Clocking Emissions model departs from the existing bus and tram’s lines and schedules.
Clocking Emissions model departs from the existing bus and tram’s lines and schedules.
We identify the segments of these lines that overlap in space and time. We select those which provide the larger spatial coverage and most frequent service, minimizing spatio-temporal gaps.
Based on the environmental literature we determine the optimal sample rates for accurate and reliable measurements of different environmental features.
We assign sensing devices for specific buses and trams to optimize the sampling rates, creating the most efficient drive-by sensing model.
Motorized transportation contributes to 9% of Amsterdam’s emissions,
with CO2 emissions reaching 360 kilotons. But emissions
greatly varies in time and space, and cities don't have tools to
produce data in such fine spatio-temporal granularity. Amsterdam
has. Deploying environmental sensing devices in only a fraction of
its buses and trams the city could implement the first real-time
environmental monitoring system.
Drive-by sensing approach opportunistically uses existing fleet to
deploy environmental sensors. In Urban Sensing we showed that only 30 taxis could cover half of the streets in
Manhattan at least once a day, proving the power of drive-by
sensing. In City Veins we demonstrated that sensing potential of cities varies based on
street network topology, number of vehicles, and their mobility
pattern.
In Clocking Emissions we propose a
model that quantifies the minimum number of Amsterdam's buses and
trams to monitor different environmental features, such as air
pollution, noise, and temperature. Clocking Emissions leverages
existing urban fleet and could be adapted in cities worldwide.
Clocking Emissions
is part of the
City Scanner
initiative, which includes the open-source sensing device
Flatburn.
Ariss, M., Wang, A., Sabouri, S., Duarte, F., & Ratti, C. (2024). Drive-by environmental sensing strategy to reach optimal and continuous spatio-temporal coverage using local transit network. Transportation Research Record.
The material on this website can be used freely in any publication
provided that
1. it is duly credited as a project by the MIT Senseable City Lab.
2. a PDF copy of the publication is sent to
senseable-press@mit.edu
Carlo Ratti Director
Fábio Duarte Project Lead
Mayar Ariss Research Lead
An Wang, Sadegh Sabouri Research
Jingrong Zhang Website
Wonyoung So Visualization