Quantifying the sensing power of vehicle fleets

Sensors can measure air quality, traffic congestion, and other aspects of urban environments. The fine-grained diagnostic information they provide could help urban managers to monitor a city’s health. Recently, a “drive-by” paradigm has been proposed in which sensors are deployed on third-party vehi...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2019-06, Vol.116 (26), p.12752-12757
Main Authors: O’Keeffe, Kevin P., Anjomshoaa, Amin, Strogatz, Steven H., Santi, Paolo, Ratti, Carlo
Format: Article
Language:English
Subjects:
Air Pollutants - analysis
Air quality
Air quality measurements
Cities
Detection
Diagnostic systems
Empirical analysis
Environmental Monitoring - instrumentation
Environmental Monitoring - methods
Environmental Monitoring - standards
Motor Vehicles
Physical Sciences
Questions
Random walk
Route selection
Sensors
Smart cities
Social Sciences
Taxicabs
Traffic congestion
Urban environments
Vehicles
Zipf's Law
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Summary:Sensors can measure air quality, traffic congestion, and other aspects of urban environments. The fine-grained diagnostic information they provide could help urban managers to monitor a city’s health. Recently, a “drive-by” paradigm has been proposed in which sensors are deployed on third-party vehicles, enabling wide coverage at low cost. Research on drive-by sensing has mostly focused on sensor engineering, but a key question remains unexplored: How many vehicles would be required to adequately scan a city? Here, we address this question by analyzing the sensing power of a taxi fleet. Taxis, being numerous in cities, are natural hosts for the sensors. Using a ball-in-bin model in tandem with a simple model of taxi movements, we analytically determine the fraction of a city’s street network sensed by a fleet of taxis during a day. Our results agree with taxi data obtained from nine major cities and reveal that a remarkably small number of taxis can scan a large number of streets. This finding appears to be universal, indicating its applicability to cities beyond those analyzed here. Moreover, because taxis’ motion combines randomness and regularity (passengers’ destinations being random, but the routes to them being deterministic), the spreading properties of taxi fleets are unusual; in stark contrast to random walks, the stationary densities of our taxi model obey Zipf’s law, consistent with empirical taxi data. Our results have direct utility for town councilors, smart-city designers, and other urban decision makers.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1821667116