Research Article
Predicting the city foot traffic with pedestrian sensor data
@INPROCEEDINGS{10.4108/eai.7-11-2017.2273699, author={Jonathan Liono and Xianjing Wang and Will Mcintosh and Flora D. Salim}, title={Predicting the city foot traffic with pedestrian sensor data}, proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={ACM}, proceedings_a={MOBIQUITOUS}, year={2018}, month={4}, keywords={prediction pedestrian count mobility patterns time series}, doi={10.4108/eai.7-11-2017.2273699} }- Jonathan Liono
Xianjing Wang
Will Mcintosh
Flora D. Salim
Year: 2018
Predicting the city foot traffic with pedestrian sensor data
MOBIQUITOUS
ACM
DOI: 10.4108/eai.7-11-2017.2273699
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.
