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Open AccessJournal ArticleDOI

Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

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TLDR
In this article, a common approach to next location prediction is proposed, which provides essential intelligence to various businesses by predicting the next location of a user in a location-based application.
Abstract
Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next locati...

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Proceedings ArticleDOI

A Location Sensitive and Utility Based Advertising System for Public Transport

TL;DR: In this paper , an advertising system that uses location and historical data for scheduling is proposed, and experiments indicate that a good compromise between utility and fairness can be found between the two objectives.
Journal ArticleDOI

Companion Mobility to Assist in Future Human Location Prediction

TL;DR: A two-phase framework for predicting an individual’s future locations that fully benefits from spatio-temporal contexts embedded in that person's and his/her companions’ mobility is proposed, and a bidirectional recurrent neural network (BRNN)-based multi-output model is proposed to predict a person's future locations in the next several time slots.
Journal ArticleDOI

Companion Mobility to Assist in Future Human Location Prediction

- 01 Jan 2022 - 
TL;DR: Zhang et al. as mentioned in this paper proposed a two-phase framework for predicting an individual's future locations that fully benefits from spatio-temporal contexts embedded in that person and his/her companions' mobility.
Proceedings ArticleDOI

A Location Sensitive and Utility Based Advertising System for Public Transport

TL;DR: In this paper , an advertising system that uses location and historical data for scheduling is proposed, and experiments indicate that a good compromise between utility and fairness can be found between the two objectives.
References
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Journal ArticleDOI

Finding the K Shortest Loopless Paths in a Network

Jin Y. Yen
- 01 Jul 1971 - 
TL;DR: The significance of the new algorithm is that its computational upper bound increases only linearly with the value of K, so it is extremely efficient as compared with the algorithms proposed by Bock, Kantner, and Haynes and others.
Journal ArticleDOI

Trajectory Data Mining: An Overview

TL;DR: A systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics, and introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors.
Proceedings ArticleDOI

WhereNext: a location predictor on trajectory pattern mining

TL;DR: This paper proposes WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object, which uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time.
Proceedings ArticleDOI

Using statistical testing in the evaluation of retrieval experiments

David A. Hull
TL;DR: It is suggested that relevance feedback be evaluated from the perspective of the user and a number of different statistical tests are described for determining if differences in performance between retrieval methods are significant.
Journal ArticleDOI

Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

TL;DR: The extensive experimental analysis shows that the proposed Spatial-Aware Hierarchical Collaborative Deep Learning model outperforms the state-of-the-art recommendation models, especially in out- of-town and cold-start recommendation scenarios.