Parking demand forecasting is an important part of urban parking planning and is also an important basis for the development of parking facilities. The primary objective of this study was to explore multiple factors that affect the curb parking price (CPP) and the changing rules of the curb parking price (CPP) with these factors and to predict the CPP in terms of urban mobility. The data were collected through a statistical survey that was administered in 81 cities in China. The cities were divided into three categories: rich cities (RCs), poor cities (PCs), and tourist cities (TCs). Both the time series method (TSM) and regression analysis method (RAM) were developed to simultaneously examine the factors associated with the CPP among parking users. The results showed that TSM and RAM can account for common urban curb parking prices. The prediction results showed that the CPP is affected by the number of urban dwellers (UD), the prevalence of car ownership (CO), and the per capita disposable income (PCDI) of urban residents; the CPP can be predicted by a model built on the basis of the above three influencing factors. The results can enhance our understanding of the factors that affect CPP. Based on the results, some suggestions regarding the use of the CPP range in parking policy planning were discussed.
Curb parking is a public resource [
Given the current grim parking situation in Chinese cities such as Beijing, Shanghai, and Shenzhen, the government has put forward restrictive measures to address the imbalance between parking supply and demand. For example, Beijing has used the experience of Japan and other countries to predict the increase in the number of parking places, but this has not been effective. Many cities have strict parking rules, but violations continue, and the expected effect of the policy has not been achieved [
The current research on the curb parking pricing (CPP) mostly uses discrete selection models, game theory, nonlinear decision analysis models, and other methods to evaluate the rationality of on-street parking pricing, while there are few studies on the curb parking pricing (CPP) prediction. The independent predictors selected by the above model are less predictable and are only applicable to the status quo evaluation. Therefore, it is difficult to help policy makers have a clearer understanding of the city’s curb parking prices (CPP) in the future years before formulating relevant policies, which is likely to cause short-sightedness and irrationality of parking policies.
To fill this gap, we propose a mixed forecast method for curb parking price forecasting, combining the time series method (TSM) and regression analysis method (RAM). By collecting historical parking prices, we can predict the future CPP for the core areas of Ningbo, Yancheng, and Kunming. Finally, the practical application shows that the forecast method can be applied to other cities in China. This prediction of the curb parking price can help decision makers develop better parking strategies to balance parking supply and demand and thoroughly solve the problem of urban parking.
This research makes the following three contributions. First, we develop a curb parking price model that combines the TSM approach and RAM approach, using historical data on the number of urban dwellers (UD), car ownership (CO), and per capita disposable income (PCDI) of urban residents to estimate the 2 h CPP in the core city for the next year. Second, we divide China’s cities into the rich cities (RCs), poor cities (PCs), and tourist cities (TCs) and analyze the CPP for the cities under different scenarios. Third, we classify the prediction results for the future year as optimistic, aggressive, or conservative to ensure the accuracy of the outcomes.
The remainder of this paper is organized as follows. Section
The time series method (TSM) is a method for establishing mathematical models based on time series data obtained by systematic observation. It is generally performed using curve fitting and parameter estimation methods (i.e., nonlinear least squares) and is widely used in the fields of economics [
In economics, Zhu [
In the field of geography, Yin et al. [
In electrical engineering, Ding et al. [
Since the regression analysis method (RAM) is simpler and more convenient for analyzing multifactor models, its application in many fields, such as mathematics [
In the field of mathematics, Lin et al. [
In the chemistry field, Yu et al. [
In the computer science field, Liu et al. [
In the medical field, Ma et al. [
Turning our attention to our research setting, we note that parking prices are actually related to a number of external factors [
In order to explore multiple factors that affect the curb parking price (CPP) and the changing rules of the CPP with these factors and to predict the CPP in terms of urban mobility, this article reviews the domestic and foreign research related to time series and regression analysis. From the analysis results of the literature review, time series methods and regression analysis methods can be used to achieve the purpose of this article.
It should be noted that the number of urban dwellers (UD), the prevalence of car ownership (CO), and per capita disposable income (PCDI) are macrolevel influencing factors of curb parking prices. Because the topic of this study is mainly curb parking prices in urban core areas, the spatial scope is relatively wide, and the degree of influence of microlevel factors such as major and minor roads and road length on curb parking prices in the sample area is assumed to be relatively negligible.
The basic data of the article come from the China Statistical Yearbook [
Except for the RCs and PCs, cities can be divided by their PCDI, and the TCs appear in this section in a separate category because of their special urban functions and positioning. Some RCs, such as Suzhou and Xiamen, and PCs, such as Guiyang, need to be attentive to the impact of tourism, which accounts for a large proportion of their gross domestic product (GDP). Therefore, some RCs and PCs can also be classified as TCs.
TCs, for their part, can be considered a city classification between RCs and PCs. This study collects data on the following variables for the core areas of 36 RCs, 26 PCs, and 31 TCs: 2 h CPP, UD, CO, and PCDI. The statistical results are shown from Tables
The curb parking price in the core areas in RCs (2017).
Cities | 2 h CPP (yuan) | UD (10 thousand) | CO (10 thousand) | PCDI (yuan) |
---|---|---|---|---|
Beijing | 40 | 1876.6 | 564 | 62406 |
Shanghai | 70 | 2120.88 | 359 | 62596 |
Suzhou | 18 | 1068.36 | 355 | 58806 |
Shenzhen | 21 | 1249.57 | 322 | 52938 |
Dongguan | 13 | 749.66 | 263 | 42944 |
Wuhan | 7 | 853.65 | 261 | 43409 |
Qingdao | 16 | 625.25 | 246 | 50817 |
Hangzhou | 22 | 727.14 | 244 | 56276 |
Guangzhou | 32 | 1248.9 | 240 | 55400 |
Nanjing | 28 | 685.89 | 239 | 54538 |
Ningbo | 18 | 579.56 | 229 | 55656 |
Foshan | 18 | 727.11 | 228 | 46849 |
Changsha | 15 | 614.38 | 217 | 46948 |
Shenyang | 17 | 668.2 | 210 | 41359 |
Fuzhou | 16 | 510.5 | 118 | 40973 |
Xiamen | 28 | 357.3 | 123 | 50019 |
Dalian | 12 | 417.7 | 140 | 40587 |
Wenzhou | 18 | 574.68 | 183.2 | 51866 |
Shaoxing | 16 | 328.2 | 149.52 | 54445 |
Jiaxing | 20 | 300.31 | 119.51 | 53057 |
Wuxi | 14 | 498.03 | 176.45 | 52659 |
Taizhou | 11 | 380.54 | 148.3 | 51374 |
Changzhou | 12 | 338.7 | 122.8 | 49955 |
Tianjin | 16 | 1291.11 | 287 | 37022 |
Harbin | 10 | 463.8 | 162 | 35546 |
Hefei | 9 | 587.4 | 169.74 | 37972 |
Nanchang | 10 | 289.78 | 167 | 37575 |
Wuhu | 9 | 240.42 | 147.68 | 35175 |
Jinan | 20 | 483.75 | 195 | 46642 |
Nantong | 9 | 482.4 | 187.3 | 42661 |
Quanzhou | 7 | 568.3 | 167 | 42696 |
Tangshan | 14 | 486.8 | 185 | 36415 |
Yantai | 8 | 451.31 | 187.31 | 41837 |
Zhuhai | 11 | 157.8 | 88.4 | 46826 |
Xi’an | 12 | 679.26 | 271 | 38636 |
Kunming | 16 | 467.7 | 215 | 39788 |
The curb parking price in the core areas in PCs (2017).
Cities | 2 h CPP (yuan) | UD (10 thousand) | CO (10 thousand) | PCDI (yuan) |
---|---|---|---|---|
Yancheng | 12 | 489.19 | 81.67 | 33115 |
Xingtai | 3 | 379.12 | 78.63 | 26179 |
Tieling | 4 | 127.8 | 43.2 | 28337 |
Liaoyuan | 3 | 59.61 | 12.91 | 30267 |
Tongliao | 3 | 95.69 | 46.2 | 29667 |
Lu Liang | 4 | 154.04 | 35.3 | 28704 |
Zhumadian | 3 | 162.02 | 81.6 | 26340 |
Linyi | 4 | 305.88 | 105 | 33266 |
Suzhou | 4 | 66.76 | 44.4 | 32392 |
Suqian | 3 | 187.5 | 44.85 | 28118 |
Huanggang | 3 | 234.1 | 43.2 | 26884 |
Huaihua | 3 | 128.9 | 51.97 | 29498 |
Hanzhong | 4 | 129.66 | 60.16 | 28812 |
Guiyang | 6 | 389.19 | 141.4 | 32186 |
Lanzhou | 8 | 226.05 | 101.7 | 31071 |
Xining | 5 | 167.53 | 100.1 | 32043 |
Yinchuan | 4 | 171.56 | 75.88 | 32981 |
Ganzhou | 5 | 343.38 | 79.86 | 29567 |
Taiyuan | 9 | 370.97 | 143.63 | 33469 |
Kaifeng | 3 | 215.73 | 54.2 | 29864 |
Nanning | 6 | 378.44 | 104.29 | 33217 |
Jingdezhen | 6 | 109.8 | 18 | 34283 |
Huainan | 4 | 221.29 | 59.6 | 32405 |
Qinhuangdao | 3 | 180.1 | 62.1 | 32795 |
Changchun | 7 | 438.3 | 83.75 | 33168 |
Fushun | 4 | 146 | 28.9 | 30346 |
The curb parking price in the core areas in TCs (2017).
Cities | 2 h CPP (yuan) | UD (10 thousand) | CO (10 thousand) | PCDI (yuan) |
---|---|---|---|---|
Tianjin | 16 | 1291.11 | 287 | 37022 |
Chengdu | 16 | 1152.81 | 452 | 38918 |
Kunming | 16 | 467.7 | 215 | 39788 |
Suzhou | 18 | 1068.36 | 355 | 58806 |
Zhuhai | 11 | 157.8 | 88.4 | 46826 |
Yantai | 8 | 451.31 | 187.31 | 41837 |
Xiamen | 28 | 357.3 | 123 | 50019 |
Yangzhou | 7 | 197.77 | 67 | 38828 |
Dalian | 12 | 417.7 | 140 | 40587 |
Qingdao | 16 | 625.25 | 246 | 50817 |
Haikou | 16 | 103.95 | 77.25 | 33320 |
Xi'an | 12 | 679.26 | 271 | 38636 |
Hangzhou | 22 | 727.14 | 244 | 56276 |
Shenzhen | 21 | 1249.57 | 322 | 52938 |
Nanjing | 28 | 685.89 | 239 | 54538 |
Guangzhou | 32 | 1248.9 | 240 | 55400 |
Shenyang | 17 | 668.2 | 210 | 41359 |
Harbin | 10 | 463.8 | 162 | 35546 |
Changchun | 7 | 438.3 | 143.75 | 33168 |
Jinan | 20 | 516.36 | 191.1 | 46642 |
Huangshan | 4 | 70.45 | 18.48 | 30821 |
Guilin | 5 | 247.34 | 56.15 | 32534 |
Weihai | 4 | 187.79 | 66.16 | 27898 |
Qinhuangdao | 3 | 180.1 | 62.1 | 32795 |
Sanya city | 7 | 443.62 | 112 | 33638 |
Xianyang | 4 | 219.94 | 83.4 | 34246 |
Dunhuang | 4 | 12.79 | 10.2 | 31322 |
Taian | 4 | 342.3 | 69.39 | 32739 |
Lijiang | 4 | 46.4 | 35.2 | 30403 |
Jingdezhen | 6 | 109.8 | 18 | 34283 |
Guiyang | 6 | 389.19 | 131.4 | 32186 |
The core area of the city is the main part of the urban public activity system. It displays a certain agglomeration effect and is an important place for urban residents to carry out various activities and exchanges. Therefore, the parking problem is an important focus in such areas. The basic hypotheses of the CPP prediction model in this paper are as follows.
The CPP is generally common to entire urban areas. Therefore, the UD and PCDI are selected as the predictor variables. At the same time, the parking prices basically apply only to cars, so CO is selected as another predictor variable. In a study, Humphrey and Swingley [
To obtain the future 2 h CPP in the core area of the city, it is necessary to calculate UD, CO, and PCDI for the coming years. If UD, CO, and PCDI have an increasing trend in terms of time, future variation in the three parameters can be obtained by using the TSM. Thus, the CPP in the core area of the city can be predicted by means of this method.
The prediction accuracy refers to the degree of density or dispersion in the prediction error distribution, that is, the dispersion between the actual and the corresponding predicted values. If the prediction error
The relationship between (
3D data visualization for rich cities: (a) the effect among CPP-UD-CO, (b) the effect among CPP-UD-PCDI, and (c) the effect among CPP-CO-PCDI.
3D data visualization for poor cities: (a) the effect among CPP-UD-CO, (b) the effect among CPP-UD-PCDI, and (c) the effect among CPP-CO-PCDI.
3D data visualization for touristed cities: (a) the effect among CPP-UD-CO, (b) the effect among CPP-UD-PCDI, and (c) the effect among CPP- CO-PCDI.
The purpose of the data visualization step was to determine the relationship and trend between the curb parking price (CPP) and the three independent variables to create a theoretical basis for the construction of the parking pricing model.
All the basic data were fitted again, and all three city types showed the highest fit with the quadratic curve. Therefore, the following ternary quadratic function can be established to describe the relationships among
For convenience, equation (
The independent variable is changed from the original variables
A time series is a sequence of successive observations of the same phenomenon at different times. Here,
Exponential smoothing is a method of predicting the weighted average of past observations, which makes the predicted value of the
It can be seen from the above equation that the predicted value of period
By analogy, it can be seen that any predicted value
It can be seen that
According to the above, the combined time series and regression analysis model should be as follows:
In this paper, the TSM-RAM method is used to calculate the parking price for future years. Due to the higher data frequency of the time series for the independent variables, the time series method is used to calculate the change in the value of the independent variables in future years. At the same time, according to the 3D data visualization results, the relationship between the parking price and the three independent variables is a quadratic function, so the regression analysis method can be used to estimate the relationship between the independent and dependent variables.
The
According to the goodness-of-fit test results, the
Goodness-of-fit test results.
City type | Adjusted | Standard error | ||
---|---|---|---|---|
RC | 0.9360 | 0.8762 | 0.8505 | 4.4401 |
PC | 0.9062 | 0.8212 | 0.7647 | 1.1696 |
TC | 0.8819 | 0.7777 | 0.7221 | 4.2778 |
An F test is used to test whether the variance of the two samples is significantly different. According to the results of a variance analysis after model fitting, the
Variance analysis.
City type | Significance | |
---|---|---|
RC | 34.1967 | 0.0000 |
PC | 14.5449 | 0.0000 |
TC | 13.9906 | 0.0000 |
The
Regression coefficient test.
City type | Coefficient | Value | Significance | |
---|---|---|---|---|
RC | 4.2979 | 3.3815 | 0.0021 | |
9.2284 | 2.2091 | 0.0352 | ||
8.8863 | 3.5412 | 0.0014 | ||
PC | 1.1069 | 3.9411 | 0.0009 | |
1.7739 | 3.5259 | 0.0023 | ||
6.2943 | 3.1700 | 0.0050 | ||
TC | 6.9089 | 2.4022 | 0.0244 | |
6.6710 | 2.1513 | 0.0417 | ||
7.4237 | 2.5686 | 0.0169 |
Ningbo is a subprovincial city with municipalities that have an independent planning status under national social and economic development. It is also the economic center of the Yangtze River Delta and of Zhejiang Province. Since 2000, the city’s economy has undergone sustained and rapid growth. The living standards of residents have increased substantially, and the amount of car ownership has also increased year by year. By 2018, it had increased 28-fold in 18 years, resulting in increasing parking pressure. The government is now focusing on how to manage parking demand through parking fees. We take Ningbo as a representative RC and collect data on UD, CO, and PCDI in Ningbo to conduct an empirical analysis of the period from 2000 to 2018.
Yancheng is located in the central part of China’s eastern coast, in the central and eastern part of Jiangsu Province in the north wing of the Yangtze River Delta. It is the largest prefecture-level city in Jiangsu Province, with a city area of 17,000 square kilometers. The city is flat and resource-rich. The rivers run north-south and east-west. Constrained by historical conditions, infrastructure, production factors, etc., Yancheng has had a low level of regional economic development for a long time. The problem of high input and low output is obvious. Its car ownership figures fall in the middle to lower levels of the distribution for Jiangsu Province overall, and the development of its parking fees system lags behind that of other provinces. We take Yancheng as a representative PC and collect UD, CO, and PCDI data in the city to conduct an empirical analysis of the period from 2000 to 2018.
Kunming is the capital of Yunnan Province. It is located in the southwestern part of China. It is warm year-round and also known as the “Spring City.” Its booming tourism industry has brought enormous opportunities to Kunming. The disposable income and car ownership of urban residents in Kunming can be compared with those of some developed cities. Therefore, parking problems have gradually become an urgent issue in Kunming. We take Kunming as a representative TC and collect UD, CO, and PCDI data for the city to conduct an empirical analysis of the period from 2000 to 2018.
We analyze the three data series for each city, as shown in Figures
Changes in the three data series for Ningbo from 2000 to 2018: (a) changes in the UD historical data, (b) changes in the CO historical data, and (c) changes in the PCDI historical data.
Changes in the three data series for Yancheng from 2000 to 2018: (a) changes in the UD historical data, (b) changes in the CO historical data, and (c) changes in the PCDI historical data.
Changes in the three data series for Kunming from 2000 to 2018: (a) changes in the UD historical data, (b) changes in the CO historical data, and (c) changes in the PCDI historical data.
The three data series for Ningbo show an increasing trend over time. Among them, the increasing trends in CO (Figure
Yancheng, which has the largest area of a prefecture-level city in Jiangsu Province, China, has a large number of permanent residents, and the proportion of urbanization is relatively low. The growth rate of UD in Yancheng city from 2007 to 2013, which appears in Figure
The development of tourism has brought enormous economic benefits to Kunming, and its urbanization level exceeded 60% in 2008, while the number of permanent urban residents (Figure
The prediction results are divided into optimistic, aggressive, and conservative scenarios. The TSM is used to predict the changes in UD, CO, and PCDI in Ningbo, Yancheng, and Kunming from 2019 to 2021. The statistical results are shown in Table
Time series prediction for 2019–2021 (optimistic).
Cities | Term | Year | UD (10 thousand) | CO (10 thousand) | PCDI (yuan) |
---|---|---|---|---|---|
NB | Short-term | 2019 | 611.19 | 278.87 | 64455 |
2020 | 625.51 | 303.75 | 68756 | ||
2021 | 639.82 | 328.63 | 73097 | ||
Long-term | 2025 | 698.95 | 428.17 | 90958 | |
2030 | 770.60 | 552.59 | 112977 | ||
YC | Short-term | 2019 | 503.22 | 101.92 | 38818 |
2020 | 509.98 | 113.06 | 41880 | ||
2021 | 516.73 | 123.84 | 45083 | ||
Long-term | 2025 | 543.77 | 172.87 | 59303 | |
2030 | 577.56 | 244.09 | 80241 | ||
KM | Short-term | 2019 | 510.17 | 246.50 | 46341 |
2020 | 521.31 | 262.25 | 49846 | ||
2021 | 532.46 | 278.00 | 53504 | ||
Long-term | 2025 | 577.05 | 341.00 | 69661 | |
2030 | 632.79 | 419.75 | 93290 |
Future changes in the three data series for Ningbo (2019–2021): (a) future changes in UD, (b) future changes in CO, and (c) future changes in PCDI.
Future changes in the three data series for Yancheng (2019–2021): (a) future changes in UD, (b) future changes in CO, and (c) future changes in PCDI.
Future changes in the three data series for Kunming (2019–2021): (a) future changes in UD, (b) future changes in CO, and (c) future changes in PCDI.
Time series prediction for 2019–2021 (aggressive).
Cities | Term | Year | UD (10 thousand) | CO (10 thousand) | PCDI (yuan) |
---|---|---|---|---|---|
NB | Short-term | 2019 | 615.13 | 279.51 | 64730 |
2020 | 630.91 | 306.19 | 69500 | ||
2021 | 645.97 | 334.11 | 74463 | ||
Long-term | 2025 | 704.09 | 458.23 | 96252 | |
2030 | 775.97 | 641.38 | 127848 | ||
YC | Short-term | 2019 | 503.80 | 104.02 | 38967 |
2020 | 511.02 | 114.21 | 42252 | ||
2021 | 518.33 | 125.53 | 45800 | ||
Long-term | 2025 | 548.76 | 183.55 | 62991 | |
2030 | 590.91 | 278.32 | 92705 | ||
KM | Short-term | 2019 | 510.51 | 247.98 | 46496 |
2020 | 525.06 | 267.18 | 50237 | ||
2021 | 538.36 | 288.43 | 54244 | ||
Long-term | 2025 | 592.17 | 389.33 | 71951 | |
2030 | 661.89 | 535.62 | 97913 |
Time series prediction for 2019–2021 (conservative).
Cities | Term | Year | UD (10 thousand) | CO (10 thousand) | PCDI (yuan) |
---|---|---|---|---|---|
NB | Short-term | 2019 | 610.32 | 279.12 | 64454 |
2020 | 624.05 | 304.24 | 68773 | ||
2021 | 637.77 | 329.33 | 73093 | ||
Long-term | 2025 | 692.50 | 429.43 | 90369 | |
2030 | 760.62 | 554.00 | 111960 | ||
YC | Short-term | 2019 | 502.75 | 101.80 | 38664 |
2020 | 508.67 | 111.86 | 41454 | ||
2021 | 514.29 | 121.93 | 44243 | ||
Long-term | 2025 | 534.01 | 162.18 | 55398 | |
2030 | 553.45 | 212.48 | 69339 | ||
KM | Short-term | 2019 | 509.42 | 246.49 | 46179 |
2020 | 519.80 | 262.22 | 49389 | ||
2021 | 530.19 | 277.93 | 52600 | ||
Long-term | 2025 | 571.75 | 340.68 | 65442 | |
2030 | 623.69 | 418.85 | 81495 |
The results of the optimistic, aggressive, and conservative estimations of the trends in UD, CO, and PCDI are ordered as follows: aggressive > optimistic > conservative. The changing characteristics of the data confirm the rigor and distinctiveness of the TSM. The three cities of Ningbo, Yancheng, and Kunming are ordered, in terms of overall economic strength, as follows: Ningbo > Kunming > Yancheng. Moreover, the levels of CO and PCDI in future years in Table
The prediction error refers to the difference between the prediction result and the real result of the development of the predicted variable and is divided into the predicted relative error and the predicted absolute error. The absolute error is the absolute difference between the predicted value and the actual observed value, and the relative error is the percentage difference relative to the observed value. Here, the absolute error is selected to characterize the error of the three data series predicted by the TSM.
Taking 2017 and 2018 as examples, the actual values for the UD, CO, and PCDI series of Ningbo, Yancheng and Kunming are shown in Tables
Comparison of the predicted and actual UD values (unit (10 thousand)).
Cities | Type | Year | Predicted value | Actual value | Error (%) |
---|---|---|---|---|---|
Ningbo | Optimistic | 2017 | 583.11 | 579.56 | 0.613 |
2018 | 595.29 | 597.93 | 0.442 | ||
Aggressive | 2017 | 589.24 | 579.56 | 1.670 | |
2018 | 601.22 | 597.93 | 0.550 | ||
Conservative | 2017 | 576.21 | 579.56 | 0.578 | |
2018 | 596.33 | 597.93 | 0.268 | ||
Yancheng | Optimistic | 2017 | 491.88 | 489.19 | 0.550 |
2018 | 494.80 | 496.50 | 0.342 | ||
Aggressive | 2017 | 496.44 | 489.19 | 1.482 | |
2018 | 504.23 | 496.5 | 1.557 | ||
Conservative | 2017 | 484.17 | 489.19 | 1.026 | |
2018 | 493.22 | 496.5 | 0.661 | ||
Kunming | Optimistic | 2017 | 489.17 | 488.72 | 0.092 |
2018 | 499.87 | 499.02 | 0.170 | ||
Aggressive | 2017 | 496.28 | 488.72 | 1.547 | |
2018 | 503.27 | 499.02 | 0.852 | ||
Conservative | 2017 | 484.32 | 488.72 | 0.900 | |
2018 | 498.21 | 499.02 | 0.162 |
Comparison of the predicted and actual CO values (unit (10 thousand)).
Cities | Type | Year | Predicted value | Actual value | Error (%) |
---|---|---|---|---|---|
Ningbo | Optimistic | 2017 | 226.82 | 229.00 | 0.010 |
2018 | 253.43 | 254.00 | 0.002 | ||
Aggressive | 2017 | 232.41 | 229 | 1.489 | |
2018 | 259.34 | 254 | 2.102 | ||
Conservative | 2017 | 227.42 | 229 | 0.690 | |
2018 | 253.84 | 254 | 0.063 | ||
Yancheng | Optimistic | 2017 | 81.57 | 81.67 | 0.122 |
2018 | 91.44 | 92.33 | 0.964 | ||
Aggressive | 2017 | 82.73 | 81.67 | 1.298 | |
2018 | 92.09 | 92.33 | 0.26 | ||
Conservative | 2017 | 80.11 | 81.67 | 1.910 | |
2018 | 92.07 | 92.33 | 0.282 | ||
Kunming | Optimistic | 2017 | 214.67 | 215.00 | 0.153 |
2018 | 236.23 | 230.75 | 2.374 | ||
Aggressive | 2017 | 215.06 | 215 | 0.028 | |
2018 | 235.32 | 230.75 | 2.145 | ||
Conservative | 2017 | 213.21 | 215 | 0.833 | |
2018 | 228.07 | 230.75 | 1.161 |
Comparison of the predicted and actual PCDI values (unit (yuan)).
Cities | Type | Year | Predicted value | Actual value | Error (%) |
---|---|---|---|---|---|
Ningbo | Optimistic | 2017 | 55178 | 55656 | 0.860 |
2018 | 59609 | 60134 | 0.875 | ||
Aggressive | 2017 | 55785 | 55656 | 0.233 | |
2018 | 60824 | 60134 | 1.147 | ||
Conservative | 2017 | 55024 | 55656 | 1.136 | |
2018 | 58145 | 60134 | 3.308 | ||
Yancheng | Optimistic | 2017 | 32933 | 33115 | 0.550 |
2018 | 35875 | 35896 | 0.059 | ||
Aggressive | 2017 | 33728 | 33115 | 1.851 | |
2018 | 36023 | 35896 | 0.354 | ||
Conservative | 2017 | 32909 | 33115 | 0.622 | |
2018 | 35372 | 35896 | 1.460 | ||
Kunming | Optimistic | 2017 | 39677 | 39788 | 0.279 |
2018 | 42990 | 42988 | 0.005 | ||
Aggressive | 2017 | 39924 | 39788 | 0.342 | |
2018 | 43178 | 42988 | 0.442 | ||
Conservative | 2017 | 39542 | 39788 | 0.618 | |
2018 | 41372 | 42988 | 3.760 |
It can be seen from the Tables
According to the test results, the goodness of fit of the TSM for the optimistic, aggressive, and conservative scenarios for the three cities is above 0.9 in all cases: the maximum is 0.999 and the minimum is 0.939, which indicates that the data estimates obtained by the TSM are highly reliable. In terms of the level of goodness of fit, the conservative prediction is the highest, the optimistic prediction falls in the middle, and the aggressive prediction is the lowest, which is also consistent with the characteristics of the results shown in Table
Testing the goodness of fit of the time series prediction results.
Types | Cities | UD | CO | PCDI |
---|---|---|---|---|
Optimistic | Ningbo | 0.980 | 0.992 | 0.994 |
Yancheng | 0.955 | 0.997 | 0.999 | |
Kunming | 0.969 | 0.999 | 0.999 | |
Aggressive | Ningbo | 0.943 | 0.962 | 0.971 |
Yancheng | 0.951 | 0.983 | 0.986 | |
Kunming | 0.958 | 0.939 | 0.967 | |
Conservative | Ningbo | 0.977 | 0.985 | 0.994 |
Yancheng | 0.970 | 0.994 | 0.989 | |
Kunming | 0.973 | 0.981 | 0.992 |
The UD, CO, and PCDI data series for future years predicted by the three TSM models are substituted into the corresponding ternary quadratic equation presented in the first part of this paper. The 2 h CPP in the core areas of NB, YC, and KM for the short term (2019–2021) and long term (2025, 2030) appear in Table
CPP prediction results.
Cities | Term | Year | CPP (yuan) | ||
---|---|---|---|---|---|
Optimistic | Aggressive | Conservative | |||
Ningbo (NB) | Short-term | 2019 | 25.89 | 26.04 | 25.92 |
2020 | 30.18 | 30.74 | 30.26 | ||
2021 | 35.06 | 36.38 | 35.19 | ||
Long-term | 2025 | 60.56 | 70.65 | 60.79 | |
2030 | 105.23 | 149.64 | 105.86 | ||
Yancheng (YC) | Short-term | 2019 | 12.05 | 11.86 | 11.96 |
2020 | 12.52 | 12.63 | 12.38 | ||
2021 | 13.42 | 13.73 | 13.05 | ||
Long-term | 2025 | 21.07 | 23.98 | 18.32 | |
2030 | 43.97 | 62.15 | 31.12 | ||
Kunming (KM) | Short-term | 2019 | 21.68 | 21.99 | 21.58 |
2020 | 26.36 | 27.3 | 26.05 | ||
2021 | 31.63 | 33.89 | 30.94 | ||
Long-term | 2025 | 59.18 | 74.44 | 54.65 | |
2030 | 110.67 | 164.02 | 93.64 |
According to the
This paper divides domestic cities into RCs, PCs, and TCs according to the differences in their parking fee systems and level of economic development. The 2 h CPP data for almost all urban core areas, as well as the UD, CO, and PCDI data for most prefecture-level cities and above, were collected to fit the models. The final calculation results also reveal future changes in the 2 h CPP of the urban core areas of Ningbo, Yancheng, and Kunming. Likewise, it would be possible to collect historical UD, CO, and PCDI data and then calculate the future CPP for other cities to which the model is fitted. For cities for which these data have not yet been collected, the model can be refitted to ensure that the ternary quadratic regression function is in accordance with the actual situation of the city, and then the CPP can be predicted after the parameters are modified.
As shown in Table Parking demand that has not been effectively regulated [ Pricing to encourage long-term parking [
The CPP in the core area of Kunming is predicted to be 5.44 yuan higher than that of Ningbo in 2035, as seen in Table
In the past, when research has evaluated the current parking pricing problem, the final goal was always to obtain the
In this study, the confidence interval is set in the model fitting and variable prediction process (the confidence level is 95%). When the TSM is used to evaluate trends in UD, CO, and PCDI, the output contains the results corresponding to the highest and lowest confidence levels for the three data series over future years. Examples of the predictions for the three data series in the optimistic TSM scenario for Ningbo city are shown in Table
Results for the confidence intervals of three types of data in Ningbo (optimistic).
Year | Confidence interval | UD (10 thousand) | CO (10 thousand) | PCDI (yuan) |
---|---|---|---|---|
2019 | Highest | 635.12 | 289.24 | 65827.4 |
Lowest | 587.26 | 268.49 | 63082.2 | |
2020 | Highest | 653.41 | 323.71 | 71483.7 |
Lowest | 597.60 | 283.79 | 66067.6 | |
2021 | Highest | 671.20 | 359.89 | 77362.5 |
Lowest | 608.44 | 297.38 | 68830.5 |
By substituting the data in the above table into the nonlinear regression in sequence, the corresponding parking pricing fee interval for future years can be obtained, as shown in Table
The 2 h CPP range in the core area of Ningbo city in future years (optimistic).
Year | CPP range (yuan) |
---|---|
2019 | (24.70, 27.11) |
2020 | (27.36, 33.26) |
2021 | (29.93, 40.93) |
At present, the issue of imbalance between parking supply and demand for urban development is still a major challenge. It is of great practical importance to accurately determine the CPP for future years so as to quickly address the imbalance between parking supply and demand and provide theoretical support to decision makers.
The present study applied a TSM-RAM model to predict the CPP and solve the traffic problem caused by the imbalance between parking supply and demand. The data were obtained through the China Statistical Yearbook. The results showed the effectiveness of the TSM-RAM model for making parking price forecasts. At the same time, we paid special attention to dividing the results into optimistic, aggressive, and conservative estimates when applying the TSM to data series. In addition, the prediction of the curb parking price (CPP) was also based on the level of urbanization, with Chinese cities divided into RCs, PCs, and TCs, and case studies of Ningbo, Yancheng, and Kunming, which were selected as representative cities corresponding to each category. The diversity of the results also provides extra information to help policymakers respond to future parking problems.
The conclusion of this article can be attributed to the following three parts. Firstly, in terms of data, we found that the goodness-of-fit test results of the curb parking prices (CPP) and the number of urban dwellers (UD), car ownership (CO), and per capita disposable income of urban residents (PCDI) are all above 0.9, indicating that the selected very high correlation between independent and dependent variables. Secondly, in terms of models and methods, we found that time series methods are used to predict the number of urban dwellers (UD), car ownership (CO), and per capita disposable income of urban residents (PCDI) results have extremely low errors, all of which are below 0.05. Combining the three types of data with a very high degree of fit for on-street parking prices can prove that the TSM-RAM method is suitable for the CPP prediction. Finally, in terms of policies, we recommend regulating parking demand that off-street parking is encouraged between on-street parking and off-street parking; on the other hand, indoor parking is encouraged as much as possible between open-air parking and indoor parking for off-street parking and then determining prices that encourage short-term parking.
However, there are some limitations to this study. First, the model in this paper only fits data for 36 RCs, 26 PCs, and 31 TCs. If sample data for more cities are properly added, the goodness of fit of the model could be improved. Second, this paper selects only three variables related to the CPP, namely, UD, CO, and PCDI. After researchers solve the difficult problems of data collection and prediction, factors such as road congestion can be added to the initial data to better improve the model fit and the accuracy of the model. Furthermore, extensions of this work should examine the categories of urban areas. This paper divides cities in China into RCs, PCs, and TCs according to their economic level, but it may also be a good choice to categorize cities according to their administrative level. This work has provided the framework for a TSM-RAM predictive curb parking price model. Under this framework, the curb parking price as affected by UD, CO, PCDI, or various other factors can be estimated to address parking problems in central urban districts.
In large cities, curb parking pricing (CPP) policies must differentiate parking charges by region. This article focuses on the selection of influencing factors of curb parking prices in core areas. Therefore, the article does not consider whether differentiated parking fees by region has an impact on curb parking pricing in a single region. This issue will be studied in detail in the next phase of the study by considering the impact of differentiated parking pricing policies on individual curb parking price.
In addition, the TSM-RAM method proposed in this paper has certain errors, but the results of the goodness-of-fit test,
The data used to support the findings of this study are included within the article.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research was funded by the Natural Science Foundation of Zhejiang Province (No. LQ19E080003), Philosophy and Social Science Program of Ningbo (G20-ZX07 and G20-ZX37), the Natural Science Foundation of Ningbo (No. 2018A610127), the National Natural Science Foundation of China (No. 71861023), the Program of Humanities and Social Science of Education Ministry of China (No. 18YJC630118), and the Foundation of a Hundred Youth Talents Training Program of the Lanzhou Jiaotong University.