Getting the Prices Right

Pierce, Gregory; Shoup, Donald

doi:10.1080/01944363.2013.787307

Cited by 207 publications

(78 citation statements)

References 12 publications

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“…A study by Millard-Ball, Weinberger & Hampshire (2014) found that SFpark moved occupancy rates closer to the target range and reduced cruising. Other studies by Pierce & Shoup, (2013a) and Millard-Ball, Weinberger & Hampshire (2013) examined parking price elasticities based on SFpark. However, studies on parking still make up a small portion of the transportation literature and many gaps remain.…”

Section: Background and Literaturementioning

confidence: 99%

“…Finally, as more smart-parking programs are implemented in growing urban centers, there is a shortage of rigorous empirical analysis that these programs will accomplish their goals. Though, early analyses of SFpark suggest SFpark has been successful thus far in achieving some of their target outcomes, particularly parking availability (e.g., Millard-Ball, Weinberger & Hampshire, 2014;Pierce & Shoup, 2013a;SFMTA, 2014), more empirical research is needed to discern other outcomes of interest for policymakers, including impacts on public transit and the effects of new meter installations.…”

Section: Background and Literaturementioning

confidence: 99%

“…Given the dearth of parking data, different aspects of the data from SFpark have been used in more recent literature to address important questions regarding different aspects of parking policies, (e.g., Millard-Ball, Weinberger & Hampshire, 2013;Millard-Ball, Weinberger & Hampshire, 2014;Pierce & Shoup, 2013a;Pierce & Shoup, 2013b;SFMTA, 2014). We supplement the SFpark data set with a rich and extremely detailed panel data set on transit buses that includes information at the bus shift-stop level.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Demand-Responsive Parking on Transit Usage and Congestion: Evidence From Sfpark

Ngo¹,

Krishnamurth²

2017

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Type of Report and Period CoveredFinal report 14. Sponsoring Agency Code Supplementary Notes AbstractParking is a serious issue in many urban areas, especially those experiencing rapid population growth. To address this problem, some cities have implemented demand-responsive pricing programs, where parking prices vary depending on the occupancy rate in a previous period. Yet, few empirical studies have rigorously evaluated these programs, though they have the potential to improve turnover and much needed parking availability in growing cities with limited infrastructure. In this study, we investigate the impacts of SFpark, a demand-responsive pricing parking program in San Francisco that began in 2011. We focus on metered, on-street parking and exploit the timing of SFpark as a natural experiment. We observe effects on three important aspects of urban transportation: parking availability, transit bus ridership and congestion. The timing of this program is plausibly exogenous to factors that affect these outcomes of interest since it is based on bureaucratic decision-making, so endogeneity is less of a concern. We generate a novel panel data set by merging detailed parking occupancy and meter rate data with micro-level transit bus ridership information at the bus stop-bus shift level. Results show that SFpark led to more areas meeting the target occupancy range of 60-80%. We also find heterogeneous effects on transit ridership and show an increase in meter rates is associated with a modest increase in ridership, suggesting people are substituting between transit and non-transit travel and that meter rates factor into mode choice. Finally, we find SFpark reduced congestion, specifically decreasing lane occupancy and increasing vehicle speed. These results have important implications for transportation policy as cities continue to expand and implement demand-responsive pricing programs globally.

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Section: Background and Literaturementioning

confidence: 99%

Section: Background and Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effects of Demand-Responsive Parking on Transit Usage and Congestion: Evidence From Sfpark

Ngo¹,

Krishnamurth²

2017

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“…More recently the city of San Francisco has introduced what is probably the first large scale scheme that applies fees that vary according to actual demand. For a comprehensive description and evaluation of this scheme see Pierce and Shoup (2013). Madrid is following the San Francisco example; from July 2014 the onstreet tariff will be related not only to the type of vehicle (see point 2 in this section) but also to the actual demand.…”

Section: Differentiated Parking Tariffsmentioning

confidence: 99%

Urban parking policy in Europe: A conceptualization of past and possible future trends

Mingardo

Wee

Rye

2015

Transportation Research Part A: Policy and Practice

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In the last two decades parking has increasingly gained importance in urban planning. Despite the growing number of papers published in recent years, an overall conceptualization of parking policy is still missing. Previous attempts (Shoup, 2005;Litman, 2006;and Barter, 2010) focus mainly on the North American planning experience. We try to bridge this gap analysing the evolution of parking policy in Europe. In this paper we first present the key aspects of parking policy, and describe their generic evolution. Next we suggest a new approach for parking policy making. We conclude by discussing some of the major challenges policy makers will face in the near future regarding parking in urban areas.

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“…There are "hard" and "soft" strategies [11]. The "hard" strategies, also called hard policy measures, use a penalty to enforce travel behavior changes [12], including road pricing [13], toll roads [14][15][16], and parking pricing [17]. The "soft" measures include two categories.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

Zhu

Gonder

Lin

2017

Journal of Advanced Transportation

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With the development of and advances in smartphones and global positioning system (GPS) devices, travelers' long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-toHome and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers' activity behavior features for an extended period. After that, the variability pattern of an individual's travel behavior is used for estimating the individual's social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment's result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.

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Getting the Prices Right

Cited by 207 publications

References 12 publications

The Effects of Demand-Responsive Parking on Transit Usage and Congestion: Evidence From Sfpark

The Effects of Demand-Responsive Parking on Transit Usage and Congestion: Evidence From Sfpark

Urban parking policy in Europe: A conceptualization of past and possible future trends

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

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