The effect of parking charges and time limit to car usageand parking behaviour

Jelena Simićević, Smiljan Vukanović, Nada Milosavljević n

University of Belgrade, Faculty of Transport and Traffic Engineering, Serbia, Vojvode Stepe 305, 11000 Belgrade, Serbia

a r t i c l e i n f o

Available online 28 September 2013

Keywords:Parking chargeTime-limited parkingStated preferencesMultinomial logit modelDirectEffects

a b s t r a c t

Parking policies are considered a powerful tool for solving parking problems as well as problems of thetransportation system in general (traffic congestion, modal split, etc.). To define parking policy properly,its effects must be estimated and predicted. In this paper, based on stated preference data and using alogistic regression, a model to predict the effects of introducing or changing the parking price and timelimitation was developed. The results show that parking prices affect car usage, while time limitationsdetermine the type of parking used (on-street or off-street). A positive finding for policy makers is thatusers with work are more sensitive to parking measures than are other users, so parking measures can beused to manage user categories. Although there is a concern that parking policy can jeopardise theattractiveness and efficiency of a zone, the results show that a very small number of users would give uptravelling into the zone.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

The main objective of parking management is to balance theparking supply with the parking demand. However, parking policyhas a strong impact not only on the operation of the parkingsubsystem but also on the entire transportation system and thecity in general. Possible driver responses to parking policy (pri-marily to the parking charge and time limitation) are complex andvaried. These include a change in the parking type, parkinglocation, transportation mode, car occupancy, destination, travelfrequency, travel time (with possible consequences on the parkingduration) and route (Scholefield et al., 1997). This mechanism ofinfluence allows parking policy to be used to achieve objectivesbeyond this subsystem. For example, studies have shown that themost important factor in reducing car usage is the parking price(Higgins, 1992). Thus, parking policy can be the most effectivepolicy for achieving the desired modal split (Victorian Competitionand Efficiency Commission (VCEC), 2006). Furthermore, the park-ing charge is considered to be the second best measure for solvingtraffic congestion after congestion charging (Albert and Mahalel,2006; Kelly and Clinch, 2006), but it is used far more often becauseof its relatively simple implementation (Marsden, 2006; Verhoefet al., 1995).

Although good parking policy has many positive implicationsfor sustainable transportation, poor parking policy can have theopposite effect. For example, analysing 16 studies from 11 inter-national cities showed that approximately 30% of the trafficvolume are vehicles cruising for parking, i.e., result of poor parkingmanagement (Shoup, 2005). In addition, recently, there is concernthat parking policy could negatively impact the competitivenessand business efficiency in an area (D’Acierno et al., 2006).

To properly set the parking policy and define the appropriatemeasures, i.e., to ensure that the objectives are met withoutadverse impact on the transportation system and other systemsof a city, the effects of the policy must be predicted.

Originally, models for the prediction of parking policy impactswere aggregate, i.e., based on group behaviour. Conventionalmodels were later replaced by disaggregate models because itwas recognised that the individual impact must be examined andincluded (Kelly and Clinch, 2006).

The user response to time limitation can be relatively easilypredicted; it depends on the parking duration and the possibilityof shortening the duration (which is associated with trip purpose(Transit Cooperative Research Programme (TCRP), 2005)). How-ever, the prediction of the user response to the parking price isvery complex and not accurately known.

It is particularly complex to determine the impact of severalmeasures (the time limitation and parking price of on-streetparking and the parking price of off-street parking) because of theirsynergistic effect. For this reason, it is not appropriate to estimatethe impacts of the measures individually; instead, this should beperformed simultaneously (see for example Ibeas et al., 2011).

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Transport Policy

0967-070X/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.tranpol.2013.09.007

n Corresponding author. Tel.: þ381 63 602142; fax: þ381 11 2468120.E-mail addresses: j.simicevic@sf.bg.ac.rs (J. Simićević), s.vukanovic@sf.bg.ac.rs

(S. Vukanović), n.milosavljevic@sf.bg.ac.rs (N. Milosavljević).

Transport Policy 30 (2013) 125–131

To investigate the parameters of significance for parkingdecision making, a logistic regression of the stated preferencedata is usually conducted (Hess, 2001; Shiftan and Burd-Eden,2001; Tsamboulas, 2001; Shiftan and Golani, 2005; Albert andMahalel, 2006; Khodaii et al., 2010; Simićević et al., 2012a). Thus,some of the socio-economic and trip characteristics significant fordecision making are identified.

Lately, more and more researchers are interested in this topic.In a review paper on parking policy, Marsden (2006) noted that“We do not understand nearly enough about how individualsrespond to parking policy interventions nor how these responsesinteract with local circumstances, the availability of alternativetransport modes or alternative destinations,” and among parkingtopics that require further research, he highlighted “the impor-tance of out-of-vehicle costs and in particular walk-times onparking behaviour”.

This paper is testing the hypotheses: (1) that time limit andparking price influence parking behaviour (2) that these influencesdiffer for specific user subgroups (depending on certain character-istics of users and trips). The aim of this paper is to forecast userbehaviour in the conditions of change in price and/or time limit ofparking, and further to forecast direct effects of such measures.The forecast was made by using multinomial logit model fittedwith the data gathered by approaches of revealed and statedpreferences.

The structure of the paper is as follows: in Section 2, the stateof parking in the central area of Belgrade is described. In Section 3,the problem this paper addresses is presented, and the procedurefor its solution is described. To test this procedure, necessary datawere gathered as shown in Section 4, while the collected data arepresented in Section 6. In the 7th and final section, final con-siderations are summarised.

2. Parking in Belgrade

Belgrade is the capital of Serbia. The urban part of the cityoccupies an area of about 77,000 ha and has approximately1.5 million inhabitants. About 96,000 inhabitants live in the citycentre, which has an approximate area of 440 ha.

Based on the traffic survey, the inhabitants of Belgrade makeapproximately three million trips per day. In the modal split,passenger cars account for 22% and public transport for 52% of alldaily trips. Coverage with the public transport network is approxi-mately 2.1 km/km2; headways are between 6 and 20 min. Publictransport users assess the quality of service as very good (marknear 4 out of a maximum of 5) (Jović and Djorić, 2009).

The parking problem in Belgrade is present in almost all itsurban area. The parking problem arises as a result of the dis-proportion between the parking demand and the number ofavailable parking spaces. The disproportion is a result of histori-cally formed city structures, flows or omissions in the planningand a lack of good parking supply management.

The basic characteristic of Belgrade is the insufficient off-streetpublic parking capacity, so the majority of vehicles are parked onthe streets. All parking spaces are owned by and under thejurisdiction of the city administration, which is, therefore, respon-sible for making the appropriate parking policy.

A restrictive parking element is implemented within thecentral area for on-street parking spaces. The area is divided intothree zones (red, yellow and green) that differ in the followingregime attributes: time limitation (1, 2 and 3 h, respectively) andparking price (56 RSD (0.53 EUR), 38 RSD (0.36 EUR) and 31 RSD(0.30 EUR) per commenced hour, respectively). Most visitors payparking by mobile phone, although other technologies are in use,such as parking metres, parking tickets and electronic tickets.

The period of regime validity is every day from 7 a.m. to 9 p.m. andon Saturdays from 7 a.m. to 2 p.m.

Residents and businesses in the area are entitled to a parkingpermit (PP), which does not guarantee a vacant parking space toits holder; however, once the user finds a vacant parking space, theuser can park there without any time limitation. The price of PP forresidents is 480 RSD per month (5.05 EUR), while the price forbusinesses depends on the zone the company is located in (9,130RSD (86.95 EUR), 6,176 RSD (58,82 EUR) and 4,106 RSD (39.10 EUR)per month, respectively).

Disabled persons can park at specially marked parking spaces(3% of the total number of parking spaces (Milosavljević et al.,2009)), which the parking regime does not take into account.

On-street parking spaces can be reserved for state institutions,city institutions, public services, diplomatic and other foreignrepresentatives, businesses and entrepreneurs. The city adminis-tration approves reservations based on previously prescribedconditions. Approximately 10% of the total number of parkingspaces in the central area is reserved (Milosavljević et al., 2009).

Parking at parking lots and garages is charged every day for24 h. The parking price varies from facility to facility and is 2–3times higher than the on-street parking price. In addition topaying for parking per commenced hour, it is possible to pay forparking per month (types and fees vary among facilities, but theyare also far higher than those for on-street parking).

During increased area attractiveness, all on-street parkingspaces are occupied, and even illegal parking occurs, making ithard to find a vacant parking space. On the other hand, off-streetcar parks and parking garages are never 100% occupied, and it canbe assumed that at every moment, a vacant parking space canbe found.

3. Problem statement and proposed solution

As already mentioned, the aim of this paper is to predict theeffects of the introduction or change of the parking policy, namely,time limitation and the parking price.

These policies refer only to users who park at public, non-reserved parking spaces and who are subject to the parking regime(so-called visitors). It is therefore necessary, in the first step, toisolate this demand and use it for the study.

To survey visitors' responses to parking policy changes, thestated preference method is used, where through interviews,various hypothetical situations are presented to the respondents,and they declare how they would behave in such situations. Thisimplies that there are scenarios with different combinations of thetime limitation and parking price levels. If the number of allpossible scenarios (full factorial) is so great that it is impractical toexamine them all, it is possible to examine only a subset, chosen torepresent a full set with certainty (fractional factorial) (Hensherand King, 2001; Wong, 2006). The possible choices for visitors areas follows: no change in behaviour, driving to the zone andchanging the type of parking space (on-street instead of off-street or vice versa), parking at the fringe of the zone andcontinuing the journey by public transport or on foot, using othertransport modes, and so on.

To better explain the response visitors faced with parkingpolicy changes, it is necessary to gather a wide set of parametersthat are assumed to have a strong impact on the travel decision.These are some socio-economic characteristics of the visitors andtrip characteristics.

All data will be used to fit the multinomial logit (MNL) model,which will predict the probability of choosing each of the statedalternatives for each visitor for any parking price and timelimitation. At the end of the modelling process, it is necessary to

J. Simićević et al. / Transport Policy 30 (2013) 125–131126

aggregate the demand in order to analyse the results and theeffects on the general level (Train, 2002).

4. Survey methodology

The proposed procedure will be tested in the Belgrade centralarea in the red zone for parking.

At 811 non-reserved on-street parking spaces in this zone,parking is time-limited to one hour and costs 56 RSD (0.53 EUR).In the zone, there are two parking garages and one off-streetparking lot, with a total of 1145 parking spaces. The charge ofparking is 75 RSD (0.71 EUR) for the first hour and 90 RSD (0.86EUR) for each additional hour.

Eight street sections were selected to represent on-streetparking, while the parking garage Obilićev Venac was chosen torepresent off-street parking (Fig. 1). The locations were chosen insuch way that based on the previous studies of parking and userscharacteristics, as well as on the basis of land use in theirneighbourhood it can be deemed with certainty that they repre-sent the entire zone. The survey was conducted by face-to-faceinterviews of users who had just parked or who were leaving theparking space.

After a pilot study, which confirmed that both the interviewersand the respondents understood the questions and which assistedin defining the levels of the parking price and time limitation to bepresented in the interview, the survey was carried out during 5days in November and December 2011. The time of the survey wasthe validity period of the regime (from 7 a.m. to 9 p.m.). Onaverage, each interview lasted 5 min.

The interview was conducted by students of the University ofBelgrade, Faculty of Transport and Traffic Engineering. Greatattention was paid to the interviewers' training and their controlin the field. Interviewers were carefully trained on how to presentthemselves and how to interview. Before the interview, their taskwas to familiarise the respondent with the research. They empha-sised that the research was conducted by the Faculty of Transportand Traffic Engineering, not by the city administration, for scien-tific purposes. Thus, respondents could not “see through” theintension of the city administration to increase the parking price

and could therefore give inaccurate responses (Kelly and Clinch,2006). Furthermore, an effort was made to overcome the tendencyof the respondents to detect and confirm interviewer attitudes,which is considered harmful to the stated preference technique(Tsamboulas, 2001).

In total, 438 users were interviewed.For aggregation, the on-street and off-street parking volume is

necessary. These data are not surveyed but taken from the study“Parking management strategy”(Milosavljević et al., 2009).

5. The collected data

The first question in the interview defined the user category(visitor or other). This question could be eliminated because thefurther procedure applies only to the category of visitors.

Based on theoretical expectations and previous experience, theparameters that were expected to influence the visitors' choicewere selected (Tsamboulas, 2001; Coppola, 2002; Shiftan andGolani, 2005; Kelly and Clinch, 2006; Van der Waerden et al.,2006; Khodaii et al., 2010, Simićević et al., 2012a). The followingparameters were surveyed: age, gender, origin and destination, caroccupancy, parking purpose, parking frequency, parking duration,parking search time, whether the employer pays for parking andwhether the respondent is car dependent. Previous studies haveshown that respondents often refuse to answer about income orgive an incorrect answer (Shiftan and Burd-Eden, 2001). Therefore,in this paper, instead of income, two proxies were surveyed: age ofthe car and engine size. One of the potential parameters iswhether a visitor uses on- or off-street parking. This parameteris considered because different measures are implemented forthese types of parking. On the other hand, the visitor's previouschoice should depict the visitor's preference towards a certain typeof parking (on- or off-street).

The second part of the interview was hypothetical scenarios,i.e., different combinations of time limitation and parking pricelevels.

Although the on-street parking price is currently lower thanthe off-street parking price, in the scenarios, these prices are equal.The reasons why we opt for this restriction are as follows:

Because of the negative effects of on-street parking spaces and,in particular, the parking search on the transportation system andenvironment, the on-street parking price should be higher than orequal to the off-street parking price. However, currently inBelgrade, the situation is the opposite, leading to underutilisedoff-street parking and over utilised on-street parking. Therefore,we decided for this initial step to set the prices to be equal.An additional reason is the small number of off-street parkingspaces in the total parking supply (Section 2).

This paper examines the mitigation and tightening of parkingmeasures. Based on the results of the pilot study the values of timelimit were chosen, as well as the prices for which visitors'responses will be studied. Five prices in the range of 30 RSD(0.29 EUR) to 200 RSD (1.90 EUR) were taken, and three values ofthe time limitation, 30, 60 and 120 min, were taken. All possiblecombinations of the stated values of regime attributes (fullfactorial) were studied, i.e. in total 15 scenarios (5�3) weretested. However, visitors' responses in certain scenarios areobvious (can be predicted with high probability). These arescenarios in which the regime attributes of the space wherethe visitor is parked are unchanged or mitigated, and the attri-butes of another type of parking are unchanged or even tightened.To reduce the number of scenarios which were investigated, wedecided not to investigate these “known” scenarios but to assumetheir outcome. Thus, four scenarios were eliminated (for on-streetand off-street parking interviews). That is, we had 11 scenarios toFig. 1. Study area.

J. Simićević et al. / Transport Policy 30 (2013) 125–131 127

investigate. Because it is unreasonable to present such a largenumber of scenarios to one respondent (Hensher and King, 2001)because of the lapse of concentration and the possibility of with-drawal from the interview, we conducted three types of interviewscontaining three or four scenarios that were randomly selected,and one or two “known” scenarios were added later. The order ofthe presented scenarios was such that the parking price increasedgradually (Kelly and Clinch, 2006) as visitors slowly considered thethreshold to which they were willing to pay for parking. It wouldbe wrong to suddenly present a situation in which parking is up to250% more expensive because this situation is difficult to conceive,and the stated response would be questionable (Hensher and King,2001). An example of a combination of scenarios is shown in Fig. 2.

6. Results

6.1. MNL model

The gathered data were used to fit the MNL model. Respon-dents had four alternatives and the possibility to write down analternative if it was not listed (“other”), as shown in Fig. 2.However, because of the small number of choices of somealternatives and because a large number of dependent variablecategories make modelling much more complicated, alternativesare grouped into the following three categories: (1) on-streetparking, (2) off-street parking or (3) not coming to the zone bycar. The probabilities to choose these alternatives are marked withP1, P2 and P3, respectively. By definition, the sum of these threeprobabilities is equal to one (Hess, 2001):

P1þP2þP3 ¼ 1 ð1Þ

The adjusted model is given by two equations:

logP1

P3¼ αaþβ1ax1þ :::þβiaxi ð2Þ

logP2

P3¼ αbþβ1bx1þ :::þβibxi ð3Þ

where α – intercepts, x – independent variables relevant to thechoice, β –parameter estimates.

The independent variables were selected from the set ofsurveyed parameters. Their parameter estimates were estimatedby the maximum likelihood method.

The final model included five independent variables. In addi-tion to the parking price and time limitation, there are cardependency, parking purpose and current choice. It should benoted that Wald statistics revealed the significance of some othervariables, namely, whether the employer pays for parking, lengthof the trip and income proxies (age of the car and engine size).However, their inclusion would not significantly contribute to themodel adjustment to the observed data. For this reason andbecause of the intension to create a realistic model that does notrequire too much data (Ortuzar and Willumsen, 2000), thesevariables were not included.

The fitted model is shown in Table 1. For model presentation,the following characteristics are selected: variable name, para-meter estimates (β), standard error, significance and exp(β) (Field,2005).

The test of the full model compared to the intercept-onlymodel is statistically significant, indicating that the set of inde-pendent variables reliably distinguish between the choices of on-street parking, off-street parking and not coming to the zone bycar (model χ2¼1047; df¼10; po0.000). Nagelkerke's R2 of 0.60

19a. IF THE SITUATION IN THE RED ZONE WAS THEFOLLOWING:

Parking price (on- and off-street): 30 RSD/h time limitation (on-street): ½ hourYOU WOULD:1) park on-street2) park off-street3) park at the fringe of the zone4) switch to public transport5) other_________________

19b. IF THE SITUATION IN THE RED ZONE WAS THEFOLLOWING:

Parking price (on- and off-street): 100 RSD/h time limitation (on-street): 2 hoursYOU WOULD:1) park on-street2) park off-street3) park at the fringe of the zone4) switch to public transport5) other_________________

19c. IF THE SITUATION IN THE RED ZONE WAS THEFOLLOWING:

Parking price (on- and off-street): 150 RSD/h time limitation (on-street): ½ hourYOU WOULD:1) park on-street2) park off-street3) park at the fringe of the zone4) switch to public transport5) other_________________

19d. IF THE SITUATION IN THE RED ZONE WAS THEFOLLOWING:

Parking price (on- and off-street): 200 RSD/h time limitation (on-street): 1 hourYOU WOULD:1) park on-street2) park off-street3) park at the fringe of the zone4) switch to public transport5) other_________________

Fig. 2. Example of SP scenarios.

Table 1MNL model results.

Alternative On-street parking Off-street parking

Variable Parameter estimate (β) (Std. error) Sig. Exp(β) Parameter estimate (β) (Std. error) Sig. Exp(β)

Intercept �0.208 (0.409) 0.611 3.773 (0.346) 0.000Car dependency 1.758 (0.236) 0.000 5.801 1.085 (0.203) 0.000 2.960Purpose work �0.662 (0.270) 0.014 0.516 �0.439 (0.223) 0.049 0.644Park on-street 2.244 (0.243) 0.000 9.434 �1.604 (0.185) 0.000 0.201Parking price (RSD/h) �0.028 (0.002) 0.000 0.972 �0.020 (0.002) 0.000 0.980Time limitation (min.) 0.020 (0.003) 0.000 1.020 �0.003 (0.002) 0.190 0.997

Number of observations¼1407.

J. Simićević et al. / Transport Policy 30 (2013) 125–131128

indicates that the independent variables explained the mostvariation of the dependent variable. The likelihood ratio index isequal to 0.65 and shows good performance of the model.

The likelihood ratio tests showed that all included independentvariables contribute significantly to the prediction.

Category “not coming to zone by car” is redundant.Generally, the effects of the independent variables on the

dependant variables are logical and expected.Car dependency is determined by the question “Do you use a

car for every trip in the city?”, where a positive answer determinescar dependent visitor. The results show that car dependent visitorsare more likely to park in the zone then to give up driving to thezone compared to visitors who are not car dependent.

The next variable is also dummy equal to 1 if the purpose ofparking is work and is 0 otherwise. The reason for this division isthat visitors with purposeful work, because of their travel timeand long parking duration, are unwanted in central areas. Unlikethem, visitors with other purposes (business, shopping, leisure…)are essential to the vitality and attractiveness of the area. It shouldbe noted that despite a 1 h time limitation, work is present at redzone on-street parking spaces. The reason for this is the change inits character, i.e., these are employees who need to use cars duringworking hours. On the other hand, a longer duration can beensured by system abuse; for example, one can pay for parking

more than once but in different ways to avoid the prescribed timelimitation (Simićević et al., 2012b).

Parameter estimates (Table 1) reveal that visitors with work aremore sensitive to a parking policy change, and it is more likely thatthese visitors will give up parking in the zone.

A visitor's previous choice depicts the visitor's preference to aspecific parking type (on- or off-street), so, for example, visitorswho use on-street parking are more likely to choose this type ofparking in the future compared to those who currently use off-street parking.

As the parking price in the zone increases, the odds of parkingin the zone decreases. With every RSD of increase, the odds ofparking on-street decrease by 0.97 and of parking off-streetdecrease by 0.98.

As the on-street parking time limitation increases, the odds ofparking on-street increase. With every 1 min, the odds increase by1.02. On the other hand, the odds of parking off-street decrease(by 0.997).

In case that the visitors, due to strict parking measures in thezone, decide not to park in such zone (3rd alternative), the ways inwhich they would come into the city centre are investigated. Theresults show that in such case the majority of visitors would shiftto public transport (51%) or would park on the zone fringe (27%).Not a single visitor stated that he/she would choose car pool.

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6.2. Demand prediction

The fitted MNL model is further used to calculate the prob-ability of the selection of alternatives, i.e., determining the visitors'sensitivity to parking policies. This is done by solving the MNLequations for probability using a wide range of parking price andtime limitation values and fixing the values of the other variables.After calculating the probabilities for each visitor individually, theresults are aggregated.

The sample consists of on-street and off-street parking visitors.As in the current situation, different regimes and parking pricesare implemented, and visitors' sensitivities are different. There-fore, because of a driver's preference towards a previous choice,aggregation is performed by dividing the sample into two seg-ments, visitors who currently park on-street and visitors whocurrently park off-street. Within each segment, the probabilities ofchoosing each alternative are summed, and the results are extra-polated to the level of the daily parking volume. The results for thesegments are then summed.

The individual effects of the parking price and time limitationchange are shown in Fig. 3(a and b), respectively, and the impactsof both policies at the same time are presented in Fig. 4.

The relations shown in the figures are logical and have alreadybeen depicted through parameter estimates (Table 1). Increasingthe parking price decreases the demand in on-street and off-streetparking, while the share of visitors who will give up driving to thered zone increases. For a parking price up to approximately 110RSD (1.05 EUR), the off-street parking demand curve is completelyhorizontal, showing visitors' propensity to a parking priceincrease. This is not surprising because of the existing off-streetparking price. After this threshold, the curve has a similar(although somewhat lower) decline as for the on-street parkingcurve. Therefore, 110 RSD (1.05 EUR) can be considered the thresholdfor off-street parking.

Unlike the parking price, the time limitation has no significanteffect on the abandonment of the car in the area, although there isa logical trend. The reason for this is primarily because of the timelimitation in the subject area (1 h), and therefore, only short-termvisitors mostly use on-street parking. On the other hand, visitorsalways have an alternative, off-street parking, where parking is nottime limited. Therefore, this is proof that visitors, if they do not fitthe time limitation, would rather use off-street parking than giveup coming to the zone by car.

However, although it is proven that this attribute has littleeffect on the amount of parking demand in the zone, it affects theparking demand redistribution by the type of parking (on- and off-street parking). By tightening the time limitation, the share ofvisitors who will park off-street (where there is no time limitation)increases. This number also increases because of the great share ofon-street parking visitors with work, such as business and perso-nal business (78%), who cannot shorten the parking duration toadopt to the new situation (TCRP, 2005). Furthermore, by mitigat-ing this attribute, some visitors who currently use off-streetparking because of the time limitation will switch to on-streetparking. This is proven by the opposite signs of the parameterestimates (Table 1).

Fig. 4 shows that the parking price and time limitation havegreat influence on visitors' behaviour and, therefore, on theparking demand. For the most stringent tested scenario (price of200 RSD/h and time limitation of 30 min), 54% of visitors wouldgive up coming to the zone by car.

It should be noted that a complete picture can be obtained by acomprehensive survey (interview of households), which wouldexamine the generation of new demand. In this paper, because offinancial constraints, it is not done.

7. Conclusion

Impacts of parking price and time limit on behaviour of usersand on car use are studied in this paper. The setup hypotheses areproven: parking regime attributes influence parking behaviourand these influences differ for specific user subgroups.

In this regard, the MNL model was fitted to predict the effectsof introducing or changing the parking price and time limitationlevels. The parameters included in the model are, in addition tothe parking price and time limitation, car dependency, parkingpurpose and previous choice. All relations of the independentvariables to the dependant variable are logical and expected.All statistics show that the model is very good at fitting the data.

The model results confirm that the parking price can affect theamount of parking demand and, therefore, parking utilisation. Theimpact on car use can be used to fulfil some sustainable transpor-tation objectives such as addressing/mitigating traffic congestionproblems, achieving the desired modal split, etc.

Tightening time limits in areas where limits already exist doesnot lead to a significant reduction in the parking demand. How-ever, it enables the parking demand to be managed according to

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J. Simićević et al. / Transport Policy 30 (2013) 125–131130

the parking type. This can be desirable, among other reasons, toreduce the parking search time, which is a consequence ofunevenly utilised types of parking. A typical example of this isthe Belgrade red zone (and entire central area), where a vacant off-street parking space can be found at any time, but visitors spendapproximately 342 h daily searching for a vacant on-street parkingspace (here are included visitors who, when failing to find a vacanton-street parking space, eventually park off-street).

Visitors with work are much more sensitive to parking policyinterventions than are others. This is a very desirable finding forpolicy makers because these visitors are unwanted in the citycentral areas. This enables managing user categories.

Finally it should be noted that in this paper, we opted to groupalternatives in this way, but depending on research objectives,grouping can be done differently. For example, a policy maker maybe interested in what visitors who give up parking in the zonewould do. In this example, the most common responses of visitorswho would give up parking in the zone are using public transport(51%) and parking at the fringe of the zone (27%). This responseleads to the need to monitor the public transport quality of theservice and state of parking at the fringe of the zone whenchanging parking policy. Only 2% of visitors who give up wouldchange the trip destination, which means that parking policies willnot significantly jeopardise zone attractiveness and efficiency.

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