Details
| Original language | English |
|---|---|
| Article number | 104039 |
| Journal | Data in Brief |
| Volume | 25 |
| Early online date | 4 Jun 2019 |
| Publication status | Published - Aug 2019 |
Abstract
This dataset contains records of the measured on-street parking availability in San Francisco, obtained from the public API of the SFpark project. In 2011, the San Francisco Municipal Transportation Agency (SFMTA) started a project on smart parking, called SFpark, whose goal was the improvement of on-street parking management in San Francisco, mostly by means of demand-responsive price adjustments [1]. One of the key points of the project was the collection of information about on-street parking availability. To this aim, about 8,000 parking spaces were equipped with specific sensors in the asphalt, periodically broadcasting availability information. The SFpark project made available a public REST API, returning the number of free parking spaces and total number of provided parking spaces per road segment, for 5,314 parking spaces on 579 road segments in the pilot area. We collected parking availability data from 2013/06/13 until 2013/07/24, by querying this API at approximately 5-min intervals. As a result, we obtained in total about 7 million observations of parking availability on the road segments. These observations represent the first dataset we are providing. In addition, we simulated the achievable sensing coverage of on-street parking availability that could be achieved by a fleet of taxis, if they were equipped with sensors able to detect free parking spaces, like side-scanning ultrasonic sensors [3], or windshield-mounted cameras [4]. In particular, by exploiting real taxi trajectories in San Francisco from the Cabspotting project [5], we first computed the frequencies of taxi visits for each road segment covered by the SFpark sensors. Then, we downsampled the first dataset, in order to have a parking availability information for a road segment at a given time only in presence of a transit of a taxi on that segment at that time. This step was replicated for 5 different sizes of taxi fleets, namely 100, 200, 300, 400, and 486. Consequently, in total six datasets are available for further research in the field of on-street parking dynamics. All these datasets can be downloaded at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YLWCSU.
ASJC Scopus subject areas
Sustainable Development Goals
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In: Data in Brief, Vol. 25, 104039, 08.2019.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - On-street parking availaibilty data in San Francisco, from stationary sensors and high-mileage probe vehicles
AU - Di Martino, Sergio
AU - Bock, Urs Fabian
PY - 2019/8
Y1 - 2019/8
N2 - This dataset contains records of the measured on-street parking availability in San Francisco, obtained from the public API of the SFpark project. In 2011, the San Francisco Municipal Transportation Agency (SFMTA) started a project on smart parking, called SFpark, whose goal was the improvement of on-street parking management in San Francisco, mostly by means of demand-responsive price adjustments [1]. One of the key points of the project was the collection of information about on-street parking availability. To this aim, about 8,000 parking spaces were equipped with specific sensors in the asphalt, periodically broadcasting availability information. The SFpark project made available a public REST API, returning the number of free parking spaces and total number of provided parking spaces per road segment, for 5,314 parking spaces on 579 road segments in the pilot area. We collected parking availability data from 2013/06/13 until 2013/07/24, by querying this API at approximately 5-min intervals. As a result, we obtained in total about 7 million observations of parking availability on the road segments. These observations represent the first dataset we are providing. In addition, we simulated the achievable sensing coverage of on-street parking availability that could be achieved by a fleet of taxis, if they were equipped with sensors able to detect free parking spaces, like side-scanning ultrasonic sensors [3], or windshield-mounted cameras [4]. In particular, by exploiting real taxi trajectories in San Francisco from the Cabspotting project [5], we first computed the frequencies of taxi visits for each road segment covered by the SFpark sensors. Then, we downsampled the first dataset, in order to have a parking availability information for a road segment at a given time only in presence of a transit of a taxi on that segment at that time. This step was replicated for 5 different sizes of taxi fleets, namely 100, 200, 300, 400, and 486. Consequently, in total six datasets are available for further research in the field of on-street parking dynamics. All these datasets can be downloaded at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YLWCSU.
AB - This dataset contains records of the measured on-street parking availability in San Francisco, obtained from the public API of the SFpark project. In 2011, the San Francisco Municipal Transportation Agency (SFMTA) started a project on smart parking, called SFpark, whose goal was the improvement of on-street parking management in San Francisco, mostly by means of demand-responsive price adjustments [1]. One of the key points of the project was the collection of information about on-street parking availability. To this aim, about 8,000 parking spaces were equipped with specific sensors in the asphalt, periodically broadcasting availability information. The SFpark project made available a public REST API, returning the number of free parking spaces and total number of provided parking spaces per road segment, for 5,314 parking spaces on 579 road segments in the pilot area. We collected parking availability data from 2013/06/13 until 2013/07/24, by querying this API at approximately 5-min intervals. As a result, we obtained in total about 7 million observations of parking availability on the road segments. These observations represent the first dataset we are providing. In addition, we simulated the achievable sensing coverage of on-street parking availability that could be achieved by a fleet of taxis, if they were equipped with sensors able to detect free parking spaces, like side-scanning ultrasonic sensors [3], or windshield-mounted cameras [4]. In particular, by exploiting real taxi trajectories in San Francisco from the Cabspotting project [5], we first computed the frequencies of taxi visits for each road segment covered by the SFpark sensors. Then, we downsampled the first dataset, in order to have a parking availability information for a road segment at a given time only in presence of a transit of a taxi on that segment at that time. This step was replicated for 5 different sizes of taxi fleets, namely 100, 200, 300, 400, and 486. Consequently, in total six datasets are available for further research in the field of on-street parking dynamics. All these datasets can be downloaded at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YLWCSU.
UR - http://www.scopus.com/inward/record.url?scp=85067387579&partnerID=8YFLogxK
U2 - 10.1016/j.dib.2019.104039
DO - 10.1016/j.dib.2019.104039
M3 - Article
AN - SCOPUS:85067387579
VL - 25
JO - Data in Brief
JF - Data in Brief
M1 - 104039
ER -