No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Optimizing Performance Objectives for Projects of Congestion Pricing for Parking
Abstract
This paper draws upon the experience of the City of Los Angeles, California, with a demonstration project, LA Express Park. Since June 2012, the Los Angeles Department of Transportation has been using demand-based pricing to set the hourly parking meter rates in the city's downtown. Drivers were informed of the current parking rates and parking space availability for on-street parking spaces and for city-owned off-street facilities by a robust parking guidance system. For privately owned off-street facilities, the data were limited to location and current parking policies. The objective of the project was to reduce the time spent looking for available parking. Through the project's success, drivers had a better parking experience, and traffic congestion was reduced. For Los Angeles, this intelligent system for parking management was essentially revenue neutral. The paper argues that increasing revenue should not be the primary objective of projects for congestion pricing of parking. New parking meter technologies have shown greater potential for increasing revenue, and public acceptance of pricing projects is greater when revenue growth is not the goal of a project. Cities are more likely to achieve success and acceptance if the goal of these projects is to reduce the number of under- and overoccupied parking spaces and thereby make finding parking easier.
... LA's Expresspark faced similar problems. In 2015, 43 percent of vehicles in Expresspark were not paying, and at least 80 percent of this nonpayment resulted from placards (Ghent 2015). The nonpayment undermined the program. ...
The curb is a monopoly good: it is network infrastructure owned entirely by cities. Unlike most network infrastructure, however, it is not managed primarily by prices. Cities instead allocate the curb with a confusing patchwork of meters, time limits, permits, and—perhaps most notably—fines. Urban parking thus stands out among network infrastructure for its low level of payment, low level of quality, and large share of revenue derived from punishment. We develop an explanation for why this is so, which emphasizes the curb’s public ownership and low production costs. We then use both qualitative and quantitative methods to examine some of our theory’s implications. We draw on descriptive data from a variety of US cities, but focus primarily on the city of Los Angeles as a case study.
... The parameters and characteristics of these four pilot programs can be compared in Figure 1. (Pierce and Shoup 2013); ** (Ghent 2015); + (Ottosson, et al. 2013); ++ (Fichman 2015) These pilots have been studied with varying degrees of rigor. Seattle's pilot has been econometrically analyzed in the peer reviewed literature (Ottosson, et al. 2013), but was not truly "dynamic" as it had only one price change. ...
This paper analyzes a small, dynamically‐priced curb parking pilot that took place in Pittsburgh, PA from 2013 to 2015. Dynamic pricing of curb parking is a recent innovation – one which is designed to manage parking congestion through price manipulation in order to optimize occupancy and reduce traffic congestion. I find that prices declined during the pilot, revenues rose and occupancies moved towards target ranges set by program administrators. In the scant few studies of such pricing schemes, disagreement has arisen amongst scholars as to whether elasticities are appropriate to measure the effect of price changes on driver behavior. This paper demonstrates the use of the regression discontinuity statistical model in estimating the effects of price change effects on driver behavior. Regression discontinuity models suggest that prices had the intended effect on driver behavior during the Pittsburgh pilot, but such effects took a couple weeks to develop.
Car-centered policies and culture have long prevailed in São Paulo, critically impacting the urban space. While a third of daily trips are made by cars, more than half of usable floor area in real estate developments built since the mid-1980s was dedicated to parking. Contradicting historical priority given to cars, a number of recent policies centered in the city’s master plan, backed up by social trends, were put in place to address that problem. In this context, this chapter reviews the current status of parking in São Paulo in a comprehensive mobility perspective, recognizing achievements and setbacks in public policy and management.
Google Books preview: https://books.google.com.br/books?id=bdq_DwAAQBAJ&pg=PA35&source=gbs_toc_r&cad=3#v=onepage&q&f=false
This book constitutes the proceedings of the second International Conference on Smart Cities, Smart-CT 2017, held in Málaga, Spain, in June 2017.
The 16 papers presented in this volume were carefully reviewed and selected from 21 submissions. The topics covered include studies and tools to improve road traffic, energy consumption, logistics, frameworks to provide new services and take decisions in a holistic way, driving assistance, electric vehicles, public transport, and surveys on smart city concepts.
After years of technological advances, parking is still a problem for many people. It is a time-consuming task that we all have to face on a day-by-day basis and it is also a problem for cities, that see how traffic and pollution increases. There have been multiple attempts to find a partial or global technological solution to this problem, ranging from using different types of sensors or cameras for automatically detecting free spaces to collaborative apps that let users share relevant information. In this paper, we give an overview of the methods developed so far, showing their main features, differences, pros, and cons, as well as other factors that may contribute to the success or failure of new proposals that will come in the future.
Dynamic parking pricing varies the cost of parking over location and time to achieve target occupancy rates. Such pricing seeks more efficient parking space use, reduced cruising for parking, and improved satisfaction with the parking experience. Evaluation practice in this field is developing as communities implement new programs. With four case studies, the programs and their evaluations are explained, and a comprehensive evaluation framework is developed. The framework differentiates between possible theory failure and program failure and distinguishes between direct, intermediate, goal, and program support outcomes. The review reveals that some metrics, such as congestion reduction, are not supported by theory or evaluation results. Other logical metrics, such as the efficiency of parking use as indicated by a ratio of district activity per parking space, are missing.
With the rapid urbanization and fast increase of car ownership, parking problem has increasingly turned to be a bottleneck for the sustainable development of urban transportation system. In order to improve the parking management level, a urban parking guidance system based on artificial intelligence is proposed. In this paper, the composition and structure of the urban parking guidance system is analyzed, the parking place control terminal hardware design and implementation methods based on the microcon troller SX52 are supplied, and the principles and processes of the control terminal software are introduced, then the principles and methods of protocol dispose in the control terminal are described based on the analysis of the TCP/IP protocol, the principles of work of guidance server are explained. The characteristics of urban parking guidance system based on artificial intelligence are as follows: the design idea is novel, the cost is low, the utility is strong, and it is easy to spread. Therefore, it can be used as an auxiliary system of urban intelligent transportation system, which plays an active role in relieving the traffic congestion and improving the traffic environment.
On-street parking, just as any publicly owned utility, is used inefficiently if access is free or priced very far from market rates. This paper introduces a novel demand management solution: using data from dedicated occupancy sensors an iteration scheme updates parking rates to better match demand. The new rates encourage parkers to avoid peak hours and peak locations and reduce congestion and underuse. The solution is deliberately simple so that it is easy to understand, easily seen to be fair and leads to parking policies that are easy to remember and act upon. We study the convergence properties of the iteration scheme and prove that it converges to a reasonable distribution for a very large class of models. The algorithm is in use to change parking rates in over 6000 spaces in downtown Los Angeles since June 2012 as part of the LA Express Park project. Initial results are encouraging with a reduction of congestion and underuse, while in more locations rates were decreased than increased.