Parking policy,parking location decisions and the distribution of congestion

The paper develops and tests a model which characterizes the parking location decisions of individual tripmakers. The model is designed to offer information concerning the effects of alternative parking policies on parking location decisions and therefore the effects on the distribution of congestion in an urban area.Own price, time price and full price elasticities for alternative parking locations are estimated. The own price elasticity is found to rise with distance from the destination point while the time price elasticity falls with distance. The full price elasticity is found to be relatively stable.One is able to determine from the calculated elasticities, the effects of alternative parking policies such as raising parking fees, time restrictions, or increasing search or transaction costs on the distribution of individuals consuming parking services; from this one can infer the impact on the distribution of congestion.The paper also offers some explanation for the low elasticity of auto use with respect to changes in parking costs found in some modal choice studies.The author is indebted to Adolf Buse, Ken Norrie and Richard Westin for helpful comments and criticism.     

Parking difficulty and parking information system technologies and costs

Before the implementation of a parking information system, it is necessary to evaluate the parking difficulty, technology choice, and system costs. In this study, the parking problem was quantified by asking parkers to express their parking difficulties in five scaled levels from the least to the most difficult. An ordered Probit model was developed to identify the factors that influence a parker to feel the parking difficulty. The results indicate that the amount of parking information parkers had before their trips was directly related to their parking search time, which in turn, influenced their perceptions of parking difficulty. Parkers' preferences to parking information technologies were identified based on developing binary and multinomial probit models. The results indicate that personal business trips and older persons would like to use the kiosk, while the more educated and males would not. Trips with shopping and social/recreation purposes and the drivers who had visited the destination areas frequently would like to choose roadside display. Drivers who had planned their parking and had Internet access would use in‐vehicle device. The system cost was estimated based on the cost for each component of the system. The results show that providing en‐route parking search information through roadside displays is more expensive than providing pre‐trip information through a web site.     

Electric vehicles and residential parking in an urban environment: Results from a stated preference experiment

This paper presents the results of a preference survey of 1545 respondents’ willingness to purchase electric vehicles (EVs) in Philadelphia. We pay particular attention to respondents’ willingness to pay for convenient charging systems and parking spaces. If the value of dedicated parking substantially outweighs the value of convenient charging systems, residential-based on-street charging systems are unlikely to ever be politically palatable. As expected, respondents are generally willing to pay for longer range, shorter charging times, lower operating costs, and shorter parking search times. For a typical respondent, a $100 per month parking charge decreases the odds of purchasing an EV by around 65%. Across mixed logit and latent class models, we find substantial variation in the willingness to pay for EV range, charge time, and ease of parking. Of note, we find two primary classes of respondents with substantially different EV preferences. The first class tends to live in multifamily housing units in central parts of the city and puts a high value on parking search time and the availability of on-street charging stations. The second class, whose members are likelier to be married, wealthy, conservative, and residing in single-family homes in more distant neighborhoods, are willing to pay more for EV range and charge time, but less for parking than the first group. They are also much likelier to consider purchasing EVs at all. We recommend that future research into EV adoption incorporate neighborhood-level features, like parking availability and average trip distances, which vary by neighborhood and almost certainly influence EV adoption.     

Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers’ behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers’ parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers’ parking behaviour can be incorporated into traffic assignment models and used to support parking systems.     

Fuel saving and ridesharing in the US: Motivations,limitations, and opportunities

Ridesharing can reduce the fuel consumed in noncommercial passenger highway vehicles by grouping individuals into fewer vehicles and reducing the number of miles that vehicles must travel. We estimate the potential fuel savings that could result from an increase in ridesharing in the US. If no additional travel is required to pick up passengers, adding one additional passenger for every 100 vehicles would reduce annual fuel consumption by 0.80–0.82 billion gallons of gasoline per year; if one passenger were added in every 10 vehicles, the potential savings would be 7.54–7.74 billion gallons per year. However, ridesharing may require extra travel to pick up additional passengers, which can reduce and possibly eliminate potential fuel savings. The tradeoff between saving fuel and spending time to pick up additional passengers is investigated, finding that, on average, ridesharing may not be attractive to travelers, but can be made more attractive by increasing per-vehicle-trip costs such as parking and tolls.     

Choice of parking: Stated preference approach

Over recent years, parking policy has become a key element of transport policy in many countries. Parking policy measures can affect many different dimensions of travel behaviour but are likely to be most significant in terms of travellers' choice of parking type and location. This dimension of travel choice has, to date, received comparatively little attention, yet is of vital importance if we are to properly understand and predict the effects of parking policy measures.This paper presents two studies addressing this issue carried out in the United Kingdom and Germany. Both studies used a stated preference approach in order to collect disaggregate data on travellers responses to changes in parking attributes and used these data to build simple logit models of parking type choice. The studies were designed in order to allow comparable choice models to be estimated from the two datasets. The results obtained strongly indicate the need to separately identify the costs associated with different components of the parking activity (e.g., general in-vehicle time, parking search time, egress time) and also point to the existence of significant differences in the relative valuation of these components across different journey purposes. Where possible, the results of the choice modelling exercises are also compared with existing revealed and stated preference results and are found to be generally in line with prior expectations.     

The trouble with minimum parking requirements

Urban planners typically set the minimum parking requirements for every land use to satisfy the peak demand for free parking. As a result, parking is free for 99% of automobile trips in the United States. Minimum parking requirements increase the supply and reduce the price – but not the cost – of parking. They bundle the cost of parking spaces into the cost of development, and thereby increase the prices of all the goods and services sold at the sites that offer free parking. Cars have many external costs, but the external cost of parking in cities may be greater than all the other external costs combined. To prevent spillover, cities could price on-street parking rather than require off-street parking. Compared with minimum parking requirements, market prices can allocate parking spaces fairly and efficiently.     

Optimal dynamic parking pricing for morning commute considering expected cruising time

This paper investigates how recurrent parking demand can be managed by dynamic parking pricing and information provision in the morning commute. Travelers are aware of time-varying pricing information and time-varying expected occupancy, through either their day-to-day experience or online information provision, to make their recurrent 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 clusters cannot be used at the same time by travelers between more than one Origin–Destination (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 charging parking prices in each area that only depend on the time or occupancy, regardless of origins and destinations of users of this area. In the two numerical experiments, the best system performance is usually achieved by pricing the more preferred (convenient) area such that it is used up to a terminal occupancy of around 85–95%. Optimal pricing essentially balances the parking congestion (namely cruising time) and the level of convenience.     

Analysis of parking cruising behaviour and parking location choice

ABSTRACT

This paper explores car drivers’ cruising behaviour and location choice for curb parking in areas with insufficient parking space based on a survey of car drivers in Beijing, China. Preliminary analysis of the data show that car drivers’ cruising behaviour is closely related to their parking duration and parking location. A multinomial probit (MNP) model is used to analyse cruising behaviour and the results show that the closer to the destination car drivers are, the more likely they choose to park on the curb. The adjacent locations are the basis of car drivers’ sequential parking decisions at different locations. The research results provide a better understanding of cruising behaviour for parking and recommendations for reducing cruising for parking. The provision of parking information can help regulate the parking demand distribution.     

Parking permits management and optimal parking supply considering traffic emission cost

Given a many-to-one bi-modal transportation network where each origin is connected to the destination by a bottleneck-constrained highway and a parallel transit line, we investigate the parking permit management methods to minimize traffic time cost and traffic emission cost simultaneously. More importantly, the optimal supply of parking spots is also discussed in the policies of parking permit. First, we derive the total travel costs and emission costs for the two cases of sufficient and insufficient parking spot provisions at the destination. Second, we propose a bi-objective model and solve the Pareto optimal parking permit distribution, given a certain level of parking supply. Third, we investigate the optimal parking supply in the policy of parking permit distribution, with the objectives of minimizing both total travel cost and traffic emission. Fourth, we provide a model of optimizing parking supply, in the policy of free trading of parking permits. Finally, the numerical examples are presented to illustrate the effectiveness of these schemes, and the numerical results show that restricting parking supply at the city center could be efficient to reduce traffic emission.     

Cognitive cost in route choice with real-time information: An exploratory analysis

Real-time traffic information is increasingly available to support route choice decisions by reducing the travel time uncertainty. However it is likely that a traveler cannot assess all available information on all alternative routes due to time constraints and limited cognitive capacity. This paper presents a model that is consistent with a general network topology and can potentially be estimated based on revealed preference data. It explicitly takes into account the information acquisition and the subsequent path choice. The decision to acquire information is assumed to be based on the cognitive cost involved in the search and the expected benefit defined as the expected increase in utility after the search. A latent class model is proposed, where the decision to search or not to search and the depth of the search are latent and only the final path choices are observed. A synthetic data set is used for the purpose of validation and ease of illustration. The data are generated from the postulated cognitive-cost model, and estimation results show that the true values of the parameters can be recovered with enough variability in the data. Two other models with simplifying assumptions of no information and full information are also estimated with the same set of data with significantly biased path choice utility parameters. Prediction results show that a smaller cognitive cost encourages information search on risky and fast routes and thus higher shares on those routes. As a result, the expected average travel time decreases and the variability increases. The no-information and full-information models are extreme cases of the more general cognitive-cost model in some cases, but not generally so, and thus the increasing ease of information acquisition does not necessarily warrant a full-information model.     

Generic model for resource allocation in transportation. Application to urban parking management

In this paper, we define the online localized resource allocation problem, especially relevant for modeling transportation applications. The problem modeling takes into account simultaneously the geographical location of consumers and resources together with their online nondeterministic appearance. We use urban parking management as an illustration of this problem. In fact, urban parking management is an online localized resource allocation problem, where the question is how to find an efficient allocation of parking spots to drivers, while they all have dynamic geographical positions and appear nondeterministically. We define this problem and propose a multiagent system to solve it. The objective of the system is to decrease, for private vehicles drivers, the parking spots search time. The drivers are organized in communities and share information about spots availability. We have defined two cooperative models and compared them: a fully cooperative model, where agents share all the available information, and a “coopetitive” model, where drivers do not share information about the spot that they have chosen. Results show the superiority of the first model.     

Truck parking in urban areas: Application of choice modelling within traffic microsimulation

Urban truck parking policies include time restrictions, pricing policies, space management and enforcement. This paper develops a method for investigating the potential impact of truck parking policy in urban areas. An econometric parking choice model is developed that accounts for parking type and location. A traffic simulation module is developed that incorporates the parking choice model to select suitable parking facilities/locations. The models are demonstrated to evaluate the impact of dedicating on-street parking in a busy street system in the Toronto CBD. The results of the study show lower mean searching time for freight vehicles when some streets are reserved for freight parking, accompanied by higher search and walking times for passenger vehicles.     

Transit-based smart parking: An evaluation of the San Francisco Bay area field test

This paper presents an evaluation of the first transit-based smart parking project in the US at the San Francisco Bay Area Rapid Transit (BART) District station in Oakland, California. The paper begins with a review of the smart parking literature; next the smart parking field test is described including its capital, operational, and maintenance costs; and finally the results of the participant survey analysis are presented. Some key user response results are: (1) most participants used the smart parking system 1–3 days a month for commute travel and (2) 37% of respondents had seen the changeable message signs with parking information, but only 32% of those used this information to decide whether to continue driving or take BART. Some key changes in participant travel behavior include: (1) increases in BART mode share, (2) reductions in drive alone modal share, (3) decreased average commute time, and (4) an overall reduction in total vehicle miles of travel.     

Home-work carpooling for social mixing

Transportation - Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban...     

A novel permit scheme for managing parking competition and bottleneck congestion

Morning commuters may have to depart from home earlier to secure a parking space when parking supply in the city center is insufficient. Recent studies show that parking reservations can reduce highway congestion and deadweight loss of parking competition simultaneously. This study develops a novel tradable parking permit scheme to realize or implement parking reservations when commuters are either homogeneous or heterogeneous in their values of time. It is found that an expirable parking permit scheme with an infinite number of steps, i.e., the ideal-scheme, is superior to a time-varying pricing scheme in the sense that designing a permit scheme does not require commuters’ value of time information and the performance of the scheme is robust to the variation of commuters’ value of time. Although it is impractical to implement the ideal-scheme with an infinite number of steps, the efficiency loss of a permit scheme with finite steps can be bounded in both cases of homogeneous and heterogeneous commuters. Moreover, considering the permit scheme may lead to an undesirable benefit distribution among commuters, we propose an equal cost-reduction distribution of parking permits where auto commuters with higher value of time will receive fewer permits.     

Stochastic Poisson game for an online decentralized and coordinated parking mechanism

This paper proposes a decentralized and coordinated online parking mechanism (DCPM), which seeks to reduce parking congestion at multiple parking facilities in a central business district (CBD) through guiding the parking decisions of a parking coordination group. To establish this DCPM, this study develops a stochastic Poisson game to model the competitions among parking vehicles en route at multiple parking facilities. The equilibrium condition for the proposed stochastic Poisson game is formulated through involving travelers’ parking choice behavior described by multinomial logit model. Furthermore, we prove that the stochastic Poisson game is a potential game with a unique equilibrium. A simultaneously updating distributed algorithm is developed to search the equilibrium solution of the DCPM. Its convergence is proved by both mathematical analysis and numerical experiments. The numerical experiments are conducted to test the efficiency of the DCPM, based on a real-world CBD covering Guicheng Community, Nanhai District at Foshan in China. The performance of the DCPM is compared to three greedy strategies following the nearest first, cheapest first, and least cruise first policies, respectively. The experimental results demonstrate that the DCPM significantly reduces cruise vehicles and average cruise distance per vehicle from all other three greedy strategies; the least cruise first strategy, which takes advantage of the real-time open spots information at parking facilities, performs better than the nearest first and the cheapest first strategies without the access to real-time information. The DCPM can further improve the benefit of the real-time information. Additionally, in terms of walking distance and parking cost, the DCPM provide a trade-off solution between the nearest first and the cheapest first strategies.     

Evaluating parking reservation policy in urban areas: An environmental perspective

This paper investigates a parking reservation mechanism to reduce car cruising to find parking. To consider the benefits for drivers and parking facility providers, we charge drivers for making reservations in addition to parking fees, by introducing a reservation pricing model that makes reservation prices equivalent to the value of saved search time. By modeling the number of vacant spaces as a stochastic variable, and applying binomial pricing methods, parking reservation prices are obtained. Numerical examples based on the data for two parking facilities in Taiwan are given.     

Bicycle parking: a systematic review of scientific literature on parking behaviour,parking preferences,and their influence on cycling and travel behaviour

ABSTRACT

Cycling is experiencing a revival in many cities. Research has focused on the determinants of cycling – in particular the role of the built environment and road infrastructure. Bicycle parking has received little attention – even though bicycles are parked most of the time. This article reviews the scientific literature on bicycle parking and identifies existing gaps in research and knowledge. The review analyses 94 peer-reviewed papers identified through a search in Scopus and Web of Science, in December 2017. The annual number of papers increased 15-fold between 1995 and 2017. Overall, the level of evidence on the importance of bicycle parking is limited. The majority of studies are based on cross-sectional data with the presence of parking as a binary independent variable. Most studies focus on bicycle parking at public transport stops and at work places. Few studies report on bicycle parking throughout cities, and hardly any on parking at residential locations. Bicycle parking supply and quality appears to be a determinant of cycling for current and potential cyclists. Our findings can serve as input for an evidence-based debate on the role of bicycle parking. For practitioners, our research supports investment in bicycle parking, but acknowledges that a proper evaluation of such initiatives needs to be conducted to increase the level of evidence.     

Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility

Interest in vehicle automation has been growing in recent years, especially with the very visible Google car project. Although full automation is not yet a reality there has been significant research on the impacts of self-driving vehicles on traffic flows, mainly on interurban roads. However, little attention has been given to what could happen to urban mobility when all vehicles are automated. In this paper we propose a new method to study how replacing privately owned conventional vehicles with automated ones affects traffic delays and parking demand in a city. The model solves what we designate as the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP), which dynamically assigns family trips in their automated vehicles in an urban road network from a user equilibrium perspective where, in equilibrium, households with similar trips should have similar transport costs. Automation allows a vehicle to travel without passengers to satisfy multiple household trips and, if needed, to park itself in any of the network nodes to benefit from lower parking charges. Nonetheless, the empty trips can also represent added congestion in the network. The model was applied to a case study based on the city of Delft, the Netherlands. Several experiments were done, comparing scenarios where parking policies and value of travel time (VTT) are changed. The model shows good equilibrium convergence with a small difference between the general costs of traveling for similar families. We were able to conclude that vehicle automation reduces generalized transport costs, satisfies more trips by car and is associated with increased traffic congestion because empty vehicles have to be relocated. It is possible for a city to charge for all street parking and create free central parking lots that will keep total transport costs the same, or reduce them. However, this will add to congestion as traffic competes to access those central nodes. In a scenario where a lower VTT is experienced by the travelers, because of the added comfort of vehicle automation, the car mode share increases. Nevertheless this may help to reduce traffic congestion because some vehicles will reroute to satisfy trips which previously were not cost efficient to be done by car. Placing the free parking in the outskirts is less attractive due to the extra kilometers but with a lower VTT the same private vehicle demand would be attended with the advantage of freeing space in the city center.