Details
| Original language | English |
|---|---|
| Pages (from-to) | 207-214 |
| Number of pages | 8 |
| Journal | Transportation Research Procedia |
| Volume | 19 |
| Publication status | Published - 28 Dec 2016 |
Abstract
Parking search traffic causes increased travel times and air pollution in many cities. Real-time parking availability maps are expected to help drivers to find a parking space faster and thus to reduce parking search traffic. A possibility to create such maps is the aggregation of parking availability information from crowdsourcing solutions like probe vehicles and mobile phone applications. Since these sources cannot sense the whole city at the same time, estimation methods are necessary to fill uncovered areas. This paper investigates the estimation of parking availability based on spatial methods using sensor data from San Francisco. First, spatial similarities in parking availability are evaluated for different aspects like time of day and number of parking spaces depending on the distance to reveal the parking characteristics. Then, interpolation methods are examined to estimate parking availability in unobserved road segments. Results show that relevant similarities mainly exist for short distances of less than hundred meters. Their similarity values are lower than the temporal similarity even for multiple hours of time gap. Nevertheless, spatial information is useful to interpolate parking availability. Investigated interpolation methods show significantly better results than random guess. Inverse distance weighting method outperforms a simple averaging by up to 5%.
Keywords
- crowd-sensing, parking availability estimation, parking statistics, similarity measures, spatial data analysis, spatial interpolation
ASJC Scopus subject areas
- Social Sciences(all)
- Transportation
Sustainable Development Goals
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In: Transportation Research Procedia, Vol. 19, 28.12.2016, p. 207-214.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Improving Parking Availability Maps using Information from Nearby Roads
AU - Bock, Fabian
AU - Sester, Monika
N1 - Funding information: This research has been supported by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931). The focus of the SocialCars Research Training Group is on significantly improving the city?s future road traffic, through cooperative approaches. This support is gratefully acknowledged.
PY - 2016/12/28
Y1 - 2016/12/28
N2 - Parking search traffic causes increased travel times and air pollution in many cities. Real-time parking availability maps are expected to help drivers to find a parking space faster and thus to reduce parking search traffic. A possibility to create such maps is the aggregation of parking availability information from crowdsourcing solutions like probe vehicles and mobile phone applications. Since these sources cannot sense the whole city at the same time, estimation methods are necessary to fill uncovered areas. This paper investigates the estimation of parking availability based on spatial methods using sensor data from San Francisco. First, spatial similarities in parking availability are evaluated for different aspects like time of day and number of parking spaces depending on the distance to reveal the parking characteristics. Then, interpolation methods are examined to estimate parking availability in unobserved road segments. Results show that relevant similarities mainly exist for short distances of less than hundred meters. Their similarity values are lower than the temporal similarity even for multiple hours of time gap. Nevertheless, spatial information is useful to interpolate parking availability. Investigated interpolation methods show significantly better results than random guess. Inverse distance weighting method outperforms a simple averaging by up to 5%.
AB - Parking search traffic causes increased travel times and air pollution in many cities. Real-time parking availability maps are expected to help drivers to find a parking space faster and thus to reduce parking search traffic. A possibility to create such maps is the aggregation of parking availability information from crowdsourcing solutions like probe vehicles and mobile phone applications. Since these sources cannot sense the whole city at the same time, estimation methods are necessary to fill uncovered areas. This paper investigates the estimation of parking availability based on spatial methods using sensor data from San Francisco. First, spatial similarities in parking availability are evaluated for different aspects like time of day and number of parking spaces depending on the distance to reveal the parking characteristics. Then, interpolation methods are examined to estimate parking availability in unobserved road segments. Results show that relevant similarities mainly exist for short distances of less than hundred meters. Their similarity values are lower than the temporal similarity even for multiple hours of time gap. Nevertheless, spatial information is useful to interpolate parking availability. Investigated interpolation methods show significantly better results than random guess. Inverse distance weighting method outperforms a simple averaging by up to 5%.
KW - crowd-sensing
KW - parking availability estimation
KW - parking statistics
KW - similarity measures
KW - spatial data analysis
KW - spatial interpolation
UR - http://www.scopus.com/inward/record.url?scp=85019105798&partnerID=8YFLogxK
U2 - 10.1016/j.trpro.2016.12.081
DO - 10.1016/j.trpro.2016.12.081
M3 - Article
AN - SCOPUS:85019105798
VL - 19
SP - 207
EP - 214
JO - Transportation Research Procedia
JF - Transportation Research Procedia
SN - 2352-1457
ER -