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.

Press Release

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1. it is duly credited as a project by the MIT Senseable City Lab.
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In collaboration with


Carlo Ratti Director

Fábio Duarte Project Lead

Mayar Ariss Research Lead

An Wang, Sadegh Sabouri Research

Jingrong Zhang Website

Wonyoung So Visualization

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