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Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment
Abstract
The city of San Francisco is undertaking a large-scale controlled parking pricing experiment. San Francisco has adopted a performance goal of 60–80% occupancy for its metered parking. The goal represents an heuristic performance measure intended to reduce double parking and cruising for parking, and improve the driver experience; it follows a wave of academic and policy literature that calls for adjusting on-street parking prices to achieve similar occupancy targets. In this paper, we evaluate the relationship between occupancy rules and metrics of direct policy interest, such as the probability of finding a parking space and the amount of cruising. We show how cruising and arrival rates can be simulated or estimated from hourly occupancy data. Further, we evaluate the impacts of the first two years of the San Francisco program, and conclude that rate changes have helped achieve the City’s occupancy goal and reduced cruising by 50%.
... In response to these negative externalities, city officials are deploying parking information and pricing systems. Parking occupancy information and market based parking pricing strategies reduce the time to find a parking space and pollution ( [6,14,30,34]). These strategies require cities to install both parking occupancy sensors and automated payment stations. ...
... Monitoring on-street parking is a much more difficult task than monitoring garage parking, which is relatively easy to compute through gate counts of entering and exiting vehicles. And cities often price on-street parking significantly lower than off-street parking, leading drivers rationally to choose to cruise [30]. ...
... We use the same occupancy dataset as in Millard-Ball et al. [30]. We collected the occupancy data set by developing a web application that interacts with the SFpark API. ...
The excessive search for parking, known as cruising, generates pollution and congestion. Cities are looking for approaches that will reduce the negative impact associated with searching for parking. However, adequately measuring the number of vehicles in search of parking is difficult and requires sensing technologies. In this paper, we develop an approach that eliminates the need for sensing technology by using parking meter payment transactions to estimate parking occupancy and the number of cars searching for parking. The estimation scheme is based on Particle Markov Chain Monte Carlo. We validate the performance of the Particle Markov Chain Monte Carlo approach using data simulated from a GI/GI/s queue. We show that the approach generates asymptotically unbiased Bayesian estimates of the parking occupancy and underlying model parameters such as arrival rates, average parking time, and the payment compliance rate. Finally, we estimate parking occupancy and cruising using parking meter data from SFpark, a large scale parking experiment and subsequently, compare the Particle Markov Chain Monte Carlo parking occupancy estimates against the ground truth data from the parking sensors. Our approach is easily replicated and scalable given that it only requires using data that cities already possess, namely historical parking payment transactions.
... For the parking search without additional information, simulator studies investigated the drivers' strategies to find parking [20]. To estimate the amount of parking search based on real-world sensor data, Millard-Ball et al. [21] simulated a random drive through the road network and measured the search length until the first free spot was found. In [19], authors simulated the potential savings of PGIs, but limiting to off-street parking. ...
... This is a quite surprising number, far from common experience in many other dense urban scenario around the world. From these numbers, it seems that the areas covered by the SFpark project are not so problematic, in terms of lack of free parking spaces, as also found by Millard-Ball et al. [21]. Following this observation, in order to have more common situation to compare with, we produced also an altered version of this dataset, described in the following section. ...
... There are some assumptions to state, to measure their impact of the different guidance strategies. Following the parking search simulation in [21], we assume that a driver could take any road segment connected to the current one, while respecting regulations of one-way roads (in contrast to [21]), following the PGI driving direction. ...
... We are also interested in the effects on traffic flow in view of the explicit goal of SFpark to reduce congestion and improve traffic flow. While prior studies (e.g., Millard-Ball et al. (2014); Chatman and Manville (2014)) have shown that SFpark is associated with decreases in one aspect of congestion, cruising for parking, there has been no similarly rigorous evaluation regarding the effects upon overall traffic flow. We use data from the SFpark pilot evaluation project ("SFpark pilot project") for on-street parking, which includes hourly data on parking occupancy, metered rates and measures of daily traffic flow, between July 2011 and July 2013. ...
... Our study contributes to the literature in several different ways: it provides the first well-identified estimate of the changes in transit usage attributable to parking management using very detailed transit micro-data, and contributes to the sparse empirical literature regarding the effects of pricing parking (van Ommeren et al. (2011);Millard-Ball et al. (2014), Millard-Ball et al. (2013; Pierce and Shoup (2013a); Pierce and Shoup (2013b); SFMTA (2014b); Chatman and Manville (2014)); it sheds light on two distinct aspects of the effects of SFpark not previously explored, thereby providing a complementary perspective to previous studies of SFpark (e.g., Millard-Ball et al. (2014)); and complements the related theoretical literature examining the effects of parking upon congestion (Arnott and Inci (2006); Arnott and Rowse (2009);Arnott et al. (2015)). ...
... Empirical studies on parking still make up a small portion of the parking-related transportation literature and many gaps remain. Even less is known regarding the influence of parking policies on modal transportation choice, which only a handful of empirical studies have addressed explicitly (e.g., Gillen (1977); Hensher and King (2001); Merriman (1998);Millard-Ball et al. (2014)). 7 While early analyses of SFpark suggest that it has been thus far successful in achieving some of its goals, particularly related to parking availability (e.g., Millard-Ball et al. (2014); Pierce and Shoup (2013a); SFMTA (2014b), Chatman and Manville (2014)), more empirical research is needed to uncover its effects upon other outcomes of interest to policymakers, including the effect upon public transit. ...
To alleviate many parking-related externalities, several rapidly growing cities globally are optimizing parking through “smart-parking” programs, involving measures such as adjusting parking prices based upon demand, making payments easier, and significantly improving parking-related information dissemination. There are few rigorous empirical estimates regarding the efficacy of these policies, particularly for outcomes such as transit ridership or traffic flow, which are of key policy relevance. Exploiting features of the roll out of SFpark, a smart-parking program for the city of San Francisco, we are able to estimate its effect upon public transit usage and traffic flow. Using a difference-in-difference strategy and a rich micro data-set on transit bus ridership along with data from SFpark, we find that SFpark led to a significant increase in bus ridership and a reduction in traffic flow. A back-of-the-envelope calculation suggests that the economic benefits resulting from avoided pollution and reduced congestion consequent to SFpark is larger than the approximate nominal costs of the program. Overall, our results suggest that smart-parking programs can help mitigate many traffic-related externalities, yielding significant economic benefits.
... For the search routing without additional information, simulator studies investigated the drivers' strategies to find parking [22]. To estimate the amount of parking search based on real-world sensor data, Millard-Ball et al. [23] simulated a random drive through the road network and measured the search length until the first free spot was found. In [21], authors simulated the potential savings of PGIs, but limiting to off-street parking. ...
... This number is far from common experience in many other dense urban scenario around the world. From these numbers, it seems that the areas covered by the SFpark project are not so problematic, in terms of lack of free parking spaces, as also found by Millard-Ball et al. [23]. Following this observation, in order to have more common situation to compare with, we produced also an altered version of this dataset, described in the following section. ...
... There are some assumptions to state, to measure their impact of the different guidance strategies. Following the parking search simulation in [23], we assume that a driver could take any road segment connected to the current one , while respecting regulations of one-way roads (in contrast to [23]), following the PGI driving direction. ...
... Since the usage of curbside spaces involves a wide spectrum of stakeholders, the curbside management is usually conceived as achieving a balance for all participants in curbside usage. Several pilot studies have been conducted for different individual components in curbside management, including efforts on commercial loading/unloading (Jones et al., 2009), on-street parking (Millard-Ball et al., 2014) and residential parking (Guo and McDonnell, 2013). There are also studies identifying the supply and demand dynamics with respect to the curbside usage, such as the competitions between curbside parking and downtown garage parking under different parking demands (Arnott and Rowse, 2013;Arnott et al., 2015) and the impacts of regular parking spillovers at popular destinations on curbside usage (Olus Inan et al., 2019). ...
... Analysis on a comprehensive curbside management plan was conducted by Seattle District Department of Transportation (DOT) on the RapidRide Roosevelt Corridor (Zimbabwe, 2018). For the management strategies, the prevailing frameworks included various charging schemes (Jones et al., 2009;Guo and McDonnell, 2013;Millard-Ball et al., 2014;Olus Inan et al., 2019), parking time limits (Arnott and Rowse, 2013), optimal metering rate (Arnott, 2014), dedicated curb spaces for specialized use (Shaheen et al., 2019;Goodchild et al., 2019) and special design of curbside to facilitate special usage (Mccormack et al., 2019;Shaheen et al., 2019). Ugirumurera et al. (2021) developed a micro-simulation based framework to simulate and evaluate curbside traffic managements policies. ...
Recent years have witnessed the rise of ride-hailing mobility services thanks to ubiquitous emerging technologies. Curbside spaces, as a category of public infrastructure, are being used by private ride-hailing services to pick up and drop off passengers, in addition to deliveries and parking access. This becomes quite common in urban areas and has led to additional congestion for ride-hailing, private and public transit vehicles on the driving lanes. Curb utilization by various traffic modes further alters travelers’ choices in modes/routes, clogging streets and polluting urban environment. However, there is a lack of theories and models to evaluate the effects of curbside ride-hailing stops in regional networks and to effectively manage ride-hailing pick-ups and drop-offs for system optimum. In view of this, this paper develops a bi-modal network traffic assignment model considering both private driving and ride-hailing modes who are competing for roads and curb spaces in general networks. To model the impact of limited curbside capacity to through traffic, a curbside queuing model is utilized to quantify the effect of congestion on both curbs and driving lanes induced by curbside stops in terms of waiting time and queue lengths. Travelers make joint choices of modes (driving or ride-hailing), curb stopping locations or parking locations. In addition, this study explores the option to regulate the amount of curbside stops to improve system performance, which is done by imposing a location-specific stopping fee on ride-hailing trips for using curbs to pick-up and drop-off. The curb pricing would influence travelers’ modal choices and parking location choices. To determine the optimal curbside pricing, a sensitivity analysis-based method is developed to minimize the total social cost of the network among all trips. The proposed methods are examined on three networks. We find that the optimal curbside pricing could effectively reduce curbside congestion and total social cost of the traffic system, benefiting all trips in the network.
... However, such measures are quite costly [1] and, sometimes, may even result in safety issues, especially within the multistory car parks [2]. Some other measures are also found to be effective, such as dynamic parking pricing [3][4][5] and residential parking permit regulations [6][7][8]. Such measures could reduce the parking demand among low-wage individuals, which may be deemed as unfair to them. ...
... Tan et al. [20] propose a truthful reverse Vickrey auction to allocate and price parking spaces in a static setting and further analyze the effects of the key factors (e.g., dynamic arrival rate and 3. Place an order and start to be charged: the ending time of sharing and overtime charging standards will be listed in advance. 4. Check the rental details, including already used time and the remaining available time. 5. Pay for the rental and leave with the departure voucher. ...
Making residential parking spots available to the public has become popularized in recent years. The sharing of residential parking spots can promote the further use of parking space and enhance the utility of parking resources in urban metropolitan areas. However, little is known about the relationship between spots’ physical or temporal factors and rental effects from practical experience. This study aims to evaluate the effects of residential parking spot sharing from both individual and social benefit perspectives. One-year real behavioral records concerning parking spots’ owners and borrowers were obtained, and the field survey of various parking spots’ physical characteristics was conducted. Two Partial Least Squares Structural Equation Modeling (PLS-SEM) models emphasizing the individual and societal points of view were adopted. Results revealed that the spots’ physical factors, including spot type, visibility, ease of parking, and distances to major surrounding buildings, along with owners’ sharing willingness and preferences, tend to pose significant influences on the rental effects from both individual and social benefit perspectives. Some differences were also discovered between the two models. For the individual model, owners’ sharing willingness was the dominant factor affecting the parking spots’ sharing effects, while for the social model, parking spots’ physical characteristics appear to be more important in determining the sharing effects. Based on these findings, suggestions were discussed to promote residential parking spot sharing and increase the benefits of sharing to individuals and society.
... The work in [10] uses a M/M/∞ queuing model to study the effect of cruising on congestion. Using a M/M/C model, the authors in [11] study the impact of variable pricing on parking occupancy and cruising in San Francisco. Assuming stationary arrivals and departures, the authors in [12,13,14] propose a bathtub model to capture the effect of cruising on traffic congestion and study parking pricing. ...
... For instance,[11] assumes that cruising drivers search until they find a parking spot. ...
We present a queuing model of parking dynamics and a model-based prediction method to provide real-time probabilistic forecasts of future parking occupancy. The queuing model has a non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data from 29 truck parking locations, each with between 55 and 299 parking spots. For each location and each spot the data specifies the arrival and departure times of a truck, for 16 months of operation. The modeling framework presented in this paper provides empirical support for queuing models adopted in many theoretical studies and policy designs. We discuss how our framework can be used to study parking problems in different environments. Based on the queuing model, we propose two prediction methods, a microscopic method and a macroscopic method, that provide a real-time probabilistic forecast of parking occupancy for an arbitrary forecast horizon. These model-based methods convert a probabilistic forecast problem into a parameter estimation problem that can be tackled using classical estimation methods such as regressions or pure machine learning algorithms. We characterize a lower bound for an arbitrary real-time prediction algorithm. We evaluate the performance of these methods using the truck data comparing the outcomes of their implementations with other model-based and model-free methods proposed in the literature.
... Furthermore, empirical evidence for on-street parking models remains limited. For example, [10] analyzed the dynamics of occupancy data in San Francisco, where a pioneering system equipped with sensors in each parking spot across downtown streets measured parking availability. However, the implementation and maintenance costs of such a system are prohibitively high. ...
Monitoring outdoor urban parking areas has traditionally relied on either costly sensing technology, such as ground sensors, or manual inspections, which are both resource-intensive. The emergence of drones equipped with advanced visual sensors provide a comprehensive aerial perspective and versatile mobility, opening up new possibilities for efficient traffic monitoring. In this research, we demonstrate the efficient monitoring of usage levels in outdoor public parking lots using drones. We deployed a number of drones flying during the peak periods for two days over four major outdoor parking lots in Pully, Switzerland, while monitoring on-street parking in their proximity. Our proposed pipeline involves identifying parking areas through geo-referencing and an image feature matching approach, followed by vehicle detection using an innovative boosted pseudo-labeling method. Central to our pipeline is an innovative boosted pseudo-labeling technique that enhances detection accuracy by generating pseudo labels from stationary vehicles observed in multiple frames, thereby reducing the need for manual data annotations. From the video collected from our drone experiment, we automate the monitoring of the outdoor parking areas over time and days. We conduct a comprehensive analysis of the occupancy rate of each parking area, encompassing both off-street and on-street parking lots, as well as dynamic interactions between different locations, and also examined the turnover rate of individual parking spots. This research represents a significant innovation in the use of drones for parking studies, providing an effective, versatile, and insightful approach for studying urban mobility and traffic management.
... Variable parking fees were set based on usage data for each region, and drivers could use a website or mobile app to find real-time information on available parking spaces. The above method can effectively manage parking demand in practice [23,24]. In conclusion, there are many studies on parking demand models for different conditions and scenarios that include different behaviors of parking characteristics. ...
The research in this article deals with verifying the deficit of parking spaces from model examples in the city of Ostrava, Czech Republic. Specifically, it deals with the possibilities of solving these deficits using automated parking systems. The main data collection took place between 2010 and 2019; later, supplemental lockdown data (up until May 2022) were obtained. The main objective of this article was to use data to determine the profitability and functionality of automated parking systems in mid-sized cities such as Ostrava. The RING system was chosen as a suitable model for the automated parking system. The data (using a least-squares approximation) were used via statistical methods to make predictions for future years, including the construction of confidence limits for a given significance level. Based on data from 2011–2019, we found that the RING system would be profitable with a probability of 92.45% in the following years. We compared these predictions with the actual data and made a new prediction.
... The plan established a fundamental aspect of the congestion management strategy through effective parking management and development control. In 2009, for Abu Dhabi, land and parking use surveys were conducted in 46 sectors, mainly in central business districts (CBDs), where parking congestion had been witnessed [2]. The parking survey showed that the parking demand was much higher in some sectors than the available spaces for on-street parking. ...
Parking accumulation profiles are time-dependent quantities at car parks that need data from longitudinal observational surveys. Car parks can be grouped into clusters based on the similarity in the shape of their accumulation profiles. This study investigates factors affecting parking accumulation and attempts to devise a methodology to construct parking accumulation profiles for different land uses in Abu Dhabi city. The study uses data available from Dubai city, which has a similar land-use type and distribution pattern as Abu Dhabi. A snapshot survey was conducted to observe the portion of the sampled car parks to calibrate the predicted parking accumulation. The estimation of parking accumulation profiles based on different land uses in Abu Dhabi city was done using the planning of the survey conducted in Dubai. The results suggest that a preliminary parking survey can help determine the initial accumulation profile and predict a robust parking accumulation profile that is applicable across different land-use types in Abu Dhabi.
... Although several real-world cases have illustrated the significant effect of price ranges due to the inelasticity of parking difficulty [47][48][49][50][51], these cases have not affected scholars' propositions of a better pricing model to optimize the parking service environment and reduce urban traffic congestion [11,52,53]. Scholars have hoped to improve the theoretical development of parking pricing policies and replicate the success of shared parking programmes in other parts of the world [7,[54][55][56][57]. ...
Using an incentive measure to encourage people to share their private parking spaces could be an effective strategy for urban parking problems. This paper discusses an innovative mechanism of shared parking, “FlexPass,” which applies a reverse auction in which drivers propose bids in line with their individual expectations to share their idle parking spaces. The auction mechanism, hypotheses on bidding process principles, the competitive environment, and the risk-averse decisions of providers with regard to parking spaces are analysed to explore the sustainability of the economic benefits obtained for FlexPass parking spaces. A total of 216 respondents from the University of California, Berkeley, were invited to participate in bidding in an actual survey during their daily use of parking spaces. The analytical results show that operational rules based on risk aversion can enable profit-seeking with a bounded capability to obtain considerable economic benefits and release parking resources in an environment of demand competition. Particularly in some scenarios, FlexPass would sacrifice a certain monetary income to ensure the perceived benefits of parking space providers. With the improvement of people’s enthusiasm for participating in shared parking, the benefits to individuals and parking lots would be further enhanced, suggesting that our mechanism can operate sustainably over the long term. These findings are helpful for policymakers to formulate feasible shared parking policies from the perspective of monetary incentives.
... Then, seven different dynamic parking pricing schemes are obtained, as shown in Table 5. Based on the relative research and travelers' acceptable levels of on-street parking price s, Scheme 1 with a price change of 2 Yuan/h, a desirable range of 60%-80%, and an adjustment threshold of 33% is regarded as the initial reference scheme [20] [31]. Based on the survey data, the highest on-street parking price is set at 20 Yuan/h, and the lowest price is 2 Yuan/h. ...
The rapid increase in the number of cars has caused many problems, such as “cruising for parking” and “illegal parking”. In this study, we conducted several on-street parking surveys in Beijing’s business districts. A parking location choice model and decision rules for the cruising process are established. A multi-agent based on-street parking simulation was constructed to explore the effects of time-varying parking prices on parking demand. It is concluded that demand-driven dynamic parking pricing can effectively regulate the distribution of parking demand and ensure the utilization of parking facilities within the desirable range in business districts. A lower desirable range and higher price change for the price adjustment can cause larger fluctuations in parking demand, fewer time intervals within the desirable range, and more price adjustment times. A higher desirable range and higher price change can result in longer cruising and driving times, and lower driving speeds. Considering the effectiveness and operating costs of the pricing schemes, it is recommended that the suitable range for parking occupancy rate is 60%–80% and the price change is 2 Yuan/h. The price adjustment threshold can be set based on the scale of the regional road network. The proposed dynamic parking pricing strategy can balance the distribution of the parking demand and reduce parking and traffic problems. The research conclusions can also provide a reference for the formulation of dynamic parking pricing strategies.
... The dependence of cruising time on parking system parameters is investigated in several papers all applying a queuing theory. Millard-Ball et al (Millard-Ball et al. 2014) established a queueing model to evaluate the relationship between occupancy and the amount of cruising vehicles when studying the impacts of the San Francisco SFPark adaptive parking pricing program. Dowling et al. (Dowling et al. 2017) (Dowling et al. 2018) (Dowling et al. 2020) consider parking search as a process that can be described by a network of interacting queues, each representing cars that want to park at a certain link, with cruising cars migrating between queues in respect to the network topology. ...
Parking occupancy in the area is defined by three major parameters - the rate of cars arrivals, the dwell time of already parked cars, and the willingness of drivers who are searching but yet did not find a vacant parking spot, to continue their search. We investigate a series of theoretical and numeric models, deterministic and stochastic, that describe parking dynamics in the area as dependent on these parameters, over the entire spectrum of the demand to supply ratio, focusing on the case when the demand is close to or above the supply. We demonstrate that a simple deterministic model provides a good analytical approximation for the major characteristics of the parking system - the average fraction of cars among the arriving that will find parking in the area, the average number of cars that cruise for parking, and average cruising time. Stochastic models make it possible to estimate the distributions of these characteristics as well as the parameters that are related to the variance of these distributions, like the fraction of the arriving cars that find parking in less than t minutes.
... The step ahead of the basic model 2 is queue models (Millard-Ball et al. 2014;Dowling et al. 2020). The queue models investigate a two-dimensional street grid network, considering each link l that has n l parking spots as a server that can serve n l requests (cars). ...
... Similar findings have been reported in the literature on the capacity of parking management strategies in encouraging people to use more sustainable modes of transport. Some researchers (Simićević et al., 2012, Arnott and Rowse, 2013, Arnott, 2014, Millard-Ball et al., 2014, Yan et al., 2019 have focused on finding the optimal occupancy rate of parking facilities to determine parking price and parking time limits leading to minimum search time and egress distance. ...
This study explores how individuals evaluate different shared parking features, as an emerging concept. In this paper, we develop a latent class behavioural model for a sample of 1,008 residents in New South Wales, Australia to explore different segments of taste and preference for shared parking. The outcome of this study extends our understanding about the emerging concept of shared parking by quantifying monetary value, in terms of willingness-to-pay, of different features of this new emerging phenomenon. This study contributes to the literature by providing firsthand insights about how demand for shared parking is formed and how different policy implication can be made accordingly. The results indicate that pricing persists to be the sole most effective parking policy, however non-price-related attributes can help decision makers to make more informative decision when it comes to different pricing schemes. The study also highlights that shared parking can perform a significant role in accommodating the surplus arising from the excessive parking demand and inadequate supply up to almost 40% of demand. The outcome of this study is anticipated to inform and equip city authorities with advanced knowledge about such an emerging phenomenon which can help developing regulations before the concept is out of control. Information from this study can also guide the private sector and the wider community to identify and develop opportunities related to shared parking.
... In the aspect of parking pricing, most existing studies use empirical and simulation methods (e.g., Axhausen and Polak, 1991;Millard-Ball et al., 2014;Soto et al., 2018). To our knowledge, the theoretical models for parking pricing are limited. ...
This paper extends the work of Zhang et al. (2008) to investigate the daily commuting patterns allowing both late arrival in the morning and early departure in the evening in a linear city. Given the fixed parking locations of morning commuters, the Nash equilibrium principle is employed to formulate commuters’ departure time choice in the evening and the user equilibrium principle in terms of daily travel cost is adopted to formulate the departure time choice of morning commuters. A combined regime of linear location-dependent parking fee and time-varying road toll is then proposed to reduce the total social cost (TSC) for the linear city. All the possible equilibrium traffic flows are analytically derived. The analytical results show that (1) commuters, who arrive at work early in the morning, may not depart early in the evening, but their evening trip costs must be lower; (2) when the parking spaces become denser, the evening commute starts earlier and the morning commute starts later; (3) a linear location-dependent parking fee could either advance or postpone the earliest departure time in the morning; (4) three parking modes, distinguished by the order in which commuters park, occur successively as the parking fee rate increases; and (5) an appropriate parking fee can always improve the system efficiency, but no matter how the parking fee is changed, it is impossible to completely eliminate the efficiency loss in the morning. Furthermore, a parking reservation regime joint with time-varying road toll is designed to minimize the TSC for the linear city. Finally, a set of numerical examples are presented to demonstrate the model's properties and these proposed regimes’ performance.
... It is noted that on-street parking has many disadvantages [17]: pollution, traffic jams, security, corruption, interference with pedestrians. Most of authors consider the logit model [18][19][20] for a parking choice. There is considered special cases of parking choice. ...
The system of two parking lots is researched: paid and free. The task of the city authorities is to determine part of the land for parking agency. The agency selects the best parking fee, and travelers determine which parking to choose. The goals of each participant are different: passengers attempt to minimize the loss of time and parking fees, the agency maximizes profits, and the city thinks about the public good (in this case, about all travelers). The mutual dependence of participants leads to the need to apply game theory to describe their interaction. The mathematical model defines restrictions on the parameters for existence Nash equilibrium. The numerical example that does not contradict the existing picture of the world is considered.
... Apart from some simplistic approaches that relate this behavior to overall metrics like vehicle population [19], most works perform future occupancy predictions based on Markov chains, using Poisson and exponential distributions to model arrivals and departures respectively. This is the case of works like [20]- [22], and [23] among many others. As a bridge between theoretical and data-driven models, the work in [24] uses a Weibull distribution to model the probability of a spot to be occupied and genetic neural networks to predict its future values. ...
Effective on-street parking is key to reduce urban traffic and pollution in densely populated cities. Thus, researchers have focused on forecasting future occupancy values depending on factors like time, space, or weather. This approach shows high average performances, but fails in predicting congested scenarios, actually the most critical. This work proposes a data-driven parking level of service (LOS) predictor that outperforms traditional methods, solving its inherent class imbalance issue by means of Random Undersampling Boost classifiers. We trained and validated the LOS classifiers using 13 months of data collected from the smart parking system in the city of Madrid, Spain. Results display average recall values above 0.94 and 0.87 at prediction horizons up to 10 and 60 minutes respectively. We compare these results with traditional regression-based occupancy predictors showing that our classifier clearly outperforms the formers predicting the minority classes, which carry the most significant information for drivers and parking managers. We further analyze the impact on performance of temporal and spatial features, revealing mid-term temporal data as the most relevant forecasting information, and low correlations between parking behaviors in bordering neighborhoods. In the light of these results, we believe that the proposed data-driven parking LOS classification has the potential to open a novel perspective on the parking occupancy forecasting field.
... Rather than build more parking, transportation planners use both pricing and built environment strategies for reducing parking demand and encouraging mode shift from driving to more sustainable travel modes. For instance, charging for parking has become a widely used approach to managing parking demand (Millard-Ball et al. 2014). Pricing is an important mechanism for controlling automobile use because (a) people are sensitive to parking cost, as well as parking search and walk times in choosing destinations and mode, and (b) parking supply and price are at least partially controllable through policy levers, such as zoning, regulation, and taxation (Inci 2015). ...
This study examines positive and negative sentiments associated with parking experiences reported in online Yelp reviews for four metropolitan areas in North America, leveraging large location-based social network (LBSN) data to understand parking sentiment as a measure of parking search or post-parking experiences. Demand from travelers and business owners for more parking is a significant issue for local transportation planners and decision-makers, but to date, there has been little study of how local parking management strategies or built environment characteristics modify parking experiences and sentiments. To understand this relationship, we first conduct a sentiment analysis (SA) to identify the emotional, affective content of parking-related reviews embedded in the Yelp reviews. We then use generalized mixed effects (GLME) models to examine the associations between parking sentiment and (a) parking management practices, and (b) characteristics of the built environment. The SA results show that positive and negative parking sentiments are significantly spatially clustered in each metropolitan area. GLME models show that sentiments are significantly associated with destination activity types, parking management strategies, and several built environment factors. The results of this study indicate how different interventions advocated by transportation policies may influence perceptions of parking in commercial and mixed-use districts with implications for overall support for neighborhood transportation planning best practice. Furthermore, the findings represent that emerging data mining and statistical methods can successfully leverage big data to reveal travel experiences and their relationship to urban contexts, providing an effective solution to obtaining experiential transportation information.
... Parking pricing, as one of the most frequently and widely used instruments for managing traffic demand in urban area, has been substantially studied in the literature. Although there are a number of empirical and simulation studies on parking pricing (e.g., Axhausen & Polak, 1991;Balac et al., 2017;Chatman & Manville, 2014;Dale et al., 2014;Millard-Ball et al., 2014;Waraich et al., 2013), theoretical models for parking pricing are limited. Arnott and Rowse (1999) developed a structural model of parking in a ring-road network and investigated optimal parking pricing strategies. ...
We analyze the commuting patterns and parking modes for integrated daily commuting under different regimes. Based on a bi-direction bottleneck network with a spatial pattern of parking, the daily commuting patterns with parking location choices are analyzed. Without road toll and parking fee, there exist two equilibrium flow patterns and one parking mode. Increasing the parking density would postpone the earliest departure time and shorten the length of departure time period. A linear location-dependent parking fee regime without road toll is then proposed. We find that three parking modes occur across the parking fee rate and the morning efficiency loss always exists no matter how the fee rate is changed. Furthermore, a combined regime of linear location-dependent parking fee and time-varying road toll is developed to minimize the total daily social cost (TDSC). Two parking modes occur as the parking fee rate increases and there exist four equilibrium flow patterns. We show that the linear location-dependent parking fees could either advance or postpone the earliest morning departure time. An inappropriate parking fee rate may increase both individual and social costs. The best system performance can be achieved with joint consideration of both the TDSC and parking supply cost.
... Smart-parking apps help drivers easily find available parking spaces. It was estimated that SFpark reduced vehicle-distance travelled by cars in search for on-street parking spaces by 50 percent [Millard-Ball et al. (2014)]. Real-time communication with the traveler about road conditions, accidents, and construction could reduce travel time as travelers pick better routes and avoid cruising. ...
... Policy measures may particularly be relevant in shaping supply side issues -accessibility, quality and cost of the public transit systems, transportation infrastructure and land use management to mention just a few while some may be oriented towards the demand side. For example, San Francisco's experiment with SFpark (Pierce & Shoup, 2013), (Millard-Ball, Weinberger, & Hampshire, 2014), (Fabusuyi & Hampshire, 2018) was designed in part, with the objective of achieving a more societal friendly travel behavior by pricing parking spaces correctly and using the revenue to subsidize public transit. ...
We develop a more nuanced understanding of commuters’ travel mode choices by generating detailed profiles that capture the travel behavior of commuters in the Pacific states of the continental US. These profiles are created by utilizing the US Census Public Use Microdata Sample (PUMS) data. The microdata sample set allows for the estimation of fine-grained models that showcase how individual commuters make travel mode choices. Our results show appreciable locational variation in mode choices and statistically significant differences in commuting profile across and within population segments. A key revealing finding demonstrates that across the three states analyzed, the total number of vehicles driven for any day of the week could be reduced by up to 10 million assuming the commuting patterns observed in San Francisco applies to the rest of the states. We conclude with insights and policy implications provided by the study on making transportation-related infrastructure decisions.
... Practical experiments for establishing performance-defined pricing schemes have had only limited success (SFMTA, 2014;Pierce and Shoup, 2013;Chatman and Manville, 2014;Millard-Ball et al., 2014). It is hypothesized that the constraints placed upon these parking programs, including lax enforcement of illegal parking, free parking permits, abuse of disabled parking placards and price ceilings are to blame for this. ...
We propose ParkSage, a set of spatially-explicit algorithms for establishing parking prices that guarantee a predetermined occupancy rate over a city, and for evaluating the achieved reduction in parking search time. We apply ParkSage for establishing overnight parking prices that guarantee 85% occupation in the Israeli city of Bat Yam. Pricing by street links ensures high parking availability and close to zero cruising everywhere in the city, but is inconvenient for drivers. Establishing prices by the large and heterogeneous city quarters results in local mismatch between demand and supply, the emergence of areas with fully occupied on-street parking and a long search time for the drivers whose destinations are in these areas. We demonstrate that pricing by the medium sized Transportation Analysis Zones, which is easy enough for drivers to comprehend and abide by, is sufficient for eliminating cruising. The software for establishing and assessing performance parking prices is based on the standard municipal GIS layers of streets and parking lots and is available for free download from https://www.researchgame.net/profile/Nir_Fulman
... Shoup (2006) estimated that 95% of a car's lifetime is spent parked. For cars, parking duration is relatively long (several hours), and a lack of parking spaces causes drivers to cruise around a neighborhood in search of available parking, increasing congestion and causing excess travel, air pollution, and greenhouse gas (GHG) emissions (Millard-Ball, Weinberger, and Hampshire 2014). Researchers found that, on average, 30% of road traffic is caused by vehicles cruising (Shoup 2006). ...
Understanding factors that drive the parking choice of commercial vehicles at delivery stops in cities can enhance logistics operations and the management of freight parking infrastructure, mitigate illegal parking, and ultimately reduce traffic congestion. In this paper, we focus on this decision-making process at large urban freight traffic generators, such as retail malls and transit terminals, that attract a large share of urban commercial vehicle traffic. Existing literature on parking behavior modeling has focused on passenger vehicles. This paper presents a discrete choice model for commercial vehicle parking choice in urban areas. The model parameters were estimated by using detailed, real-world data on commercial vehicle parking choices collected in two commercial urban areas in Singapore. The model analyzes the effect of several variables on the parking behavior of commercial vehicle drivers, including the presence of congestion and queueing, attitudes toward illegal parking, and pricing (parking fees). The model was validated against real data and applied within a discrete-event simulation to test the economic and environmental impacts of several parking measures, including pricing strategies and parking enforcement.
... Curbside parking still remains the most common way to park for drivers in the USA. Hence, Millard-Ball et al.[66] assess the curbside parking pricing in San Francisco. They evaluate the relationship between occupancy rules and metrics of direct policy interest, such as the probability of finding a parking space and the amount of cruising with the San Francisco performance goal of 60-80% occupancy of its on-street parking. ...
This thesis focuses on the problem of urban parking, especially in peak traffic hours. One of the main objectives is to explore the solution methods from operational research perspective and to provide practical solutions through mathematical programming and heuristics.We first consider a simplified, static version of the problem in which all the necessary data is fixed and deterministic over a one-day planning horizon. A 0-1 programming model derived from the generalized assignment problem is proposed and tested on a randomly generated set of instances. Extensions that include different objective functions and other modes of transport are also examined. In addition, we proposed a heuristic based on variable neighborhood search to quickly obtain a good quality solutions.The dynamic nature of the problem has led us to adapt this model so that it can take into account the continuous data updates. We have proposed and evaluated several policies and scenarios, with the goal of developing a system that is as adaptive and robust as possible. The proposed system should be able to guide users to a parking lot assigned to them when possible, or to their destination when their is no parking slot available.Our approach is corroborated via simulation over a set of real data collected from three major European cities.
... Practical experiments for establishing performance-defined pricing schemes have had only limited success (SFMTA, 2014;Pierce and Shoup, 2013;Chatman and Manville, 2014;Millard-Ball et al., 2014). It is hypothesized that the constraints placed upon these parking programs, including lax enforcement of illegal parking, free parking permits, abuse of disabled parking placards and price ceilings are to blame for this. ...
We propose ParkSage, a set of spatially-explicit algorithms for establishing parking prices that guarantee a predetermined occupancy rate over a city, and for evaluating the achieved reduction in parking search time. We apply ParkSage for establishing overnight parking prices that guarantee 85% occupation in the Israeli city of Bat Yam. Pricing by street links ensures high parking availability and close to zero cruising everywhere in the city, but is inconvenient for drivers. Establishing prices by the large and heterogeneous city quarters results in local mismatch between demand and supply, the emergence of areas with fully occupied on-street parking and a long search time for the drivers whose destinations are in these areas. We demonstrate that pricing by the medium sized Transportation Analysis Zones, which is easy enough for drivers to comprehend and abide by, is sufficient for eliminating cruising. The software for establishing and assessing performance parking prices is based on the standard municipal GIS layers of streets and parking lots and is available for free download from https://www.researchgame.net/profile/Nir_Fulman
... Accurate prediction of such parameters using past information is today's need which helps in better management of parking lots. Analysis/acceptability of change in parking rates have its own importance in determination of dynamic prices [120,121]. Survey such as [121], shows that dynamic pricing regulates parking facilities, and solves many issues such as maximization of revenue generated, and minimization of parking prices, cruising time. LA Express Park survey [122] reported dynamic pricing based on sensors and smart meters with various factors such as one which influence parking decisions, distance between parked and intended location, awareness of parking prices through dynamic pricing stickers, and feelings about dynamic pricing. ...
Dynamic pricing plays an important role in modern Intelligent Transportation System (ITS) in solving problems such as congestion control, peak load reduction, mobility management of traditional or Electric Vehicles (EVs) in cost-effective manner. It also facilitates the construction of an eco-friendly environment in the society by optimized route planning of vehicles. However, framing dynamic pricing strategies for ITS has always been a challenging task keeping in view of various constraints. An in-efficient dynamic pricing technique may lead to the mismanagement of vehicles, which results an increase in the waiting time of vehicles, an increase in air and noise pollution, wastage of electric and other sources of energies. On the contrary, efficient dynamic pricing strategies can provide satisfaction to all stakeholders, including service providers and service consumers. Motivated from these facts, this paper presents an extensive literature review and analysis of dynamic pricing techniques used in the literature for ITS. The analysis presented in the paper gives new insights to the readers for the applications of one of the techniques in comparison to its merits over the others. Various problems solved by the dynamic pricing techniques, importance of various evaluation parameters, limitations of dynamic pricing techniques and their applications are discussed in-depth in this paper. Different taxonomies used for exploring various issues of dynamic pricing are also presented in a structured manner. Moreover, advantages and limitations of various dynamic pricing techniques are explored and discussed in the paper. Finally, existing challenges and future research directions of dynamic pricing in ITS are presented.
Curb spaces are important assets to cities. They are often used by travelers to switch transportation means, visitors to access curbside properties, and municipalities to place roadside infrastructure. The promotion of multi-modal transportation and the emergence of new mobility services have complicated both curb environments and their management. Consequently, some cities have started to explore new strategies for curb management, but lacked the anticipation on how different curb regulations and built-environment features may collectively influence curb-use patterns across user groups. We make a step toward smart curb environment by proposing a graph-based deep learning approach, i.e., MultiGCN-LSTM, to predict diverse curb uses across time and space. We used two graph convolution layers and an LSTM layer to capture the spatial, temporal, and semantic dependencies between curb regulations, built-environment semantics, and diverse curb uses. Two place-specific models were developed separately for a medium-sized college town and a metropolitan in the U.S. The effectiveness of the proposed models was validated with ablation studies and demonstrated in three scenario experiments. The research contributes to smart curb management in the face of more diversified and intensified curb uses with new mobility services and emerging vehicular technologies.
Parking space availability is valuable information to travelers. This paper aims at modeling drivers’ behavioral changes in arrivals/departures over time of day and developing an adaptive forecasting approach for parking space availability. We propose a stochastic model that consists of two inter-connected Markov processes. First, the lower level of the model focuses on the parking behavior within a short time period, based on conventional M/M/C/C queueing theory with the assumption of fixed arrival and parking rates. Next, to account for the behavioral changes in drivers’ arrivals/departures over a longer time period (e.g. time of day), we incorporate a Markov regime switching process to describe the regime switching mechanism of the arrival/departure behavior. The integrated model leads to an adaptive forecasting formula with time-varying forecasting coefficients adaptively adjusted based on the arrival/departure regimes. We investigate two real traffic applications to illustrate the developed stochastic model and to test the performance of the adaptive forecasting method using out-of-sample data.
The problem of optimizаtion the distribution of urban territory near a center of gravity (airport, railway station, mall, etc.) between two types of parking lots: paid and free is considered. Obviously, under otherwise equal conditions the car user will choose free parking, but this will lead to an increase in the time to find a parking space. Therefore, paying for parking is beneficial for people who have a higher their value of time. Paid parking operator determine the size of the parking fee - if the price is low enough, all parking spaces will be occupied and there will be problems with parking, in addition, there will be lost profits. Lost profits will also occur if the price is high enough, because if the price goes up, fewer car users will be ready to use paid parking services. The city authorities' task is to determine part of the land which should be allocated for paid parking on the understanding that the rest of the land goes to free parking. The paid parking owners' task is to determine the optimal price in order to maximize the profit. Car users choose parking based on their value of time. A mathematical model was built with three types of participants: car users, municipal authorities and parking operators. The strategy of the passenger is the choice of parking, the goal is to minimize the cost and time. The strategy of the municipality is the allocation of territory for parking, the goal is similar to the goals of passengers. Parking operator with the strategy "parking price" achieves to maximize profit. The interaction of the participants is described within the framework of game theory, the objective functions of the players are considered, and the existence of the Nash equilibrium is proved. Numerical examples are considered, options for the development of the presented model are described. The dependence of characteristics on the total allocated number of parking spaces in two types of parking lots, the price of parking and the intensity of the car flow to the center of gravity is presented.
Scarcity of on-street parking in city centers is a known factor motivating drivers to drive slowly (“to cruise”) while searching for an available parking place and is associated with negative externalities e.g. congestion, accidents, fuel waste, and air pollution. Finding the correct prices is suggested to bring cruising to a sustainable level. Current research methods based on surveys and simulations fail to provide a complete understanding of drivers’ cruising preferences and their behavioral response to price changes. We used the PARKGAME serious game, which provides a real-world abstraction of the dynamic cruising experience. Eighty-three players participated in an experiment under two pricing scenarios. Pricing was spatially designed as “price rings” decreasing when receding from the desired destination point. We analyzed search time, parking distance, parking location choice, and spatial searching patterns. We show that such a pricing policy may substantially reduce the cruising problem, motivating drivers to park earlier—further away from the destination or in the lot, especially when occupancy levels are extremely high. We further discuss the policy implications of these findings.
This article analyses the impact that different parking management policies may have on public roads. Policies were simulated using a new parking model based on two sub models: choice of parking place and search for parking place. The model considers curb traffic and was implemented into a traditional microsimulation traffic software. The parameters for the sub models were estimated from data collected in the city centre of Santander (Spain) and from a stated preferences survey asked to users of parking spaces. The model for testing policies was run on Aimsun simulation software creating a personalised API programmed using Python 3.7. The proposed model was able to dynamically simulate various policies based on charging for on-street parking spaces with fare updates at short time intervals of between 5 and 15 min. A sensitivity analysis was performed on different fare scenarios and considering different levels of information available to the users. As a result, this work demonstrates some benefits of dynamic fares such as reducing searching time, curb induced traffic and emissions as well as a new modal redistribution of parking choice between off-street and on-street supply. On the contrary, dynamic fares implied that users needed to spend a bit more time from their parking location to their destinations.
Dockless electric scooters challenge cities seeking to regulate them amidst broader goals such as universal access. Cities are particularly concerned with improper scooter parking, which can impede access by other travelers. Despite an important role that scooter parking plays in both regulations and public discourse, no comprehensive view of current scooter parking regulations exists, nor is it clear how regulations align with broader city objectives. This research asks: how have U.S. cities regulated scooter parking to date and what issues do parking regulations aim to address? Data from 37 U.S. cities show that while areas of widespread agreement exist—nearly all (95%) cities allow scooters to park in the furniture zone—a wide range in scooter parking requirements exists. About three quarters of cities allow scooters to park at bike racks (78%) and against buildings (70%), while fewer than two-thirds allow scooters to park either on landscaping (62%) or against signs (60%). Even among cities with similar regulations, however, considerable nuance exists. Interviews with staff from six U.S. cities and existing research highlight motivations for scooter parking regulations. The regulations employed—and the high degree of variability across cities—yield implications for scooter parking policies, as well as scooters’ role in advancing broader city objectives. Scooter parking regulations play an important role in access, but cities should approach regulations as just one piece within a broader agenda of reclaiming streets for people and promoting mobility, sustainability, and access for all.
This paper uses a novel block-by-block panel data set of the price and location of garages and curbside parking to map and assess the congestion cost of on-street parking in New York City. For the average 9-to-5 car commuter, the difference-indifferences estimates show a 10% increase in travel time due to delays caused by other drivers parking on-street. Simulations based on the theoretical model show that: (i) 43% of the free on-street parking consumer surplus is eroded by self-generated congestion externalities, (ii) most drivers have a significant incentive to cruise for parking, especially in congested locations (Manhattan south of 96 th street).
Practitioners need to know the level of cruising for parking when designing parking poli- cies. Existing methodologies, such as counting, experiments, and survey, are either too ex- pensive or infeasible to be undertaken on a large scale. Inci et al. (2017) introduce an instrumental-variables-based econometric methodology using administrative data to esti- mate the average level of cruising when parking is close to full occupancy. This paper introduces a novel methodology to estimate the marginal external cruising time (and thus cost) across time and space. Our methodology is easier to implement, requires even less data, estimates the whole distribution rather than the average, and does not require park- ing to be near full occupancy. It also allows for welfare evaluations of parking fees and supply. To illustrate all these, we apply our methodology to Melbourne, which generates rich policy insights. We also apply it to the same dataset that Inci et al. (2017) use for Istanbul and find consistent results, rendering confidence to both methodologies.
This paper presents a method for determining parking search behavior using GPS traces. The research takes advantage of a GPS based household travel survey, an extensive dataset of GPS with video, and a commercially purchased set of trip segments. Strategies for data cleaning, matching traces to digitized networks, assessing the probability that a trace is of good quality, and strategies for determining whether or not a trip involves excess travel due to parking search are described. We define and operationalize two definitions of excess search – popularly known as cruising. Our results suggest that cruising in San Francisco, CA and Ann Arbor, Michigan is acute in some locations but overall experienced in less than 5–6% of vehicle trips, and that it accounts for less than 1% of vehicle travel in these cities–considerably less than in previous estimates.
The present study develops a spatial unconditional quantile regression by extending Firpo et al.’s (Econometrica 77:953–973, 2009) unconditional quantile regression and empirically investigates the determinants of parking prices at different quantiles of prices in Japan. The empirical results suggest that spatial competition in terms of unit price and the unit time play important roles in determining parking prices. On the contrary, price is unaffected by demand, approximated by adopting several employment density variables and aggregated people flow data obtained from cell phones. Besides, significant differences exist among the factors that affect parking prices during the day and at night as well as among the unconditional quantiles.
This paper explores the impact of parking prices on the decision to drive to work using a California household travel survey dataset and a discrete choice model. The paper tackles estimation challenges posed by insufficient parking information. The first challenge is the estimation of parking prices for those who do not drive, which is addressed by using a sample selection model. The second challenge is to understand the effect of the extent of the prevalence of Employer-Paid parking coupled with incentive programs offered in-lieu of parking. To address this challenge, two extreme scenarios are examined, and a range for the marginal effects of parking prices is estimated; one scenario assumes everyone receives Employer-Paid parking coupled with in-lieu of parking incentives, and the second assumes that no one is offered such incentives. The results suggest that higher parking prices reduce driving, regardless of the followed approach. It is estimated that a 10% increase in parking prices leads to a 1–2 percentage point decline in the probability of driving to work. This range varies with initial parking prices, where the lower end of the range increases at a decreasing rate, and the higher end peaks at $2.5 and decreases with higher prices. Moreover, there seems to be no evidence of sample selection bias. The evidence confirms that parking pricing can indeed be an effective transportation demand management tool.
This paper aims to identify the most used urban mobility metrics by means of a bibliometric analysis. Therefore, a qualitative and quantitative study was carried out from a selection of 49 articles, between 1989 and 2016. Scopus and Web of Science were used as databases. The performed study verified the predominance of papers focused on environmental and efficiency perspectives in urban transport systems. Furthermore, it was possible to verify the emergence of new approaches such as sustainable mobility, resilient transport and smart mobility. Two hundred and twenty eight categories of metrics and performance indicators were identified and grouped into twelve perspectives. The categorization proposed by this paper can assist researchers on future works on the topic addressed.
A deep learning model is adopted 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 GCNN-based 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 min 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.
Adam Millard-Ball, Rachel Weinberger, and Robert Hampshire critically comment on Pierce and Shoup's article 'Evaluating the Impacts of Performance-Based Parking'. In principle, a performance-based parking system such as SFpark, which adjusts prices in a bid to achieve target occupancy for curb parking, is an excellent way to reduce congestion and to improve the driver experience. Pierce and Shoup's findings suggest that the SFpark program has had a remarkable impact in an extremely short time, an impact that is substantially faster and greater than that shown in other analyses. Their empirical analysis ignores the endogeneity of prices specifically the possibility that fluctuations in demand trigger price changes under SFparks rate adjustment rules. The authors feel that it is too soon to draw firm conclusions about the impact of performance-based parking pricing programs such as SFpark.
This paper investigates the relations between dynamic parking prices and provision of parking information in a general parking network. Travelers are provided with the real-time occupancy and pricing information to make their parking choices. We first formulate the parking choices under the User Equilibrium (UE) conditions using the Variational Inequality (VI) approach. More importantly, the system optimal (SO) parking flow pattern and SO parking prices are also derived and solved efficiently using Linear Programming. Under SO, any two parking lots cannot be used at the same time by travelers between more than one O-D pairs. The SO parking flow pattern is not unique, which offers sufficient flexibility for operators to achieve different management objectives while keeping the flow pattern optimal. We show that any optimal flow pattern can be achieved by lot-based parking pricing schemes that only depend on the time or real-time occupancy. We finally solve both UE and SO in two numerical examples. The best system performance is usually achieved by the parking prices such that the more preferred (convenient) lot should be used fully up to a certain terminal occupancy of around 85%-95%. This essentially balances the parking congestion (namely cruising time) and the convenience of preferred lots. We also obtain the SO prices from the SO solution set, to produce constant arrival rates to each lot. This could mitigate the potential roadway congestion and queuing comparing to intensive arrival rates.
The need to park a car is a key consideration in any trip. The need can affect or determine travel mode, departure time, and even the entire trip chain, as well as impose pressure on the network; drivers can create traffic jams as they seek places to park. Given the complexity of motorist parking behavior, no reliable methodology has been developed to model parking activities realistically. To date, the studies have focused on the effects of parking on travel demand (especially on mode choice). The study reported here developed a convenient and easy-to-use methodology, which integrated parking choice with the traffic assignment. The methodology responds in particular to the needs of practitioners who carry out traffic impact study projects in which a detailed analysis is sought of a confined area (study area). The proposed methodology splits a study area's zonal trips into two main parts: (a) walking trips to and from parking lots and (b) vehicular trips between origin parking lots and destination parking lots. A logit model was adapted to model parking choice, which could accommodate factors that influenced motorist behavior (e.g., a lot's price, security, protection from the elements). The methodology was tested through its application to a part of the central business district of the city of Abu Dhabi, United Arab Emirates. A pragmatic approach to the problem of how to price parking to alleviate its shortage and to use its supply efficiently was proposed.
Problem, research strategy, and findings: Underpriced and overcrowded curb parking creates problems for everyone except a few lucky drivers who find a cheap space; all the other drivers who cruise to find an open space waste time and fuel, congest traffic, and pollute the air. Overpriced and underoccupied parking also creates problems; when curb spaces remain empty, merchants lose potential customers, workers lose jobs, and cities lose tax revenue. To address these problems, San Francisco has established SFpark, a program that adjusts prices to achieve availability of one or two open spaces per block. To measure how prices affected on-street occupancy, we calculated the price elasticity of demand revealed by over 5,000 price and occupancy changes during the program's first year. Price elasticity has an average value of -0.4, but varies greatly by time of day, location, and several other factors. The average meter price fell 1% during the first year, so SFpark adjusted prices without increasing them overall. This study is the first to use measured occupancy to estimate the elasticity of demand for on-street parking. It also offers the first evaluation of pricing that varies by time of day and location to manage curb parking. Takeaway for practice: San Francisco can improve its program by making drivers more aware of the variable prices, reducing the disabled placard abuse, and introducing seasonal price adjustments. Other cities can incorporate performance parking as a form of congestion pricing.
The distribution of the difference between two independent Poisson random variables involves the modified Bessel function of the first kind. Using properties of this function, maximum likelihood estimates of the parameters of the Poisson difference were derived. Asymptotic distribution property of the maximum likelihood estimates is discussed. Maximum likelihood estimates were compared with the moment estimates in a Monte Carlo study. Hypothesis testing using likelihood ratio tests was considered. Some new formulas concerning the modified Bessel function of the first kind were provided. Alternative formulas for the probability mass function of the Poisson difference distribution are introduced. Finally, two new applications for the Poisson difference distribution are presented. The first is from the Saudi stock exchange (TASI) and the second from Dallah hospital.
We investigate when Arrivals See Time Averages (ASTA) in a stochastic model; i.e., when the stationary distribution of an embedded sequence, obtained by observing a continuous-time stochastic process just prior to the points (arrivals) of an associated point process, coincides with the stationary distribution of the observed process. We also characterize the relation between the two distributions when ASTA does not hold. We introduce a Lack of Bias Assumption (LBA) which stipulates that, at any time, the conditional intensity of the point process, given the present state of the observed process, be independent of the state of the observed process. We show that LBA, without the Poisson assumption, is necessary and sufficient for ASTA in a stationary process framework. Consequently, LBA covers known examples of non-Poisson ASTA, such as certain flows in open Jackson queueing networks, as well as the familiar Poisson case (PASTA). We also establish results to cover the case in which the process is observed just after the points, e.g., when departures see time averages. Finally, we obtain a new proof of the Arrival Theorem for product-form queueing networks.
This paper presents an explicit agent-based model of parking search in a city. In the model, "drivers" drive toward their destination, search for parking, park, remain at the parking place, and leave. The city's infrastructure is represented by a high-resolution geographic information system (GIS) of the street network and parking lots; information is included on traffic directions and permitted turns, on-street parking permissions, and layers of off-street parking places and lots. Destinations are presented by layers of dwellings and public places. Driver agents belong to one of four categories: residents and guests with dwellings as destinations and employees and customers with public places as destinations. Each agent has its own destination, willingness to pay, time of arrival, and duration of stay. In the model, driver agents are "landed" at a distance of approximately 250 m from their destination, that is, close to the area in which drivers start searching for parking. First, a driver estimates the parking situation in the area and then starts to search for a parking place. During the search, a driver agent accounts for the availability of parking places, differences in pricing, and parking enforcement efforts. The model outputs include distributions of (a) search time, (b) distance between parking place and destination, (c) fees paid by the drivers, and (d) parking revenues for the proprietor of paid parking places (whether local authority or private operator). The model is implemented as an ArcGIS application and applied to analyze parking dynamics in an inner city neighborhood in Tel Aviv, Israel, during the course of a regular weekday.
1. Introduction 2. Causal and non-causal models 3. Microeconomic data structures 4. Linear models 5. ML and NLS estimation 6. GMM and systems estimation 7. Hypothesis tests 8. Specification tests and model selection 9. Semiparametric methods 10. Numerical optimization 11. Bootstrap methods 12. Simulation-based methods 13. Bayesian methods 14. Binary outcome models 15. Multinomial models 16. Tobit and selection models 17. Transition data: survival analysis 18. Mixture models and unobserved heterogeneity 19. Models of multiple hazards 20. Models of count data 21. Linear panel models: basics 22. Linear panel models: extensions 23. Nonlinear panel models 24. Stratified and clustered samples 25. Treatment evaluation 26. Measurement error models 27. Missing data and imputation A. Asymptotic theory B. Making pseudo-random draw.
Urban planners typically set minimum parking requirements to meet the peak demand for parking at each land use, without considering either the price motorists pay for parking or the cost of providing the required parking spaces. By reducing the market price of parking, minimum parking requirements provide subsidies that inflate parking demand, and this inflated demand is then used to set minimum parking requirements. When considered as an impact fee, minimum parking requirements can increase development costs by more than 10 times the impact fees for all other public purposes combined. Eliminating minimum parking requirements would reduce the cost of urban development, improve urban design, reduce automobile dependency, and restrain urban sprawl.
The potential impact of parking-pricing on trip generation and modal choice is gaining greater acknowledgement within transport demand management research. However, although the aggregate effect of a transport demand management pricing measure is often noted or estimated, the potential varied impact of pricing measures on specific subsets of the market are often overlooked in the policy process. The variance of price impacts on different trip purposes, initially, and as tariffs increase progressively, is an important consideration for policy makers. Using the results from a survey on 1007 on-street parkers in Dublin, Ireland, this paper shows a progressively widening gap in price sensitivity between trips made for business purposes relative to non-business purposes, as the suggested parking pricing scenarios are increased. The results highlight the complication that the varied price sensitivity of particular market subsets can bring to development of a pricing policy and warns of threshold points where the gap between the price responsiveness of specific market subsets become considerably more pronounced.
Suppose curb parking is free but all the spaces are occupied, and off-street parking is expensive but immediately available. In this case, you can cruise to find a curb space being vacated by a departing motorist, or pay for off-street parking right away. This paper presents a model of how drivers choose whether to cruise or to pay, and it predicts several results: you are more likely to cruise if curb parking is cheap, off-street parking is expensive, fuel is cheap, you want to park for a long time, you are alone in the car, and you place a low value on saving time. The model also predicts that charging the market price for curb parking—at least equal to the price of adjacent off-street parking—will eliminate cruising. Because the government sets curb parking prices, planners and elected officials strongly influence drivers’ decisions to cruise. The failure to charge market rates for curb parking congests traffic, pollutes the air, wastes fuel, and causes accidents. Between 1927 and 2001, studies of cruising in congested downtowns have found that it took between 3.5 and 14 min to find a curb space, and that between 8 and 74 percent of the traffic was cruising for parking.
In this paper, we present PARKAGENT, an agent-based, spatially explicit model for parking in the city. Unlike traditional parking models, PARKAGENT simulates the behavior of each driver in a spatially explicit environment and is able to capture the complex self-organizing dynamics of a large collective of parking agents within a non-homogeneous (road) space. The model generates distributions of key values like search time, walking distance, and parking costs over different driver groups. It is developed as an ArcGIS application, and can work with a practically unlimited number of drivers.The advantages of the model are illustrated using a real-life case from Tel Aviv. Taking detailed data from field surveys, the model is used to study the impact of additional parking supply in a residential area with a shortage of parking places. The PARKAGENT model shows that additional parking supply linearly affects the occurrence of extreme values, but has only a weak impact on the average search time for a parking place or the average walking distance between the parking place and the destination.
Parking policies are considered a powerful tool for solving parking problems as well as problems of the transportation system in general (traffic congestion, modal split, etc.). To define parking policy properly, its effects must be estimated and predicted. In this paper, based on stated preference data and using a logistic regression, a model to predict the effects of introducing or changing the parking price and time limitation was developed. The results show that parking prices affect car usage, while time limitations determine the type of parking used (on-street or off-street). A positive finding for policy makers is that users with work are more sensitive to parking measures than are other users, so parking measures can be used to manage user categories. Although there is a concern that parking policy can jeopardise the attractiveness and efficiency of a zone, the results show that a very small number of users would give up travelling into the zone.
For a queuing system with Poisson input, a single waiting line without defections, and identically distributed independent negative exponential service times, the equilibrium distribution of the number of service completions in an arbitrary time interval is shown to be the same as the input distribution, for any number of servers. This result has applications in problems of tandem queuing. The essence of the proof is the demonstration of the independence of an interdeparture interval and the state of the system at the end of the interval.
In many stochastic models, particularly in queueing theory, Poisson arrivals both observe (see) a stochastic process and interact with it. In particular cases and/or under restrictive assumptions it has been shown that the fraction of arrivals that see the process in some state is equal to the fraction of time the process is in that state. In this work the author presents a proof of this result under one basic assumption: the process being observed cannot anticipate the future jumps of the Poisson process.
The purpose of this study was to explore travellers’ responsiveness to congestion pricing (cordon
pricing) and parking attributes (especially parking pricing) in deciding whether to drive and park in the
central business district (CBD). Employing the stated-preference method, data were collected by
personal interviews in Mashhad City CBD. The respondents were requested to report their current trip
to the CBD by answering a set of questions and scenarios to consider five alternatives, including two
parking locations in the CBD: park inside the cordon-pricing zone or outside and access the cordon area
by walking or public transport, shift to public transport (taxi and bus), or cancelling of their trip to the
CBD. The two parking locations were categorised by cordon and parking tariff schedule, search and
queue time for a parking space, and access time from the parking space to the final destination.
The preferences regarding mode of transportation and parking choices were determined using a
multinomial logit model, which was used to estimate elasticity values and the willingness-to-pay
among attributes. The results indicate that drivers are highly sensitive to cordon charge (?1.145),
significantly more than to parking cost, search and egress times in switching mode and parking choice.
Furthermore, respondents’ reactions suggest that drivers’ willingness to pay for parking fees is higher
than their tendency to pay for cordon tolls.
This paper presents an explicit agent-based model of parking search in a city. In the model, "drivers" drive toward their destination, search for parking, park, remain at the parking place, and leave. The city's infrastructure is represented by a high-resolution geographic information system (GIS) of the street network and parking lots; information is included on traffic directions and permitted turns, on-street parking permissions, and layers of off-street parking places and lots. Destinations are presented by layers of dwellings and public places. Driver agents belong to one of four categories: residents and guests with dwellings as destinations and employees and customers with public places as destinations. Each agent has its own destination, willingness to pay, time of arrival, and duration of stay. In the model, driver agents are "landed" at a distance of approximately 250 m from their destination, that is, close to the area in which drivers start searching for parking. First, a driver estimates the parking situation in the area and then starts to search for a parking place. During the search, a driver agent accounts for the availability of parking places, differences in pricing, and parking enforcement efforts. The model outputs include distributions of (a) search time, (b) distance between parking place and destination, (c) fees paid by the drivers, and (d) parking revenues for the proprietor of paid parking places (whether local authority or private operator). The model is implemented as an ArcGIS application and applied to analyze parking dynamics in an inner city neighborhood in Tel Aviv, Israel, during the course of a regular weekday.
This article proposes two models to analyse parking search: an analytical model called PARKANALYST and a geosimulation model, termed PARKAGENT, which explicitly accounts for street network and drivers' parking-related decisions. We employ both models to analyse the impact of occupancy rate and demand-to-supply ratio on cruising for parking and to compare the models' outcomes. We estimate the main characteristics of parking dynamics, and find that the spatial effects influence system dynamics starting from an occupancy rate of 85% while become really important for analysing parking when the occupancy rate is above 92–93%.
In recent years several European cities have introduced road pricing as a tool for managing transport demand, especially to reduce traffic congestion and rebalance the modal split between private vehicles and mass-trans it systems. Indeed, user behaviour brings about a User Equilibrium condition, which does not correspond to overall utility maximisation and fails to take account of external costs. Hence, in order to achieve the efficient use of transportation systems (System Equilibrium), tolls can be charged on urban roads so that the social surplus is maximised. For several reasons (theoretical, political, social acceptability) it is impossible to charge efficient tolls (first-best solutions) proposed in the literature; therefore in real networks sub-optimal tolls (second-best solutions) are applied. In this study we analyse the effects on optimal fare design when pricing revenues are wholly or partly used for improving public transport. In particular, we formulate a model according to economic theory in a multimodal and multiuser context, where multimodal features are calculated explicitly on the network for each fare configuration. The model is applied on a trial network (built with heterogeneous values of relative accessibility among different traffic zones) and several second-best strategies are analysed with particular attention to the use of pricing revenue.
In this paper we propose an assignment model on urban networks to simulate parking choices; this model is able to simulate the impact of cruising for parking on traffic congestion. For simulating parking choice and estimating the impact of cruising on road congestion we propose a multi-layer network supply model, where each layer simulates a trip phase (on-car trip between the origin and destination zone, cruising for parking at destination zone and walking egress trip). In this model the cruising time is explicitly simulated on the network. The proposed model is tested on a trial network and on a real-scale network; numerical tests highlighted that the proposed model is able to simulate user parking choice behaviour and the impact of cruising for parking upon road congestion, particularly when the average parking saturation degrees exceed 0.7.
There are two main sources of inefficiency in urban transport markets. First, transport prices fail to reflect the external costs of travel, notably peak-period external congestion costs. Secondly, a large percentage of drivers park for free, particularly at the workplace. Economic theory suggests, in the absence of other market distortions, that efficiency can be restored with a perfectly differentiated external cost charge in conjunction with resource-cost pricing of all parking spots. In practice, urban transport authorities can try various combinations of imperfect road-pricing systems and imperfect parking charges. One example might be the use of a single cordon charge to enter a city, together with a tax on workplace parking. In this paper, we use a numerical simulation model of an urban transport market to examine the efficiency gains from various parking policies with and without a simple cordon system. As would be expected, we show that pricing of parking and road use need to be simultaneously determined. As the level of the parking fee becomes more efficient, or as the number of free parkers is reduced, so the level of optimally determined cordon charge falls. Additionally, by introducing a cordon charge, the level of the optimally determined parking fee falls. The model results show that the second-best pricing of all parking spaces produces higher welfare gains than the use of a single-ring cordon scheme, though marginally lower than the combination of a cordon charge with resource-cost pricing of parking spots.
This study underscores the importance of adopting integrated parking management policies that ensure not only more rational use of the available parking spaces, evenly balancing supply and demand and bringing in revenues to cover the parking facilities costs, but also the improved attractiveness of alternative transportation modes. Parking supply and demand flows within the UC campus are estimated. The results indicate that the parking facility is underpriced and that there is overcrowding. To reflect critically on these issues and identify research areas to address their socioeconomic implications, a survey regarding the characterization of campus commuters and their travel options is presented. Logistic regression modelling is applied to determine the relative importance of UC campus commuters’ attributes in their level of willingness to pay to have reserved parking on the campus. Finally, some policy proposals are discussed.Highlights► Parking is a central topic in urban transportation and traffic management. ► Parking facilities at the UC campus are overcrowded and underpriced. ► Commuters are willing to pay to have access to conditional parking. ► Commuters are willing to accept a compensation to revise private car mode option. ► Integrated parking management (with public transport) might have positive impacts.
Arnott and Inci [Arnott, R. and Inci, E., 2006. An integrated model of downtown parking and traffic congestion. Journal of Urban Economics 60, 418–442] developed an integrated model of curbside parking and traffic congestion in a downtown area. Curbside parking is exogenously priced below its social opportunity cost, and the stock of cars cruising for parking, which contributes to traffic congestion, adjusts to clear the market for curbside parking spaces. Denser downtown areas have garage as well as curbside parking. Because of economies of scale in garage construction, garages are discretely spaced. The friction of space confers market power on parking garages. Spatial competition between parking garages, as modeled in Arnott [Arnott, R., 2006. Spatial competition between downtown parking garages and downtown parking policy. Transport Policy 13, 458–469], determines the equilibrium garage parking fee and spacing between parking garages. Also, the stock of cars cruising for parking adjusts to equalize the full prices of curbside and garage parking. This paper combines the ingredients of these two models, hence presenting an integrated model of curbside parking, garage parking, and traffic congestion, and examines curbside parking policy in this context through a numerical example with parameters representative of a medium-sized US city. The central result is that raising the curbside parking fee appears to be a very attractive policy since it generates efficiency gains that may be several times as large as the increased revenue raised.
This paper presents a simple model of parking congestion focusing on drivers' search for a vacant parking space in a spatially homogeneous metropolis. The mean density of vacant parking spaces is endogenous. A parking externality arises because individuals neglect the effect of their parking on this mean density. We examine stochastic stationary-state equilibria and optima in the model. Due to the model's nonlinearity, multiple equilibria may exist and the effects of parking fees are complex. Several extensions are discussed, including determining the social value of a particular parking information system.
This paper is the first to look at cruising for parking from an economic perspective. We present a downtown parking model that integrates traffic congestion and saturated on-street parking; the stock of cars cruising for parking adds to traffic congestion. Two major results emerge from the model, one of which is robust. The robust one is that, whether or not the amount of on-street parking is optimal, it is efficient to raise the on-street parking fee to the point where cruising for parking is eliminated without parking becoming unsaturated. The other is that, if the parking fee is fixed at a sub-optimal level, it is second-best optimal to increase the amount of curbside allocated to parking until cruising for parking is eliminated without parking becoming unsaturated
Congestion can be caused by through-traffic and by traffic destined for the area where consumers park. It may appear that congestion should be reduced by increasing the price of parking. This paper shows that if road usage is suboptimally priced, then a lump-sum parking fee can increase welfare, but a parking fee per unit time does not. Indeed, an increase in the price of parking induces each person to park for a shorter time, allows more persons to use parking spaces each day, and can thereby increase traffic. For the same reason, consumers may prefer that parking not be free.
The paper explores ways in which economists view parking charges within the context of policy formulation. Recent trends in economic analysis have taken more note of the institutional structure in which decisions are made; institutions embracing both formal structures such as laws but also the de facto ways in which actual outcomes emerge. While this distinction is often applied to final consumers, it also has relevance for those setting and enforcing micro economic policies such as parking policies. Taking a neo-classical economic approach would lead parking policies in one direction, but allowing for transactions costs, hysteresis, second-best factors, game-playing, etc. as well as normative concerns over equity of various kinds, all of which reflect institutional structures, can lead into a variety of others. The aim is to explain why current parking policies deviate from classical economic ideals.
Time dependent behavior has an impact on the performance of telecommunication models. Examples include: staffing a call center, pricing the inventory of private line services for profit maximization, and measuring the time lag between the peak arrivals and peak load for a system. These problems and more motivate the development of a queueing theory with time varying rates. Queueing theory as discussed in this paper is organized and presented from a communications perspective. Canonical queueing models with time-varying rates are given and the necessary mathematical tools are developed to analyze them. Finally, we illustrate the use of these models through various communication applications.
We treat parking as a common property resource and examine the benefits of pricing it. Without pricing, parking close to the destination will be excessive, and will fall off more rapidly than is socially optimal. The optimal pattern is attained under private ownership if each parking owner prices in a monopolistically competitive manner. When cruising for parking congests both parkers and through traffic, the benefits from pricing are substantially reduced.
Too little, too soon? A preliminary evaluation of congestion-based parking pricing in San Francisco
- D G Chatman
- M Manville
Chatman, D.G. and Manville, M., 2013. Too little, too soon? A preliminary evaluation of congestion-based parking pricing in San Francisco. Working paper.
Estimating environmental and congestion effects from cruising for parking
- D King
King, D., 2010. Estimating environmental and congestion effects from cruising for parking. Paper Presented at Transportation Research Board Annual
Meeting. Washington, DC.
Kleinrock, L., 1976. Queueing Systems. Wiley.
Evaluating the Impacts of Performance-Based Parking
- A Millard-Ball
- R R Weinberger
- R C Hampshire
Millard-Ball, A., Weinberger, R.R., Hampshire, R.C., in press. Evaluating the Impacts of Performance-Based Parking, J. Am. Plann. Assoc. (2014).
Curbing Cars: Shopping, Parking and Pedestrian Space in SoHo
- Schaller Consulting
Schaller Consulting, 2006. Curbing Cars: Shopping, Parking and Pedestrian Space in SoHo, New York.
Driven to Excess: What Under-Priced Curbside Parking Costs the Upper West Side
- D C Shoup
Shoup, D.C., 2008. Driven to Excess: What Under-Priced Curbside Parking Costs the Upper West Side, New York.
Coin-in-Slot Parking Meter Brings Revenue to City. Popular Mechanics
Popular Mechanics, 1935. Coin-in-Slot Parking Meter Brings Revenue to City. Popular Mechanics, p. 519.
Coin-in-Slot Parking Meter Brings Revenue to City
- G Pierce
- D Shoup
Pierce, G., Shoup, D., 2013. Getting the prices right. J. Am. Plann. Assoc. 79 (1), 67-81.
Popular Mechanics, 1935. Coin-in-Slot Parking Meter Brings Revenue to City. Popular Mechanics, p. 519.