Turning meter transactions data into occupancy and payment behavioral information for on-street parking

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 timevarying 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 timevarying 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.

A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources

“…Parkers are most likely to park in a block and purchase a ticket from any of the affiliated meters near this block. Locations of the 97 parking meters and centroids of the 39 street blocks are plotted in Fig 3. Previously, we have shown that true parking occupancies can be approximated using meter transaction data with a few days of occupancy ground truth collected manually (Yang and Qian, 2017). Since the main focus of this case study is evaluating the performance of the proposed model, we convert meter transactions to parking occupancies in a straight forward wa: we make the assumption that all vehicles start parking right after the payment is made and leave at the moment parking session expires, and there is no unpaid parking.…”

“…In the literature, most studies "estimate" parking occupancies at the current moment (Boyles et al, 2015;Yang and Qian, 2017;Alajali et al, 2017), as opposed to "predicting" (or "forecasting") parking occupancies in the near future. There are generally two ways to predict short-term parking occupancy: (1) Model individual drivers' stochastic arrival and departure behaviors in a microscopic manner (Caicedo et al, 2012;Boyles et al, 2015;Caliskan et al, 2007).…”

“…This clearly offers cost advantage: since 95% of the on-street paid parking are managed by meters, a prediction model based on transactions is more adaptive and cost efficient than relying on parking sensors. In our previous study, we have shown that the estimation of parking occupancy based on parking ticket transactions can be calibrated to a satisfying level given a small amount of ground truth data (Yang and Qian, 2017).…”

“…• We propose a deep neural network based occupancy prediction model that utilizes multiple data sources, including parking meter transactions, traffic conditions, and weather conditions. While meter transactions data alone have been previously explored to estimate current parking occupancy (SFMTA, 2014;Yang and Qian, 2017), this study is the first to use parking meter transactions with other data sources to forecast (predict) short-term on-street parking occupancies.…”

Optimizing curbside parking resources subject to congestion constraints

“…Recent research has observed, however, that transaction data can be used to estimate parking occupancy and, in consequence, used to estimate resource performance [Yang andQian, 2017, Dowling et al, 2017]. The distinction that occupancy below 100% results in congestion has recently been noted by [Millard-Ball et al, 2014] in their own analysis of the SFPark pilot study parallel to [Pierce and Shoup, 2013], however the authors of [Millard-Ball et al, 2014] view block-face parking as a Bernoulli random variable, between being full or not.…”