Predicting Next Locations with Object Clustering and Trajectory Clustering

Chen, Meng; Liu, Yang; Yu, Xiaohui

doi:10.1007/978-3-319-18032-8_27

Cited by 29 publications

(25 citation statements)

References 17 publications

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“…Pang et al [10] detect the anomalous behavior of taxis in Beijing metropolitan area. Chen et al [2] propose a model given the past trajectories and predict the next station of an object. Kong et al [8] aim to deal with the problem of traffic congestion and propose a method to predict the traffic congestion using the floating car trajectories data.…”

Section: Urban Traffic Analysismentioning

confidence: 99%

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Bus-OLAP: A Data Management Model for Non-on-Time Events Query Over Bus Journey Data

et al. 2018

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Increasing the on-time rate of bus service can prompt the people's willingness to travel by bus, which is an effective measure to mitigate the city traffic congestion. Performing queries on the bus arrival can be used to identify and analyze various kinds of non-on-time events that happened during the bus journey, which is helpful for detecting the factors of delaying events, and providing decision support for optimizing the bus schedules. We propose a data management model, called Bus-OLAP, for querying bus journey data, considering the characteristics of bus running and the scenarios of nonon-time analysis. While fulfilling typical requirements of bus journey data queries, Bus-OLAP not only provides a flexible way to manage the data and to implement multiple granularity data query and update, but it also supports distributed queries and computation. The experiments on real-world bus journey data verify that Bus-OLAP is effective and efficient.

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Section: Urban Traffic Analysismentioning

confidence: 99%

“…2 Next, we introduce our method to fuse external factors with bus journey records for bus non-on-time queries by taking weather data as an example.…”

Section: Query With Data From Different Sourcesmentioning

confidence: 99%

Bus-OLAP: A Data Management Model for Non-on-Time Events Query Over Bus Journey Data

et al. 2018

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“…Petzold et al () evaluated five machine learning techniques and found that Markov predictor provided higher accuracy and lower resource requirements. Since then, Markov predictor has been common in location prediction models (Song et al, ; Ashbrook & Starner, ; Yu et al, ; Chen et al, ; Herder et al, ; Park et al, ). Many variants of Markov model have been proposed, for example, for complex mobility patterns, high order Markov is adopted (Likhyani et al, ).…”

Section: Related Workmentioning

confidence: 99%

“…(Ashbrook & Starner, 2003) considered the mobility pattern between two continuous frequently visited locations that are identified from GPS trajectory data. These mobility patterns of locations have been used in many models (Song et al, 2004;Sadilek et al, 2012;Gao et al, 2012b;McGee et al, 2013;Mathew et al, 2012;Yang et al, 2014;Chen et al, 2015). Cao et.…”

Section: Spatio-temporal Characterizationmentioning

confidence: 99%

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MIAC: A mobility intention auto‐completion model for location prediction

Feng

Gao

Wang

et al. 2018

Intell Sys Acc Fin Mgmt

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Summary Location prediction is essential to many commercial applications and enables appealing experience for business and governments. Many research work show that human mobility is highly predictable. However, existing work on location prediction reported limited improvements in using generalized spatio‐temporal features and unsatisfactory prediction accuracy for complex human mobility. To address these challenges, in this paper we propose a Mobility Intention and Auto‐Completion (MIAC) model. We extract those mobility patterns that generalize common spatio‐temporal features of all users, and use the mobility intentions as the hidden states from mobility dataset. A new predicting algorithm based on auto‐completion is then proposed. The experimental results on real‐world datasets demonstrate that the proposed MIAC model can properly capture the regularity of a user's mobility by simultaneously considering the spatial and temporal features. The comparison results also indicate that MIAC model significantly outperforms state‐of‐the‐art location prediction methods, and also can predicts long range locations.

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