Aslam, NS;
Ibrahim, MR;
Cheng, T;
Chen, H;
Zhang, Y;
(2021)
ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs.
Geo-spatial Information Science
10.1080/10095020.2021.1985943.
(In press).
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Abstract
Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility. Here, we propose a framework, ActivityNET, using Machine Learning (ML) algorithms to predict passengers’ trip purpose from Smart Card (SC) data and Points-of-Interest (POIs) data. The feasibility of the framework is demonstrated in two phases. Phase I focuses on extracting activities from individuals’ daily travel patterns from smart card data and combining them with POIs using the proposed “activity-POIs consolidation algorithm”. Phase II feeds the extracted features into an Artificial Neural Network (ANN) with multiple scenarios and predicts trip purpose under primary activities (home and work) and secondary activities (entertainment, eating, shopping, child drop-offs/pick-ups and part-time work) with high accuracy. As a case study, the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose. The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.
| Type: | Article |
|---|---|
| Title: | ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/10095020.2021.1985943 |
| Publisher version: | https://doi.org/10.1080/10095020.2021.1985943 |
| Language: | English |
| Additional information: | © 2021 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Trip purpose prediction, smart card data, POIs, neural networks, machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10136689 |
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