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Determination of car park locations to accommodate maximum dynamic demand
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Currently, the problem of organizing permanent car storage places is relevant due to the high growth rate of motorization in the world. At the same time, excessively large Parking lots have a negative impact on the environment. The article considers various types of Parking lots and the main factors of their impact on the environment. A map of dispersion of emissions of harmful substances such as nitrogen oxides, carbon monoxide (II), total hydrocarbons for the city of Naberezhnye Chelny is constructed. To calculate the amount of emissions from one car in the Parking lot, a model is built in the AnyLogic simulation environment. The relationships between the number of Parking spaces and the amount of emissions from cars in the Parking lot were calculated and analyzed. It has been experimentally established that the more Parking spaces in a Parking lot, the longer cars search for Parking and, consequently, the more impact on the environment.
This paper proposes an optimal parking site selection scheme to alleviate CO2 emissions of the traffic flows for green urban road networks. Through the creative dynamic traffic zone programming, a constrained optimization model is set up to assess the impact of potential public parking locations on road traffic emissions. In each scenario, Thiessen Polygon based zoning method is applied to investigate the distributions of road traffics. The main contribution of this study is as follows. Firstly, this proposed model takes the CO2 emission of the whole traffic network of sustainable city development as the optimization goal, instead of the traditionally discussed travel distance or cost efficiency. Secondly, a Thiessen polygon based public parking zoning method is developed and implemented realistically. This zoning method provides a precise approach to traffic distribution and parking demand estimation. Rather than the quadrilateral or radial zoning, this method pays more attention to the parking supply demand and its impact on parking congestion. Thirdly, the genetic algorithm (GA) is used to find the optimal public parking location (PPL) sets. GA has a great application value in speeding up stochastic search for global optimization. It is especially suitable to simulate complex and large capacity problems concerning the realistic solutions. By implementing the dynamic zoning and modelling method into intelligent transportation system (ITS), the efficiency of parking induction and dynamic optimization of traffic distribution could be ensured for the future smart mobility. Therefore, this model not only serves as a novel method for public parking allocations, but hold potential to support intelligent parking guidance, as a part of the intelligent traffic system for smart city development.
The increasing population growth and subsequent rise in vehicle use highlight the necessity of efficient urban planning and supporting effective transportation systems. Experiencing the same challenges as many cities in developing countries, the study area targeted in this research suffers from inadequate public parking sites. The historical background of the city and hosting tourists, population growth, traditional design of the city with narrow streets and passages, and lack of effective municipal planning in the past are the factors that signify the necessity of establishing new public parking sites. Siting new parking lots requires application of an efficient location selection approach to minimize the corresponding economic and environmental costs. That is, siting a parking lot at a location is economically reasonable and socially acceptable when multiple economic, social, and environmental criteria are considered. Hence, the present study uses an efficient hybrid multicriteria decision-making (MCDM) method by coupling the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) methods under a fuzzy environment (i.e., fuzzy AHP-TOPSIS) to effectively address site selection criteria and uncertainties associated with a decision-making process for locating a new public parking lot.
We propose a new way to measure the share of traffic that is cruising for parking: observe how many cars pass a newly vacated space before a driver parks in it. This statistical method provides a quick, cheap and approximate way to estimate what share of traffic is cruising. Using 876 observations of newly vacated on-street parking spaces in central Stuttgart, we estimated that 15 per cent of the traffic was cruising for parking.
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.
Mismatch between urban development and public parking lots have caused problems in transportation systems and urban traffic so that occupied passages by on-street stopping disrupt smoothing traffic flow. This makes it inevitable to increase the capacity of passages and to devote more space to off-street parking spots. The present study aims at finding the most suitable sites for public parking lots in one of the most crowded cities of western Iran. Before the site selection process is getting started, the area required for construction of new parking lots was estimated using Parking Generation Method. Afterwards, site selection criteria including “distance from travel absorber centers”, “distance from passages”, “the cost of real estate”, and “suitable land uses for parking lots” were determined while “unsuitable land uses for public parking spaces” such as historical monuments were recognized as a constraint factor and excluded from being further analyzed. The selected criteria were then weighted using pair wise comparisons. The map layers were finally overlaid once by Boolean Method. Since the selected spots on the output map had not adequate distribution throughout the study area, the overlaying was done once again by fuzzy Ordered Weighted Average (OWA) by which a total number of four spots were recognized suitable for public parking lots in the city.
The paper deals with the selection of traffic infrastructure facility location by applying the AHP (Analytic Hierarchy Process) multi-criteria analysis methodology. The proposed methodology is applied in a case study to solve the problem of selecting a location for the garage-parking facility in the town of defined characteristics. The paper analyses the characteristics of five potential locations (alternatives), the selection of criteria and measures for assessing the alternatives and presents the input data preparation, the application of the selected method and the analysis results. All the relevant criteria for the analyses were included: the traffic, the economic criteria and those which nowadays are of great significance: the influence of the facility on the environment and the social criteria which is in accordance with the sustainable development principles. The goal of the paper is to present the procedure of the AHP method application on the complex issue of traffic planning and to confirm the adequacy of the chosen method on the traffic facility strategic planning.
In order to optimize the location of public parking facilities based on the optimal total social cost, location selection and scale settings are considered as the main contents. By analyzing the cost factors and the restrictions, a location model of public parking facilities is formulated with the objective to minimize the total social cost. Under the assumption that the requirement distribution of the public parking is agreed to the Gravity Model, the optimization algorithm of the schemes selection is proposed to fully search the solution space. Finally, a numerical example is carried out to illustrate the proposed model and the algorithm.
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.
As an important part of the sharing economy, the usage of car sharing increases world widely with the help of developments in the technology. Especially after COVID-19 the demand for private car ownership and car sharing systems increased tremendously. Therefore, its market share attracts new investors and causes existing service providers to enlarge their service area. In this article, a novel multi-objective location-dependent two-stage stochastic optimization model is proposed to determine the most appropriate locations for car sharing system and allocate the demand to these locations. The model is applied to determine the best locations among 15 candidates, and three objectives are considered, which are the minimization of total cost that comprises locating costs minus income from satisfying the demand, minimization of CO emission occurs by the usage of car sharing system's cars and minimization of average unsatisfied demand. Both location-independent and location-dependent demands are taken into account. The proposed model delivers a more precise decision process framework for problems include stochasticity and multiobjectivity, and it easily can be implemented to any region, providing region sensitive parameters.
In smart parking guidance systems, the ability to estimate the availability of vacant parking spaces is important to make effective guidance. In this paper, we propose a general architecture for building crowdsensing-based parking guiding system, in which the occupancy state of parking lots can be detected by smart vehicles equipped with sensors and wireless communication devices, or by parking meters and parking fee-paying terminals, and the state information can be used to estimate the probability of finding available spaces for incoming smart vehicles. Five representative scenarios that can be used in such a framework were investigated. The problem to estimate the availability of parking spaces in each scenario was modeled as an M/M/c/c queuing problem with closed-form analytical solutions. The scenarios were validated in a simulation platform and their performance in various parking environments was investigated. Experimental results revealed that the crowdsensing-based parking prediction method can lead to 30.91% or more relative improvement on average estimation error than steady-state prediction in typical parking environments.
In the smart city environment, parking vehicle management is an essential requirement for citizens in the current situation because every city is rapidly growing as a crowded environment. Specific planning, operation, and thinking can address this problem with the Internet of things (IoT) and Information Communication Technologies (ICT). Existing research provides various solutions and methods for parking systems in the smart city. However, smart parking has many challenges, such as centralization, communication bandwidth, energy efficiency, integrity, security, and privacy. Inspired by Blockchain and AI technology, we propose a Blockchain-enabled Secure Framework for Energy-Efficient Smart Parking in Sustainable City Environment. The transport layer implements the ECC algorithm to encrypt and decrypt the parking zones data for secure communication. The RSU-based-Blockchain network offers authentication and verification of data at the security layer in a distributed manner. Virtualization technology is used for data storage and provides an energy-efficient environment using virtual machines. With Deep LSTM networks, we analyze the parking zone's data and offer the best parking space to the drivers with the best location and timing. We evaluate the proposed architecture using quantitative, qualitative analysis and provide the driver's best parking space.
Parking is one of the biggest challenges faced by megacities experiencing rapid growth. With more than 15 million population and approximately 3 million passenger cars, Istanbul is a megacity having a considerable parking problem. Sustainable planning of the parking lots in the city requires smarter spatial decision making approaches to use limited land resources efficiently. In order to determine the optimum locations for parking supply in the case of four districts of Istanbul, a GIS-based fuzzy analytical hierarchy process (AHP) approach is employed. In this context, the relevant criteria are identified and collected under three main categories: (1) land use, (2) finance and (3) transportation. Raster grid layers are created in GIS for those criteria through spatial data processing and analysis techniques. Those layers are then combined after being weighted with the fuzzy AHP approach. Thus, the scores of all grid cells in the study area are obtained and then transferred to the land parcels to rank them by their suitability. Besides, the parcels are also evaluated with respect to shape and area. Finally, those assigned to the highest ranking category as well as having proper shape and area are recognized as the feasible parcels for parking lot generation and prioritized according to those factors. The findings showed that the GIS-based fuzzy AHP approach applied in this study enabled multi-criteria investigation of each location (i.e. grid cell) in the study area based on highly detailed spatial data and could therefore be used to determine the feasible parcels for parking lot generation. Among the main criteria, transportation had the greatest weight while finance had the least weight. According to the case study, the need for parking and the number of feasible parcels are greater in Uskudar than the other three districts.
The capacity utilisation of car parks is an important issue because of space deficiency for infrastructure improvement in city centres. Intelligent Parking Guidance Systems (IPGSs) provide a solution to such shortages by managing car park capacity. In this study, an IPGS model is proposed concerning the occupancy condition of three surface car parks, and the proposed model is compared with the conventional system (CS) where drivers tend to prefer the closest parking utility. A multi-agent-based simulation program investigates five scenarios and the results compared regarding the occupancy ratio, wasted Value of Time (VoT), and emission of harmful gases. The simulation results illustrate that the proposed IPGS model manages to equilibrate the capacity utilisation of car parks and the parking search period compared with the CS model. Analysis results show that emissions for CS of CO2, CO, HC, and NOx are 398.71, 19.90, 3.29, and 21.14 g/s/veh, respectively. This is attributed to the extra search period in the CS models. Besides, the cost of this extra searching period in CS is estimated in terms of the value of time for Turkey, Germany, U.K., and France as 0.49, 0.93, 1.42, and 1.42 euro/veh respectively.
While the traditional facility location problem considers exogenous demand, in some applications, locations of facilities could affect the willingness of customers to use certain types of services, e.g., carsharing, and therefore they also affect realizations of random demand. Moreover, a decision maker may not know the exact distribution of such endogenous demand and how it is affected by location choices. In this paper, we consider a distributionally robust facility location problem, in which we interpret the moments of stochastic demand as functions of facility-location decisions. We reformulate a two-stage decision-dependent distributionally robust optimization model as a monolithic formulation, and then derive exact mixed-integer linear programming reformulation as well as valid inequalities when the means and variances of demand are piecewise linear functions of location solutions. We conduct extensive computational studies, in which we compare our model with a decision-dependent deterministic model, as well as stochastic programming and distributionally robust models without the decision-dependent assumption. The results show superior performance of our approach with remarkable improvement in profit and quality of service under various settings, in addition to computational speed-ups given by formulation enhancements. These results draw attention to the need of considering the impact of location decisions on customer demand within this strategic-level planning problem.
Parking at megacities has become a major problem that is garnering increasing attention. The fundamental cause of the parking problem is the imbalance of demand and supply in core areas, where parking demand is high but parking provision is limited owing to exorbitant land prices. The idea of multistory parking facilities is proposed to serve larger parking demands with fewer land possessions. The newly developed automated multistory parking facilities are able to pick-up and place cars on different stories automatically. This paper proposes online operations method (OOM) of automated multistory parking facilities in response to intensive parking demands to reduce customers’ waiting time. The proposed online optimization model is composed of two tiers: in the first tier, a reinforcement learning framework is adopted to determine parking spot selections for incoming parking demands, and the second tier executes the plan acquired from the first tier by optimizing the action sequences of the automated elevator. Numerical experiments with multiple demand patterns are conducted to verify the proposed methodology. The results show that the learned strategy distinguishes from common practice in that it shows non-greedy patterns for some time during the day, and achieves significant improvements in various cases.
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.
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.
Illegal parking adversely impacts traffic and creates unsafe conditions for other road users. We present parking enforcement policies to maximize social welfare and profit. We use the bilateral meet function to quantify the friction and characterize key factors of illegal parking behavior. The model shows that the citation probability increases with the violator dwell time but decreases with the violators arrival rate, and drivers are inclined to park illegally when their dwell time is short. We also present a counter-intuitive finding that the citation fine may increase the dwell time of the violators.
Strategic development of parking spaces is considered as one of the leading solutions to alleviate urban congestion. However, few studies have quantified the interactions between land use and parking to investigate their emission impacts on traffic congestion. This study introduced a novel dimensionless ratio, the Extra Carbon Emission Index (ECEI) which estimates carbon dioxide (CO2) increments in the congestion condition of free-flowing traffic. Using a publicly accessible web-mapping service application, we collected the traffic speed data from dynamic urban road networks during peak hours of Xi’an city, China in 2017. A multiple regression model was applied to analyse the tripartite relationship among land use, parking availability and the ECEI. The results suggest that 1) supply-demand ratio of parking spaces, density and land use mix are negatively correlated to congestion; 2) parking availability is positively associated with spatial-temporal distribution of traffic flows, and this land use differentiates congestion in time and throughout the built environment; and 3) low-density parking lots and high-density parking spaces increase traffic congestion in residential districts, while adversely affect the compact land use towards sustainability. Compared to the reconstruction of land use, parking reform provides a more efficient way to alleviate congestion by coordinating the density of lots and spaces in residential districts.
This paper explores the extent to which high quality public transport can support reduced car parking requirements for new residential apartment buildings. Using a case study of Melbourne, the demand for car parking at residential apartment buildings in proximity to high frequency public transport is assessed, while controlling for a range of socio-demographic, urban design and demand management variables. Key findings indicate that while lower demand for car parking is associated with proximity to high quality public transport, this association is not significant when controlling for other factors that influence car ownership. Public transport service supply within 800 m of residential apartment buildings was instead found to be significant, rather than simple distance to transit. Modelling results suggest an inelastic relationship whereby a 10% increase in public transport service supply is associated with a 0.9–1.2% reduction in car parking demand as measured by levels of car ownership. Notwithstanding broader criticisms of residential off-street parking minimums, the findings have important implications for the development of residential car parking policies, suggesting that city-wide car parking requirements should appropriately reflect the spatial distribution and quality of public transport services.
The problem in designing a public car park layout entails selecting several car parks for construction and then determining the assigned number of parking spaces in each selected car park within a planning region containing many parking demand points and candidate car parks. In this paper, we propose a mathematical programming model to formulate the layout of public car parks with the objective of minimizing the comprehensive costs, including the walking time of all users, and the construction cost of the car parks. The model is designed to consider dynamic multiperiodic parking demands and guarantee that the parking needs of users are met during each period. Using this model, the number and locations of car parks and the number of parking spaces at each car park to be constructed can be simultaneously determined. Moreover, the number of cars at each parking demand point that will enter or leave each car park during each time period can also be determined. A typical numerical example, which is solved using a genetic algorithm (GA), is created to demonstrate the proposed model.
The demand for urban mobility has seen a marked increase over the last few decades. The expansion of road networks, the increase in distance traveled and in the number of trips made has created car-dependent cities. One solution for achieving the switch from private car to public transport is the introduction of a new public transport line that improves the accessibility and frequency of service. However, an important behavioral process underpins travel choices and often a change in choice context is not sufficient to evoke behavior change. Voluntary travel behavior change (VTBC) programs were developed for heightening individuals’ awareness of the consequences of their travel choices and of the benefits to be reaped from using sustainable alternatives, through information provision and communication strategies. The objective of the present paper is to analyze the effect on travel mode choice of introducing a new light railway line into the choice set (hard measure) when implementing a VTBC program on a large scale, but with a high degree of personalization (soft measure). Although numerous studies have demonstrated the need to implement a (hard) measure that acts on the choice context, in combination with a (soft) measure that acts in a personalized way on demand, as far as the authors are aware, few have put this into practice. The first results have shown that a combination of hard and soft measures is more effective in evoking travel behavior change and personalized travel plans produce better results than generalized soft measures.
The parking problem is a very important issue in city life because many citizens waste a large amount of energy and time trying to find suitable parking lots. To resolve this problem, various intelligent parking guidance systems have been introduced. However, the method of operating an intelligent parking guidance system remains in the infant stage. For successful operation, it is important to develop an effective method that assesses and selects the best parking lot in a real-time environment. In this vein, this study proposes a neural network-based predictive control approach that finds suitable weights for multiple factors dynamically so that the best performance of the intelligent parking guidance system can be achieved. The proposed method can enhance the performance of an intelligent parking guidance system via dynamic control in selecting the best parking lot. To evaluate the proposed approach, simulation tests and comparison with a traditional model have been conducted. As a result, the proposed approach provides a robust solution in an efficient manner under diverse parking environments. With the proposed approach, from the public interest viewpoint, the car parking problem can be approached more effectively.
Tehran, the capital of Iran and one of the largest cities in the world, faces uncontrolled urban expansion. Over the last few decades, urban expansion and traffic congestion in Tehran has greatly increased the demand for public parking facilities. In recent years, urban policy makers and the local municipalities of Tehran have focused their efforts on increasing the number of public parking facilities in different areas of the city. Their approach to parking site selection has been centralized, and has received some criticism for a perceived failure to represent certain interest groups and stakeholders coupled with an inability to provide a platform for active participation and collaboration. Due to many conflicting issues, the number of factors inherent to parking site selection, the increasing diversity of expertise areas, and the current trend to democratize planning, the use of participatory or collaborative planning methods for parking site selection has proven to be more effective. This paper presents a Web-based group GIS-MCDA procedure and tool to address the issue of parking site selection in Tehran. The integration of GIS and Multicriteria Decision Analysis (MCDA) capabilities into the Web platform offers an effective Multicriteria Spatial Decision Support System (MC-SDSS) with which to involve stakeholders and other groups in site selection processes. Such a system makes it possible to find appropriate sites that may reconcile the conflicting objectives resulting from different opinions and the final site selection outcome that can be accepted by the majority. The paper demonstrates the implementation of the proposed system for tackling the parking site selection problem in the center of District # 22 of Tehran.
Parking Structures: Planning, Design, Construction, Maintenance and Repair
- A P Chrest
- M S Smith
- S Bhuyan
- M Iqbal
- D R Monahan
- Chrest A. P.
Identifying Public Parking Sites Using Integrating GIS and Ordered Weighted Averaging Approach in Sanandaj City, Iran
- H Karimi
- B M Herki
- S Gharibi
- S Hamiditehrani
- A Kakhani
- Karimi H.
Transportation Engineering: An Introduction
- C J Khisty
- B K Lall
- Khisty C. J.
Study on Location Planning Model for Urban Public Parking Area
- J Chen
- W Wang
- Chen J.