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Counting Passengers in Public Buses by Sensing Carbon Dioxide Concentration: Data Collection and Machine Learning

Published: 24 October 2018 Publication History

Abstract

As a new initiative by smart city projects that are going viral in worldwide ICT developments, mostly by governments, IoT sensors and their applications have been exploited and adopted proactively nowadays. A useful but relatively low-tech application is counting human presence by using carbon dioxide sensor. Such CO2 sensors are durable and inexpensive, with their compact sizes they could be deployed anywhere for estimating head counts ubiquitously. In this paper, a case study of applying CO2 sensors in public buses is investigated. Counting passengers in public buses or public transport in general has great economics advantages. However, a few technical challenges include but not limited to the mobility of the bus, the dynamic air flows, and factors such as windows were open, ventilation and even urban pollution etc, would affect the accuracy of occupancy counting. Hardly there would be a simple linear mapping between the number of people in a bus and the measurement of CO2 level. Hence, non-linear machine learning tool is used for inferring the non-linear relation between the two, with the consideration of the mentioned influential factors. Empirical data are collected from experiments conducted in several different buses over different times. The results can point to a promising conclusion that satisfactory accuracy could be achieved.

References

[1]
Jiang, C., Masood, M. K., Soh, Y. C., & Li, H. (n.d.). Indoor occupancy estimation from carbon dioxide concentration. Retrieved July 20, 2016.
[2]
M. A. ul Haq, M. Y. Hassan, H. Abdullah, H. A. Rahman, M. P. Abdullah, F. Hussin, and D. M. Said, "A review on lighting control technologies in commercial buildings, their performance and affecting factors," Renew. Sustain. Energy Reviews, vol. 33, 2014.
[3]
T. Teixeira, G. Dublon, and A. Savvides, "A survey of human-sensing: Methods for detecting presence, count, location, track, and identity,"ACM Comput. Surveys, vol. 5, 2010.
[4]
S. Depatla, A. Muralidharan, and Y. Mostofi, "Occupancy estimation using only WIFI power measurements," IEEE J. Sel. Areas Commun., vol. 33, no. 7, 2015.
[5]
CO2 sensor MH-Z16, {Online}. Available: https://www.google.com/url?sa=t&rct=j&q&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwiRrfzhlM3YAhXDUbwKHXd2DqcQFghBMAI&url=https%3A%2F%2Fraw.githubusercontent.com%2FSeeedDocument%2FGrove-CO2_Sensor%2Fmaster%2Fres%2FMH-Z16_CO2_datasheet_EN.pdf&usg=AOvVaw1hRnxhBsH78Ws-9m1OkEOj
[6]
P. Liu, S. K. Nguang, and A. Partridge, "Occupancy inference using pyroelectric infrared sensors through hidden Markov models," IEEE Sensors J.
[7]
T. Ekwevugbe, N. Brown, V. Pakka, and D. Fan, "Real-time building occupancy sensing using neural-network based sensor network," IEEE Int. Conf. Digital Ecosys. Tech., 2013
[8]
G. Ansanay-Alex, "Estimating occupancy using indoor carbon dioxide concentrations only in an office building: a method and qualitative assessment," 11th REHVA World Congr. Energy Eff., Smart Healthy Build. 2013
[9]
G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1, pp. 489--501, 2006.

Cited By

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  • (2025)Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT TechnologiesData and Metadata10.56294/dm20254674(467)Online publication date: 1-Jan-2025
  • (2024)AirVA - Indoor Air Quality Monitoring and Control with Occupants Alerting SystemInformation Systems and Technologies10.1007/978-3-031-45648-0_8(71-81)Online publication date: 14-Feb-2024
  • (2023)Monitoring Indoor Air Quality and Occupancy with an IoT System: Evaluation in a Classroom Environment2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10211974(1-6)Online publication date: 20-Jun-2023
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Published In

cover image ACM Other conferences
BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things
October 2018
217 pages
ISBN:9781450365192
DOI:10.1145/3289430
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 ACM 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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  • Deakin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2018

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Author Tags

  1. CO2 sensor
  2. Machine learning
  3. human occupancy estimation
  4. neural network

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • FDCT Macau

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BDIOT 2018

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Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

View all
  • (2025)Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT TechnologiesData and Metadata10.56294/dm20254674(467)Online publication date: 1-Jan-2025
  • (2024)AirVA - Indoor Air Quality Monitoring and Control with Occupants Alerting SystemInformation Systems and Technologies10.1007/978-3-031-45648-0_8(71-81)Online publication date: 14-Feb-2024
  • (2023)Monitoring Indoor Air Quality and Occupancy with an IoT System: Evaluation in a Classroom Environment2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10211974(1-6)Online publication date: 20-Jun-2023
  • (2023)Machine Learning Applied to Public Transportation by Bus: A Systematic Literature ReviewTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812311551892677:7(639-660)Online publication date: 17-Mar-2023
  • (2023)Artificial intelligence for improving public transport: a mapping studyPublic Transport10.1007/s12469-023-00334-716:1(99-158)Online publication date: 20-Nov-2023
  • (2022)Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor DeploymentSensors10.3390/s2210377022:10(3770)Online publication date: 16-May-2022
  • (2022)An Indoor Multi-Environment Sensor System Based on Intelligent Edge ComputingElectronics10.3390/electronics1201013712:1(137)Online publication date: 28-Dec-2022
  • (2022)Survey of Automated Fare Collection Solutions in Public TransportationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316160623:9(14248-14266)Online publication date: 1-Sep-2022
  • (2022)A Low-Cost Bidirectional People Counter Device for Assisting Social Distancing Monitoring for COVID-19Journal of Control, Automation and Electrical Systems10.1007/s40313-022-00916-z33:4(1148-1160)Online publication date: 11-Apr-2022

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