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ScienceDirect Transportation Research Procedia 20 (2017) 193 – 199

12th International Conference "Organization and Traffic Safety Management in large cities", SPbOTSIC-2016, 28-30 September 2016, St. Petersburg, Russia

Method for Evaluating Economic Efficiency of Parking Management Tools Dmitriy Fadeyev * Irkutsk National Research Technical University (INRTU), 83 Lermontov str., Irkutsk, 664074, Russia

Abstract Parking policy determined and carried out in today’s cities is based on a system of priorities. The main of them is the formation of a comfortable, favorable, safe and environmentally friendly city space. The goal can be reached if a certain city area is forbidden for cars. This study is aimed at economic evaluation of parking management tools, and analysis of users’ behavior with a glance to applied management tools. The study allowed obtaining comparative evaluations of efficiency of paid parking and time constraint by the example of Irkutsk. Besides, the study determined the optimum rate and the way how costs of searching for a parking spot influence the efficiency of management tools. ©2017 2016The TheAuthors. Authors. Published by Elsevier © Published by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the organizing committee of the 12th International Conference "Organization and Traffic (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the 12th International Conference “Organization and Traffic Safety Safety Management in large cities". Management in large cities” Keywords: parking; parking policy; payment for parking; parking time constraint; economic efficiency; parking duration

1. Introduction One of the most serious problems of central parts of big cities is inability to meet the vehicle owners’ demand for parking. Various popular objects concentrated on a relatively small area are often located in the historical part of the city with its established pattern and city-planning program.

* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address: [email protected]*

2352-1465 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the 12th International Conference “Organization and Traffic Safety Management in large cities” doi:10.1016/j.trpro.2017.01.050

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In this situation, the parking policy concept should be determined with due consideration for the peculiarities of a certain city, especially in requirements for preserving historical and cultural heritage and public space availability. The world experience in application of parking management tools makes it possible to identify the most common of them: differential payment in different kinds of parking areas; assigning zones for time constraint parking; creating priority groups (for example, according to the place of residence); and minimization the total number of parking spots. Pricing is a powerful tool of parking management in modern conditions. It should be emphasized that money for parking as a rule comes to the municipal budget and often are used by the authorities to maintain and develop the public transport infrastructure and create new livable streets and public places. The aim of this study is the economic evaluation of parking management tools and analysis of users’ behavior with a glance to applied management tools. The object of this study is the whole of on-street and off-street parking areas for cars in the central part of a big city. The subject of this study is the range of parking management tools from the viewpoint of efficiency and users’ behavior. 2. Methods In order to achieve the aim of this study, the following steps were determined and made: (1) to choose and justify an economic mathematical model for the economic evaluation of parking management tools, and the assessment of its possibility and consistency; (2) to collect and evaluate the required data; (3) to analyze and interpret the obtained data; (4) to present results of the study. 2.1. The choice and justification of a model As it is said above, the main instruments regulating the access of cars to the city center are the parking management tools (limited number of parking spots): either by parking payment, or by parking time constraint. The paper of Douglas (1975) shows that the choice of a management tool for parking is of high importance as it influences drivers’ behavior, when they search for a parking spot, and their additional costs. Every driver has to find either an on-street parking spot or a direct way to an off-street parking court. According to Brown (1991) and Gillen (1978), if on-street parking areas are free of charge but have a time constraint, while off-street parking courts require payment, drivers as a rule prefer the first type of parking, in spite of the fact that they have to spend time to find a spot. Calthrop (2001), Calthrop et al. (2000) hold the same view. It is noted in papers of Kodransky and Hermann (2011) that this situation creates a serious problem: drivers searching for a parking spot induce 50% of traffic jams. There are many studies of management strategies for parking. Some authors consider pricing as a means to recover costs generated by street traffic congestion. Glazer and Niskanen (1992) show in their studies that the increase of parking payment together with restricted trips to the city center can lead to transit traffic. Vickrey (1994) describes in his study a mechanism of pricing in on-street parking areas in peak hours. According to this mechanism, the price of empty parking spots is a function of the number of unused areas. The paper of Arnott and Rowse (1999) gives a simulation of the work of parking network aimed at minimizing people’s time expenditures for movement to the city center. In the view of existing aims of the study, a decision was made not to apply models which take into account only one management tool without possibility to assess the balance of different types of parking areas. The most complex model was proposed by Calthrope and Proost (2000). It describes parking management by two tools: payment for parking and time constraint. Table 1 shows the main parameters of the considered model. Table 1. The main parameters of the model Parameter designation Parameter Х The number of spots in on-street parking areas QX Total parking time in off-street parking courts in peak hours Y The number of spots in off-street parking courts C The price of parking per time unit in off-street parking courts N The number of drivers who want to park their cars in peak hours

Dmitriy Fadeyev / Transportation Research Procedia 20 (2017) 193 – 199 Curve of demand α

β q d ‫݌‬௜ ሺ݅ ൌ ܺǡ ܻሻ ‫ݐ‬௜ ሺ‫݌‬௜ ሻ ൌ ሺߙ െ ‫݌‬௜ ሻȀߚ

Parameter of demand curve (autonomous demand) Parameter of demand curve (price reaction of demand) Time used for parking Costs of searching for an empty spot in on-street parking area Price in each parking area per time unit Parking duration

Management problems arise if the constant supply of spots in on-street parking areas without payment is not enough to meet the demand: (1) ܰ‫ݐ‬௑ ሺͲሻ ൌ ܰߙȀߚ ൐ ܳ௑ Suppose that municipal authorities want to send certain drivers to certain parking spots for a certain period of time. At that, the authorities need to control parking and reach maximum economic efficiency W specified by a function which takes into account parking duration and the number of users in each type of parking area (NX is the number of users of on-street parking areas Х): ௧ೊ ௧೉ ௠௔௫ (2) ே೉ ௧೉ ௧ೊܹ ൌ  ܰ௑ ‫׬‬଴ ሺߙ െ ߚ‫ݍ‬ሻ݀‫ ݍ‬൅ ሺܰ െ  ܰ௑ ሻ‫׬‬଴ ሺߙ െ ߚ‫ ݍ‬െ ‫ܥ‬ሻ݀‫ݍ‬ with restrictions: ܰ௑ ‫ݐ‬௑ ൑ ܳ௑ ሺ‫ݕ‬ଵ ሻǢܰ௑ ൑ ܰሺ‫ݕ‬ଶ ሻǢܰ௑ ǡ ‫ݐ‬௑ ǡ ‫ݐ‬௒ ൒ Ͳ y1 and y2 are corresponding multipliers of restrictions. There are three different scenarios of the situation depending on total time of on-street parking QX, number of users N and price in off-street parking court C. In our case, the most adequate is the model of parking when the number of supplied parking spots in on-street areas is less than the demand. At the same time, the optimum price is equal to the price in an off-street parking court. As a result, only a part of drivers use on-street areas, the rest of them use off-street parking courts: ఈି஼  ൐ ܳ௑ (3) ܰ ఉ

2.2. Data collection and evaluation A parking network in the center of a large city of Irkutsk (population is 623,424; the number of cars is 207,000) was assigned an object for data collection. The historical central part of the city has a specific feature: the area of a shopping center is 12–15 hectares involving popular massively visited places (Fig.1). The scale and concentration of trade activity is reflected by figure: 65 thousand people per day which is the total number of passengers who get on the public transport in this zone.

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Fig. 1. Concentration of trading objects in the center of the city.

Fig. 2. Layout of parking areas in the center of the city.

The object of the study is 25 parking areas (Fig. 2) near massively visited places (offices, shops and educational institutions). Parking areas were divided into the following groups: (a) on-street and off-street; (b) by characteristics: paid (information about payment) and free of charge; (c) organized (i.e. with signified entrance and exit, road markings, etc.) and unorganized. In order to ascertain the number of parking users, all vehicles were registered in on-street and off-street parking areas of the city. The method of the study consists in hourly registration of the number of cars in off-street parking courts and along the drive way. Cars were being continuously registered at entrances and exits in parking areas to collect data on parking duration. The survey took the whole day and lasted for 10–12 hours, from 8–9 a.m. to 7–8 p.m.

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2.3. Analysis and interpretation As a result of the survey, data were obtained with respect to the following parameters: maximum and minimum number of cars per hour at every road section and parking area; average time gap between arrivals of cars at the parking area; distribution of average parking duration throughout the day depending on the time of arrival (see example in Fig. 3, 4). This allowed revealing the busiest sections of streets and peak points of parking cars number every hour. The average number of vehicles arriving to the center of the city for parking is 1900–2000 units per hour, insignificantly depending on a season.

Fig. 3. Distribution of parking cars in the center of the city by the hours of the day.

Fig.4. Distribution of average duration of parking in on-street (blue line) and off-street (red dashed line) parking areas near large trade objects.

In order to continue calculations, it was necessary to check the condition of inequality for a case when demand is relatively high and supply is low in on-street parking areas which is typical for the central parts of big cities. Obtained numerical data were inserted into the inequality the following way: ܰ

ఈି஼ ఉ

ൌ ͳͲͲͲͲ ή

ଶ଴ି଼ǡଶଷ ସ

ൌ ʹͻͶʹͷ ൐ ܳ௑ ൌ ͺͲͲͲ

(4)

As it can be seen, the inequality is satisfied. Consequently, the considered model is applicable to the obtained data. 2.4. Study results For calculations, a special application was developed for software MATLAB 6.1 and additional set of tools Optimization Toolbox, which makes it possible to find (simulate) optimum values for managing a parking network in the center of a big city: (а) values of minimum/optimum*/maximum price level; (b) number of drivers and probability of an empty spot in an on-street parking area; (с) economic efficiency. While simulating the parking management process, two management tools for on-street parking were compared with a glance at the influence of costs of searching for an empty parking spot.

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Fig. 5. Dependence of the number of drivers searching for an empty parking spot on the price in an on-street parking area.

Fig. 5 shows an example of found values ‫݌‬௑஼ (lower limit of price) which corresponds to N = 10 000, и ‫݌‬௑஼ (upper limit) which corresponds to N = 2 719. The area to the left of the solid line, as the line of supply-demand balance in on-street parking areas, corresponds to probability 1; the area to the right has the value lower than 1. Figure 6 shows the dependence of economic efficiency on parking payment and time constraint.

Fig. 6. Dependence of economic efficiency function on the applied parking management tool.

Table 2 presents data describing the sensitivity of economic efficiency function on the rate of costs of searching for an empty parking spot depending on the applied management tool and values of corresponding restrictions. Table 2. Dependence of economic efficiency on the rate of costs of searching for an empty spot in an on-street parking Costs of searching for an empty spot

j

d d = 0.00 rub.

С

Optimum price

Lower price limit

’‫כ‬ଡ଼୨ , rub/h

’ଡ଼୨ , rub/h

8.23

8.23

Upper price limit

’ଡ଼୨ , rub/h 8.23

Probability of successful search

ɏ୶

Economic efficiency

W, rub/day 1

239006.13

W, % 100 .00

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Dmitriy Fadeyev / Transportation Research Procedia 20 (2017) 193 – 199

d = 0.50 rub.

d = 1.00 rub.

d = 4.00 rub.

О

8.23

16.17

16.17

0.2719

239006.13

С

8.23

7.76

8.23

1

239006.13

О

8.40

16.14

16.17

0.2759

235375.47

С

8.23

7.26

8.23

1

239006.13

О

8.57

16.12

16.17

0.2801

231765.33

С

8.23

3.53

8.23

1

239006.13

О

9.68

15.88

16.17

0.3100

210594.30

100 .00 100 .00 98. 48 ↓ 100 .00 96. 97 ↓ 100 .00 88. 11 ↓

Notes: j is a street parking management tool; С is payment for parking; О is parking time constraint. So, the optimum tool for street parking management in the central part of Irkutsk is payment for parking which would maximize the function of economic efficiency. 3. Conclusion The study of innovative approaches in parking policy implemented in European countries gives a clear idea about the emerging system of priorities: air pollution control; development of bicycle movement; improvement of the quality of life and comfort of public places; and correction of street traffic congestion. All these goals can be achieved by limiting the access of vehicles to certain parts of the city. The reduction of the total number of parking areas, as well as economic (payment system) and technological (payment options) mechanisms, are powerful tools in the hands of municipal authorities. The study confirmed that payment for parking is more efficient than time constraint if parking price in on-street parking areas is optimum (i.e. equal to the price in off-street parking courts). If there are no costs of searching for an empty parking spot, the tool for on-street parking management does not matter. As the costs of searching for a spot increase, the effect becomes lower in linear fashion at time constraint than at payment. This is confirmed by experimental data. This model shows an important correlation in the simplest view: the best on-street parking management reduces the necessity of unsuccessful search and costs of movement to an offstreet parking court. In conclusion, a point to be noted is that municipal authorities should clearly realize what goal they want to achieve while developing and carrying out the parking policy and use those mechanisms which are the most efficient. References Arnott, R., Rowse, J. (1999). Modeling parking. Journal of urban economics, 45(1): 97–124. Brown, M. (1991). Car parking: the economics of policy enforcement. UK: Cranfield Press. Calthrop, E. (2001). Privatising enforcement. Mimeo. Calthrop, E., Proost, S. (2000). Regulating urban parking: the choice between meter fees and time restrictions. ETE working paper 2000-06, CES, K. U. Leuven. Calthrop, E., Proost S., Van Dender K. (2000). Parking policies and road pricing. Urban Studies, 37(1): 63–76. Douglas, R. W. (1975). A Parking model — the effect of supply on demand. American Economist, 19(1): 85–86. Gillen, D. W. (1978). Parking Policy, Parking Location Decisions and the Distribution of Congestion. Transportation, (7): 69–85. Glazer, A., Niskanen, E. (1992). Parking fees and congestion. Regional Science and Urban Economics, pp. 123–132. Kodransky, M., Hermann, G. (2011). Europe’s Parking U-Turn: From Accommodation to Regulation. New York: ITDP (Institute for Transportation and Development Policy). Vickrey, W. (1994). Statement to the Joint Committee on Washington DC Metropolitan Problems: Exhibit 53 — Economizing on Curb Parking Space — a suggestion for a new approach to parking meters, reprinted in Journal of Urban Economics, (36): 42–65.