Measurement of Air Pollution by Measurement of Traffic Density

Leon Rothkrantz

doi:10.1109/SCSP54748.2022.9792549

Measurement of Air Pollution by Measurement of Traffic Density

^*Corresponding author for this work

Interactive Intelligence

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Abstract

The areas of many cities in the Netherlands are covered by a network of stationary sensors, measuring special components of air pollution such as CO2, NO2, PM2.5 and PM10. The pollution with fine dust along roads, surrounding and crossing the city is primarily related to traffic density. To measure traffic density, we used a license plate recognizer based on a special Neural Network Neocognitron, analyzing the video footage of surveillance cameras along the roads. We also studied the onset and offset of traffic density to predict traffic density, using the first recorded sparse traffic data. In cooperation with MIT Senseable City Lab the Technical University of Delft has developed special mobile, low cost sensors to measure air pollution. These mobile sensors are integrated with stationary sensors to a heterogeneous sensor network and enable measurement of air pollution out of the reach of the stationary sensor network..

Original language	English
Title of host publication	2022 Smart Cities Symposium Prague, SCSP 2022
Editors	Jiri Ruzicka
Publisher	IEEE
Number of pages	8
ISBN (Electronic)	978-1-6654-7923-3
DOIs	https://doi.org/10.1109/SCSP54748.2022.9792549
Publication status	Published - 2022
Event	2022 Smart Cities Symposium Prague, SCSP 2022 - Prague, Czech Republic Duration: 26 May 2022 → 27 May 2022

Publication series

Name	2022 Smart Cities Symposium Prague, SCSP 2022

Conference

Conference	2022 Smart Cities Symposium Prague, SCSP 2022
Country/Territory	Czech Republic
City	Prague
Period	26/05/22 → 27/05/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Air Quality Monitoring
Distributed Sensor Network
License Plate Recognizer
Traffic Density

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SCSP54748.2022.9792549

Measurement_of_Air_Pollution_by_Measurement_of_Traffic_DensityFinal published version, 1.89 MB

Cite this

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title = "Measurement of Air Pollution by Measurement of Traffic Density",

abstract = "The areas of many cities in the Netherlands are covered by a network of stationary sensors, measuring special components of air pollution such as CO2, NO2, PM2.5 and PM10. The pollution with fine dust along roads, surrounding and crossing the city is primarily related to traffic density. To measure traffic density, we used a license plate recognizer based on a special Neural Network Neocognitron, analyzing the video footage of surveillance cameras along the roads. We also studied the onset and offset of traffic density to predict traffic density, using the first recorded sparse traffic data. In cooperation with MIT Senseable City Lab the Technical University of Delft has developed special mobile, low cost sensors to measure air pollution. These mobile sensors are integrated with stationary sensors to a heterogeneous sensor network and enable measurement of air pollution out of the reach of the stationary sensor network.. ",

keywords = "Air Quality Monitoring, Distributed Sensor Network, License Plate Recognizer, Traffic Density",

author = "Leon Rothkrantz",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2022 Smart Cities Symposium Prague, SCSP 2022 ; Conference date: 26-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1109/SCSP54748.2022.9792549",

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AU - Rothkrantz, Leon

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2022

Y1 - 2022

N2 - The areas of many cities in the Netherlands are covered by a network of stationary sensors, measuring special components of air pollution such as CO2, NO2, PM2.5 and PM10. The pollution with fine dust along roads, surrounding and crossing the city is primarily related to traffic density. To measure traffic density, we used a license plate recognizer based on a special Neural Network Neocognitron, analyzing the video footage of surveillance cameras along the roads. We also studied the onset and offset of traffic density to predict traffic density, using the first recorded sparse traffic data. In cooperation with MIT Senseable City Lab the Technical University of Delft has developed special mobile, low cost sensors to measure air pollution. These mobile sensors are integrated with stationary sensors to a heterogeneous sensor network and enable measurement of air pollution out of the reach of the stationary sensor network..

AB - The areas of many cities in the Netherlands are covered by a network of stationary sensors, measuring special components of air pollution such as CO2, NO2, PM2.5 and PM10. The pollution with fine dust along roads, surrounding and crossing the city is primarily related to traffic density. To measure traffic density, we used a license plate recognizer based on a special Neural Network Neocognitron, analyzing the video footage of surveillance cameras along the roads. We also studied the onset and offset of traffic density to predict traffic density, using the first recorded sparse traffic data. In cooperation with MIT Senseable City Lab the Technical University of Delft has developed special mobile, low cost sensors to measure air pollution. These mobile sensors are integrated with stationary sensors to a heterogeneous sensor network and enable measurement of air pollution out of the reach of the stationary sensor network..

KW - Air Quality Monitoring

KW - Distributed Sensor Network

KW - License Plate Recognizer

KW - Traffic Density

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U2 - 10.1109/SCSP54748.2022.9792549

DO - 10.1109/SCSP54748.2022.9792549

M3 - Conference contribution

AN - SCOPUS:85133443089

T3 - 2022 Smart Cities Symposium Prague, SCSP 2022

BT - 2022 Smart Cities Symposium Prague, SCSP 2022

A2 - Ruzicka, Jiri

PB - IEEE

T2 - 2022 Smart Cities Symposium Prague, SCSP 2022

Y2 - 26 May 2022 through 27 May 2022

ER -

Measurement of Air Pollution by Measurement of Traffic Density

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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