Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States
Abstract
:1. Introduction
2. Literature Review
3. Data
3.1. Data Source
3.2. Descriptive Analysis of Ridesharing Usage
3.3. Variable Definitions and Descriptive Statistics
3.3.1. Dependent Variable
3.3.2. Independent Variables
3.3.3. Control Variables
4. Methodology
4.1. Model Selection
4.2. Zero-Inflated Negative Binomial Regression Model
4.2.1. ZINB Distribution
4.2.2. ZINB Fixed Model
4.2.3. Application of the ZINB Model
5. Results
5.1. Results for the Relationship between Household Vehicle Ownership and Ridesharing Usage
5.2. Results for the Relationship between Count of Household Vehicles and Ridesharing Usage Varying by Population Density
5.3. Results for the Relationship between Household Vehicle Ownership Level and Ridesharing Usage Varying by Population Density
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- United States Environmental Protection Agency (EPA). Inventory of U.S. Greenhouse Gas Emissions and Sinks. 2018. Available online: https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks (accessed on 14 May 2018).
- Adie, T. America’s Commuting Choices: 5 Major Takeaways from 2016 Census Data. 2017. Available online: https://www.brookings.edu/blog/the-avenue/2017/10/03/americans-commuting-choices-5-major-takeaways-from-2016-census-data/ (accessed on 16 May 2018).
- Nielsen, J.R.; Hovmøller, H.; Blyth, P.L.; Sovacool, B.K. Of “white crows” and “cash savers:” A qualitative study of travel behavior and perceptions of ridesharing in Denmark. Transp. Res. Part A Policy Pract. 2015, 78, 113–123. [Google Scholar] [CrossRef]
- Schneider, R.J. Theory of routine mode choice decisions: An operational framework to increase sustainable transportation. Transp. Policy 2013, 25, 128–137. [Google Scholar] [CrossRef]
- Chakrabarti, S. How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles. Transp. Policy 2017, 54, 80–89. [Google Scholar] [CrossRef]
- Efthymiou, D.; Antoniou, C.; Waddell, P. Factors affecting the adoption of vehicle sharing systems by young drivers. Transp. Policy 2013, 29, 64–73. [Google Scholar] [CrossRef]
- Beirão, G.; Cabral, J.S. Understanding attitudes towards public transport and private car: A qualitative study. Transp. Policy 2007, 14, 478–489. [Google Scholar] [CrossRef]
- Morency, C. The ambivalence of ridesharing. Transportation 2007, 34, 239–253. [Google Scholar] [CrossRef]
- Chan, N.D.; Shaheen, S.A. Ridesharing in North America: Past, present, and future. Transp. Rev. 2012, 32, 93–112. [Google Scholar] [CrossRef]
- Erdoğan, S.; Cirillo, C.; Tremblay, J.M. Ridesharing as a green commute alternative: A campus case study. Int. J. Sustain. Transp. 2015, 9, 377–388. [Google Scholar] [CrossRef]
- Hamari, J.; Sjöklint, M.; Ukkonen, A. The sharing economy: Why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 2016, 67, 2047–2059. [Google Scholar] [CrossRef]
- Jacobson, S.H.; King, D.M. Fuel saving and ridesharing in the US: Motivations, limitations, and opportunities. Transp. Res. Part D Transp. Environ. 2009, 14, 14–21. [Google Scholar] [CrossRef]
- Firnkorn, J.; Müller, M. Selling mobility instead of cars: New business strategies of automakers and the impact on private vehicle holding. Bus. Strategy Environ. 2012, 21, 264–280. [Google Scholar] [CrossRef]
- Furuhata, M.; Dessouky, M.; Ordóñez, F.; Brunet, M.E.; Wang, X.; Koenig, S. Ridesharing: The state-of-the-art and future directions. Transp. Res. Part B Methodol. 2013, 57, 28–46. [Google Scholar] [CrossRef]
- Fagnant, D.J.; Kockelman, K.M. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 2014, 40, 1–13. [Google Scholar] [CrossRef]
- Yu, B.; Ma, Y.; Xue, M.; Tang, B.; Wang, B.; Yan, J.; Wei, Y.M. Environmental benefits from ridesharing: A case of Beijing. Appl. Energy 2017, 191, 141–152. [Google Scholar] [CrossRef]
- Morrison, C.N.; Jacoby, S.F.; Dong, B.; Delgado, M.K.; Wiebe, D.J. Ridesharing and Motor Vehicle Crashes in 4 US Cities: An Interrupted Time-Series Analysis. Am. J. Epidemiol. 2017, 187, 224–232. [Google Scholar] [CrossRef] [PubMed]
- Srinivasan, S.; Walker, J.L. Vehicle ownership and mode use: The challenge of sustainability. Transportation 2009, 36, 367–370. [Google Scholar] [CrossRef]
- Cirillo, C.; Liu, Y.; Tremblay, J.M. Simulation, numerical approximation and closed forms for joint discrete continuous models with an application to household vehicle ownership and use. Transportation 2017, 44, 1105–1125. [Google Scholar] [CrossRef]
- Shay, E.; Khattak, A.J. Household travel decision chains: Residential environment, automobile ownership, trips and mode choice. Int. J. Sustain. Transp. 2012, 6, 88–110. [Google Scholar] [CrossRef]
- Roorda, M.J.; Carrasco, J.A.; Miller, E.J. An integrated model of vehicle transactions, activity scheduling and mode choice. Transp. Res. Part B Methodol. 2009, 43, 217–229. [Google Scholar] [CrossRef]
- Huang, X.; Cao, X.J.; Yin, J.; Cao, X. Effects of metro transit on the ownership of mobility instruments in Xi’an, China. Transp. Res. Part D Transp. Environ. 2017, 52, 495–505. [Google Scholar] [CrossRef]
- Shaheen, S.; Cohen, A.; Zohdy, I. Shared Mobility: Current Practices and Guiding Principles (No. FHWA-HOP-16-022). 2016. Available online: https://trid.trb.org/view/1415740 (accessed on 7 July 2016).
- Pelzer, D.; Xiao, J.; Zehe, D.; Lees, M.H.; Knoll, A.C.; Aydt, H. A partition-based match making algorithm for dynamic ridesharing. IEEE Trans. Intell. Transp. Syst. 2015, 16, 2587–2598. [Google Scholar] [CrossRef]
- Nourinejad, M.; Roorda, M.J. Agent based model for dynamic ridesharing. Transp. Res. Part C Emerg. Technol. 2016, 64, 117–132. [Google Scholar] [CrossRef]
- Masoud, N.; Lloret-Batlle, R.; Jayakrishnan, R. Using bilateral trading to increase ridership and user permanence in ridesharing systems. Transp. Res. Part E Logist. Transp. Rev. 2017, 102, 60–77. [Google Scholar] [CrossRef]
- Masoud, N.; Jayakrishnan, R. A real-time algorithm to solve the peer-to-peer ride-matching problem in a flexible ridesharing system. Transp. Res. Part B Methodol. 2017, 106, 218–236. [Google Scholar] [CrossRef]
- Li, Y.; Chen, R.; Chen, L.; Xu, J. Towards social-aware ridesharing group query services. IEEE Trans. Serv. Comput. 2017, 10, 646–659. [Google Scholar] [CrossRef]
- Aïvodji, U.M.; Gambs, S.; Huguet, M.J.; Killijian, M.O. Meeting points in ridesharing: A privacy-preserving approach. Transp. Res. Part C Emerg. Technol. 2016, 72, 239–253. [Google Scholar] [CrossRef]
- Sánchez, D.; Martínez, S.; Domingo-Ferrer, J. Co-utile P2P ridesharing via decentralization and reputation management. Transp. Res. Part C Emerg. Technol. 2016, 73, 147–166. [Google Scholar] [CrossRef]
- Zhang, J.; Wen, D.; Zeng, S. A discounted trade reduction mechanism for dynamic ridesharing pricing. IEEE Trans. Intell. Transp. Syst. 2016, 17, 1586–1595. [Google Scholar] [CrossRef]
- Liu, Y.; Li, Y. Pricing scheme design of ridesharing program in morning commute problem. Transp. Res. Part C Emerg. Technol. 2017, 79, 156–177. [Google Scholar] [CrossRef]
- Lee, A.; Savelsbergh, M. Dynamic ridesharing: Is there a role for dedicated drivers? Transp. Res. Part B Methodol. 2015, 81, 483–497. [Google Scholar] [CrossRef]
- Greenwood, B.N.; Wattal, S. Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities. MIS Q. 2017, 41, 163–187. [Google Scholar] [CrossRef]
- Mahesh, R. From Jitneys to App-Based Ridesharing: California’s Third Way Approach to Ride-for-Hire Regulation. South. Calif. L. Rev. 2014, 88, 965. [Google Scholar]
- Posen, H.A. Ridesharing in the Sharing Economy: Should Regulators Impose Uber Regulations on Uber. Iowa Law Rev. 2015, 101, 405. [Google Scholar]
- De Donnea, F.X. Consumer behaviour, transport mode choice and value of time: Some micro-economic models. Reg. Urban Econ. 1972, 1, 355–382. [Google Scholar] [CrossRef]
- Devarasetty, P.C.; Burris, M.; Arthur, W., Jr.; McDonald, J.; Muñoz, G.J. Can psychological variables help predict the use of priced managed lanes? Transp. Res. Part F Traffic Psychol. Behav. 2014, 22, 25–38. [Google Scholar] [CrossRef]
- Salon, D.; Boarnet, M.G.; Handy, S.; Spears, S.; Tal, G. How do local actions affect VMT? A critical review of the empirical evidence. Transp. Res. Part D Transp. Environ. 2012, 17, 495–508. [Google Scholar] [CrossRef]
- Khan, M.; Kockelman, K.M.; Xiong, X. Models for anticipating non-motorized travel choices, and the role of the built environment. Transp. Policy 2014, 35, 117–126. [Google Scholar] [CrossRef] [Green Version]
- Taylor, B.D.; Morris, E.A. Public transportation objectives and rider demographics: Are transit’s priorities poor public policy? Transportation 2015, 42, 347–367. [Google Scholar] [CrossRef]
- Quinn, T.D.; Jakicic, J.M.; Fertman, C.I.; Gibbs, B.B. Demographic factors, workplace factors and active transportation use in the USA: A secondary analysis of 2009 NHTS data. J. Epidemiol. Community Health 2016. [Google Scholar] [CrossRef] [PubMed]
- Geng, J.; Long, R.; Chen, H.; Yue, T.; Li, W.; Li, Q. Exploring multiple motivations on urban residents’ travel mode choices: An empirical study from Jiangsu province in China. Sustainability 2017, 9, 136. [Google Scholar] [CrossRef]
- Aziz, H.A.; Nagle, N.N.; Morton, A.M.; Hilliard, M.R.; White, D.A.; Stewart, R.N. Exploring the impact of walk–bike infrastructure, safety perception, and built-environment on active transportation mode choice: A random parameter model using New York City commuter data. Transportation 2017, 2, 1–23. [Google Scholar] [CrossRef]
- Rayle, L.; Dai, D.; Chan, N.; Cervero, R.; Shaheen, S. Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transp. Policy 2016, 45, 168–178. [Google Scholar] [CrossRef]
- Zolnik, E.J. The effect of gasoline prices on ridesharing. J. Transp. Geogr. 2015, 47, 47–58. [Google Scholar] [CrossRef]
- Dias, F.F.; Lavieri, P.S.; Garikapati, V.M.; Astroza, S.; Pendyala, R.M.; Bhat, C.R. A behavioral choice model of the use of car-sharing and ride-sourcing services. Transportation 2017, 44, 1307–1323. [Google Scholar] [CrossRef]
- Ding, C.; Wang, D.; Liu, C.; Zhang, Y.; Yang, J. Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transp. Res. Part A Policy Pract. 2017, 100, 65–80. [Google Scholar] [CrossRef]
- Clewlow, R.R. Carsharing and sustainable travel behavior: Results from the San Francisco Bay Area. Transport Policy 2016, 51, 158–164. [Google Scholar] [CrossRef]
- Coll, M.H.; Vandersmissen, M.H.; Thériault, M. Modeling spatio-temporal diffusion of carsharing membership in Québec City. J. Transp. Geogr. 2014, 38, 22–37. [Google Scholar] [CrossRef] [Green Version]
- U.S. Department of Transportation. Federal Highway Administration, 2017 National Household Travel Survey. Available online: http://nhts.ornl.gov (accessed on 15 March 2018).
- Lord, D.; Mannering, F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transp. Res. Part A Policy Pract. 2010, 44, 291–305. [Google Scholar] [CrossRef] [Green Version]
- Cameron, A.C.; Trivedi, P.K. Regression Analysis of Count Data; Cambridge University Press: Cambridge, UK, 2013; Volume 53. [Google Scholar]
- Zuur, A.F.; Ieno, E.N.; Walker, N.J.; Saveliev, A.A.; Smith, G.M. Zero-truncated and zero-inflated models for count data. In Mixed Effects Models and Extensions in Ecology with R; Springer: New York, NY, USA, 2009; pp. 261–293. [Google Scholar]
- Fang, R.; Wagner, B.D.; Harris, J.K.; Fillon, S.A. Zero-inflated negative binomial mixed model: An application to two microbial organisms important in oesophagitis. Epidemiol. Infect. 2016, 144, 2447–2455. [Google Scholar] [CrossRef] [PubMed]
- Dong, C.; Clarke, D.B.; Yan, X.; Khattak, A.; Huang, B. Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections. Accid. Anal. Prev. 2014, 70, 320–329. [Google Scholar] [CrossRef] [PubMed]
- Shen, S.; Neyens, D.M. Factors affecting teen drivers’ crash-related length of stay in the hospital. J. Transp. Health 2017, 4, 162–170. [Google Scholar] [CrossRef]
- Greene, W.H. Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models; NYU Working Paper No. EC-94-10; New York University: New York, NY, USA, 1994. [Google Scholar]
- Yau, K.K.; Wang, K.; Lee, A.H. Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros. Biom. J. 2003, 45, 437–452. [Google Scholar] [CrossRef]
Variable | Definition | Type | Obs. | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|---|---|
Dependent Variable | |||||||
Rideshare | Frequency of ridesharing usage in the last 30 days | Ordinal | 219,026 | 0.309 | 1.757 | 0 | 99 |
Independent Variables | |||||||
HHvehcount | Count of household vehicles | Ordinal | 219,026 | 2.232 | 1.231 | 0 | 12 |
Vehicle 0 | Count of household vehicles is zero | Dummy | 219,026 | 0.033 | 0.180 | 0 | 1 |
Vehicle 1 | Count of household vehicles is one | Dummy | 219,026 | 0.229 | 0.420 | 0 | 1 |
Vehicle 2 | Count of household vehicles is two | Dummy | 219,026 | 0.415 | 0.493 | 0 | 1 |
Vehicle 3 | Count of household vehicles is three or more | Dummy | 219,026 | 0.322 | 0.467 | 0 | 1 |
Control Variables | |||||||
Individual characteristics | |||||||
Female | Individual is female (Yes = 1, No = 0) | Dummy | 219,026 | 0.531 | 0.499 | 0 | 1 |
Age | Individual’s age (years) | Ordinal | 219,026 | 53.025 | 18.245 | 16 | 92 |
Education | Individual’s education level: 1 = less than high school, 2 = high school/General Educational Development (GED), 3 = some college/associate, 4 = bachelor, 5 = graduate/professional | Ordinal | 219,026 | 3.332 | 1.185 | 1 | 5 |
White | Individual’s race is white (Yes = 1, No = 0) | Dummy | 219,026 | 0.822 | 0.383 | 0 | 1 |
Worker | Individual is a worker (Yes = 1, No = 0) | Dummy | 219,026 | 0.549 | 0.498 | 0 | 1 |
Driver | Individual is a driver (Yes = 1, No = 0) | Dummy | 219,026 | 0.919 | 0.272 | 0 | 1 |
Household characteristics | |||||||
HHincome | Household income level: 1 = less than $10 k, 2 = $10 k to $15 k, 3 = $15 k to $25 k, 4 = $25 k to $35 k, 5 = $35 k to $50 k, 6 = $50 k to $75 k, 7 = $75 k to $100 k, 8 = $100 k to $125 k, 9 = $125 k to $150 k, 10 = $150 k to $200 k, 11 = $200 k or more | Ordinal | 219,026 | 6.315 | 2.593 | 1 | 11 |
Homerent | Home is rental (Yes = 1, No = 0) | Dummy | 219,026 | 0.212 | 0.409 | 0 | 1 |
Regional characteristics | |||||||
Pdensity | Population density (persons per square mile) in the census block group of household’s home location in log | Continuous | 219,026 | 7.172 | 1.752 | 3.9 | 10.3 |
Rail | Home location has heavy rail service (Yes = 1, No = 0) | Dummy | 219,026 | 0.161 | 0.368 | 0 | 1 |
Urban | Household is in an urban area (Yes = 1, No = 0) | Dummy | 219,026 | 0.770 | 0.421 | 0 | 1 |
Public transit usage and season | |||||||
Ptused | Count of public transit usage in the last 30 days | Ordinal | 219,026 | 0.895 | 4.345 | 0 | 240 |
Spring | The survey was conducted in March, April, or May | Dummy | 219,026 | 0.205 | 0.403 | 0 | 1 |
Summer | The survey was conducted in June, July, or August | Dummy | 219,026 | 0.259 | 0.438 | 0 | 1 |
Fall | The survey was conducted in September, October, or November | Dummy | 219,026 | 0.266 | 0.442 | 0 | 1 |
Winter | The survey was conducted in December, January, or February | Dummy | 219,026 | 0.270 | 0.444 | 0 | 1 |
(A) Independent Variable: Count of Household Vehicles | (B) Independent Variable: Household Vehicle Ownership Level | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Variables | Coef. | Std. Err. | z Value | p Value | Marginal Effects | Coef. | Std. Err. | z Value | p Value | Marginal Effects |
Non−zero state (not always 0) | Non−zero state (not always 0) | |||||||||
HHvehcount | −0.082 *** | 0.009 | −8.75 | 0.000 | −7.9% | |||||
Vehicle 0 | 0.288 *** | 0.050 | 5.74 | 0.000 | 33.3% | |||||
Vehicle 2 | −0.266 *** | 0.027 | −9.71 | 0.000 | −23.4% | |||||
Vehicle 3 | −0.359 *** | 0.033 | −10.95 | 0.000 | −30.1% | |||||
Female | −0.100 *** | 0.022 | −4.63 | 0.000 | −9.5% | −0.106 *** | 0.021 | −4.95 | 0.000 | −10.1% |
Age | −0.010 *** | 0.001 | −12.70 | 0.000 | −1.0% | −0.011 *** | 0.001 | −13.65 | 0.000 | −1.1% |
Education | −0.032 ** | 0.013 | −2.58 | 0.010 | −3.2% | −0.035 ** | 0.013 | −2.77 | 0.006 | −3.4% |
White | 0.014 | 0.027 | 0.50 | 0.618 | 1.4% | 0.025 | 0.027 | 0.92 | 0.357 | 2.5% |
Worker | 0.009 | 0.029 | 0.30 | 0.762 | 0.9% | 0.008 | 0.029 | 0.27 | 0.784 | 0.8% |
Driver | −0.445 *** | 0.044 | −10.02 | 0.000 | −35.9% | −0.342 *** | 0.046 | −7.39 | 0.000 | −29.0% |
HHincome | 0.059 *** | 0.005 | 12.67 | 0.000 | 6.0% | 0.067 *** | 0.005 | 14.37 | 0.000 | 6.9% |
Homerent | 0.234 *** | 0.027 | 8.81 | 0.000 | 26.3% | 0.181 *** | 0.027 | 6.78 | 0.000 | 19.8% |
Pdensity | 0.138 *** | 0.010 | 13.64 | 0.000 | 14.8% | 0.126 *** | 0.010 | 12.44 | 0.000 | 13.5% |
Rail | 0.109 *** | 0.025 | 4.39 | 0.000 | 11.5% | 0.087 *** | 0.025 | 3.54 | 0.000 | 9.1% |
Urban | −0.358 *** | 0.055 | −6.48 | 0.000 | −30.1% | −0.317 *** | 0.055 | −5.75 | 0.000 | −27.2% |
Ptused | 0.012 *** | 0.002 | 7.17 | 0.000 | 1.2% | 0.010 *** | 0.002 | 6.23 | 0.000 | 1.0% |
Spring | 0.131 *** | 0.031 | 4.24 | 0.000 | 14.0% | 0.124 *** | 0.031 | 4.06 | 0.000 | 13.2% |
Summer | −0.090 ** | 0.030 | −3.05 | 0.002 | −8.6% | −0.084 ** | 0.030 | −2.85 | 0.004 | −8.1% |
Winter | 0.004 | 0.028 | 0.14 | 0.885 | 0.4% | 0.005 | 0.028 | 0.17 | 0.868 | 0.5% |
Intercept | 0.572 *** | 0.105 | 5.45 | 0.000 | 0.561 *** | 0.105 | 5.35 | 0.000 | ||
Zero state (odds of always 0) | Zero state (odds of always 0) | |||||||||
HHvehcount | 0.207 *** | 0.012 | 17.74 | 0.000 | 23.0% | |||||
Vehicle 0 | −0.546 *** | 0.071 | −7.71 | 0.000 | −42.1% | |||||
Vehicle 2 | 0.442 *** | 0.031 | 14.10 | 0.000 | 55.5% | |||||
Vehicle 3 | 0.670 *** | 0.036 | 18.58 | 0.000 | 95.4% | |||||
Female | 0.109 *** | 0.023 | 4.77 | 0.000 | 11.6% | 0.115 *** | 0.023 | 4.99 | 0.000 | 12.2% |
Age | 0.040 *** | 0.001 | 50.49 | 0.000 | 4.1% | 0.041 *** | 0.001 | 50.86 | 0.000 | 4.1% |
Education | −0.464 *** | 0.012 | −38.15 | 0.000 | −37.1% | −0.465 *** | 0.012 | −38.21 | 0.000 | −37.2% |
White | −0.160 *** | 0.029 | −5.43 | 0.000 | −14.8% | −0.164 *** | 0.030 | −5.55 | 0.000 | −15.1% |
Worker | −0.368 *** | 0.028 | −13.13 | 0.000 | −30.8% | −0.372 *** | 0.028 | −13.23 | 0.000 | −31.0% |
Driver | −0.342 *** | 0.050 | −6.77 | 0.000 | −28.9% | −0.456 *** | 0.054 | −8.49 | 0.000 | −36.6% |
HHincome | −0.242 *** | 0.005 | −44.29 | 0.000 | −21.5% | −0.253 *** | 0.006 | −45.30 | 0.000 | −22.4% |
Homerent | −0.610 *** | 0.029 | −20.71 | 0.000 | −45.6% | −0.573 *** | 0.030 | −19.23 | 0.000 | −43.6% |
Pdensity | −0.264 *** | 0.011 | −24.45 | 0.000 | −23.2% | −0.262 *** | 0.011 | −24.26 | 0.000 | −23.1% |
Rail | −0.307 *** | 0.028 | −11.01 | 0.000 | −26.4% | −0.299 *** | 0.028 | −10.71 | 0.000 | −25.9% |
Urban | −0.238 *** | 0.051 | −4.71 | 0.000 | −21.2% | −0.249 *** | 0.051 | −4.92 | 0.000 | −22.0% |
Ptused | −0.059 *** | 0.003 | −17.96 | 0.000 | −5.7% | −0.052 *** | 0.003 | −16.16 | 0.000 | −5.1% |
Spring | 0.072 * | 0.033 | 2.19 | 0.028 | 7.5% | 0.068 * | 0.033 | 2.06 | 0.039 | 7.0% |
Summer | 0.114 *** | 0.032 | 3.58 | 0.000 | 12.1% | 0.114 *** | 0.032 | 3.59 | 0.000 | 12.1% |
Winter | −0.080 ** | 0.031 | −2.60 | 0.009 | −7.6% | −0.078 * | 0.031 | −2.55 | 0.011 | −7.5% |
Intercept | 6.071 *** | 0.106 | 57.07 | 0.000 | 6.293 *** | 0.107 | 58.85 | 0.000 | ||
Number of observations (obs.) | 219,026 | 219,026 | ||||||||
Nonzero obs. | 16,817 | 16,817 | ||||||||
Zero obs. | 202,209 | 202,209 | ||||||||
Log likelihood | −82,654.76 | −82,462.38 | ||||||||
LR chi2 | 1749.03 *** | 1881.69 *** |
High Population Density (Dependent Variable: Rideshare) | Low Population Density (Dependent Variable: Rideshare) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Variables | Coef. | Std. Err. | z Value | p Value | Marginal Effects | Coef. | Std. Err. | z Value | p Value | Marginal Effects |
Non−zero state (not always 0) | Non−zero state (not always 0) | |||||||||
HHvehcount | −0.136 *** | 0.012 | −10.92 | 0.000 | −12.7% | −0.057 *** | 0.015 | −3.79 | 0.000 | −5.5% |
Female | −0.077 ** | 0.027 | −2.82 | 0.005 | −7.5% | −0.077 * | 0.037 | −2.07 | 0.038 | −7.4% |
Age | −0.011 *** | 0.001 | −10.48 | 0.000 | −1.1% | −0.013 *** | 0.001 | −10.86 | 0.000 | −1.3% |
Education | −0.01 | 0.016 | −0.64 | 0.521 | −1.0% | 0.047 * | 0.020 | 2.29 | 0.022 | 4.8% |
White | 0.122 *** | 0.034 | 3.63 | 0.000 | 13.0% | −0.207 *** | 0.049 | −4.19 | 0.000 | −18.7% |
Worker | 0.011 | 0.039 | 0.27 | 0.788 | 1.1% | 0.130 ** | 0.045 | 2.88 | 0.004 | 13.9% |
Driver | −0.438 *** | 0.051 | −8.64 | 0.000 | −35.5% | 0.052 | 0.073 | 0.72 | 0.474 | 5.4% |
HHincome | 0.077 *** | 0.006 | 13.12 | 0.000 | 8.0% | 0.075 *** | 0.008 | 9.37 | 0.000 | 7.8% |
Homerent | 0.241 *** | 0.032 | 7.51 | 0.000 | 27.2% | 0.301 *** | 0.049 | 6.12 | 0.000 | 35.1% |
Rail | 0.193 *** | 0.029 | 6.65 | 0.000 | 21.3% | 0.120 ** | 0.046 | 2.63 | 0.009 | 12.8% |
Ptused | 0.009 *** | 0.002 | 5.23 | 0.000 | 0.9% | 0.004 | 0.003 | 1.54 | 0.122 | 0.4% |
Spring | 0.113 ** | 0.040 | 2.84 | 0.005 | 11.9% | 0.169 ** | 0.052 | 3.23 | 0.001 | 18.4% |
Summer | −0.071 | 0.038 | −1.87 | 0.061 | −6.8% | −0.076 | 0.051 | −1.50 | 0.134 | −7.3% |
Winter | −0.011 | 0.036 | −0.31 | 0.758 | −1.1% | 0.093 | 0.048 | 1.93 | 0.053 | 9.8% |
Intercept | 1.337 *** | 0.100 | 13.32 | 0.000 | −0.273 * | 0.125 | −2.19 | 0.029 | ||
Zero state (odds of always 0) | Zero state (odds of always 0) | |||||||||
HHvehcount | 0.262 *** | 0.018 | 14.91 | 0.000 | 29.9% | 0.263 *** | 0.019 | 13.74 | 0.000 | 30.1% |
Female | 0.102 ** | 0.033 | 3.14 | 0.002 | 10.8% | 0.153 *** | 0.042 | 3.65 | 0.000 | 16.6% |
Age | 0.047 *** | 0.001 | 40.48 | 0.000 | 4.8% | 0.038 *** | 0.001 | 27.07 | 0.000 | 3.9% |
Education | −0.466 *** | 0.017 | −26.99 | 0.000 | −37.3% | −0.487 *** | 0.022 | −21.98 | 0.000 | −38.6% |
White | −0.294 *** | 0.039 | −7.50 | 0.000 | −25.5% | 0.093 | 0.058 | 1.60 | 0.109 | 9.8% |
Worker | −0.413 *** | 0.040 | −10.26 | 0.000 | −33.8% | −0.360 *** | 0.050 | −7.25 | 0.000 | −30.2% |
Driver | −0.360 *** | 0.063 | −5.69 | 0.000 | −30.2% | 0.011 | 0.110 | 0.10 | 0.920 | 1.1% |
HHincome | −0.216 *** | 0.008 | −28.51 | 0.000 | −19.4% | −0.311 *** | 0.010 | −30.70 | 0.000 | −26.7% |
Homerent | −0.650 *** | 0.039 | −16.59 | 0.000 | −47.8% | −0.669 *** | 0.058 | −11.44 | 0.000 | −48.8% |
Rail | −0.465 *** | 0.036 | −13.05 | 0.000 | −37.2% | −0.363 *** | 0.061 | −5.97 | 0.000 | −30.4% |
Ptused | −0.042 *** | 0.003 | −12.86 | 0.000 | −4.1% | −1.536 *** | 0.109 | −14.13 | 0.000 | −78.5% |
Spring | 0.086 | 0.047 | 1.82 | 0.068 | 9.0% | 0.154 ** | 0.060 | 2.59 | 0.010 | 16.7% |
Summer | 0.154 *** | 0.045 | 3.43 | 0.001 | 16.6% | 0.192 ** | 0.059 | 3.27 | 0.001 | 21.1% |
Winter | −0.101 * | 0.044 | −2.32 | 0.020 | −9.6% | 0.013 | 0.056 | 0.23 | 0.822 | 1.3% |
Intercept | 3.125 *** | 0.106 | 29.45 | 0.000 | 3.330 *** | 0.153 | 21.79 | 0.000 | ||
Number of obs. | 64,468 | 162,356 | ||||||||
Nonzero obs. | 9023 | 8007 | ||||||||
Zero obs. | 55,445 | 154,349 | ||||||||
Log likelihood | −41,684.62 | −42,105.86 | ||||||||
LR chi2 | 940.26 *** | 388.41 *** |
High Population Density (Dependent Variable: Rideshare) | Low Population Density (Dependent Variable: Rideshare) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Variables | Coef. | Std. Err. | z Value | p Value | Marginal Effects | Coef. | Std. Err. | z Value | p Value | Marginal Effects |
Non−zero state (not always 0) | Non−zero state (not always 0) | |||||||||
Vehicle0 | 0.252 *** | 0.054 | 4.69 | 0.000 | 28.7% | 0.167 | 0.100 | 1.68 | 0.093 | 18.2% |
Vehicle2 | −0.293 *** | 0.033 | −8.84 | 0.000 | −25.4% | −0.266 *** | 0.052 | −5.09 | 0.000 | −23.4% |
Vehicle3 | −0.443 *** | 0.043 | −10.43 | 0.000 | −35.8% | −0.346 *** | 0.058 | −6.00 | 0.000 | −29.2% |
Female | −0.088 ** | 0.027 | −3.19 | 0.001 | −8.4% | −0.079 * | 0.037 | −2.13 | 0.034 | −7.6% |
Age | −0.012 *** | 0.001 | −11.06 | 0.000 | −1.2% | −0.015 *** | 0.001 | −11.58 | 0.000 | −1.5% |
Education | −0.011 | 0.016 | −0.69 | 0.490 | −1.1% | 0.045 * | 0.020 | 2.22 | 0.027 | 4.6% |
White | 0.128 *** | 0.033 | 3.83 | 0.000 | 13.7% | −0.195 *** | 0.049 | −3.94 | 0.000 | −17.7% |
Worker | 0.006 | 0.039 | 0.14 | 0.887 | 0.6% | 0.134 ** | 0.045 | 2.95 | 0.003 | 14.3% |
Driver | −0.368 *** | 0.053 | −6.92 | 0.000 | −30.8% | 0.148 | 0.077 | 1.93 | 0.054 | 15.9% |
HHincome | 0.082 *** | 0.006 | 13.94 | 0.000 | 8.6% | 0.087 *** | 0.008 | 10.64 | 0.000 | 9.1% |
Homerent | 0.202 *** | 0.032 | 6.26 | 0.000 | 22.4% | 0.238 *** | 0.050 | 4.74 | 0.000 | 26.8% |
Rail | 0.163 *** | 0.029 | 5.60 | 0.000 | 17.7% | 0.108 * | 0.046 | 2.36 | 0.018 | 11.5% |
Ptused | 0.008 *** | 0.002 | 4.59 | 0.000 | 0.8% | 0.004 | 0.003 | 1.46 | 0.145 | 0.4% |
Spring | 0.105 ** | 0.040 | 2.66 | 0.008 | 11.1% | 0.169 ** | 0.052 | 3.23 | 0.001 | 18.4% |
Summer | −0.069 | 0.038 | −1.83 | 0.068 | −6.7% | −0.074 | 0.051 | −1.46 | 0.146 | −7.1% |
Winter | −0.015 | 0.036 | −0.41 | 0.685 | −1.4% | 0.098 * | 0.048 | 2.03 | 0.043 | 10.3% |
Intercept | 1.239 *** | 0.101 | 12.27 | 0.000 | −0.317 * | 0.130 | −2.44 | 0.015 | ||
Zero state (odds of always 0) | Zero state (odds of always 0) | |||||||||
Vehicle0 | −0.569 *** | 0.083 | −6.88 | 0.000 | −43.4% | −1.160 *** | 0.199 | −5.84 | 0.000 | −68.6% |
Vehicle2 | 0.482 *** | 0.042 | 11.59 | 0.000 | 62.0% | 0.528 *** | 0.063 | 8.39 | 0.000 | 69.5% |
Vehicle3 | 0.729 *** | 0.050 | 14.44 | 0.000 | 107.2% | 0.933 *** | 0.069 | 13.59 | 0.000 | 154.3% |
Female | 0.102 ** | 0.033 | 3.12 | 0.002 | 10.8% | 0.167 *** | 0.043 | 3.91 | 0.000 | 18.1% |
Age | 0.048 *** | 0.001 | 40.73 | 0.000 | 4.9% | 0.039 *** | 0.001 | 26.90 | 0.000 | 4.0% |
Education | −0.465 *** | 0.017 | −26.88 | 0.000 | −37.2% | −0.493 *** | 0.022 | −22.03 | 0.000 | −38.9% |
White | −0.299 *** | 0.039 | −7.59 | 0.000 | −25.9% | 0.082 | 0.059 | 1.39 | 0.165 | 8.6% |
Worker | −0.418 *** | 0.040 | −10.36 | 0.000 | −34.2% | −0.363 *** | 0.050 | −7.24 | 0.000 | −30.5% |
Driver | −0.479 *** | 0.068 | −7.10 | 0.000 | −38.1% | −0.135 | 0.116 | −1.16 | 0.244 | −12.7% |
HHincome | −0.228 *** | 0.008 | −29.42 | 0.000 | −20.4% | −0.325 *** | 0.011 | −30.84 | 0.000 | −27.8% |
Homerent | −0.618 *** | 0.040 | −15.65 | 0.000 | −46.1% | −0.628 *** | 0.060 | −10.48 | 0.000 | −46.6% |
Rail | −0.449 *** | 0.036 | −12.52 | 0.000 | −36.1% | −0.369 *** | 0.062 | −5.98 | 0.000 | −30.9% |
Ptused | −0.037 *** | 0.003 | −11.06 | 0.000 | −3.6% | −1.586 *** | 0.117 | −13.60 | 0.000 | −79.5% |
Spring | 0.083 | 0.047 | 1.76 | 0.078 | 8.7% | 0.150 * | 0.060 | 2.48 | 0.013 | 16.1% |
Summer | 0.150 *** | 0.045 | 3.34 | 0.001 | 16.2% | 0.186 ** | 0.059 | 3.14 | 0.002 | 20.5% |
Winter | −0.100 * | 0.044 | −2.27 | 0.023 | −9.5% | 0.014 | 0.056 | 0.25 | 0.800 | 1.4% |
Intercept | 3.431 *** | 0.108 | 31.85 | 0.000 | 3.617 *** | 0.159 | 22.69 | 0.000 | ||
Number of obs. | 64,468 | 162,356 | ||||||||
Nonzero obs. | 9023 | 8007 | ||||||||
Zero obs. | 55,445 | 154,349 | ||||||||
Log likelihood | −41,582.4 | −42,028.79 | ||||||||
LR chi2 | 1012.03 *** | 433.12 *** |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, Y.; Zhang, Y. Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States. Sustainability 2018, 10, 2720. https://doi.org/10.3390/su10082720
Zhang Y, Zhang Y. Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States. Sustainability. 2018; 10(8):2720. https://doi.org/10.3390/su10082720
Chicago/Turabian StyleZhang, Yuanyuan, and Yuming Zhang. 2018. "Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States" Sustainability 10, no. 8: 2720. https://doi.org/10.3390/su10082720