A Context- and Trajectory-based Destination Prediction of Public Transportation Users

Malte Bieler, Raghava Rao Mukkamala, Tor-Morten Grønli*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Location prediction based on contextual information is the core of a plethora of personalized locationbased services (LBSs). Several applications require the use of techniques for predicting travel destinations based on human movement. Network analyses of human behavioral data show how the spatial-temporal regularity of human movement can be harnessed for inferring human mobility patterns. However, techniques are often based on a limited number of contextual features, which may limit prediction accuracy, especially if only historical location data are used. Using movement data obtained from public transportation users, we investigate the utility of contextual features derived via the installation of Bluetooth beacons in transportation vehicles and software tools in end-users’ travel applications. Using a multiclass random forest classifier, we show that contextual information of a user’s past travel history and at journey onset goes beyond spatial information and boosts destination prediction accuracy. The likely destination and travel-path length obtained at journey onset can then serve as the input for a stochastic-based model to predict a destination based on acquired trajectory information. Here we show that previously predicted destinations boost the performance of a Markov chain network. Thus, various contextual information at the start of a journey provides information beyond the location information acquired during a journey’s progression that can be employed for destination prediction. These findings have strong implications for LBSs as they require accurate destination prediction at early stages of a journey while at the same time mitigating the privacy concerns associated with collection of location data
Original languageEnglish
JournalIEEE Intelligent Transportation Systems Magazine
Issue number1
Pages (from-to)300-317
Number of pages18
Publication statusPublished - Jan 2023

Bibliographical note

Published online: 11 January 2022.


  • Trajectory
  • Hidden markov models
  • Predictive models
  • Global positioning system
  • Smart phones
  • Prediction alorithms
  • Markov processes

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