Book ChapterDOI
Popular Route Planning with Travel Cost Estimation
Huiping Liu,Cheqing Jin,Aoying Zhou +2 more
- pp 403-418
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TLDR
A novel structure, called popular traverse graph, is devised, based on which an efficient route planning algorithm is proposed to search the popular route with minimal travel cost, and extensive experimental reports show that the method is both effective and efficient.Abstract:
With the increasing number of GPS-equipped vehicles, more and more trajectories are generated continuously, based on which some urban applications become feasible, such as route planning. In general, route planning aims at finding a path from source to destination to meet some specific requirements, i.e., the minimal travel time, fee or fuel consumption. Especially, some users may prefer popular route that has been travelled frequently. However, the existing work to find the popular route does not consider how to estimate the travelling cost. In this paper, we address this issue by devising a novel structure, called popular traverse graph, to summarize historical trajectories. Based on which an efficient route planning algorithm is proposed to search the popular route with minimal travel cost. The extensive experimental reports show that our method is both effective and efficient.read more
Citations
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Journal ArticleDOI
A survey of trajectory distance measures and performance evaluation
TL;DR: A comprehensive survey of the trajectory distance measures is conducted, classified into four categories according to the trajectory data type and whether the temporal information is measured.
Journal ArticleDOI
Finding Top-k Shortest Paths with Diversity
TL;DR: It is proved that the KSPD problem is NP-hard and a greedy framework is proposed that supports a wide variety of path similarity metrics which are widely adopted in the literature and is able to efficiently solve the traditional KSP problem if no path similarity metric is specified.
Journal ArticleDOI
Feature Grouping-Based Outlier Detection Upon Streaming Trajectories
TL;DR: A feature grouping-based mechanism that divides all the features into two groups, where the first group is used to find close neighbors and the second group is use to find outliers within the similar neighborhood, which is both effective and efficient to detect outliers upon trajectory data streams.
Book ChapterDOI
TrajSpark: A Scalable and Efficient In-Memory Management System for Big Trajectory Data
TL;DR: TrajSpark is presented, a distributed in-memory system to consistently offer efficient management of trajectory data, which introduces a new abstraction called IndexTRDD to manage trajectory segments, and exploits a global and local indexing mechanism to accelerate trajectory queries.
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