Research Design

After the data processing process, the cross-sectional dataset contains variables related to the parking dynamics and parking fees. Every variable incorporated in our model is standardized by the unit of analysis, the traffic zone. We summarize the attributes and the expected relationship of independent variable with the dependent variables in Table 4.11 and provide the descriptive statistics in Table 4.12. The variables used in the analysis are as follows.

4.3.1 Dependent Variable

Aiming at knowing how parking fee affects parking occupancy and results in better land use efficiency, we explore the indirect relationship between parking fee and occupancy rate in the previous chapter. Parking fee affects the quantity demanded of parking first, then leads to a change in occupancy rate. Therefore, to analyze such a relationship, the dependent variable is twofold.

Figure 4.7 Indirect Relationship Between Curb Parking Fees and Occupancy Rate

1. Quantity Demanded of Curb Parking

The first section of empirical analysis attempts to uncover the first part of such an indirect relationship between the parking fee and the quantity demanded. We employ the quantity demanded of curb parking as the dependent variable to examine the price mechanism. The data of the quantity demanded here is the number of cars parked on curb spaces at peak time.

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2. Vacancy Rate of Curb Parking

Vacant curb spaces at peak time signify the inefficient use of land. Therefore, the second section of empirical analysis employs the parking vacancy as the dependent variable to measure the land use efficiency. This number is the outcome after dividing the surplus of curb parking by the quantity supplied of which.

4.3.2 Independent Variable

1. Curb Parking Fee

Curb parking fee is the primary explanatory variables that we focus on in the research.

If curb parking is a normal good, the parking fee will have a negative relationship with the quantity demanded supposing the income is fixed. Higher parking fees reduce the number of cars parked on curb spaces because on-street parking becomes too expensive.

Drivers shift their demand to alternative parking options such as off-street parking or illegal parking. Therefore, the coefficient of curb parking fee is predicted to have a negative sign. Moreover, as the quantity demanded of curb parking increases after the decrease in parking fees, the vacancy rate decreases correspondingly. Hence, curb parking fee should have a positive relationship with the vacancy rate of curb parking.

2. Quantity Supplied of Parking Spaces

Quantity supplied of parking spaces contains both the on-street parking and the off-street parking in Taipei City. We include this variable in that higher quantity supplied of parking spaces, though, are not necessarily equivalent to higher quantity demanded of parking, the bigger capacity of the area accommodates a higher number of cars parked. Therefore, to prevent from attributing the wrong portion of variations of the

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quantity demanded of curb spaces to price mechanism, we incorporate the quantity supplied of on-street and off-street parking all at once. We use this variable to specify the exact effect of parking fees on quantity demanded of curb parking and anticipate that the parameter of the quantity supplied of parking spaces has a positive sign.

However, we are uncertain about the relationship between the quantity supplied of parking spaces and the vacancy rate of curb parking. On the one hand, if on-street parking accommodates most of the parking demand, the vacancy rate is likely to reduce.

On the other hand, if most parking demand is served by the off-street parking instead, the vacancy rate of curb parking will then increase or remain at the same level.

3. The Number of Illegally Parked Cars

Illegal parking generally occurs when people demand for a short period to stop by. For example, drivers stop at the curbside because they need a quick check for something outside their cars. Though legally speaking, these drivers should park their cars properly in the space. Parking in the curb space for such a short time, however, is often inefficient.

First, the huge amount of time drivers spent to find a curb space is utterly disproportionate to the short parking duration. Not yet do drivers get to enjoy the services provided by curb parking, they must leave. Even worse, when drivers parked their cars in curb spaces, they might bump into the parking fee collectors. This situation is similar to forcing someone to pay for the goods or services that they never bought.

Therefore, illegal parking is not just about the risk for the sake of convenience; it implies drivers’ willingness to pay and their ideas of whether curb parking is priced right. In this scenario, we view illegal parking as the implicit demand for on-street

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parking. Illegal parking complements the demand that curb parking could not perfectly meet. We, thus, expect that the number of illegally-parked cars is positively correlated with the quantity demanded of curb parking. Additionally, as the quantity demanded of curb spaces increases, the surplus of curb parking decreases. Illegal parking has a negative relationship with the vacancy rate of curb parking.

4. Type of Land Use

The only dummy variable in our model is the type of land use. This variable includes 14 classifications defined by Taipei Parking Supply and Demand Survey. These types of land use are different from the zoning in urban planning; they combine zoning categories with neighborhood attributes. For instance, the classification of “Center (Commercial Area with Night Market)” not only indicates the land use zoning but also implies the surrounding parking pattern. We expect to observe more short-term pick-up and drop-off demand for illegal parking near the night market. Meanwhile, the quantity demanded of curb parking will surge as most night markets do not provide nearby off-street parking. More types of land use can be found in Table 4.12. We expect that traffic zones in the city center have higher parking demand but lower vacancy rate than those on the outskirts. The residential areas have the highest quantity demanded and the lowest vacancy rate of curb parking.

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Table 4.11 Description of Variables and Functions

Notation Description Type Expected Sign

D_CURB Vacancy D_CURB Number of cars parked on curb spaces Continuous

Vacancy Rate The surplus of curb parking divided by

the quantity supplied of which Continuous

P_RATE Average parking fee (NTD) Continuous Negative Positive S_ALL Quantity supplied of parking spaces Continuous Positive Uncertain D_Illegal The number of cars parked illegally Continuous Positive Negative LU Different types of land use

(See all classifications in Table 4.12) Categorical － －

Table 4.12 Descriptive Statistics of Each Variable

Variable Mean Std. Dev. Min Max

Dummy: Types of Land Use Frequency Percentage

LU1: Center (Residential Area) 124 18.13%

LU2: Center (Commercial Area with Shopping Center) 41 5.99%

LU3: Center (Commercial Area with Night Market) 26 3.80%

LU4: Center (Mixed Residential and Commercial Area) 189 27.63%

LU5: Center (Cultural District) 7 1.02%

LU6: Center (Hospital) 8 1.17%

LU7: Outskirts (Residential Area) 177 25.88%

LU8: Outskirts (Commercial Area with Shopping Center) 4 0.58%

LU9: Outskirts (Commercial Area with Night Market) 3 0.44%

LU10: Outskirts (Mixed Residential and Commercial Area) 81 11.84%

LU11: Outskirts (Cultural District) 6 0.88%

LU12: Outskirts (Hospital) 4 0.58%

LU13: Outskirts (Industrial Area) 9 1.32%

LU14: Outskirts (Public Facility) 5 0.73%

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In this chapter, we first study how effective parking fees are in changing the quantity demanded of curb parking. We regress the number of cars parked on curb spaces on curb parking fee, the quantity supplied of parking spaces, the illegal parking and the types of land use. We employ the OLS estimator to analyze the average variations and incorporate quantile regression to examine coefficient change in the different percentiles of data.

The second section of this chapter delves into how effective parking fees are in reducing parking vacancy. The regression model adopted here is almost identical to the previous one with one important modification of the dependent variable from the quantity demanded of curb parking to the vacancy rate of which. We use parking vacancy to help us understand whether adjusting parking fees is a practical policy to improve the land use efficiency.

5.1 How Parking Fees Affect the Quantity Demanded of Curb Parking?

5.1.1 The Ordinal Least Square Regression Model

In this regression model, we leave out 34 traffic zones which have no quantity supplied of curb spaces and keep 641 observations of traffic zones in the dataset. In Figure 5.1, we use squared residuals as a proxy for the underlying squared error terms; the scatter plot does not suggest that the error term of the quantity demanded of curb parking is homoscedastic. We, thus, conduct the Breusch-Pagan test to examine the