The sensitivity of on-street parking demand in response to price changes: A case study in Seattle, WA

Ottosson, Dadi Baldur; Chen, Cynthia; Wang, Tingting; Lin, Haiyun

doi:10.1016/j.tranpol.2012.11.013

Cited by 86 publications

(42 citation statements)

References 11 publications

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“…Quite a few methods have been developed for estimating current parking occupancies and parking demand, Ottosson et al (2013) discovered that parking occupancies estimated directly from aggregating transactions (namely every parker leaves exactly at the time ticket expiration) are generally lower than their actual observations, and such discrepancies are substantial in some cases. Similar conclusion was reached in a study comparing data from parking kiosks and actual counts for major paid parking blocks in Seattle (Seattle, 2011b,a), where the estimation error varies from 6% to 55%, and occupancies derived from transactions always being lower than the counted data.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Yang

et al. 2019

Transportation Research Part C: Emerging Technologies

168

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A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture the temporal features. In addition, the model is capable of taking multiple heterogeneously structured traffic data sources as input, such as parking meter transactions, traffic speed, and weather conditions. The model performance is evaluated through a case study in Pittsburgh downtown area. The proposed model outperforms other baseline methods including multi-layer LSTM and Lasso with an average testing MAPE of 10.6% when predicting block-level parking occupancies 30 minutes in advance. The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations. We found that incorporating traffic speed and weather information can significantly improve the prediction performance. Weather data is particularly useful for improving predicting accuracy in recreational areas.

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Section: Literature Reviewmentioning

confidence: 99%

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

Yang

et al. 2019

Transportation Research Part C: Emerging Technologies

168

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“…Ottosson et al (2013) has shown the potential to vary parking prices by time of day within a geographic area and Caicedo (2012) to have pay by the minute parking. In addition, the growing range of mobility services such as car sharing mean that incorporating car share into residential parking standards is now an important issue (Engel-Yan andPassmore, 2013 andShaheen et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

Parking Policy

Marsden¹

2014

Transport and Sustainability

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PurposeThis Chapter provides an overview of parking policy. The Chapter takes as its start point that parking is first and foremost a land-use issue. It looks at the conflicts and synergies between parking policy for the purposes of traffic management and parking policy to support various key land-uses and policy objectives. Methodology/approachThis Chapter discusses the main practice oriented viewpoints on what is meant by parking policy and what it aims to achieve. It then provides a state-of-art review of the evidence base on residential, retail and workplace parking as the three key parking destinations before drawing together these findings. FindingsThe reviews reveal that there has been an over emphasis on the importance of the impact of parking pricing to trip frequency, destination and walk times in the literature. Much greater emphasis should be put in to establishing the extent to which parking restraint supports the economy, the environment and social equity. Only then will we be able to develop a consistent policy framing within which good parking management policy can play out and make a long-term difference to travel patterns and the quality of life in our cities. Practical implicationsIf parking policy is to work well as part of an overall package of demand restraint, it needs to be applied in conjunction with and understanding of land-use planning. In transport terms this means connecting parking policy to non-car accessibility. If the overarching land-use and transport accessibility policies are right then there is a greater possibility for other parking management policies to be effectively applied and integrated in broader transport strategies. Originality/value of paperThe paper suggests that without a clear understanding of the broader objectives that parking policy supports it will not be possible to design effective parking management approaches.

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“…The target occupancy rate was chosen to be 71-86% in this experiment. Ottosson et al (2013) provide the main findings of the experiment. They obtain price elasticity of parking demand by time of the day and neighborhood characteristics.…”

Section: Toward Temporal-spatial Pricing Of Parkingmentioning

confidence: 99%