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 -