Drive-by Environmental Sensing Strategy to Reach Optimal and Continuous Spatio-Temporal Coverage Using Local Transit Network
Name
ariss-et-al-2024-drive-by-environmental-sensing-strategy-to-reach-optimal-and-continuous-spatio-temporal-coverage-using.pdf
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9.36 MB
Format
Adobe PDF
Checksum (MD5)
6217d9b2bae655183ed2023f96ad1501
Author(s)
Ariss, Mayar
Wang, An
Sabouri, Sadegh
Duarte, Fabio
Ratti, Carlo
Date Issued
May 23, 2024
Journal
Transportation Research Record
Publisher
SAGE Publications
Citation
Ariss, Mayar, Wang, An, Sabouri, Sadegh, Duarte, Fabio and Ratti, Carlo. 2024. "Drive-by Environmental Sensing Strategy to Reach Optimal and Continuous Spatio-Temporal Coverage Using Local Transit Network." Transportation Research Record, 2678 (11).
Version
Final published version
Abstract
Monitoring environmental features, such as air pollution, carbon dioxide emissions, noise, and heat, gives cities key data-driven insights to advise sustainable policies and city design. However, given the high variability of the environmental data, achieving good spatio-temporal resolution and coverage remains a major challenge. Even in well-monitored cities, such as Amsterdam, environmental sensors are usually placed in very few fixed locations, implying limited spatial coverage and an inability to adapt to changes in the urban environment. As cities evolve, they experience shifts in pollution sources, and fixed sensors might not adequately capture these changes without a costly and time-consuming reconfiguration process. To monitor the environmental qualities of Amsterdam’s roads, we present a “drive-by” sensing solution for a structured network of vehicles, meaning that sensors are designed to be deployed on buses and tramways, the trajectories and schedules of which are known. We propose a deployment strategy that combines the available fleets to reach optimal spatio-temporal coverage for different environmental features. For example, by optimizing the deployment of sensors on public transit vehicles, our proposal significantly enhances the monitoring of pollution-sensitive areas in Amsterdam. Depending on the desired spatio-temporal granularity and noting that one vehicle only hosts one sensor, the required number of sensors to be deployed on the structured network varies between 43 and 142, with the latter achieving the finest possible resolution.
MIT Department
Senseable City Laboratory
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
10.1177/03611981241247051
