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Uncovering divergences in parking payment behavior
... While the work outlined in this paper focuses on fitting predictive count models, the MetaCountRegressor package can also be applied to linear regression. An implementation of MetaCountRegressor for linear regression can be found in Florian Heraud et al. (2025), which focuses on identifying the best specification for differences in parking payments, as well as the relationship between the amount spent and the remaining time. ...
{Analyzing and modeling rare events in count data presents significant challenges due to the scarcity of observations and the complexity of underlying processes, which are often overlooked by analysts due to limitations in time, resources, knowledge, and the influence of biases. This paper introduces MetaCountRegressor, a Python package designed to facilitate predictive count modeling of rare events guided by metaheuristics. The MetaCountRegressor package offers a wide range of functionalities specifically tailored for the unique characteristics of rare event prediction. This package offers a collection of metaheuristic algorithms that efficiently explore the solution space, facilitating effective optimisation and parameter tuning. These algorithms are specifically engineered to deal with the inherent challenges of modeling rare events for predictive purposes, and capturing causative effects that are easily interpretable. State-of-the-art models are produced by the decision-based optimization framework. This includes the ability to capture unobserved heterogeneity through random parameters and allows for correlated and grouped random parameters. It also supports a range of distributions for the random parameters, and can capture heterogeneity in the means. The package also supports panel data, among other features, and serves as a systematic framework for analysts to discover optimization-driven results, saving time, reducing biases, and minimizing the need for extensive prior knowledge.
Growing awareness of the environmental impact of abundant parking has led to recent measures focused on decreasing car use in urban areas. This paper employs interpretable time series models to analyze the effects of these measures on parking demand. The study utilizes a dataset of more than 22 million parking transactions from 3,594 on-street selling points and 8 park-and-ride (P&R) locations in Amsterdam. Three models with external regressors, namely, Error Trend Seasonality (ETSX) models, Seasonal Autoregressive Integrated Moving Average (SARIMAX) models, and Interpretable Multi-Variate Long Short-Term Memory (IMV-LSTM) models, are compared against a Seasonal Naïve benchmark model. The ETSX model achieved the lowest error values, as indicated by both RMSE and SMAPE. The results show a significant decrease in parking (up to a 77% decline), primarily attributed to the tariff change, which had a greater impact than the introduction of a metro line. Moreover, both measures caused a shift in parking to P&R locations and peripheral areas. The introduction of the metro line led to more parking near a new metro station. In addition, COVID-19 measures resulted in a significant decrease in parking demand. These results are presented in an application that visualizes the influence of external regressors on parking ticket demand.
Cities worldwide are striving to find more efficient approaches to address the prevalent parking challenges in urban areas. A key aspect of achieving an optimal parking environment is the collection of curbside parking data, which enables informed decision-making and effective management of on-street parking spaces. This study proposes a solution for curbside parking monitoring and data collection using roadside LiDAR systems. By leveraging laser beam variation detection, this solution can extract essential information about parking usage. Unlike existing solutions, such as imagery or embedded sensor-based monitoring, our solution offers portability and ease of deployment for short-term or long-term curbside parking data collection. Additionally, the LiDAR sensor captures only three-dimensional data and is independent of illumination conditions, ensuring stable operation throughout the day while safeguarding privacy by not capturing imagery. These features align with the requirements of city agencies for parking data collection. The workflow follows a simple trend without the need for complex training, as typically seen in machine learning-based methods, and instead relies on parameter tuning based on real-world environmental factors. To validate the effectiveness of our method, we collected curbside parking data for five days at a midtown traffic junction with eight parking spaces. Manual validation confirmed a 95% match between identified parking events and observed data across different time periods. The study further presents parking statistics based on the identified events, revealing crucial insights about parking usage in the study area.
Intelligent parking services can provide parking recommendations and reservations for travelers. They are an effective method for solving the cruising for parking problems in big cities. This research conducted a sequential parking decision behavior survey and analyzed travelers’ parking choices and reservation behaviors at different stages of the travel process. Then, a parking recommendation model was established to consider the travelers’ psychological thresholds and the attention to parking factors. The effects of different parking recommendation schemes in different situations were further explored based on parking simulations. It was concluded that travelers were more willing to accept and use the parking recommendation system. A total of 56% of travelers chose to make parking reservations during the travel process. The satisfaction proportion of the psychological threshold for the parking reservation group was higher than that for the non-parking reservation group. A dynamic parking recommendation scheme with a regulation threshold can change the recommendation strategy according to the overall utilization of parking lots. The implementation of the scheme can not only improve travelers’ parking experience, but it can also effectively balance the utilization of parking resources. It can be applied to different parking utilization situations and produce good performance. The research results provide references for the design and application of intelligent parking services in order to solve parking problems.
This study provides empirical evidence and analyses of cruising time for parking in a dense urban area. A survey was conducted across three distinct geographical areas in the central business district (CBD) of Brisbane, Australia, over one week to assess the effect of parking and driving habits on search time for parking. Drivers’ parking behavior was observed and responses to a questionnaire were obtained from 138 drivers. Several large transactional datasets were also used to complement the survey data and evaluate the impact of parking supply/demand and traffic volume on cruise time. Overall, 25% of the participants did not cruise for parking, 40% spent less than 5 min, and 35% spent more than 5 min to find parking. Our results reveal that arrival time is a significant determinant of cruising behavior. Approximately 80% of the drivers who parked every day in the CBD found parking in less than 3 min, while almost 50% of those who parked occasionally in the CBD had to search for parking more than 5 min. The trip purpose also impacted cruise time for parking; drivers who traveled for work or business were more likely to cruise for parking compared to all other trip purposes. One surprising finding of this study is the negative association between relative traffic volume and cruising time; drivers who tended to search for on-street parking in the Brisbane CBD were more likely to avoid peak traffic. Our findings, in general, highlight the need to provide real-time and reliable parking information to regular commuters to minimize their total travel time and to reduce congestion in the CBD. Provision of a parking information system can mitigate cruising for on-street parking in the CBD, especially if it considers the prices of both on-street and off-street parking facilities and enables customized comparisons between them.
The recent development of shared-bike systems in China has brought convenience for users, as well as great pressure on bicycle parking management. There is limited empirical evidence regarding the effectiveness of incentive measures from the perspective of guiding people to park their shared bikes regularly. By defining a mixed logit model based on the theory of planned behaviour, this study focuses on exploring the conformity effects of policy compliance among dockless shared-bike users in the behavioural decisions of bicycle parking. A total of 453 respondents who had just finished parking a shared bike were invited to provide intentional information by participating in a stated preference survey. The empirical results indicated that, as a positive and negative incentive measure, both monetary rewards and financial penalties can motivate people to park the shared bikes in an unsaturated place near their destinations. However, the significant difference is that, with increasing incentive intensity, a monetary reward can motivate shared-bike users to shift the bicycles away more effectively than a financial penalty, especially when the shifting distance requires more than 10 min of walking. In addition, some factors used to characterize the individual heterogeneity, such as gender, education and searching time for dockless shared bikes, also have obvious impacts on policy compliance regarding bicycle parking guidance. These findings can help policy makers to create appropriate measures in the form of incentives to reduce illegal parking by shared-bike users.
The effect of parking fees on parking demand has been investigated in many different contexts, applying a wide variety of study designs. Research that brings together the knowledge derived in these studies systematically, however, is scarce. We conduct a meta-analysis of parking price elasticities based on 50 studies. Using seemingly unrelated regression models we account for the interdependence between the price elasticity of occupancy (EPO), dwell time (EPD), and volume (EPV). We show that estimates of parking elasticities based on stated preference (SP) data lead to different managerial and policy insights than estimates based on revealed preference (RP) data. Given a large amount of reported and unreported variation between the existing studies on parking price elasticities, we provide concrete recommendations for future studies that should warrant a higher degree of comparability and coherence in research design, conduction, and reporting.
Cruising-for-parking is a common phenomenon in many urban areas worldwide. Properly understanding and mitigating cruising can reduce travel times, alleviate traffic congestion, and improve the local environment. Most of the existing studies estimating cruising traffic are based on empirical data and/or detailed simulation models. Both approaches have large data requirements, and the detailed simulation models tend to have high computational costs. In this paper, we present a case study of an area within the city of Zurich, Switzerland, using a recently proposed macroscopic model to analyze the current conditions of cruising-for-parking. The results are validated with empirical data. The macroscopic model, inspired by a bottleneck model, reproduces the dynamics of both, the parking and the traffic system, as well as their interactions. As such, it calculates the delays encountered by drivers while waiting for parking, and the impact of such delays on the overall traffic stream, which involves not only the searching traffic but also the through traffic. It is shown that the macroscopic parking model could, additionally, incorporate the data generated by agent-based models, cooperatively producing valid and trustworthy results of cruising estimations, while requiring comparatively few data inputs and relatively low computational costs. The study shows that in a small area of Zurich (0.28 km²) with a demand of 2687 trips in a typical working day, cruising-for-parking generates 83 h of additional travel time and 1038 km of additional travel distance. Surprisingly, the worst conditions are observed at noon, corresponding to a maximum number of 30 searchers with an average search time of 13 min. Additionally, four types of parking policies are discussed, and their potential impacts on traffic performance are either quantitatively or qualitatively illustrated. The four policies include: the adjustment of the parking supply, the adjustment of parking time controls, the adoption of dynamic parking charges, and the provision of parking forecasts.
In this paper, we incorporate activity-based trip chaining behavior into the network equilibrium analysis. An integrated model which combines Beckman-type congestion link terms and entropy-type logit demand terms is proposed to describe the traveler behavior. The convexity and equivalency conditions of the model are discussed. Based on the integrated model, a bi-level model is designed to maximize the social welfare by appropriate parking fee. Also, an expanded network is developed to eliminate the non-additivity of the utilities of activities and travelling in the original network. Then, the Simulated Annealing (SA) method is used to solve the proposed bi-level model. Numerical examples are presented to examine the model’s availability and effects of the parking fee scheme on traveler behavior and social welfare. Results show that the model is effective in describing the trip chaining behavior in the network.
Car ownership and car use depend on numerous factors, among which are parking availability at destination and at home. While the former has attracted considerable research efforts for decades the latter, home parking, has recently become subject to increasing research interest. We use evidence from the 2013/14 Norwegian National Travel Survey, and combine this with additional geographical context data. In this way, we analyse the impact of parking availability at home and at destination on car use, while controlling for different urban contexts like housing and job density, and population density. The analysis is divided into three. The first part links car use to availability and payment arrangements of workplace parking. The second is concerned with the impact of home parking accessibility and car use. The third set of regressions looks at the combined effect of parking at trip start and the trip end on car use. Reduced access to free workplace parking stands out as one of the most effective ways of reducing car use on work trips. Maximum parking standards are therefore important parts of an urban planner's toolbox. Workplace parking fees can be effective when parking capacity is abundant and especially so if payment is made on a daily basis. Car use is in fact almost doubled where employees can pay monthly instead of daily. Limited access to parking at home also affects car use. The decision to drive decreases with increasing walking distance to the car park, especially in densely built-up areas. Restricted parking both at home and at the trip destination add up to very low odds of car use. Parking restrictions will have the greatest effect in compact cities.
Suppression effect in multiple regression analysis may be more common in research than what is currently recognized. We have reviewed several literatures of interest which treats the concept and types of suppressor variables. Also, we have highlighted systematic ways to identify suppression effect in multiple regressions using statistics such as: R 2 , sum of squares, regression weight and comparing zero-order correlations with Variance Inflation Factor (VIF) respectively. We also establish that suppression effect is a function of multicollinearity; however, a suppressor variable should only be allowed in a regression analysis if its VIF is less than five (5).
Parking enforcement is increasingly perceived as a key feature of urban transport policy. Illegal parking can lead to reduced traffic speeds, congestion, changes in modal choice, loss of revenue from valid parking spaces, a decline in respect for the law and even to accidents. Its potential impact is of such magnitude that it is now widely recognized that policies aimed at its control should be incorporated into any sensible transport plan. However, since parking enforcement is the policy maker's response to illegal parking and in order to implement a response that is appropriate, it is vital that there exists a sound understanding of the causes and patterns of illegal parking. Similarly, the ability to assess the impact of a particular enforcement strategy is equally vital. These considerations, together with the need to specify an overall objective for the enforcement effort, justify the exploration of the various relationships that exist between illegal parking and enforcement. This paper reviews and codifies the relevant available literature and presents these relationships within the general context of transport policy.
Parking policy is one of the key links between transport and land-use policy. Parking policies are often compromised in their effectiveness due to the perceived tension between three of the objectives that parking supports: regeneration, restraint and revenue. In particular the belief that parking restraint measures could damage the attractiveness of city centres to both retail and commercial enterprises limits the political acceptability of pricing policies and planning.
This paper presents a review of the evidence base upon which commuter, leisure and shopping and residential parking policies are based. Whilst underdeveloped, the literature suggests that greater attention should be given to analysing and presenting the accessibility impacts that different parking restraint measures have on travelers of all modes. The research base in many instances does not support, or provides evidence counter to, the assumption that parking restraint makes centres less attractive. Further disaggregate work is needed to understand how context specific these findings might be.
Estimating crash data count models poses a significant challenge which requires extensive knowledge, experience, and meticulous hypothesis testing to capture underlying trends. Simultaneous consideration of multiple modelling aspects is required including, among others, functional forms, likely contributing factors, and unobserved heterogeneity. However, model development, frequently affected by time and knowledge, can easily overlook crucial modelling aspects such as identification of likely contributing factors, necessary transformations, and distributional assumptions. To facilitate model development and an estimation that can extract as many insights as possible, an optimization framework is proposed to generate and simultaneously test a diverse array of hypothesis. The framework comprises a mathematical programming formulation and three alternative solution algorithms. The objective function involves minimizing the Bayesian Information Criterion (BIC) to avoid overfitting. The solution algorithms include metaheuristics to deal with an NP-hard problem and search through a complex and nonconvex space. The metaheuristics also enable to handle unique datasets through varying search strategies. The effectiveness of the proposed framework was ascertained using three distinct datasets, and published models used as benchmarks. The results highlighted the ability of the proposed framework to estimate crash data count models, surpassing benchmark models in terms of insights and goodness-of-fit. The framework provides several advantages, such as robust hypothesis testing, uncovering unique specifications and vital insights in the data, and leveraging existing knowledge to enhance search efficiency. The framework also exposes the vulnerability of traditional analyst efforts to fall into local optima, bias, and limitations in creating more efficient models. In a compelling example using crash data from Washington, the proposed framework unveiled insights overlooked by a benchmark published model, identifying speed, interchanges, and grade breaks as likely crash contributors, and revealing the potential danger of excessively wide shoulders. Conversely, the benchmark model identified fewer contributing factors and missed a crucial non-linear relationship between crash safety and shoulder widths. While wider shoulders are typically associated with improved safety, the proposed models suggest a safety threshold beyond which further widening could decrease safety. The introduction of random parameters in the analysis revealed a more nuanced relationship with crash frequency, thereby underlining the limitations of models incapable of capturing heterogeneity.
In-advance availability of parking information plays an important role in parker/ traveller decision-making for parking, curbing congestion, and managing parking lots efficiently. Specifically on-street parking poses many challenges compared to the off-street ones. Many users such as, store owners, municipal authorities, and police demand slots for on-street parking Free of Charges (FoC) for a short duration. In last few years, parking authorities collected data to attract the attention of researchers to present data-centric solutions for various problems such as, minimization of parking prices, maximization of revenue, and balacing the congestion at parking lots associated with smart parking systems. Motivated from the aforementioned problem, this paper proposes a scheme based on machine learning and game theory for dynamic pricing and allocation of parking slots in on-street parking scenarios. The dynamic pricing and allocation problem is modeled as Stackelberg game and is solved by finding its Nash equilibrium. Two types of Parking Users (PUs), i.e., Paid Parking Users (PPUs) and Restricted Parking Users (RPUs) are considered in this work. RPUs avail parking slots FoC once a day. PPUs compete to minimize the prices, and RPUs compete to maximize the FoC granted duration. The Parking Controllers (PCs) compete to maximize revenue generated from PPUs and to minimize total FoC parking duration granted to the RPUs. The random forest model is used to predict occupancy, which in turn is used to generate parking prices. Seattle city parking and its prices data sets are used to predict occupancy and to generate prices, respectively. In order to test the performance of communication system, the proposed
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PARK Pricing and Allocation Scheme (PAS) is compared with its four variants and is found worth. The proposed scheme is also compared with other state-of-the-art schemes using various performance evaluation metrics. Simulated results prove the superiority of the proposed scheme in comparison to the other state-of-the-art schemes.
Parking pricing is considered a powerful tool for improving parking management and relieving traffic congestion in metropolitan areas. This study uses field-collected panel data over four time periods before and after the implementation of a new parking pricing policy in Nanning to investigate the time-varying effect of pricing on on-street parking characteristics, including parking duration and parking turnover. Results show that parking duration decreases as parking price increases, with the relationship presenting a growing elasticity. The elasticity of parking turnover fluctuates due perhaps to the influence of vehicle ownership. Kolmogorov-Smirnov (K-S) tests show that on-street parking characteristics differ significantly before and after the implementation of a new parking pricing structure. Strategies that can improve parking services are also investigated based on the empirical data and modeling results.
The sharp contradiction between the rapidly growing number of vehicles and limited on-street parking space has resulted in the phenomenon of “difficult and disorderly parking”, which has adverse impacts on performance of urban roads. Propensity towards usage of private vehicles coupled with ineffective public transport has led to unmanageable demand for parking, especially in CBD with observance of illegal parking every day. In order to formulate enforcement measures to reduce occupancy of illegal parking, it becomes imperative to investigate behavioural aspects associated with illegal parking. To comprehend this objective, two busy urban streets in CBD area of Surat city were selected for comparative assessment of legal and illegal parking demand. Preliminary, on-street parking inventory was carried out by licence plate method at 1 h interval during business hours to determine peak parking hours. The survey observed dominancy of two wheel-motorized vehicles on both sides for both roads, and hence all analyses were carried out in terms of two wheel-motorized vehicles. Microscopic parking inventory was then carried for both legal and illegal parking demands at 10 min data monitoring interval using licence plate survey method during peak parking hours. Statistical check on demand accumulation revealed significant difference in legal and illegal parking demand. To probe further, microscopic behavioural analysis was carried by obtaining percentage repetition of parked vehicles (PRPVs) between two successive sets of observations for different data monitoring intervals. Paired t-test at 5% significance level between legal and illegal PRV values for different data monitoring intervals revealed isotropic behaviour between legal and illegal parking regimes. Further, efficient utilization of the available parking space in terms of turnover in 2 W per hour was analysed for each data frequency which indicated high value for high data frequency and vice versa. However, efficiency in terms of turnover was varying for both the demands with poor value for illegal parking. Research suggests that illegal problem in Surat city is a behavioural problem, and henceforth, stringent enforcement measures need to be formulated to reduce its occupancy.
The well-differentiated impact that parking supply options produce on congestion, pollution and land consumption arouses the interest of policy makers for a better understanding of car user’s behavior when choosing a parking option. Despite the evidence on the advantages of hybrid discrete choice models, most literature on parking choice only involves observable factors while leaving aside issues related to the latent variables. The behavioral hypothesis is that parking choice process depends not only on a set of observable factors but also has to do with individual-specific latent attributes. A hybrid discrete choice model with interactions among attitudes and observable factors, as well as among socioeconomic characteristics and observable factors, was estimated in order to consider individual heterogeneity. The results showed that, in addition to parking fee, search time and access time, a Risk-averse attitude and a Positive car care (maintenance) attitude are determinants for parking choice. The inclusion of these latent attributes and their interactions also resulted in a large improvement in the goodness-of-fit of the model and affected the time valuations.
Employers often provide employees with either subsidized or free parking at work. This distorts relative prices of alternative commuting modes and produces inefficiencies in the transport market. To mitigate this price distortion, parking cash-out has been suggested as an effective and efficient policy to reduce single occupancy car commuting trips. By rewarding the abandonment of car parking use rather than penalizing continued parking, parking cash-out makes commuters sensitive to the opportunity cost of workplace parking while, at the same time, circumventing the opposition usually associated with the imposition of transport user fees. However, practical experiences are rare and only few studies investigate the effectiveness and efficiency of this policy option. This paper empirically tests the effects of parking cash-out on modal choice by performing a two-stage mode choice experiment among commuters in Germany. The first stage collects revealed commuting behavior data. The second stage proposes cash-out scenarios and observes stated mode choice behavior. The joint treatment of regular travel costs and cash outs allows comparing the traditional car travel demand cost elasticity and the parking cash-out elasticity. Results indicate that regardless of model specifications, parking cash-out has a negative and significant effect on the private car choice probability. This bears important implications for future transport policies.
Most of motorized trips in cities of middle and small size are made in public transport and mainly in private vehicle, this has caused a saturation in parking systems of the cities, causing important problems to society, one of the most important problems is high occupancy of public space by parking systems. Thus, is required the estimation of models that reproduce users' behaviour when they are choosing for parking in cities, to carry out transport policies to improve transport efficiency and parking systems in the cities.The aim of this paper is the specification and estimation of models that simulate users' behaviour when they are choosing among parking alternatives in the city: free on street parking, paid on street parking, paid on underground parking and Park and Ride (not currently available). For this purpose, we propose a multinomial logit model that consider systematic and random variations in tastes. Data of users' behaviour from the different alternatives of parking have been obtained with a stated preference surveys campaign which have been done in May 2015 in the principal parking zones of the city of Santander.In this paper, we provide several improvements to previously developed methodologies because of we consider much more realism to create the scenarios stated preference survey, obtaining better adjustments.
For better management of on-street parking, cities are searching for ways to understand current usage of their on-street supply. Options for collecting on-street parking occupancy information range from manual data collection, which tends to be slow and not in real time, to in-ground sensors, which can provide real-time data but are costly and may have other issues related to battery life and their placement in the roadbed. This paper evaluates the various data collection methods and technologies for on-street parking. The District of Columbia Department of Transportation’s experience with various on-street technologies to detect parking occupancy is described, with a focus on how the technologies were able to identify solutions to the urban challenges inherent in the District. Criteria for a more in-depth analysis of on-street occupancy detection technology are described and presented as the next steps in the evaluation.
The goal of this study is to explore the perceptions and behaviors of drivers who cruise for parking. We conducted surveys with drivers in Brisbane, Australia, to understand potential factors that influence drivers’ cruising behavior. This study reveals that errors in drivers’ perception of parking cost are one of the leading factors encouraging drivers to cruise for on-street parking. Drivers are not necessarily well informed about parking costs, even when they claim to be familiar with these costs. The survey also reveals that the more informed drivers are about the local traffic and parking conditions, the less likely they are to cruise for extended periods of time. This finding demonstrates the value of traffic and parking information to effectively mitigate cruising for parking. The interview results also demonstrate that the on-street parking premium (i.e., accessibility or convenience factor) could be much larger than our common assumptions and a significant contributor to increased cruising time. Finally, this study introduces the sunk cruising cost and its potential impact on cruising time. Our hypothesis is that the effect of the sunk cost may manifest in a greater tendency for drivers to continue cruising because the time spent cruising is simply unrecoverable past expenditure. The survey data supports our hypothesis, and with findings on the drivers’ misperception about parking cost and the familiarity factor, this result highlights the value of accurate and timely parking cost and availability of information to drivers to tackle the cruising-for-parking issue.
This paper focuses on improving the effectiveness of parking enforcement patrol by optimizing the schedule of visit at each parking lot and the routing plan of patrol vehicles. Meanwhile, individual parking driver makes his/her parking payment decision based on knowledge of the patrol visit frequencies. Game-theoretic models are proposed to capture the interactions among the parking enforcement agency and parking drivers. We first develop a discrete formulation of the problem in the form of a mixed-integer program and propose a Lagrangian relaxation based solution approach. For large-scale instances, we also develop a continuum approximation model that can be reduced to a simpler non-linear optimization problem. A series of numerical experiments are conducted to show that, for small problem instances, both modeling approaches can yield reasonable solutions, although the continuum approximation approach is able to produce a solution within a much shorter time. For large-scale instances, the discrete model incurs prohibitive computational burdens, while the continuum approximation approach still provides a near-optimum solution effectively. We also discuss impacts of various system parameters, as well as the performance of different policy options (e.g., whether to allow multiple parking tickets to be issued to a vehicle with a long time of parking violation).
A case study of how big-data analytics can help to evaluate the effectiveness of existing policies and to formulate new policies is presented in this paper. The District Department of Transportation (DOT) in Washington, D.C., analyzed data for meter time limit adherence, identifying “overstays” at on-street metered parking spaces beyond the prescribed time limit. This analysis assessed the prevalence of meter overstays, citation patterns, and characteristics of that area. This information could help to determine the validity of existing time limits and develop a pricing structure that would shift longer-duration parkers to off-street garages. The analysis of overstays was conducted by using parking transaction data from the District DOT’s pay-by-cell program, transactions at networked single- and multispace meters, and parking citation data for overstays. Maps were created to identify areas experiencing historically, chronically, or persistently high rates of overstays. An assessment based on existing lan...
Commercial vehicles are of particular interest in parking enforcement because of their heavy presence in central business districts and their recurrent behavior of illegal parking. To deter illegal commercial vehicle parking, enforcement policies are defined by the citation fine and level of enforcement. This paper investigates how rational carriers react to a policy under steady state equilibrium conditions. To model the equilibrium, the paper uses the theory of bilateral searching and meeting where enforcement units meet illegally parked commercial vehicles at a rate which depends on the size of the two agents (illegally parked commercial vehicles and enforcement units). In assessing policy effectiveness, two objectives are defined which are profit maximization and social cost minimization. With the two objectives, the paper presents three market regimes and studies the equilibrium of each market. The proposed model covers several gaps in the parking literature by introducing illegal parking behavior elasticity with respect to parking dwell time, level of enforcement, citation fine, and citation probability. The model is applied on a case study of the City of Toronto and the results show that the citation probability increases with dwell time and the level of enforcement. Increasing either the citation fine or level of enforcement will hinder illegal parking but the obtained profit remains approximately constant. Sensitivity analysis on the meeting rate elasticity shows that profits are low when both elasticities are either high or low.
This paper reviews the literature on parking with an emphasis on economic issues. Parking is not just one of the most important intermediate goods in the economy; it is also a vast use of land. Many theoretical and empirical papers analyze the quantity and pricing of parking by concentrating on particular aspects of the issue. The aspects covered in this review are cruising for parking, spatial competition, (minimum and maximum) parking requirements, parking pricing and road pricing in the bottleneck model, and temporal-spatial pricing. Various forms of parking, including residential parking, shopping mall parking, and employer-provided parking, are also reviewed before identifying understudied topics that should be on the research agenda.
The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner.
Previous work in the economic theory of parking has treated parkers as homogeneous. In almost all policy contexts, however, heterogeneity among individuals matters not only quantitatively but also qualitatively. For example, providing both tolled and untolled alternatives allows those with high values of time to pay largely with money and those with low values of time to pay only with time. This paper extends the authors' (2009) integrated model of parking and traffic congestion in an isotropic downtown in steady state to treat heterogeneity in the value of time and parking duration. It develops the theory, and then presents an extended numerical example that applies the theory to several policy scenarios. With homogeneous individuals, underpricing curbside parking leads to wasteful cruising for parking. With heterogeneous individuals, however, curbside time limits can be used to ration out those with longer parking durations, so that cruising for parking is eliminated. With curbside parking time limits, underpricing curbside parking downtown may be a sound policy response to the free parking provided by suburban shopping centers.
The objective of this research is to investigate the behavior of Greek drivers with respect to their parking behavior through a staged approach. Available data collected from previous traffic studies was augmented with data collected by the authors for the year 2010, thus establishing different time periods and allowing comparisons over time (besides among the survey locations). First, at the national level, illegal parking behavior is examined in six Greek cities. Specifically, three of these cities are placed in Athens and the other three are smaller Greek cities. Second, the evolution of controlled parking systems in Athens is investigated by analyzing available data (as well as data collected within the scope of this study) and observing the change of parking characteristics, compliance and enforcement level.
This paper addresses and extends upon the recent upsurge of interest in market-oriented reform of parking policy, which has been reinvigorated by the work of Donald Shoup. His market-oriented approach to parking policy is shown to be the more ambitious of two distinct challenges to the conventional supply-focused approach. The other is 'parking management'. However, off-street parking markets and their post-reform dynamics have been neglected so far in proposals to deregulate the quantity of off-street parking. The paper highlights additional barriers to the emergence of off-street parking markets and several likely problems within them. Rather than suggesting the rejection of market-oriented parking policy, these findings are taken to imply a need for a more vigorous policy effort than has so far been called for. Achieving well-functioning off-street parking markets would require efforts both to actively foster such markets and to regulate to ensure their health. Deregulation would not be enough.
Multiple linear regression (MLR) and geographically weighted regression (GWR) models are used for estimating parking demand in areas with paid short stay parking systems. These models have been applied to the city of Santander (Cantabria, Spain) to check their goodness of fit and their predictive ability. The results show the main advantages and disadvantages of using GWR models. The technique proved to be useful in this case study because it offered a better fit and made better predictions in a scenario showing a certain degree of spatial heterogeneity unexplained by any of the variables introduced into the global model. However, the GWR model also presented situations of local correlation although this was considered moderate given the results provided by the variance inflation factors and the local condition indexes.
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
Measuring Consumer Utility
- Zikmund
Australia’s future transport and mobility
- KPMG
A Multi-Objective Framework for the Estimation of Advanced Crash Frequency Models Considering Random Parameters and Heterogeneous Means
- Ahern
Brisbane City Council
- Brisbane City
- Council
Empirical evidence on cruising for parking
- Van Ommeren