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Predicting the city foot traffic with pedestrian sensor data

Authors:

Jonathan Liono,

Flora D. SalimAuthors Info & Claims

MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 1 - 10

https://doi.org/10.1145/3144457.3152355

Published: 07 November 2017 Publication History

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.

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Cited By

Lee C(2024)The relationship between foot traffic and commercial land pricesGeografie10.37040/geografie.2024.005129:1(1-13)Online publication date: 26-Mar-2024
https://doi.org/10.37040/geografie.2024.005
Anh Dang NVu TLuong SNgoc Dinh TSeyedmahmoudian MChandran JStojcevski A(2024)Forecasting Pedestrian Traffic in Melbourne Central Business District: Integrating Sensor Data with COVID-19 Impacts to Inform City Planning2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS)10.1109/ICCAIS63750.2024.10814385(1-6)Online publication date: 26-Nov-2024
https://doi.org/10.1109/ICCAIS63750.2024.10814385
Navon Yde Silva A(2023)Measuring Local Economic Activity Using Pedestrian Count Data*Economic Record10.1111/1475-4932.1275699:S1(35-49)Online publication date: 27-Jul-2023
https://doi.org/10.1111/1475-4932.12756
Show More Cited By

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Predicting the city foot traffic with pedestrian sensor data

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Information & Contributors

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Published In

cover image ACM Other conferences

MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2017

555 pages

ISBN:9781450353687

DOI:10.1145/3144457

General Chairs:
Tao Gu
RMIT University, Australia
,
Ramamohanarao Kotagiri
University of Melbourne, Australia
,
Publications Chair:
Huai Liu
Victoria University, Australia

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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EAI: The European Alliance for Innovation
SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

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MobiQuitous 2017

MobiQuitous 2017: Computing, Networking and Services

November 7 - 10, 2017

VIC, Melbourne, Australia

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Overall Acceptance Rate 26 of 87 submissions, 30%

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Citations

Cited By

Lee C(2024)The relationship between foot traffic and commercial land pricesGeografie10.37040/geografie.2024.005129:1(1-13)Online publication date: 26-Mar-2024
https://doi.org/10.37040/geografie.2024.005
Anh Dang NVu TLuong SNgoc Dinh TSeyedmahmoudian MChandran JStojcevski A(2024)Forecasting Pedestrian Traffic in Melbourne Central Business District: Integrating Sensor Data with COVID-19 Impacts to Inform City Planning2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS)10.1109/ICCAIS63750.2024.10814385(1-6)Online publication date: 26-Nov-2024
https://doi.org/10.1109/ICCAIS63750.2024.10814385
Navon Yde Silva A(2023)Measuring Local Economic Activity Using Pedestrian Count Data*Economic Record10.1111/1475-4932.1275699:S1(35-49)Online publication date: 27-Jul-2023
https://doi.org/10.1111/1475-4932.12756
Zaouche MBode N(2023)Bayesian spatio-temporal models for mapping urban pedestrian trafficJournal of Transport Geography10.1016/j.jtrangeo.2023.103647111(103647)Online publication date: Jul-2023
https://doi.org/10.1016/j.jtrangeo.2023.103647
Mohammadsalehi ASafaei BMonazzah ABauer LHenkel JEjlali A(2022)ARMOR: A Reliable and Mobility-Aware RPL for Mobile Internet of Things InfrastructuresIEEE Internet of Things Journal10.1109/JIOT.2021.30883469:2(1503-1516)Online publication date: 15-Jan-2022
https://doi.org/10.1109/JIOT.2021.3088346
Panay BBaloian NPino JPeñafiel SFrez JFuenzalida CSanson HZurita G(2021)Forecasting Key Retail Performance Indicators Using Interpretable RegressionSensors10.3390/s2105187421:5(1874)Online publication date: 8-Mar-2021
https://doi.org/10.3390/s21051874
Sevtsuk ABasu RChancey B(2021)We shape our buildings, but do they then shape us? A longitudinal analysis of pedestrian flows and development activity in MelbournePLOS ONE10.1371/journal.pone.025753416:9(e0257534)Online publication date: 21-Sep-2021
https://doi.org/10.1371/journal.pone.0257534
Bolin DVerendel VBerghauser Pont MStavroulaki IIvarsson OHåkansson E(2021)Functional ANOVA Modelling of Pedestrian Counts on Streets in Three European CitiesJournal of the Royal Statistical Society Series A: Statistics in Society10.1111/rssa.12646184:4(1176-1198)Online publication date: 9-Jan-2021
https://doi.org/10.1111/rssa.12646
Lu YChen XWang BWang TZhang PLi YHarle RFarrahi KLane N(2019)Recycling price prediction of renewable resourcesAdjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3349326(571-576)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1145/3341162.3349326
DeFazio DRamesh ASeetharam ASchulzrinne HLi P(2018)NYCERProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3286978.3287010(187-196)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3286978.3287010
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