Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China
Abstract
:1. Introduction
2. Modeling Methodology
- Model specification: The threshold value was introduced for ordered probit model and the basic likelihood functions of household q can be formulated, which were shown in Equations (1) and (4) respectively.
- Model estimation: Developing cross-sectional multivariate ordered probit model (CMOP) with the form of pairwise marginal likelihood function as shown in Equation (5). The estimation approach is composite marginal likelihood (CML) to adapt the underlying complex dependencies. By this method, both coefficients of explanatory variables and correlations in the error covariance matrix can be obtained.
- Elasticity calculation: In the case of estimated results, AE and ASE can be calculated for continuous variables and key explanatory variables respectively, as Equation (8) displayed.
3. Data Collection and Descriptive Analysis
3.1. Introduction to the Research Area
3.2. Descriptive Analysis of Sample Data
3.3. Explanatory Variables
4. Empirical Results
4.1. Estimated Results and Elasticity Analysis
4.2. Discussions on Error Correlation Matrix
5. Conclusions and Discussions
- As an important explanatory variable, household income plays a positive role in automobile ownership, but a negative role in bicycle ownership. This factor is less important to affect automobile ownership with a small value of elasticity, which is similar to the situation in developed countries.
- The households with high education level incline to own more automobiles and dislike to use vehicles of the other types, especially of motorcycles. And the education level is a more elastic factor for automobile ownership than household income.
- Household size appears positive in all the four utility functions as generally we speculate. Motorcycles have less elasticity than the other three types of vehicle.
- The households with more elderly members prefer to own more automobiles as expected. Based on the elasticities, the degree of intolerance of elderly people for vehicle types can be ranked (from high to low): Motorcycle, bicycle, and e-bicycle.
Author Contributions
Funding
Conflicts of Interest
References
- Zhang, Z.; Jin, W.; Jiang, H.; Xie, Q.; Shen, W.; Han, W. Modeling heterogeneous vehicle ownership in China: A case study based on the chinese national survey. Transp. Policy 2017, 54, 11–20. [Google Scholar] [CrossRef]
- Tang, R. The rise of China’s auto industry and its impact on the us motor vehicle industry. Fed. Publ. 2009, 688. Available online: http://digitalcommons.ilr.cornell.edu/key_workplace/688 (accessed on 15 August 2018).
- Huo, H.; Wang, M. Modeling future vehicle sales and stock in China. Energy Policy 2012, 43, 17–29. [Google Scholar] [CrossRef]
- Wu, T.; Zhao, H.; Ou, X. Vehicle ownership analysis based on GDP per capita in China: 1963–2050. Sustainability 2014, 6, 4877–4899. [Google Scholar] [CrossRef]
- Chung, W.; Zhou, G.; Yeung, I.M. A study of energy efficiency of transport sector in China from 2003 to 2009. Appl. Energy 2013, 112, 1066–1077. [Google Scholar] [CrossRef]
- Anowar, S.; Eluru, N.; Miranda-Moreno, L.F. Alternative modeling approaches used for examining automobile ownership: A comprehensive review. Transp. Rev. 2014, 34, 441–473. [Google Scholar] [CrossRef]
- Ling, Z.; Cherry, C.R.; Yang, H.; Jones, L.R. From e-bike to car: A study on factors influencing motorization of e-bike users across China. Transp. Res. Part D Transp. Environ. 2015, 41, 50–63. [Google Scholar] [CrossRef] [Green Version]
- Yang, C.-J. Launching strategy for electric vehicles: Lessons from China and Taiwan. Technol. Forecast. Soc. Chang. 2010, 77, 831–834. [Google Scholar] [CrossRef]
- Wells, P.; Lin, X. Spontaneous emergence versus technology management in sustainable mobility transitions: Electric bicycles in China. Transp. Res. Part A Policy Pract. 2015, 78, 371–383. [Google Scholar] [CrossRef] [Green Version]
- Pendyala, R.M.; Kostyniuk, L.P.; Goulias, K.G. A repeated cross-sectional evaluation of car ownership. Transportation 1995, 22, 165–184. [Google Scholar] [CrossRef]
- Bhat, C.R.; Pulugurta, V. A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions. Transp. Res. Part B Methodol. 1998, 32, 61–75. [Google Scholar] [CrossRef]
- Chu, Y.L. Automobile ownership analysis using ordered probit models. Transp. Res. Rec. J. Transp. Res. Board 2002, 1805, 60–67. [Google Scholar] [CrossRef]
- Senbil, M.; Zhang, J.; Fujiwara, A. Motorization in Asia: 14 countries and three metropolitan areas. IATSS Res. 2007, 31, 46–58. [Google Scholar] [CrossRef]
- Sanko, N.; Dissanayake, D.; Kurauchi, S.; Maesoba, H.; Yamamoto, T.; Morikawa, T. Household car and motorcycle ownership in Bangkok and Kuala Lumpur in comparison with Nagoya. Transp. A Transp. Sci. 2014, 10, 187–213. [Google Scholar] [CrossRef]
- Sanko, N.; Maesoba, H.; Dissanayake, D.; Yamamoto, T.; Kurauchi, S.; Morikawa, T. Inter-temporal analysis of household car and motorcycle ownership behaviors: The case in the Nagoya metropolitan area of Japan, 1981–2001. IATSS Res. 2009, 33, 39–53. [Google Scholar] [CrossRef]
- Greene, W.H.; Hensher, D.A. Modeling Ordered Choices: A Primer; Cambridge University Press: Cambridge, UK, 2009. [Google Scholar]
- Gómez-Gélvez, J.A.; Obando, C. Joint disaggregate modeling of car and motorcycle ownership: Case study of Bogotá, Colombia. Transp. Res. Rec. J. Transp. Res. Board 2014, 2451, 149–156. [Google Scholar] [CrossRef]
- Yamamoto, T. Comparative analysis of household car, motorcycle and bicycle ownership between Osaka metropolitan area, Japan and Kuala Lumpur, Malaysia. Transportation 2009, 36, 351–366. [Google Scholar] [CrossRef]
- Fang, H.A. A discrete-continuous model of households’ vehicle choice and usage, with an application to the effects of residential density. Transp. Res. Part B Methodol. 2008, 42, 736–758. [Google Scholar] [CrossRef]
- Scott, D.M.; Kanaroglou, P.S. An activity-episode generation model that captures interactions between household heads: Development and empirical analysis. Transp. Res. Part B Methodol. 2002, 36, 875–896. [Google Scholar] [CrossRef]
- Bhat, C.R.; Srinivasan, S. A multidimensional mixed ordered-response model for analyzing weekend activity participation. Transp. Res. Part B Methodol. 2005, 39, 255–278. [Google Scholar] [CrossRef] [Green Version]
- Wu, Z.; Ye, X. Joint modeling analysis of trip-chaining behavior on round-trip commute in the context of Xiamen, China. Transp. Res. Rec. J. Transp. Res. Board 2008, 2076, 62–69. [Google Scholar] [CrossRef]
- Ferdous, N.; Eluru, N.; Bhat, C.R.; Meloni, I. A multivariate ordered-response model system for adults’ weekday activity episode generation by activity purpose and social context. Transp. Res. Part B Methodol. 2010, 44, 922–943. [Google Scholar] [CrossRef] [Green Version]
- Abay, K.A.; Paleti, R.; Bhat, C.R. The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity. Transp. Res. Part B Methodol. 2013, 50, 74–89. [Google Scholar] [CrossRef] [Green Version]
- Chiou, Y.-C.; Hwang, C.-C.; Chang, C.-C.; Fu, C. Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach. Accid. Anal. Prev. 2013, 51, 175–184. [Google Scholar] [CrossRef] [PubMed]
- Russo, B.J.; Savolainen, P.T.; Iv, W.H.S.; Anastasopoulos, P.C. Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model. Anal. Methods Accid. Res. 2014, 2, 21–29. [Google Scholar] [CrossRef]
- Zou, Y.; Ye, X.; Henrickson, K.; Tang, J.; Wang, Y. Jointly analyzing freeway traffic incident clearance and response time using a copula-based approach. Transp. Res. Part C Emerg. Technol. 2018, 86, 171–182. [Google Scholar] [CrossRef]
- Ye, X.; Wang, K.; Zou, Y.; Lord, D. A semi-nonparametric poisson regression model for analyzing motor vehicle crash data. PLoS ONE 2018, 13, e0197338. [Google Scholar] [CrossRef] [PubMed]
- Tang, J.; Liu, F.; Zhang, W.; Ke, R.; Zou, Y. Lane-changes prediction based on adaptive fuzzy neural network. Expert Syst. Appl. 2018, 91, 452–463. [Google Scholar] [CrossRef]
- Zhao, Y.; Kockelman, K.M. Household vehicle ownership by vehicle type: Application of a multivariate negative binomial model. In Proceedings of the 81st Annual Meeting of the Transportation Research Board, Washington, DC, USA, 13–17 January 2002. [Google Scholar]
- Liu, Y.; Tremblay, J.-M.; Cirillo, C. An integrated model for discrete and continuous decisions with application to vehicle ownership, type and usage choices. Transp. Res. Part A Policy Pract. 2014, 69, 315–328. [Google Scholar] [CrossRef]
- Bhat, C.R.; Guo, J.Y. A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transp. Res. Part B Methodol. 2007, 41, 506–526. [Google Scholar] [CrossRef] [Green Version]
- Bhat, C.R.; Sen, S. Household vehicle type holdings and usage: An application of the multiple discrete-continuous extreme value (MDCEV) model. Transp. Res. Part B Methodol. 2006, 40, 35–53. [Google Scholar] [CrossRef]
- West, S.E. Distributional effects of alternative vehicle pollution control policies. J. Public Econ. 2004, 88, 735–757. [Google Scholar] [CrossRef]
- Matas, A.; Raymond, J.L.; Roig, J.L. Car ownership and access to jobs in Spain. Transp. Res. Part A Policy Pract. 2009, 43, 607–617. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Li, Y.; Yang, X.; Liu, Q.; Li, C. Built environment and household electric bike ownership: Insights from Zhongshan metropolitan area, China. Transp. Res. Rec. J. Transp. Res. Board 2013, 2387, 102–111. [Google Scholar] [CrossRef]
- Huang, X.; Cao, X.; Cao, J. The association between transit access and auto ownership: Evidence from Guangzhou, China. Transp. Plan. Technol. 2016, 39, 269–283. [Google Scholar] [CrossRef]
- Zhang, Y.; Li, C.; Liu, Q.; Wu, W. The socioeconomic characteristics, urban built environment and household car ownership in a rapidly growing city: Evidence from Zhongshan, China. J. Asian Arch. Build. Eng. 2018, 17, 133–140. [Google Scholar] [CrossRef]
- Zhang, Y.; Wu, W.; Li, Y.; Liu, Q.; Li, C. Does the built environment make a difference? An investigation of household vehicle use in Zhongshan metropolitan area, China. Sustainability 2014, 6, 4910–4930. [Google Scholar] [CrossRef]
- Ao, Y.; Chen, C.; Yang, D.; Wang, Y. Relationship between rural built environment and household vehicle ownership: An empirical analysis in rural Sichuan, China. Sustainability 2018, 10, 1566. [Google Scholar] [CrossRef]
- Jamerson, F.; Benjamin, E. Electric Bikes Worldwide Reports: Light Electric Vehicles; Electric Battery Bicycle Company: Newport Beach, CA, USA, 2009. [Google Scholar]
- Bhat, C.R. Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Transp. Res. Part B Methodol. 2001, 35, 677–693. [Google Scholar] [CrossRef] [Green Version]
- Bhat, C.R. Simulation estimation of mixed discrete choice models using randomized and scrambled halton sequences. Transp. Res. Part B Methodol. 2003, 37, 837–855. [Google Scholar] [CrossRef]
- Bhat, C.R.; Sidharthan, R. A new approach to specify and estimate non-normally mixed multinomial probit models. Transp. Res. Part B Methodol. 2012, 46, 817–833. [Google Scholar] [CrossRef] [Green Version]
- Godambe, V.P. An optimum property of regular maximum likelihood estimation. Ann. Math. Stat. 1960, 31, 1208–1211. [Google Scholar] [CrossRef]
- Zhao, Y.; Joe, H. Composite likelihood estimation in multivariate data analysis. Can. J. Stat. 2005, 33, 335–356. [Google Scholar] [CrossRef]
- Weinert, J.; Ma, C.; Cherry, C. The transition to electric bikes in China: History and key reasons for rapid growth. Transportation 2007, 34, 301–318. [Google Scholar] [CrossRef]
- Tang, J.; Zhang, S.; Chen, X.; Liu, F.; Zou, Y. Taxi trips distribution modeling based on entropy-maximizing theory: A case study in Harbin city—China. Phys. A 2018, 493, 430–443. [Google Scholar] [CrossRef]
- Chen, P.; Tong, R.; Lu, G.; Wang, Y. The α-reliable path problem in stochastic road networks with link correlations: A moment-matching-based path finding algorithm. Expert Syst. Appl. 2018, 110, 20–32. [Google Scholar] [CrossRef]
- Ling, Z.; Cherry, C.R.; MacArthur, J.H.; Weinert, J.X. Differences of cycling experiences and perceptions between e-bike and bicycle users in the United States. Sustainability 2017, 9, 1662. [Google Scholar] [CrossRef]
Description of Discrete Variable | ||||
Attribute | Percent | Attribute | Percent | |
Annual household income [Yuan] 1 | Automobile ownership | |||
Mean | 1.69*105 | Mean | 1.13 | |
≤100,000 | 30.5% | 0 | 15.9% | |
100,000~300,000 | 58.1% | 1 | 57.4% | |
300,000~500,000 | 9.9% | 2+ | 26.7% | |
500,000~1000,000 | 1.3% | Motorcycle ownership | ||
Household size | Mean | 0.07 | ||
Mean | 3.99 | 0 | 93.5% | |
1 | 0.1% | 1 | 6.1% | |
2 | 7.2% | 2+ | 0.4% | |
3 | 32.7% | Electric bicycle ownership | ||
4 | 26.0% | Mean | 1.31 | |
5 | 23.6% | 0 | 15.1% | |
6 | 8.2% | 1 | 45.5% | |
7+ | 2.2% | 2+ | 39.4% | |
The population under the age of 6 | Human-powered bicycle ownership | |||
Mean | 0.29 | Mean | 0.32 | |
0 | 73.6% | 0 | 72.6% | |
1 | 24.1% | 1 | 23.6% | |
2+ | 2.3% | 2+ | 3.8% | |
Home ownership | Zone | |||
Self-owned house | 91.0% | Beigan Street | 14.7% | |
Non-self-owned house | 9.0% | Chengxiang Street | 24.9% | |
Licensed household members2 | Ningwei Town | 9.7% | ||
Have a license | 88.2% | Puyang Town | 0.4% | |
Not have a license | 11.8% | Shushan Street | 13.3% | |
Real estate price [Yuan/m2] | Suoqian Street | 3.0% | ||
Mean | 9855 | Wenyan Street | 5.9% | |
<10,000 | 49.3% | ETD Zone 3 | 4.7% | |
10,000~15,000 | 46.8% | Xinjie Town | 7.2% | |
15,000~20,000 | 3.2% | Xintang Street | 15.8% | |
20,000~30,000 | 0.5% | Yanqian Town | 0.4% | |
>30,000 | 0.2% | Beigan Street | 14.7% | |
Description of continuous variable | ||||
Attribute | Mean | Standard Deviation | ||
Population density [thousand people] 4 | 9.45 | 14.690 | ||
Average age of household members | 37.05 | 7.697 | ||
Average education years of household members | 12.10 | 2.357 | ||
Male proportion of household members | 0.48 | 0.193 | ||
Employed proportion of household members | 0.89 | 0.219 | ||
Average number of trips per person | 2.94 | 0.810 | ||
Average commute travel time [min] | 22.23 | 13.290 |
Name | Type | Description | Mean | S.D. |
Household sociodemographic attributes | ||||
Household income [10 thousand Yuan] | Continuous | Annual household income converted from discrete variable in household survey data. | 16.89 | 11.016 |
Household size | Ordinal | Number of family members in the household. | 3.99 | 1.193 |
Home ownership | Dummy | 1 if the house is self-owned; 0 otherwise. | 0.91 | 0.286 |
Real estate price [10 thousand Yuan/m2] | Continuous | House price of the residence place converted from discrete variable in household survey data (similar to household income variable). | 0.99 | 0.340 |
Individual sociodemographic attributes | ||||
Age of household members | Continuous | Average age of household members based on personal survey data. | 37.05 | 7.697 |
Education level ofhousehold members | Continuous | Average education years of household members based on personal survey data. | 12.10 | 2.357 |
Licensed household members | Dummy | 1 if at least one person has a license in household; 0 otherwise. | 0.88 | 0.322 |
Built environment attributes | ||||
Population density [thousand people] | Continuous | The population density is obtained based on the small zone level (communities) where the household is located. | 9.45 | 14.690 |
Zone: Street/Town | Categorical | The area (11 streets/towns) where the household is located in Xiaoshan District. | — | — |
Explanatory Variable | Estimate | S.E. | T-Statistic |
---|---|---|---|
Automobile ownership (0, 1 or > = 2) | |||
Household income | 0.0368 | 0.0006 | 58.343 |
Household size | 0.2749 | 0.0074 | 37.284 |
Home ownership | 0.7340 | 0.0300 | 24.469 |
Population density | −0.0032 | 0.0006 | −5.451 |
Age of household members | 0.0119 | 0.0012 | 9.737 |
Education level of household members | 0.1160 | 0.0041 | 27.975 |
Licensed household members | 1.9041 | 0.0342 | 55.670 |
Zone: ETD Zone (dummy) | 0.5712 | 0.0447 | 12.773 |
Zone: Xinjie Town (dummy) | −0.2601 | 0.0348 | −7.465 |
4.4965 | 0.0880 | 51.087 | |
6.8033 | 0.0927 | 73.375 | |
Motorcycle ownership (0, 1 or > = 2) | |||
Household size | 0.1301 | 0.0116 | 11.201 |
Population density | −0.0147 | 0.0014 | −10.758 |
Age of household members | −0.0186 | 0.0022 | −8.473 |
Education level of household members | −0.0901 | 0.0075 | −11.995 |
Licensed household members | −0.2224 | 0.0390 | −5.700 |
Zone: Suoqian Town (dummy) | −0.6533 | 0.1083 | −6.033 |
Zone: ETD Zone (dummy) | −0.5296 | 0.0824 | −6.427 |
−0.0314 | 0.1462 | −0.215 | |
1.1692 | 0.1589 | 7.357 | |
Electric bicycle ownership (0, 1 or > = 2) | |||
Household income | −0.0088 | 0.0008 | −10.893 |
Household size | 0.2632 | 0.0064 | 41.178 |
Real estate price | −0.3143 | 0.0225 | −13.999 |
Age of household members | −0.0116 | 0.0011 | −10.582 |
Education level of household members | −0.0462 | 0.0038 | −12.059 |
Licensed household members | −0.5357 | 0.0261 | −20.487 |
Zone: Beigan Street (dummy) | −0.1914 | 0.0223 | −8.599 |
Zone: Shushan Street (dummy) | −0.1786 | 0.0220 | −8.113 |
Zone: Wenyan Town (dummy) | 0.1973 | 0.0359 | 5.493 |
−2.0325 | 0.0806 | −25.217 | |
−0.6211 | 0.0793 | −7.829 | |
Human-powered bicycle ownership (0, 1 or > = 2) | |||
Household size | 0.1138 | 0.0076 | 14.957 |
Age of household members | −0.0117 | 0.0012 | −9.398 |
Education level of household members | −0.0391 | 0.0048 | −8.146 |
Licensed household members | −0.0977 | 0.0141 | −6.915 |
Zone: Wenyan Town (dummy) | 0.2979 | 0.0380 | 7.835 |
Zone: Xinjie Town (dummy) | 0.4210 | 0.0331 | 12.718 |
Zone: Xintang Street (dummy) | −0.2493 | 0.0264 | −9.451 |
0.0398 | 0.0916 | 0.435 | |
1.2558 | 0.0930 | 13.509 | |
LLCM(β) 1 | −19,233.35 | ||
LLCM(c) 2 | −22,128.39 | ||
ρ2CM(c) 3 | 0.131 |
Variable | Automobile | Motorcycle | E-Bicycle | Bicycle |
---|---|---|---|---|
Household Income | 0.216 | 0.000 | −0.069 | 0.000 |
Education Level of Household Members | 0.501 | −1.946 | −0.858 | −0.599 |
Age of Household Members | 0.156 | −1.288 | −0.197 | −0.555 |
Household Size * | 0.097 | 0.011 | 0.117 | 0.153 |
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Ma, J.; Ye, X.; Shi, C. Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China. Sustainability 2018, 10, 3660. https://doi.org/10.3390/su10103660
Ma J, Ye X, Shi C. Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China. Sustainability. 2018; 10(10):3660. https://doi.org/10.3390/su10103660
Chicago/Turabian StyleMa, Jie, Xin Ye, and Cheng Shi. 2018. "Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China" Sustainability 10, no. 10: 3660. https://doi.org/10.3390/su10103660