源:2019 20TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2019)
出版年:2019
页码:216-225
DOI:10.1109/MDM.2019.00-54
关键词:Next Location Prediction; Travel Time Difference Model; Markov Model;; Traffic Trajectory Data
摘要:Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Unfortunately, due to the time and space complexity, these methods (e.g., Markov models) only use the just passed locations to predict next locations, without considering all the passed locations in the trajectory. In this paper, we seek to enhance the prediction performance by considering the travel time from all the passed locations in the query trajectory to a candidate next location. In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Further, we integrate the TTDM with a Markov model via a linear interpolation to yield a joint model, which computes the probability of reaching each possible next location and returns the top-rankings as results. We have conducted extensive experiments on two real datasets: the vehicle passage record (VPR) data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over existing solutions. For example, compared with the Markov model, the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi data.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070969666&doi=10.1109%2fMDM.2019.00-54&partnerID=40&md5=905c78951b960a26bc8b7bb753cc9036
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