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Counting Passengers in Public Buses by Sensing Carbon Dioxide Concentration: System Design and Implementation

Authors:

Tengyue Li,

Simon FongAuthors Info & Claims

BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things

Pages 218 - 221

https://doi.org/10.1145/3289430.3289462

Published: 24 October 2018 Publication History

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Abstract

There are a lot of technology for crowd sensing. Such as ultrasonic and pyroelectric infrared. These techniques have some shortcomings that they could not use on a large scale. Our purpose is to develop a sensor base carbon dioxide sensor group technology. Technology is used to evaluate the number of public transport and flow. The first is to set a carbon dioxide sensor and data collection. Then through the extreme learning machine (ELM) and Liner regression algorithm analysis data. We try to combine existing bus real-time information platform, refer to the number of real-time bus function. According to the traffic movements to improve bus stops and routes. To help with city planning.

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Cited By

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Monti LTse RTang SMirri SDelnevo GManiezzo VSalomoni P(2022)Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus ContextSensors10.3390/s2210369222:10(3692)Online publication date: 12-May-2022
https://doi.org/10.3390/s22103692
Pištek PHarvan SValicek M(2022)Automated People Counting in Public TransportIndustry 4.0 Challenges in Smart Cities10.1007/978-3-030-92968-8_5(75-93)Online publication date: 21-Jun-2022
https://doi.org/10.1007/978-3-030-92968-8_5
Ceccarini CMirri SSalomoni PPrandi C(2021)On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart CampusMobile Networks and Applications10.1007/s11036-021-01742-426:5(2066-2075)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s11036-021-01742-4

Index Terms

Counting Passengers in Public Buses by Sensing Carbon Dioxide Concentration: System Design and Implementation
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods

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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

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Published: 24 October 2018

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FDCT Macau

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BDIOT 2018

BDIOT 2018: 2018 2nd International Conference on Big Data and Internet of Things

October 24 - 26, 2018

Beijing, China

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Overall Acceptance Rate 75 of 136 submissions, 55%

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Monti LTse RTang SMirri SDelnevo GManiezzo VSalomoni P(2022)Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus ContextSensors10.3390/s2210369222:10(3692)Online publication date: 12-May-2022
https://doi.org/10.3390/s22103692
Pištek PHarvan SValicek M(2022)Automated People Counting in Public TransportIndustry 4.0 Challenges in Smart Cities10.1007/978-3-030-92968-8_5(75-93)Online publication date: 21-Jun-2022
https://doi.org/10.1007/978-3-030-92968-8_5
Ceccarini CMirri SSalomoni PPrandi C(2021)On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart CampusMobile Networks and Applications10.1007/s11036-021-01742-426:5(2066-2075)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s11036-021-01742-4
Monti LMirri SPrandi CSalomoni PBujari AManzoni PForster AMota EGaggi O(2019)Smart Sensing Supporting Energy-Efficient BuildingsProceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good10.1145/3342428.3342691(171-176)Online publication date: 25-Sep-2019
https://dl.acm.org/doi/10.1145/3342428.3342691

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Bus Crowdedness Sensing System Based on Carbon Dioxide Concentration

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

Impacts on Carbon Dioxide Emissions from the Replacement of Conventional Buses by Electric Buses

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