Turning meter transactions data into occupancy and payment behavioral information for on-street parking. (May 2017)

• Record Type: Journal Article
• Authors: Yang, Shuguan; Qian, Zhen (Sean)
• Is Part Of: Transportation research. Volume 78(2017)
• Page Start: 165
• Page End: 182
• Publication Date: 2017-05
• Subjects: Time-dependent parking occupancy -- Payment behavior -- Parking meter transactions; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.02.022
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We propose to mine parking transaction data to estimate time-varying parking occupancy in a cheap and effective way. A probabilistic payment model is proposed to simulate individual payment and parking behavior. Payment behavior can be learned from analyzing transaction data, and can provide insights for enforcement patrol. There exists an effective granularity, namely the highest spatial resolution for this model to perform reliably. Abstract: Over 95% of on-street paid parking stalls are managed by parking meters or kiosks. By analyzing meter transactions data, this paper provides a methodology to estimate on-street time-varying parking occupancy and understand payment behavior in an effective and inexpensive way. We propose a probabilistic payment model to simulate individual payment and parking behavior for each parker. Aggregating the payment/parking of all transactions leads to time-varying occupancy estimation. Two data sets are used to evaluate the methodology, parking spaces near Carnegie Mellon University (CMU) campus, and near the Civic Center in San Francisco. The proposed model generally provides reliable estimations of occupancies at a low error rate and substantially outperforms other naive models in the literature. From the results of the experiments we find that people generally tend to slightly underpay in CMU area, whereas for Civic Center area, payment behavior varies by time of day and day of week. For Fridays, people generally tend to overpay and stay longer in the mornings, compared to underpaying and parking for shorter durations in the late afternoons. Parkers' payment behavior, in general, is more variable and noisier around Civic Center than around CMU. Moreover, we explore the effective granularity, defined as the highest spatial resolution for this model to perform reliably. For CMU areas, the effective granularity is around 10–20 spaces for each block of streets, while it is 150–200 spaces for the Civic Center area due to more random parking behavior.