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Quantifying the sensing power of vehicle fleets
- Source :
- Proceedings of the National Academy of Sciences of the United States of America
- Publication Year :
- 2019
- Publisher :
- National Academy of Sciences, 2019.
-
Abstract
- Significance Attaching sensors to crowd-sourced vehicles could provide a cheap and accurate way to monitor air pollution, road quality, and other aspects of a city’s health. But in order for so-called drive-by sensing to be practically useful, the sensor-equipped vehicle fleet needs to have large “sensing power”—that is, it needs to cover a large fraction of a city’s area during a given reference period. Here, we provide an analytic description of the sensing power of taxi fleets, which agrees with empirical data from nine major cities. Our results show taxis’ sensing power is unexpectedly large—in Manhattan; just 10 random taxis cover one-third of street segments daily, which certifies that drive-by sensing can be readily implemented in the real world.<br />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.
- Subjects :
- 010504 meteorology & atmospheric sciences
Computer science
Taxis
Social Sciences
01 natural sciences
Sustainability Science
urban monitoring
Transport engineering
city science
11. Sustainability
0502 economics and business
Cities
Air quality index
Randomness
0105 earth and related environmental sciences
050210 logistics & transportation
Air Pollutants
Multidisciplinary
Zipf's law
05 social sciences
Random walk
Motor Vehicles
Traffic congestion
urban sustainability
13. Climate action
Physical Sciences
Key (cryptography)
mobile sensing
Environmental Sciences
Street network
Environmental Monitoring
Subjects
Details
- Language :
- English
- ISSN :
- 10916490 and 00278424
- Volume :
- 116
- Issue :
- 26
- Database :
- OpenAIRE
- Journal :
- Proceedings of the National Academy of Sciences of the United States of America
- Accession number :
- edsair.doi.dedup.....7d2e3d6dacbcdce97ee97dc58037de4e