Does Parking Type Preference Behavior Differ According to Whether It Is Paid or Free? A Case Study in Istanbul, Türkiye

Abstract

Parking behavior depends on drivers’ choice of parking type and willingness to pay for parking. Generally, the parking type refers to off-street and on-street parking facilities. The main factors affecting the preference for parking types are driver, vehicle, travel, and parking characteristics. Understanding drivers’ parking type preference behavior and accurately modeling drivers’ tendencies helps develop sustainable parking management policies. This study examines the parking preferences of drivers in Istanbul with binary logit models according to whether they pay for parking. The results of the models show that the number of factors influencing parking type preference is higher for free parking than for paid parking, including driver, vehicle, travel, and parking characteristics. Moreover, some factors in the models affect drivers’ parking type preferences differently for paid and free parking. Namely, low-income individuals tend to use on-street parking when parking is free and off-street parking when it is paid. Conversely, individuals who drive small-size vehicles prefer off-street parking for free parking and on-street parking for paid parking. Individuals who prefer off-street parking for free parking expect shorter walking distances to the final destination and parking duration. On the contrary, individuals who choose on-street parking for paid parking anticipate shorter walking distances to the final destination and parking duration.

1. Introduction

Due to urbanization, increasing population, and car ownership, the balance between parking supply and demand becomes more critical, especially in metropolitan areas. Obviously, the optimum and sustainable management of existing parking places is necessary. According to Shoup [1], the necessary car parking area in the United States of America (USA), based on the number of vehicles in 2002, covers an area on the scale of the state of Connecticut. In a scenario where car ownership in the world is at the same level as in the USA in 2000, if one parking space per vehicle is considered, an area the size of the United Kingdom (UK) or Greece would be required to park the vehicles in all countries, and if four parking spaces per vehicle are considered, an area the size of Spain or France would need to be reserved for car parking. However, a study conducted in London (UK) found that shifting from minimum to maximum parking requirements reduced parking spaces by approximately 40%. Moreover, almost all of the reduction in parking supply was caused by eliminating the minimum standards, with only 2.2% of the decline attributed to adopting maximum standards [2]. This suggests that more sustainable parking policies should be developed rather than providing a minimum number of parking spaces to meet the needs of individual users. In a study analyzing parking policies in 12 cities on five continents, it becomes clear that parking problems are similar everywhere; however, urban planners are often unaware of successful parking policies implemented in other cities that could be beneficial in their own. Effective parking policies can enhance transportation, the economy, and the environment, while poor parking policies can lead to traffic congestion, slow public transport, and increased air pollution [3]. Therefore, developing efficient and sustainable parking policies helps optimize the use of parking places, reduce traffic congestion, and improve the quality of life in city centers. On the other hand, since parking policies aim to influence the choices during the parking process positively, it seems necessary to examine parking behavior in particular [4].

Once a driver reaches the travel destination, she/he starts to search for the most suitable parking place. Depending on the preferred parking type, the driver might travel an additional amount of time on the road network. In particular, drivers who search for on-street parking can increase traffic and cause environmental and road safety problems [5,6]. A numerical example with parameters representative of a medium-sized USA city shows that although only 14% of vehicles on the road are searching for a parking place, these vehicles cause an almost 50% increase in time lost due to traffic congestion [7]. Furthermore, according to 16 field studies conducted in 11 cities in six different countries (USA, UK, Germany, Israel, Australia, and South Africa), drivers who search for parking places caused 30% of the traffic, and the average parking search time was 8.1 min [6]. Additionally, when drivers approach their destination but cannot find an available parking spot, 5% and 17% will choose to park illegally [8]. In order to minimize these problems, transportation planners try to influence individual travel behavior in general and parking behavior in particular through various parking policies [9]. Accordingly, it is necessary to understand drivers’ behavior accurately when planning for parking facilities.

Parking type preferences are closely related to drivers’ approaches to paying for parking. Indeed, from a transportation policy perspective, parking fee regulation is a powerful tool to manage the parking demand and, in a broader sense, the travel demand. For the parking fee payment status or parking fee change to have the desired effect on driver behavior, it is necessary to understand individuals’ reactions to various fee schemes [10]. Therefore, it is critical to identify the factors influencing the choice of off-street or on-street parking according to payment status [11]. The factors related to parking behavior basically consist of individual, vehicle, travel, and parking characteristics. Accordingly, the factors that affect parking behavior include gender, age, income level, and car ownership for individual characteristics [12,13,14,15,16,17,18]; vehicle age, size, and purchasing cost for vehicle characteristics [10,17,19]; and travel purpose, travel time, parking search time, walking distance, and parking duration for travel and parking characteristics [4,10,12,13,14,15,16,19,20,21].

This study separately examines drivers’ parking type preference behavior for free and paid parking. The effects of drivers’ individual, vehicle, travel, and parking characteristics on parking type choice are analyzed by using binary logit models. Moreover, with the help of the parking type preference models, recommendations for implementing effective parking policies are developed. This paper is organized as follows: This section describes the need for the study and its general framework. Section 2 provides the literature review. Section 3 explains the dataset, methodology, and modeling approach. Section 4 is devoted to descriptive statistics and model results. Section 5 discusses the topic, and Section 6 presents conclusions.

2. Literature Review

In general, analyzing individual travel choices (parking, walking, traveling, etc.) in detail paves the way for developing sustainable policies through the lens of urban mobility. Driver behavior, especially concerning parking location choice, is difficult to formulate due to the complexity and multiplicity of influencing factors. On the other hand, parking policies produced by understanding drivers’ parking type preferences and observing the problems experienced during the parking search process can be more influential. Parking policies are one of the effective methods decision-makers and experts use to manage travel demand and mobility in cities efficiently. In addition, understanding drivers’ reactions to the developed parking policies makes it easier to take immediate and accurate measures.

Parking pricing is among the most influential factors in drivers’ choice of parking location. Locations with low or non-existent parking fees are more attractive for drivers [14]. However, some drivers tend to pay for parking to shorten the process of searching for a parking place. Early studies have focused on the flexibility of individuals to pay for parking in the parking search process [22]. Considering the parking type choice, if the on-street parking fee is lower than the off-street parking fee, it is observed that the drivers’ parking search process is negatively affected, and the efficiency decreases. On-street parking spaces are generally more accessible to the final destination, resulting in higher demand for these low-fee spaces. Consequently, drivers tend to cruise longer in search of an available parking space, which contributes to increased traffic congestion, a higher likelihood of accidents, fuel wastage, air pollution, and degradation of the pedestrian environment [6,23]. It was also found that non-resident drivers were willing to pay more for underground car parks or regulated street parking than residents, considering the location they traveled to. On the other hand, when a driver decides to pay for parking, she/he tends to park her/his vehicle at off-street facilities, which are perceived to be safer than on-street parking [24]. Furthermore, it was found that time-sensitive drivers tend to prefer off-street parking, while cost-sensitive drivers are more likely to prefer on-street parking [25].

The studies in the literature analyze the effect of driver characteristics on parking preference. Some of these studies have examined the effect of characteristics such as gender, age, income, and car ownership [12,13,14]. For example, a study conducted in Greece reported that parking choice does not depend on gender, age, and income [14]; however, a study in Belgium and the Netherlands mentioned that parking type choice habits are associated with gender but not with age [15]. A study conducted in China found that individuals’ gender, age, and attitudes towards parking risk significantly influence their choice of parking type [26]. Moreover, a study conducted in Türkiye showed that men prefer off-street parking more than women [16]. Furthermore, another study from Tunisia found that gender is not effective in determining parking choice behavior [13], while research from Israel indicated that young individuals are more likely and high-income individuals are less likely to change their travel behavior according to parking restriction policies [27]. Also, a study in Colombia showed that women and high-income individuals are less likely to care about paying for parking. In this case, these individuals prefer to pay for parking rather than unsecured and uncontrolled on-street parking [17]. A study in Spain found that low-income individuals agree to pay less for parking than high-income individuals [24]. A study on nighttime parking in Türkiye found that a lower household income increases the likelihood of free on-street parking [28]. Therefore, it is expected that these individuals tend to use free parking. In another study conducted in China, it was shown that paid parking preference is sensitive to individuals’ car ownership status. The car ownership status of an individual who pays for parking significantly affects their parking behavior, which considers improving parking policies [18]. By examining the effect of vehicle characteristics on parking type preference, individuals with cheaper vehicles take more risks and might prefer uncontrolled on-street parking instead of paying for parking [17]. A study on overnight parking in Türkiye concluded that the likelihood of using paid parking increases with the vehicle’s newness [28]. This situation shows that there is a significant relationship between parking payment status and monetary risk-avoidance behavior.

In the literature regarding travel and parking characteristics, a study in the UK and Germany found that drivers’ parking behavior is related to trip purposes and trip costs [21]. However, a study in Belgium and the Netherlands indicated that parking type preference habits are not related to trip purpose [15]. Obviously, some of the factors affecting the parking choice are related to time. These include the parking duration, parking search time, and walking time from the parking spot to the final destination. Therefore, in addition to the cost, time factors influence parking choices, as in all transportation-related choices. Accordingly, a study from Greece noted that parking choice does not depend on trip frequency, while the most critical factors are determined as the parking cost, parking search time, parking duration, and walking time between the parking spot and the final destination [14]. A study conducted in China found that individuals pay more attention to the walking distance after parking, driving time, and parking price when making their parking choices [29]. Moreover, a study in Israel showed that drivers who park for long durations are less likely to change their behavior in response to parking restriction policies [27]. On the other hand, a study conducted in Serbia found that a shorter parking duration limit (a restrictive measure) decreases the likelihood of choosing on-street parking while increasing the likelihood of preferring off-street parking [30]. A study conducted in the Netherlands indicated that time restrictions are more effective than pricing strategies in managing the length of parking stay [31]. Furthermore, a study in China indicated that the parking choice in off-street or on-street parking, parking duration, and parking period decisions are influenced by the parking fee [32]. Moreover, in a study in the Netherlands, it is reported that drivers prefer on-street parking for the short term when off-street parking is discretely supplied over space [33]. Another study in the same country noted a significant impact of walking time on drivers’ parking choices [34]. In addition, a study in Türkiye also determined that parking choice behavior is related to trip purpose, travel time, and walking distance. Accordingly, off-street parking is more likely to be used by individuals whose trip purpose is work and whose travel time and walking distance to the final destination are higher [16]. Another study conducted in Türkiye found that on-street parking is more commonly preferred for short-term parking [35]. Moreover, a study from Israel shows that increasing the time required to search for parking will change people’s travel behavior [27], and another study conducted in Greece also supports this result, showing that when the on-street parking search time increases, the attractiveness of off-street parking increases [14].

In conclusion, a review of the related literature shows that parking behavior has been researched in numerous countries from different parts of the world. In these studies, various parking behavior models have been developed by analyzing factors influencing parking behavior. The outcomes of these models are used to develop effective parking policies. This study differs from the literature by making the parameters effective in parking behavior comprehensive (driver, vehicle, travel, and parking characteristics) and distinguishing between the parking type choice as free and paid parking. With this approach, parking type choice models are developed for Istanbul, a growing megacity in Türkiye, and parking policies that meet the province’s expectations are generated. In such megacities, drivers, in particular, need help searching for and finding a parking spot for a long time. For these reasons, it becomes essential to determine the factors affecting the preference of parking types in Istanbul and to implement parking policies according to the developed models. The results of the models also have the potential to help form the basis of a parking behavior model approach for other cities.

3. Parking Choice Model

This section consists of three parts. These parts present the data, methodology, and modeling approach.

3.1. The Data

The data source of this study is an online survey that was conducted in Istanbul, Türkiye, between June and August 2021. The survey was designed for individuals who live in Istanbul, have a driving license, and actively drive a vehicle. The survey distribution resulted in a convenience sample of 464 participants, and 405 correctly filled surveys were received. Convenience sampling is considered one of the most valuable methods when randomization is very difficult, such as in huge populations or when the researchers have limited resources and a restricted workforce [36].

The recent report published by the General Directorate of Security (2017) shows that the number of drivers in Istanbul is approximately 5.9 million [37]. According to the 2017 population data of Istanbul, 39% of the population are drivers [38]. As stated by the Turkish Statistical Institute, the population in 2021 is approximately 15.8 million [39]. Based on the same ratio (39%), the number of drivers at the time of the survey was estimated to be around 6.2 million. Accordingly, the minimum required sample size was calculated based on Krejcie and Morgan’s Equation (1) [40]. The minimum required sample size was determined to be approximately 384, indicating that the study had a sample size that exceeded the minimum threshold.

$$s={\displaystyle \frac{X^2·N·P·\left(1-P\right)}{d^2·\left(N-1\right)+X^2·P·\left(1-P\right)}}$$

(1)

where $s$ is required sample size, $X^2$ is the table value of chi-square for 1 degree of freedom at the desired confidence level (95% confidence level, 3.841), $N$ is the population size, $P$ is the population proportion (assumed to be 0.50, since this would provide the maximum sample size), and $d$ is the degree of accuracy expressed as a proportion (0.05).

The survey questions presented to the participants were formed by using the information obtained from field observations and studies given in the literature review section, and additional questions were added as needed. The dataset obtained from this survey, designed to collect information about the parking preferences of individuals, was used in the analyses.

The survey consists of four parts. In the first part, the demographic and socioeconomic characteristics of the participants were asked. These were gender, age, household income, driving experience, and car ownership. In addition to these, the participants’ habits of parking spot type (parallel, angled, perpendicular parking) were also learned.

In the second part, the characteristics of the vehicle at the participants’ disposal were collected. These were vehicle type, age, purchasing cost, fuel type, and presence of parking technologies. For vehicle type, participants were asked to classify the vehicle in size. Vehicle age was continuously measured in years, and vehicle cost was taken in Turkish Lira (TRY). Fuel type was selected from the given options of gasoline, diesel, hybrid, electric, and liquefied petroleum gas (LPG). For the vehicle parking technologies, participants were asked about sensors, cameras, and automatic parking systems (APS). In order to verify vehicle information, participants were also asked to input the brand name and model of the vehicle.

In the third part, data on travel time, parking search time, walking distance, and parking duration from the most recent trips were collected. Moreover, the survey included a stated preference part specifically designed to gather data on these travel and parking characteristics from actual trips, with separate responses for compulsory and non-compulsory trip purposes. Accordingly, this survey part employed a balanced sample approach [41], ensuring equal representation for both trip purposes, with these characteristics being derived from actual travel and parking behavior. Regarding the definition of these trip purposes, compulsory trips were school or work trips in both the general and survey contexts. Since these trips are usually periodic and repetitive, especially on weekdays, the use of parking places is more likely to occur in a planned manner. On the other hand, non-compulsory trips are usually shopping, socializing, or entertainment trips, which are sporadic, and parking locations are less likely to be planned. Furthermore, a detailed description of each factor related to travel and parking characteristics is provided below.

• Travel time (min): This refers to the average time from the beginning of the trip until the destination is reached and the vehicle is parked.
• Parking search time (min): This refers to the average time the driver searches for an available parking spot after reaching the destination.
• The walking distance (m): This refers to the average distance from the parking spot to the final destination. Here, the final destination is the end of the trip, which is reached by getting out of the parked vehicle and walking.
• Parking duration (h): This refers to the average time the vehicle remains parked at the parking spot.

In the last part of the survey, participants were asked about the parking types they preferred for their trips. There were four different types based on the parking fee and the location of the parking facility: (i) free off-street, (ii) free on-street, (iii) paid off-street, and (iv) paid on-street parking. Although there are notable examples of parking meters or mobile applications for parking worldwide, for paid on-street parking in Istanbul, the existing payment system involves paying parking attendants. Payments for off-street parking are made at points located at the exits. Parking occupancy information is shared in two ways. The first method involves signage that directs drivers while on the road. The second method provides access to parking occupancy information through mobile applications before or during the trip [42].

Table 1. Information about the survey.

Survey Parts	Description	Questions
Driver characteristics	Participants’ demographic, socioeconomic characteristics, and habits of parking spot type	Gender, age, household income *, driving experience, car ownership, and preference of parking spot type
Vehicle characteristics	Vehicle characteristics used by the participants	Vehicle type, vehicle age, vehicle purchasing cost *, fuel type, and presence of parking technologies
Travel and parking characteristics	Participants’ travel and parking characteristics for both compulsory and non-compulsory trip purposes	Travel time, parking search time, walking distance, and parking duration
Parking preference information	Parking fee and location	Free off-street and on-street parking Paid off-street and on-street parking

* Household incomes and vehicle purchasing costs were taken in Turkish Lira (TRY), and it was determined that 1 Euro (EUR) was equal to approximately TRY 10.20 at the time of the survey.

summarizes the parts of the survey with brief descriptions.

3.2. Methodology

The flowchart in Figure 1 shows the analysis process of the dataset obtained from the surveys. Accordingly, participants’ parking type preferences, as well as information on the driver, vehicle, travel, and parking characteristics, were collected either categorically or continuously. Figure 1 also presents information on categorical and continuous variables, including the units of continuous variables. For categorical variables, the options given to the participants are given in Section 3.1. Participants’ preference for off-street or on-street parking was analyzed, and parking behavioral models were developed by considering whether the parking was paid or free. The analysis and the model consisted of two steps: descriptive statistics and binary logit models. In the descriptive statistics, the first descriptive measures (average and standard deviation) for continuous variables were presented and interpreted. Secondly, continuous variables were grouped, presented, and evaluated by adding categorical variables. In the binary logit model section, the relationships between parking type preference and driver, vehicle, travel, and parking characteristics were modeled with binary logit models, and models of parking type preference behavior were developed. After that, the effects of driver, vehicle, travel, and parking characteristics on parking type preference were presented and evaluated. Finally, parking policies were developed and recommended based on the results of the parking behavior models.

3.3. Modeling Approach

In the literature, one of the popular approaches in the analysis of parking behavior involves using different forms of discrete choice models. Among these, the binary logit model [10,16], multinomial logit model [11,21,43], and mixed logit model [4,19,44] are frequently used. This study examines the distinct and comprehensive factors affecting the choice of parking type with binary logit models. In the binary logit model approach, the choices are assumed to have utilities, and the individuals will choose the alternative with the highest utility among the two alternatives. The utilities of the alternatives are determined by the utility function given in general form in Equation (2) [45].

$$U_i=v_i+{\epsilon }_i$$

(2)

where $U_i$ represents the utility of the alternatives, $v_i$ represents the deterministic component, and ${\epsilon }_i$ represents the error component.

Equation (3) determines the probability of choosing $i$ from two alternatives, $i$ and $j$, in a choice set [46].

$$P_i=Pr\left(v_i+{\epsilon }_i\ge v_j+{\epsilon }_j, i \in C, j\in C, i\ne j\right)=Pr\left(v_i-v_j\ge {\epsilon }_j-{\epsilon }_i\right).$$

(3)

where $P_i$ represents the probability of choosing $i$, $i$ and $j$ represent two alternatives, $C$ represents a choice set, $v_i$ and $v_j$ represent the deterministic component of the $i$ and $j$ alternatives, and ${\epsilon }_i$ and ${\epsilon }_j$ represent the error component of the $i$ and $j$ alternatives.

By assuming that the variance of the difference in the unobservable utilities (error component) in Equation (3) fits the logistic distribution, Equation (4) gives the probability of choosing alternative i out of the two alternatives $i$ and $j$ [45].

$$P_i={\displaystyle \frac{e^{v_i}}{e^{v_i}+e^{v_j}}} , i \in C, j\in C, i\ne j$$

(4)

where $P_i$ represents the probability of choosing $i$, $i$ and $j$ represent the two alternatives, $C$ represents a choice set, and $v_i$ and $v_j$ represent the deterministic component of the $i$ and $j$ alternatives.

4. The Analysis and the Model

This study analyzes the effects of driver, vehicle, travel, and parking characteristics on the choice of parking type by distinguishing between whether the facility is free or paid. In this section, the descriptive statistics of the data are introduced first, and the results of the binary logit models are presented next.

4.1. Descriptive Statistics

In this analysis of the descriptive statistics, the effects of the determined variables on the choice of parking type are examined in two parts. The first part interprets the average values of the continuous variables, while the second part evaluates the categorical variables. In the second part, the continuous variables are converted into categorical variables. In this way, the aim is to determine the critical thresholds of the variables that are effective in determining parking choices.

4.1.1. The Evaluation of the Continuous Variables

In this part, the parking preferences are interpreted by using the descriptive statistics of the driver, vehicle, travel, and parking characteristics. The dataset that examines travel and parking characteristics is generated according to certain considerations. As every car is parked both at the origin and the destination, every trip contains two data items for the parking type choice. In addition, parking is closely related to trip purposes, as this enables fulfilling activities after a car is parked. For this reason, in the survey, each participant was asked about the parking types they preferred for their compulsory and non-compulsory trips. As a result, 810 data items were generated for the 405 participants, in accordance with the balanced sample approach for both trip purposes. Accordingly, in analyzing the distribution of parking type preferences, it was found that free parking was the most preferred choice at 64.3%, while paid on-street parking was the least selected at 7.3%. In line with these findings, recent research for the Parking Master Plan of Istanbul revealed an even higher preference for free parking at 73.2% and a lower preference for paid on-street parking at 5.6% in the districts where individuals reside [42].

Table 2. Descriptive statistics of driver, vehicle, travel and parking characteristics.

Features	Variables	Free Parking			Paid Parking			All Parking
		Off- Street	On- Street	Total	Off- Street	On- Street	Total	Off- Street	On- Street	Total
		[333]	[188]	[521]	[230]	[59]	[289]	[563]	[247]	[810]
Driver Characteristics	Age	32.0 (9.0)	31.8 (10.5)	31.9 (9.5)	34.0 (10.0)	34.5 (11.1)	34.1 (10.2)	32.8 (9.4)	32.5 (10.7)	32.7 (9.8)
	Household Income *	4.0 (2.9)	3.4 (2.1)	3.8 (2.6)	4.0 (2.9)	4.6 (4.9)	4.1 (3.4)	4.0 (2.9)	3.7 (3.0)	3.9 (2.9)
	Driving Experience (year)	9.8 (8.0)	10.8 (9.8)	10.1 (8.7)	11.4 (9.2)	11.7 (10.7)	11.5 (9.5)	10.4 (8.6)	11.1 (10.0)	10.6 (9.0)
Vehicle Charac.	Vehicle Age	7.5 (5.9)	8.4 (6.5)	7.8 (6.1)	6.7 (5.7)	6.1 (5.7)	6.6 (5.7)	7.2 (5.9)	7.8 (6.4)	7.4 (6.0)
	Vehicle Purchasing Cost *	61.6 (42.0)	60.4 (47.7)	61.1 (44.1)	80.2 (62.7)	76.0 (78.8)	79.3 (66.2)	69.2 (52.2)	64.1 (56.9)	67.6 (53.7)
Travel and Parking Characteristics	Travel Time (min)	38.3 (27.0)	40.7 (32.1)	39.2 (28.9)	54.0 (36.6)	53.0 (39.0)	53.8 (37.0)	44.7 (32.2)	43.7 (34.2)	44.4 (32.8)
	Parking Search Time (min)	5.4 (7.3)	7.2 (7.8)	6.0 (7.5)	9.7 (9.1)	10.1 (8.3)	9.8 (8.9)	7.2 (8.3)	7.9 (8.0)	7.4 (8.2)
	Walking Distance (m)	94 (156)	127 (197)	106 (173)	283 (326)	263 (319)	279 (324)	171 (258)	159 (238)	168 (252)
	Parking Duration (h)	4.7 (4.0)	5.8 (5.5)	5.1 (4.6)	4.4 (4.7)	3.1 (2.7)	4.1 (4.4)	4.6 (4.3)	5.1 (5.1)	4.7 (4.6)
	Parking Fee (TRY)	-	-	-	21.3 (10.4)	18.5 (11.5)	20.8 (10.7)	-	-	-

Note: The numeric values in the table are presented as indicated: [number of observations] and average (standard deviation). * The household income and vehicle purchasing cost averages are shown as times the minimum wage (MW). MW: EUR 277 = TRY 2,825.90 for the year 2021.

shows the averages and the standard deviations calculated according to the type of parking (free or paid), along with the number of observations for each parking type. As can be seen from the table, the average age of the participants was close to each other for both parking types, although the average age of those who preferred paid parking was slightly higher. The average age of the participants for all parking types was 32.7 (std. dev.= 9.8), which is close to Istanbul’s average age of 33.1 [39]. On the other hand, low-income individuals seemed to prefer free on-street parking, while high-income individuals preferred paid on-street parking, as indicated by the higher average household income of paid parking users (4.1 times the minimum wage). Regarding parking fees, the average parking fee per hour was calculated as TRY 6.1 for on-street parking and TRY 4.9 for off-street parking, and the income-related results were consistent with the parking fees. Moreover, while the driver experiences of participants who preferred paid parking types were close to each other, experienced drivers preferred on-street parking when parking was free. Furthermore, it can be seen that drivers with relatively low-age vehicles preferred paid parking. Parallelly, participants who drove vehicles with high purchasing costs preferred paid off-street parking. On the other hand, the standard deviations for driver and vehicle characteristics indicate a wide range of respondents and vehicles with various characteristics in the sample. Specifically, when the average hourly parking fee was considered, it was found that drivers who preferred paid on-street parking included individuals with significantly high household incomes. Similarly, the purchasing costs of the vehicles used by these individuals were also relatively high.

The similarities between the statistical findings on travel and parking characteristics and the results of the studies conducted throughout Istanbul are presented. The standard deviations of the travel and parking characteristics were also distributed in a wide range, with particularly high values in the parking search time and walking distance data. According to data from the latest available Transportation Master Plan of Istanbul, the average travel time for trips with motorized vehicles is 45.5 min (std. dev. = 31.5), which is close to the travel time of 44.4 min (std. dev. = 32.8) calculated in the sample [47]. The average parking search time and walking distance in the sample were 7.4 min and 168 m, respectively, with relatively high standard deviations for these variables. This indicates that there were individuals among the respondents with significantly higher parking search times and walking distances. In the Parking Master Plan, 47.1% of the parking search time is in the range of 5–10 min, while 17.3% of the parking search time is over 20 min. On the other hand, this report shows that almost all the respondents were willing to accept a walking time of 5 min or less from the parking spot to the final destination. However, only 21.6% of the respondents were willing to accept a walking time of 15 min or more [42]. Assuming that the average walking speed of the individuals was 1.2–1.4 m/s [48], there were some individuals who agreed to walk over 1 km. The average parking duration for all parking types in the sample was 4.7 h, which was higher in free parking (5.1 h) than in paid parking (4.1 h). These findings are consistent with the results of the Parking Master Plan, where the parking duration distribution for all parking types is as follows: 58.1% for 6 h or less, 25.4% for 6–12 h, and 16.5% for 12 h or more. Specifically, free parking is preferred for durations of 6 h or less at 43.7%, while paid parking is preferred more for intervals with the same duration at 59.6% [42].

Compared to paid parking, the average travel time, parking search time, and walking distance were lower, whereas the parking duration was higher for free parking. Accordingly, drivers who used paid parking had longer travel times on average. Although some of this difference can be attributed to higher parking search times, other reasons could be the additional time spent during the entry and payment processes in parking facilities. Several advantages of paid parking facilities contribute to drivers’ tolerance of longer travel times. These include serving a wider destination area, a higher likelihood of finding empty and available parking spots, and significant aesthetic and comfort improvements such as enhanced walkways, lighting, and cleanliness. Additionally, these facilities are considered safer in various weather conditions (such as sun, rain, hail, and snow) and provide better protection against vandalism or personal attacks [49]. On the other hand, searching for a parking spot is an essential portion of the travel time. Although this portion was higher for paid parking (18%), it was also significant for free parking (15%). Similarly, the parking search time was lower for free parking facilities. Moreover, as expected, the search time for a parking spot was higher for on-street parking spots. These results show that the participants primarily preferred free parking, and as noted in the literature, drivers tend to use off-street parking when on-street parking is unavailable [13]. Another aspect of the parking process was that the walking distance from the parking spot to the final destination was longer for paid parking. Regarding walking distance, free off-street parking had the lowest distance among all the parking types. Considering the fact that not all the parking in Istanbul is regulated, it looks like the drivers benefit from this and manage to park their vehicles as close to their destinations as possible. Moreover, paid on-street parking spots had the lowest parking duration and the highest hourly parking fee. In addition, it was observed that the average hourly parking fee for on-street parking was 24% higher than for off-street parking. Free on-street parking spots were also used for longer parking durations.

4.1.2. The Evaluation by Grouping the Variables

Descriptive statistics of the driver and vehicle characteristics are shown in

Table 3. Driver and vehicle characteristics.

Features	Variables	Group	#	%	Features	Variables	Group	#	%
Driver Characteristics	Gender	Male	320	79	Vehicle Characteristics	Vehicle Size	Small-size	131	32
		Female	85	21			Mid-size	194	48
	Age	18–29	166	41			Large-size	68	17
		30–39	155	38			Extra-large-size	12	3
		40–49	49	12		Vehicle Age	0–2	73	18
		50–59	28	7			3–5	118	29
		≥60	7	2			6–9	105	26
	Household Income	≤2∙MW	96	24			≥10	109	27
		2∙MW–3∙MW	84	21		Vehicle Purchasing Cost	≤36∙MW	113	28
		3∙MW–4∙MW	79	19			36∙MW–60∙MW	111	28
		4∙MW–5∙MW	39	10			60∙MW–120∙MW	139	34
		≥5∙MW	107	26			≥120∙MW	42	10
	Driving Experience	0–2	67	17		Fuel Type	Gasoline	153	38
		3–5	65	16			Diesel	220	54
		6–9	86	21			Hybrid	5	1
		10–14	89	22			LPG	27	7
		≥15	98	24		Parking Technology	None	123	30
	Car Ownership	Yes	202	50			Only sensor	170	42
		None	203	50			Camera or APS	112	28

by grouping the continuous variables (and converting them into categories) shown in Table 2 and adding other categorical variables. The characteristics of the participants in the survey show that there were more males (79%), more young people (41%), varying income levels (low income 24%, high income 26%), high experience in driving (10 years or more, 46%) and high car ownership (50%). Participants who did not own a vehicle drove the vehicle from relatives or friends, rented or had their vehicle provided by their workplace, or rented themselves. Considering that this study was conducted on individuals who had a driving license and actively drove a vehicle, the rate of female drivers in Türkiye increased from 19% to 29% between 2010 and 2022, according to the Turkish Statistical Institute report [50]. It is observed that the rate of female drivers in the sample remained within this range. Similarly, the average age in the sample (32.7) was close to the average age in Istanbul (33.1) [39]. The oldest respondent was 66 years old, and the relatively slight difference in the overall average age was probably due to older individuals’ lower tendency to participate in online activities such as filling out web-based surveys.

Moreover, driven vehicles were mainly in the small-size (supermini, mini-compact, subcompact) and mid-size groups (80%), and there was a low number of new vehicles (2 years and below, 18%). Accordingly, vehicle purchasing costs were also concentrated in the low and medium ranges (56%). Most vehicles used diesel and gasoline fuel types, with a small percentage of LPG (7%) and hybrid (1%) vehicles. Parking technologies were relatively popular; in particular, 42% of the vehicles had parking sensors. In addition, when the habits of parking spot type were evaluated, it was seen that although the drivers had the highest preference for perpendicular parking (42%), they had a relatively high preference for parallel parking (19%) as well.

The continuous variables related to travel and parking characteristics are grouped and presented in a heatmap in Figure 2 by adding the category variable of trip purpose, which includes compulsory (C) and non-compulsory (NC). Here, a color scale is given separately for each travel and parking characteristic. Based on the relevant parking type preference, dark red tones indicate a high preference rate, while dark blue tones indicate a low preference rate. Therefore, the effects of variables related to travel and parking characteristics on drivers’ parking type preferences, as well as the differentiation of these preferences according to payment status, were examined. Accordingly, the aim was to determine the different situations in free and paid parking preferences using the heatmap according to each travel (travel purpose, travel time) and parking (parking search time, walking distance, parking duration) characteristic. Based on these analyses, the following conclusions were reached.

• It was observed that free on-street parking was preferred, especially for compulsory trips, at 64%. In other parking types (free off-street, paid off-street, and paid on-street), there was a more balanced distribution according to trip purposes. According to the Parking Master Plan, individuals making compulsory trips are more likely to prefer free parking, whereas those making non-compulsory trips are more likely to choose paid parking [42].
• Travel time was divided into five groups with 15 min intervals. Accordingly, drivers’ parking type preferences differed according to the payment status, especially for trips lasting 15 min or less. Drivers tended to use free parking more for trips lasting 15 min or less (22%), while paid parking was used less for these short trips (7%). Moreover, paid parking was used more frequently for trips lasting 1 h or more (33%).
• Parking search time was divided into four groups. Accordingly, the parking type preferences of drivers who spent less time searching for a parking spot differed by payment status, especially for search times of 2 min or less. Drivers who spent less time searching for parking were more likely to prefer free parking (43%), especially free off-street parking (47%), and were less likely to prefer paid parking (23%), particularly paid on-street parking (14%). Considering drivers’ motivation to reduce parking search time, the high search time in paid parking indicates that drivers initially look for free parking spots. Moreover, the higher search time (over 10 min) for on-street parking is an issue that needs attention due to its potential effects on overall traffic.
• The walking distance from the parking spot to the final destination was divided into three groups: short, medium, and long. Accordingly, the parking type preferences of drivers with short walking distances differed more significantly based on payment status. The share of short walking distances (100 m and below) was higher for free parking (77%), especially for free off-street parking (81%), and lower for paid parking (41%), especially for paid off-street parking (39%). Conversely, the share of long walking distances (over 200 m) was higher for paid parking choices (42%).
• Parking duration was divided into four groups. Accordingly, drivers’ parking type preferences for short-term parking, which represents a parking duration of 2 h or less, were more significantly differentiated by payment status. It was observed that short-term parking was higher for paid parking (42%), especially for paid on-street parking (61%). Conversely, short-term parking was lower for free parking (34%), especially for free on-street parking (28%). Additionally, long (5–9 h) and very long (10 h or more) parking durations were more commonly used for free parking (21% and 13%, respectively).

4.2. Binary Logit Model

The binary logit model is a statistical technique used to model binary outcome variables where the dependent variable has two possible alternatives. This method assumes that each alternative has an associated utility, and individuals are expected to choose the alternative with the highest utility [45,46]. Thus, it helps to understand the impact of independent variables on the probability of choosing an alternative. In this study, two binary logit models stratified for free and paid parking were estimated in an effort to effectively predict parking type preferences with respect to the determined variables. The independent variables were dummy variables consisting of driver, vehicle, travel, and parking characteristics. Driver characteristics are related to driving experience, household income, car ownership, and preference of parking spot type; vehicle characteristics are related to vehicle type, vehicle purchasing cost, fuel type, and presence of parking technology; travel and parking characteristics are related to trip purpose, travel time, parking search time, walking distance, and parking duration. In the free and paid parking models, t-statistic values were calculated to determine the relationship between parking type preference and the independent variables. The variables that did not have a statistically significant relationship at a 90% confidence level in both models were excluded. Accordingly, gender and age variables were not found to have a significant relationship in the developed models and were not included. Similar to these findings, some studies in the literature indicate that these variables are not effective in determining parking decisions; however, other studies show they are influential [13,14,15,16,17]. In addition, although some of these variables are continuous, they were changed into dummy variables by using the specified threshold values observed in Table 2 and Table 3 and in Figure 2. These thresholds, explained in detail in the following paragraphs, are associated with driving experience and household income for driver characteristics, vehicle purchasing cost for vehicle characteristics, travel time, parking search time, walking distance, and parking duration for travel and parking characteristics.

Regarding driver characteristics, the continuous variables of driving experience and household income were turned into dummy variables by considering the overall averages of the participants (Table 2). Thus, an effect of relatively high driving experience (10 years or more) and low household income (4 times the minimum wage or lower) on the parking type choice was observed. Among the dummy variables of driver characteristics, car ownership indicates whether the driver owns the vehicle or not. At the same time, preference for parking spot type represents whether the driver can park at relatively difficult spots, such as parallel parking. Related to vehicle characteristics, vehicle type represents small-size vehicles with easy maneuvering and parking characteristics. In order to change the continuous variable of vehicle purchasing cost into a dummy variable, the cut-off value was chosen as three times the yearly minimum wage and below (Table 3, 28%), which was also below the general average. Fuel type represents LPG-fueled vehicles, which are restricted in some parking types. The presence of parking technology represents the availability of sensors, cameras, and APS technologies that enable easy parking.

The heatmap in Figure 2 helped to determine the selected groups or threshold values for travel and parking characteristics used in the parking type preference models. In the process of determining these threshold values, the focus was on the color scale in the heat map, which shows the preference of parking types for each travel and parking characteristic group. Accordingly, groups where travel and parking characteristics were concentrated or differentiated for the preferred parking types were chosen. While the tendency to prefer parking for compulsory trips was higher in free on-street parking (64%), the distribution was more balanced in other types of parking according to travel purposes. Therefore, the group selected for trip purposes was compulsory trips, representing repeated and routine trips. Regarding travel time, drivers’ parking preferences differed significantly, especially for trips of 15 min or less. For these short-term trips, 22% of drivers preferred free parking, while only 7% preferred paid parking. Therefore, the group chosen for travel time was short-term trips of 15 min or less. Regarding parking search time, the parking preferences of drivers with relatively low search time expectations for a parking spot (2 min or less) differed by payment status. Drivers who had relatively low search time expectations for parking were more likely to prefer free parking (43%), especially free off-street parking (47%), and were less likely to prefer paid parking (23%), particularly paid on-street parking (14%). Thus, 2 min or less was chosen for this group to reflect a parking type preference process that results in relatively low search time expectations for parking. Regarding walking distance, the parking preferences of drivers with short walking distances differed more significantly based on payment status. The percentage of short walking distances (100 m and below) was higher for free parking (77%), especially for free off-street parking (81%), and lower for paid parking (41%), especially for paid off-street parking (39%). Therefore, the threshold for walking distance from the parking spot to the final destination was selected as 100 m or less to reflect situations where it is relatively easy to reach the final destination. Regarding parking duration, drivers’ parking preferences for short-term parking, which represents a parking duration of 2 h or less, were more significantly distinguished by payment status. Short-term parking was higher for paid parking (42%), especially for paid on-street parking (61%). Contrarily, short-term parking was lower for free parking (34%), especially for free on-street parking (28%). Therefore, the threshold value for parking duration was chosen as 2 h or less to determine the parking type preference, especially for short-term parking.

By using the variables mentioned above, two behavioral models (free and paid parking) for parking type preference were developed. In the design of the model, only one utility function was defined for each stratum. In the models, the choice was designed to be the preference between off-street and on-street parking, and on-street parking was selected as the reference alternative with no utility function.

Table 4. Binary logit model results.

Variables		Free Parking		Paid Parking
		Coefficients	t-Statistics	Coefficients	t-Statistics
Off-Street Parking
Driver Charac.	High driving experience (≥10 years = 1; otherwise = 0)	−0.76	−3.44 *	0.31	0.91
	Low household income (≤4∙MW = 1; otherwise = 0)	−0.46	−2.02 *	0.57	1.70 **
	Car ownership (owning a vehicle = 1; otherwise = 0)	0.87	3.94 *	0.28	0.84
	Having habit of parallel parking (Yes = 1; otherwise = 0)	−0.45	−1.76 **	−0.33	−0.78
Vehicle Charac.	Small-size vehicles (yes = 1; otherwise = 0)	0.47	2.07 *	−0.98	−2.90 *
	Low vehicle purchasing cost (≤36∙MW = 1; otherwise = 0)	−0.78	−3.12 *	0.29	0.58
	LPG-fueled vehicles (yes = 1; otherwise = 0)	−0.77	−1.97 *	−0.31	−0.39
	Vehicles equipped with parking technology (yes = 1; otherwise = 0)	−0.67	−2.67 *	−0.12	−0.28
Travel and Parking Charac.	Compulsory trips (compulsory = 1; otherwise = 0)	−0.98	−4.53 *	−0.32	−0.99
	Short-term trips (≤15 min = 1; otherwise = 0)	−0.60	−2.35 *	−1.00	−1.68 **
	Low search time expectations for parking (≤2 min = 1; otherwise = 0)	0.67	2.87 *	1.07	2.24 *
	Short walking distance to the destination (≤100 m = 1; otherwise = 0)	0.54	2.13 *	−0.55	−1.65 **
	Short-term parking (≤2 h = 1; otherwise = 0)	0.47	2.10 *	−0.91	−2.69 *
Constant		1.38	3.35 *	1.93	3.55 *
Number of observations		521		289
$LL\left(\beta \right)$		−340.68		−146.26
$LL\left(M\right)$		−292.04		−129.92
$-2LL$		97.29		32.69
${\rho }^2$		0.14		0.11

Note: The comparison parking type is on-street parking; all coefficients are in comparison to this parking type. * Significant at 95% confidence level. ** Significant at 90% confidence level.

shows the models’ estimated coefficients, t-statistics, log-likelihood $LL\left(\beta \right)$, and pseudo-$R^2$ (${\rho }^2$) values. According to the results of the developed models, the $-2LL$ test statistic was above the critical ${\mathsf{\chi }}^2$ value of 23.69 at 14 degrees of freedom at the 95% confidence level. This indicates that the estimated models are improved models.

4.2.1. The Effect of Driver Characteristics on Parking Type Preference

The deductions from the coefficient estimates of the driver characteristics presented in Table 4 are given below, respectively.

• High driving experience: The variable was significant only in the free parking model. The negative-signed coefficient estimate shows that experienced drivers prefer and do not hesitate to use on-street spots if the parking is free. Even though free on-street parking generally poses problems because of not-well-designed parking spots, safety issues, etc., they are preferred more, possibly due to expected proximity to the destination. On the other hand, there was no significant relationship between the preference for paid parking types and high driving experience. This result is because the paid parking spots are planned and standardized. Thus, even with a lack of experience, drivers are not disadvantaged and discouraged from using them. A study conducted in China also found that driving experience is associated with shared parking choices, with a negative correlation between them [51].
• Low household income: The variable was significant in both models. The coefficient was negative for free parking and positive for paid parking. These estimates indicate that low household income influences the choice between off-street and on-street parking differently for free and paid facilities. Accordingly, individuals who belong to low-income households will more likely use on-street facilities if free parking is available and off-street facilities if the parking requires a fee. This situation is also related to where low-income individuals are located, which might also be an indication of fewer free off-street parking places in these locations. On the other hand, low-income drivers are more inclined to park at more affordable off-street parking spots. In the literature, while some studies show that income is not effective in determining parking preference [13,14], other studies have found a relationship between income and willingness to pay for parking [17,24].
• Car ownership: The variable was significant only in the free parking model. The positive-signed coefficient estimate reveals that in terms of safety, car owners are more inclined to use relatively safer off-street parking facilities rather than uncontrolled and unprotected on-street parking, as anticipated. In Istanbul, the disorganized and unregulated use of free on-street parking spots, combined with the lack of physical design elements such as lines and barriers, has numerous adverse effects on traffic. These include increased traffic from vehicles searching for parking spots, double parking, and parking in unsuitable locations, all of which contribute to making these parking spots unsafe [52]. Moreover, it has been noted that on-street parking is particularly hazardous on main streets, where its presence increases the likelihood of accidents [53]. On the other hand, there was no significant relationship between preference of paid parking type and car ownership. Thus, this is an expected result, considering that paid parking is generally available in all planned and protected parking areas. However, vehicle owners are obviously more affected by parking expenses than non-vehicle owners [18].
• Habit of parallel parking: The variable was significant only in the free parking model. The negative-signed coefficient estimate indicates that the likelihood of using on-street facilities is higher for drivers who get used to parallel parking when there is a free parking alternative. On the contrary, there was no significant relationship between parking type preference and having the habit of parallel parking for paid parking. These outcomes suggest that when the cost of parking is not influential in the decision, driver habits might be the factor in preferring parking places that are more difficult to park in.

In summary, among the driver characteristics, high driving experience, car ownership, and parallel parking habits are effective only when choosing free parking types. Specifically, high driving experience and parallel parking habits increase the tendency to use on-street parking, while car ownership increases the tendency to use off-street parking. Conversely, low household income, one of the driver characteristics, is effective in determining the preference of parking type for both free and paid parking. Accordingly, while low household income increases the tendency to use on-street parking for free parking, it also increases the tendency to use off-street parking for paid parking.

4.2.2. The Effect of Vehicle Characteristics on Parking Type Preference

The findings from the coefficient estimates of the vehicle characteristics presented in Table 4 are given below, respectively.

• Small-size vehicles: The variable was significant in both models. The coefficient had a positive sign for free parking and a negative sign for paid parking. These estimates show that small-size vehicles distinctly affect the preference between off-street and on-street parking for free and paid facilities. Accordingly, in the free parking model, driving a small-size vehicle increases the probability of choosing off-street parking, while in the paid parking model, it increases the likelihood of selecting on-street parking. Individuals who drive vehicles with a high ability to enter smaller parking spots are more inclined to prefer isolated parking places such as free off-street parking, possibly due to the risks associated with unregulated on-street parking places (damage, theft). Oppositely, drivers with easily maneuverable vehicles are more likely to use paid on-street parking due to better safety. This situation also shows that in cases where parking fees are not present, safety is more critical in choosing parking types, followed by ease of parking.
• Low vehicle purchasing cost: The variable was significant only in the free parking model. The negative-signed coefficient estimate reveals that drivers with relatively low-cost vehicles are likelier to use on-street parking when parking is free. Indeed, low-cost vehicles might cause less concern to their drivers against the risk of theft and damage. In parallel, the literature suggests that drivers with relatively cheaper vehicles take more risks and may prefer uncontrolled on-street parking to pay for parking [17]. On the other hand, since safety risks are minimized for paid parking, drivers’ choice between off-street and on-street parking is unaffected by vehicle purchasing costs.
• LPG-fueled vehicles: The variable was significant only in the free parking model. The negative-signed coefficient estimate indicates that drivers who want to avoid paying for parking prefer on-street parking due to safety requirements, since free off-street parking is unsuitable for LPG vehicles. According to the regulations published for LPG vehicles in Türkiye, these vehicles can only use off-street parking under certain conditions. As stated in the regulation, these conditions pertain to the operation and safety of parking facilities. The operational conditions require that parking garages in shopping and commercial centers obtain a service qualification certificate and relevant approval from the local fire department. The safety requirements specify that LPG vehicles must be informed at the entrance to the parking area, parking must be restricted to designated floors, and spark safety must be ensured for ventilation and electrical installations [54]. On the other hand, the fuel type of the vehicle does not affect the driver’s preference for paid parking types due to sufficient safety and control services for LPG vehicles.
• Vehicles equipped with parking technology: The variable was significant only in the free parking model. The negative-signed coefficient estimate shows that with the confidence of parking in narrow places provided by these technologies, drivers can park in a cost-effective and more accessible way, increasing drivers’ tendency towards on-street parking. In brief, among the participants, the users of vehicles equipped with parking technologies have the benefit of parking at difficult locations with relative ease. This is more important for free on-street parking that is generally used irregularly, unplanned, and sometimes occurs on narrow streets. On the other hand, paid on-street parking provides more appropriately sized, regular parking spots where horizontal signs designate vehicle parking spots. Thus, drivers who use these parking spots do not need much assistance from parking technologies, and these technologies do not affect the preference of parking type.

In summary, among the vehicle characteristics, low purchasing cost, LPG-fueled vehicles, and vehicles equipped with parking technologies are only effective in determining the preference for free parking types and increase the tendency to use on-street parking. On the contrary, small-size vehicles, as one of the vehicle characteristics, are effective in determining the choice of parking type for both free and paid parking. Accordingly, small-size vehicles increase the tendency to use off-street parking for free parking while also increasing the tendency to use on-street parking for paid parking.

4.2.3. The Effect of Travel and Parking Characteristics on Parking Type Preference

The conclusions from the coefficient estimates of the travel and parking characteristics presented in Table 4 are given below, respectively.

• Compulsory trips: The variable was significant only in the free parking model. The negative-signed coefficient estimate reveals that the tendency of drivers to park for compulsory trips in free parking is towards on-street parking. Drivers are more likely to use on-street parking because they have faster access to the desired final destination, and there needs to be more free off-street parking in the districts. Also, in one study, the parking choice probability was found to be affected by trip purposes, which was higher for business trips than for other purposes [55]. Another study shows that off-street parking is preferred for business purposes, while on-street parking is preferred for other trip purposes [16]. On the other hand, there was no significant relationship between parking type preference and trip purpose for paid parking. Thus, trip purposes do not affect parking type preference when there is a parking fee.
• Short-term trips: The variable was significant in both models. In terms of signs, there was a negative-signed coefficient in both models. Accordingly, regardless of the parking fee, individuals who make short trips are more inclined to use on-street parking. Similarly, in the literature, it is indicated that off-street parking is more likely to be used by drivers whose travel time is higher [16]. For short-term trips, drivers aim to reach their destinations quickly and easily. In addition, these trips may be more frequent within a specific region (district, neighborhood, etc.). In this case, it is essential to determine the region’s parking supply and parking type distribution, and the dominance of on-street car park use shows that traffic congestion in the region will increase. Especially when the coefficients of the models developed for free and paid parking are examined, the tendency to prefer on-street parking is higher in the case of a choice between paid parking facilities.
• Low search time expectations for parking: The variable was significant in both models. With regard to signs, the coefficients had positive signs in both models. Accordingly, when drivers have low search time expectations for parking, they mostly prefer off-street parking, with a higher probability of finding an empty parking spot, especially if they pay for parking. One study shows that when the search time for on-street parking increases, the attractiveness of off-street parking also increases [14]. On the other hand, parking search time is often higher for on-street parking facilities, which creates more traffic, time loss, and additional costs.
• Short walking distance to the destination: The variable was significant in both models. The coefficient had a positive sign for free parking and a negative sign for paid parking. These estimates show that although the parking type preference differs for paid and free parking, the effect levels are similar due to the coefficients of this variable being close. When the walking distance expectations to the final destination are relatively shorter, there is a higher tendency to choose off-street parking for free, while on-street parking is more preferred for paid parking. Free off-street parking facilities are generally located in shopping malls, public institutions, business centers, and residential buildings. Therefore, these parking places may be closer to the final destination. On the other hand, paid on-street parking facilities are closer to the city center, commercial places, and attractive trip destinations. The parking behavior tendency is also to prefer a parking spot close to the final destination. In one study, nearly half of the commuters stated that the main reason for choosing a parking spot was proximity to the final destination [13].
• Short-term parking: The variable was significant in both models. The coefficient had a positive sign for free parking and a negative sign for paid parking. These estimates indicate that short-term parking causes the preference between off-street and on-street parking to be different when they are free or paid. Although drivers who park for shorter durations choose off-street parking facilities for free parking, they choose on-street parking facilities when they pay for parking. The causes of this situation are that among the free off-street parking facilities, those belonging to shopping malls, public institutions, and business centers are generally suitable for short-term parking. In contrast, those belonging to residential areas are more suitable for long-term parking. Here, it is seen that drivers are more inclined to park on-street in residential areas, probably due to the low amount of residential off-street parking available in some districts. This situation in free parking shows that off-street parking is used for relatively shorter parking durations. As opposed to free parking, paid on-street parking is preferred for relatively short-term durations. A study has shown that the attractiveness of off-street parking increases as the parking duration increases [14]. Similarly, when the parking duration rises, the share of on-street parking decreases by 15% versus 3% for off-street parking compared to underground parking spots [13]. Considering the time-based fee tariffs in paid parking, it is seen that drivers looking for short-term parking tend to use on-street locations. Paid off-street parking is preferred by drivers for longer-term parking because this parking type offers more advantageous parking fees when the parking duration increases.

In summary, among the travel and parking characteristics, compulsory trips are only effective in determining the choice of free parking type and increase the tendency to use on-street parking. Among these characteristics, short-term trips and low search time expectations for parking are effective in determining the choice of parking type for both free and paid parking. Accordingly, in free and paid parking, short-term trips increase the tendency to use on-street parking, while low parking search time expectations increase the tendency to use off-street parking. On the other hand, while a short walking distance to the destination and short-term parking are effective in the selection of parking types for both free and paid parking, a short walking distance and short-term parking increase the tendency to use off-street parking for free parking and increase the tendency to use on-street parking for paid parking.

5. Discussion

Parking policies improve the responsiveness of parking management systems to parking demand. In congested urban areas, parking pricing strategies are practical and commonly used as travel demand management policies [56]. Parking pricing is also recognized as able to ease traffic and parking congestion and reduce other social opportunity costs [57]. In addition, parking policies include pricing parking spots, developing parking facility infrastructure, imposing time restrictions for parking, and directing vehicles to the most suitable parking spots. However, limited resources make it challenging to develop efficient parking policies due to high supply costs and unexpected demands [58]. Furthermore, if finding a parking spot is difficult or costly, owning a car becomes less attractive as both usage and parking costs increase [59]. Similarly, free and uncongested on-street parking increases car ownership even for drivers who park their vehicles at off-street parking locations [60]. This shows that on-street parking spots have a demand-generating effect. Hence, parking regulations are essential to balance the modal split between private and public transportation systems. By increasing the supply of parking, parking policies might assist in relieving city centers from the negative impacts of excessive vehicle density. These include shifting to more sustainable modes of transportation, such as public transport systems and bicycles, and imposing various restrictions on people coming to park through paid zones [61]. Eventually, parking regulations can be used strategically to manage traffic and improve urban quality of life by controlling individual car use and making alternative modes of transport attractive.

The parking master plan reports for Istanbul in 2016 and 2022 established short-term and medium-term goals. The Parking Master Plan (2016) focused on seven goals in total, with three short-term (2014–2019) and four long-term (2019–2023) goals. The short-term goals were to provide sufficient parking spaces to meet demand, to use parking supply effectively and efficiently, and to improve the standards for on-street parking usage. The long-term goals were to measure and evaluate the performance of parking planning, to reduce the negative impacts of parking, to decrease parking demand and control supply, and to support alternative transportation modes and pedestrian access [52]. However, the Parking Master Plan (2022) shows that most targets have not been achieved. Accordingly, the latest Parking Master Plan determined the objectives of defining zones and developing parking management, duration, and fee policies to manage parking supply and reduce traffic volumes, congestion, and car dependency. It aims to establish and develop park-and-ride areas and technologically intelligent payment systems to create an accessible, affordable, integrated, and inclusive transportation system. The strategies determined within the goals of this plan can be categorized into two main groups, focusing on the management of parking supply and parking demand. For parking supply management, the plan emphasizes areas where no new supply will be created, areas with limited supply production, and areas where new supply will be generated. On the other hand, parking demand management strategies include implementing pricing policies, promoting park-and-ride facilities and public transportation usage, and improving technological infrastructure to facilitate the pre-planning of private car trips [42].

This study examines the relationship between parking type preferences and driver, vehicle, travel, and parking characteristics in detail with the help of two binary logit models by considering the availability of free parking that meets expectations. Contributions to the literature and the proposed parking policies are presented in order by referring to the estimation results of the model. Additionally, current or planned local parking policies related to the study’s findings are noted where applicable.

• Since free on-street parking spots need to be organized and planned, the current arrangement of these spots in Istanbul reduces the tendency of relatively inexperienced drivers to park in these locations. In order to ensure equal use of these public parking spaces by all individuals and to prevent potential traffic incidents, a parking policy should be developed to organize these parking spots according to vehicle size and maneuvering movements. One of the strategies aimed at Istanbul is to establish usage standards and legal regulations for managing on-street parking throughout the city. This includes implementing paid on-street parking around residential areas, offering user-based discounted rates (for residents, visitors, disabled individuals, and commercial users), and ensuring safe and appropriate parking spots. This approach aims to make parking management more sustainable and safer, ensuring residents can easily find affordable and safe parking spots [42].
• Low-income drivers, who are more affected by the situation of paying parking fees, are more likely to park at free on-street parking places, which increases irregular parking, causes long parking search times, and negatively affects traffic and road safety. Moreover, low-income individuals tend to park farther away and walk to save money on parking fees [62]. Local authorities might not charge for on-street parking and may continue to require significant off-street parking for all land uses. If this occurs, free parking will shift transportation mode choices towards car use. This will result in more time wasted in traffic, increased energy consumption, more air pollution, and higher costs for everything except parking. Thus, everyone, including those who do not use parking facilities, will pay the cost. Local authorities will even impose high free parking costs on everyone, including low-income individuals. Therefore, instead of planning for free on-street parking, planning based on variable pricing is recommended [42,63]. On the other hand, a better approach is to determine the maximum rather than the minimum off-street parking requirements in residential areas [2]. According to this approach, providing off-street parking spots (such as underground parking spaces) in existing and planned residential buildings would provide an alternative to free on-street parking for everyone, including low-income individuals.
• In cases where free car parking that meets expectations is not available, considering the desire of low-income individuals to minimize parking costs, parking fee policies should be implemented and developed for short-term use of paid on-street parking places by individuals at these income levels. However, if it is desired to reduce access to the districts by private cars, it is seen that providing access by public transport systems supported by park-and-ride services will be more easily adapted by low-income individuals due to their parking type preferences. Accordingly, the planned parking strategies in Istanbul include developing high-capacity park-and-ride facilities near existing and under-construction stations as well as enhancing pedestrian access from these parking facilities to the stations [42].
• Car owners tend to use free off-street parking due to their concerns for safety and comfort. Especially in residential, work, and school places, providing free off-street parking is necessary by considering car ownership of the districts and accurately determining the existing parking demand. Therefore, the construction of fully automated and mechanical parking systems that serve the region’s needs is planned in Istanbul, especially in regions with high vehicle ownership and where no new parking supply will be constructed [42].
• Since drivers with parallel parking habits are more likely to use on-street parking, there is a need to develop applications where on-street parking spots can be found or reserved online. Thus, drivers can find a parking spot that serves their needs and use on-street parking spots more efficiently.
• While driving a small-size vehicle is an advantage in paid on-street parking, it does not positively affect free on-street parking. This advantageous situation is because of the fact that paid on-street parking spots are organized and planned to be more suitable for vehicle size. In free on-street parking spots, the safety risks due to vehicles parking closer to each other cause threats, even for small-sized vehicles.
• Drivers of low-purchasing-cost vehicles are more inclined to use free on-street parking, which shows that their risk level against possible accidents or theft is low. Indeed, this situation contributes to irregular vehicle parking at free on-street parking places. As a result, in addition to the regulations for on-street parking, it is necessary to determine and control these specific individual groups that cause irregularity.
• While individuals driving LPG-fueled vehicles tend to use free on-street parking, this is not the case for paid parking. It is possible to interpret this as a need to examine the suitability of free off-street parking for LPG-fueled vehicles. Moreover, it is also understood that safe off-street parking facilities are available for LPG-fueled vehicles in paid parking under Türkiye’s regulations [54]. Therefore, according to the regulations, developing or constructing free off-street parking for LPG-fueled vehicles is necessary. Thus, the parking demands of these vehicle drivers can be balanced between different types of parking.
• The availability of vehicle parking technologies increases the tendency to use free on-street parking. The widespread use of these technologies in vehicles eases the use of these facilities, especially where parking spots are smaller than standard ones. This situation increases the number of unsafe parking spots and the time it takes to search for a parking spot, and accordingly, it has negative traffic impacts. Therefore, designing free on-street parking spots and guiding vehicles to free parking spots will increase efficiency. Hence, horizontal and vertical markings in locations where free on-street parking is allowed will prevent these safety risks.
• Drivers tend to use free on-street parking for compulsory trips. This situation is due to the lack of sufficient accessible off-street parking facilities. Free or low-priced on-street parking is sometimes seen as causing traffic congestion and increasing parking search time problems [6,64]. Therefore, a strategy of reducing on-street parking capacity and making it paid (except for residents) has been proposed for certain regions of Istanbul [42]. Thus, it can be proposed to use the revenue generated from on-street parking for improvements and to remove off-street parking requirements [64].
• The share of short-term trips in total trips is significant. The high tendency for the short-term use of free on-street facilities leads to more unregulated parking and additional traffic. Furthermore, where free parking is unable to meet expectations, there is a higher inclination to use paid on-street parking spots for short-term trips. Therefore, the share of short-term trips and the tendency toward the parking locations for these trips should be considered when determining the supply of parking types.
• Drivers with low search time expectations for parking are more likely to prefer off-street parking. On the other hand, it is known that the parking search time is higher for on-street parking, which contributes to traffic congestion, time loss, and additional costs. Therefore, planning on-street parking, locating it only in the required areas, and using Parking Guidance and Information (PGI) systems to inform users in real time about empty parking places can significantly reduce traffic. Future plans in Istanbul include developing PGI systems, implementing mobile payment systems, introducing reservation systems for facility parking on a zone basis, and using parking meter systems for on-street parking [42]. Furthermore, implementing off-street parking reservation mechanisms is an appropriate parking policy to reduce the demand for on-street parking [65]. These plans aim to increase parking management efficiency and reduce traffic congestion by minimizing parking search times. The literature also emphasizes that successful parking demand management is the principal action to reduce the parking search time [66].
• For free parking, drivers with short walking distance expectations prefer off-street parking facilities, while in situations where free parking that meets expectations is not available, these individuals choose paid on-street parking facilities. In both cases, the main tendency of drivers is to choose the location that is the closest to their destination. Therefore, the locations of free and paid parking types affect parking demand, and distances to attraction centers should be considered to create a balanced supply of parking types.
• In general, off-street parking facilities are used for relatively short-term parking among free alternatives. When free facilities that meet expectations are not available, paid on-street parking is preferred more for relatively short-term parking. Therefore, parking efficiency levels can be increased in these parking types with parking duration or period limitations. Accordingly, flexible pricing techniques and the organization of a parking subscription system are among Istanbul’s parking strategies for managing the parking supply [42]. As another parking policy, on-street parking fees must be higher than off-street parking fees. Studies have shown that underpricing on-street parking results in significant efficiency losses [23,64]. Accordingly, it is aimed to charge 25% more for on-street parking than off-street parking in Istanbul [42]. Therefore, it is recommended that on-street parking be regulated and priced to ensure the full capacity utilization of off-street parking facilities [67].

6. Conclusions

This study observed that drivers prefer free parking at a rate of 64.3%. Preferences for off-street (63.9%) and on-street (36.1%) parking within free parking are more balanced compared to off-street (79.6%) and on-street (20.4%) preferences within paid parking. This study specifically focused on the differentiation between drivers’ parking type preferences according to payment status. Accordingly, two binary logit models were developed to determine type preferences for paid and free parking. The number of variables related to driver, vehicle, travel, and parking characteristics that impact parking type preference is higher for free parking than for paid parking. In terms of parking type preference, the analyses observed that some variables are effective only for free parking, while others affect similar or different tendencies for free and paid parking. Furthermore, the analysis details how parking type preferences (off-street and on-street) for free and paid parking differ by individual, vehicle, travel, and parking characteristics. These differing preference patterns can be observed as follows:

• For driver characteristics, low-income individuals tend to use on-street parking for free parking and off-street parking for paid parking.
• Concerning vehicle characteristics, individuals who drive small-size vehicles tend to park in off-street parking for free parking and on-street parking for paid parking.
• Regarding parking characteristics, individuals with short walking distance expectations to their final destination tend to use off-street parking for free parking and on-street parking for paid parking. Additionally, it is observed that for short-term parking, off-street parking for free parking and on-street parking for paid parking is preferred.

Parking behavior is a process that needs to be examined in order to develop parking policies and predict their results. In particular, the choice of parking type helps determine the distribution of parking demand in the region and the direction in which the existing parking supply should be increased or improved. This study modeled the parking type choice behavior by dividing parking types according to whether they are paid or free. These behavioral analyses make it clear that parking payment status is a critical threshold for drivers’ choice of parking type. In conclusion, the findings suggest that the distribution of parking types should be carefully regulated based on factors such as the driver types (with respect to behavioral approaches), vehicles, travel and parking priorities in the region, and they also provide a basis for essential policy recommendations in urban planning and traffic management. Apparently, modeling the parking preference behavior in a simplified way with driver, vehicle, travel, and parking characteristics will guide the development of parking policies and investments.

To shed light on future studies, the parking behavior models developed in this study focus on type preference behavior for both paid and free parking. Considering the region’s characteristics under examination, these models can calculate the rate at which users will prefer off-street and on-street parking based on their payment status. Combined with the existing parking supply situation and the demand model to be developed for parking spaces, these parking preference models can provide outputs that will form the basis of sustainable parking strategies for the region.