Predicting the city foot traffic with pedestrian sensor data

Wang, X, Liono, J, McIntosh, W and Salim, F 2017, 'Predicting the city foot traffic with pedestrian sensor data', in Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017), Melbourne, Australia, 7-10 November 2017, pp. 1-10.


Document type: Conference Paper
Collection: Conference Papers

Title Predicting the city foot traffic with pedestrian sensor data
Author(s) Wang, X
Liono, J
McIntosh, W
Salim, F
Year 2017
Conference name MobiQuitous 2017
Conference location Melbourne, Australia
Conference dates 7-10 November 2017
Proceedings title Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 1
End page 10
Total pages 10
Abstract In this paper, we focus on developing a model and system for predicting the city foot traffic. We utilise historical records of pedestrian counts captured with thermal and laser-based sensors installed at multiple locations throughout the city. A robust prediction system is proposed to cope with various temporal foot traffic patterns. The empirical evaluation of our experiment shows that the proposed ARIMA model is effective in modelling both weekdays and weekend patterns, outperforming other state-of-art models for short-term prediction of pedestrian counts. The model is capable of accurately predicting pedestrian numbers up to 16 days in advance, on multiple look-ahead times. Our system is evaluated with a real-world sensor dataset supplied by the City of Melbourne.
Subjects Pattern Recognition and Data Mining
Ubiquitous Computing
Keyword(s) prediction
pedestrian count
mobility patterns
time series
DOI - identifier 10.1145/3144457.3152355
Copyright notice © 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
ISBN 9781450353687
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