Predicting the city foot traffic with pedestrian sensor data

Publication Year: 2017 Publication Type : ConferenceProceeding


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.


    author = {Xianjing Wang and Jonathan Liono and Will McIntosh and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Melbourne, Australia, November 7-10, 2017},
    doi = {10.1145/3144457.3152355},
    editor = {Tao Gu and Ramamohanarao Kotagiri and Huai Liu},
    pages = {1--10},
    publisher = {ACM},
    timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
    title = {Predicting the city foot traffic with pedestrian sensor data},
    url = {},
    year = {2017}


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