Contributing Factors to the Changes in Public and Private Transportation Mode Choice after the COVID-19 Outbreak in Urban Areas of China
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
2. Data
2.1. Data Collection and Survey Design
2.2. Sociodemographic Characteristics
2.3. Travel Frequency
2.4. Mode Choice
3. Model and Methods
3.1. The Definition of Variables
- (1)
- Gender was a binary variable (1 = male, 0 = female).
- (2)
- Age was a binary variable indicating (relatively) older people (1 = older than 24, 0 = 24 and younger).
- (3)
- Ownership was clustered into a three-level variable: (a) two-wheelers (including scooter and bicycle in Table 1), (b) car, and (c) none. The reference level was none.
- (4)
- Occupation was clustered into a four-level variable: (a) office worker and non-office worker, (b) self-employed and freelancer, (c) retired and no job, and (d) student. The reference level was student.
- (5)
- Residence was clustered into a binary variable: (a) first-tier and new first-tier cities, and (b) others (including second-tier cities, third-tier cities and rural areas). The reference level was others.
3.2. Bivariate Probit Model
3.3. Random-Parameter Approach
4. Modeling Results
4.1. Bus and Subway
4.2. Private Car
4.3. Two-Wheelers
4.4. Walking
5. Discussion
5.1. Discussions of Changes in Mode Choice after the COVID-19 Outbreak
5.2. Discussion of Modeling Results
5.3. Policy Implications about Effective Restrictions and Sustainable Transportation
- (1)
- Many travelers, especially young travelers, still use public transportation as their primary or sole means of transportation, which leads to unequal risks of virus transmission. Public transport needs to maintain strict anti-pandemic measures, especially in developed cities. Hygiene measures (such as sanitizations of vehicles and standard temperature checks for drivers and passengers) are expected to mitigate public perceived safety threats. Considering that students who do not own cars are the group most severely affected by COVID-19, the stakeholders of public transport are suggested to provide them with better information services related to the prevention of disease spreads (e.g., real-time number of passengers, last disinfection time).
- (2)
- It is necessary to provide facilities and hygiene measures for non-motorized vehicles (i.e., bicycles) to promote them, especially in underdeveloped cities and rural areas due to the potential safety hazard. In Toronto and London, it was recommended to construct physically separated bike lanes and connect existing bike networks to facilitate and maintain cycling safety [50].
- (3)
- It is also required to better connect bike networks to public transportation, namely multi-modal transportation [51,52,53]. Multi-modal transportation is recommended in developed cities where there are more younger citizens with diversified travel demands. Specifically, it is suggested to optimize the scheduling of shared bikes near public transport hubs in order to improve the efficiency of transport networks. At the same time, it is necessary to strengthen disinfection measures for shared bikes such as antiseptic wipes, masks, etc. Additionally, more intelligent recommendation and more various situational contexts (e.g., hygiene information and weather) could be integrated into navigation applications [51].
5.4. Limitations
6. Conclusions
- (1)
- Gender was generally not a significant factor affecting the change in travel mode choice;
- (2)
- Older people showed a trend of switching from transit to private cars or two-wheelers (heterogeneity was estimated because of heterogeneous ownership);
- (3)
- Ownership of vehicles or two-wheelers was a significant contributing factor to the changes in mode choice;
- (4)
- Students had the most drastic changes in the use of the bus and subway compared to other occupations;
- (5)
- Changes in transit and private car travel were more apparent for people in more developed cities (i.e., first-tier and new first-tier cities).
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ritchie, H.; Mathieu, E.; Rodés-Guirao, L.; Appel, C.; Giattino, C.; Ortiz-Ospina, E.; Roser, M. Coronavirus Pandemic (COVID-19). Our World in Data. 2020. Available online: https://ourworldindata.org/coronavirus (accessed on 5 February 2023).
- Central People’s Government of China. Wuhan Novel Coronavirus Pneumonectomy Prevention and Control Headquarters Notification (No. 1). 2020. Available online: http://www.gov.cn/xinwen/2020-01/23/content_5471751.htm (accessed on 5 February 2023).
- Ministry of Transport of China. Transportation economy first fell and then rose and continued to recover in 2020. 2021. Available online: http://xxgk.mot.gov.cn/2020/jigou/zhghs/202101/t20210128_3520349.html (accessed on 5 February 2023).
- Anke, J.; Francke, A.; Schaefer, L.-M.; Petzoldt, T. Impact of SARS-CoV-2 on the mobility behaviour in Germany. Eur. Transp. Res. Rev. 2021, 13, 10. [Google Scholar] [CrossRef]
- Kazemzadeh, K.; Koglin, T. Electric bike (non)users’ health and comfort concerns pre and peri a world pandemic (COVID-19): A qualitative study. J. Transp. Health 2021, 20, 101014. [Google Scholar] [CrossRef]
- Cui, Q.; He, L.; Liu, Y.; Zheng, Y.; Wei, W.; Yang, B.; Zhou, M. The impacts of COVID-19 pandemic on China’s transport sectors based on the CGE model coupled with a decomposition analysis approach. Transp. Policy 2021, 103, 103–115. [Google Scholar] [CrossRef] [PubMed]
- Luan, S.; Yang, Q.; Jiang, Z.; Wang, W. Exploring the impact of COVID-19 on individual’s travel mode choice in China. Transp. Policy 2021, 106, 271–280. [Google Scholar] [CrossRef] [PubMed]
- Shamshiripour, A.; Rahimi, E.; Shabanpour, R.; Mohammadian, A. How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transp. Res. Interdiscip. Perspect. 2020, 7, 100216. [Google Scholar] [CrossRef] [PubMed]
- Politis, I.; Georgiadis, G.; Papadopoulos, E.; Fyrogenis, I.; Nikolaidou, A.; Kopsacheilis, A.; Sdoukopoulos, A.; Verani, E. COVID-19 lockdown measures and travel behavior: The case of Thessaloniki, Greece. Transp. Res. Interdiscip. Perspect. 2021, 10, 100345. [Google Scholar] [CrossRef]
- Tian, X.; An, C.; Chen, Z.; Tian, Z. Assessing the impact of COVID-19 pandemic on urban transportation and air quality in Canada. Sci. Total Environ. 2021, 765, 144270. [Google Scholar] [CrossRef]
- Cartenì, A.; Di Francesco, L.; Henke, I.; Marino, T.V.; Falanga, A. The Role of Public Transport during the Second COVID-19 Wave in Italy. Sustainability 2021, 13, 11905. [Google Scholar] [CrossRef]
- Sohrabi, S.; Shu, F.; Gupta, A.; Sabbaghian, M.H.; Mehrara Molan, A.; Sajjadi, S. Health Impacts of COVID-19 through the Changes in Mobility. Sustainability 2023, 15, 4095. [Google Scholar] [CrossRef]
- Zhou, E.; Lee, J. How has COVID-19 changed trip patterns by purpose in China? Transp. Saf. Environ. 2022, 4, tdac030. [Google Scholar] [CrossRef]
- Marra, A.D.; Sun, L.; Corman, F. The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area. Transp. Policy 2020, 116, 258–268. [Google Scholar] [CrossRef] [PubMed]
- Gutiérrez, A.; Miravet, D.; Domènech, A. COVID-19 and urban public transport services: Emerging challenges and research agenda. Cities Health 2021, 5 (Suppl. 1), S177–S180. [Google Scholar] [CrossRef]
- Beck, M.J.; Hensher, D.A. Insights into the impact of COVID-19 on household travel and activities in Australia—The early days under restrictions. Transp. Policy 2020, 96, 76–93. [Google Scholar] [CrossRef]
- Monterde-i-Bort, H.; Sucha, M.; Risser, R.; Kochetova, T. Mobility Patterns and Mode Choice Preferences during the COVID-19 Situation. Sustainability 2022, 14, 768. [Google Scholar] [CrossRef]
- Zubair, H.; Karoonsoontawong, A.; Kanitpong, K. Effects of COVID-19 on Travel Behavior and Mode Choice: A Case Study for the Bangkok Metropolitan Area. Sustainability 2022, 14, 9326. [Google Scholar] [CrossRef]
- Andreana, G.; Gualini, A.; Martini, G.; Porta, F.; Scotti, D. The disruptive impact of COVID-19 on air transportation: An ITS econometric analysis. Res. Transp. Econ. 2021, 90, 101042. [Google Scholar] [CrossRef]
- Wen, W.; Yang, S.; Zhou, P.; Gao, S.Z. Impacts of COVID-19 on the electric vehicle industry: Evidence from China. Renew. Sustain. Energy Rev. 2021, 144, 111024. [Google Scholar] [CrossRef]
- Goenaga, B.; Matini, N.; Karanam, D.; Underwood, B.S. Disruption and Recovery: Initial Assessment of COVID-19 Traffic Impacts in North Carolina and Virginia. J. Transp. Eng. Part A Syst. 2021, 147, 06021001. [Google Scholar] [CrossRef]
- Hara, Y.; Yamaguchi, H. Japanese travel behavior trends and change under COVID-19 state-of-emergency declaration: Nationwide observation by mobile phone location data. Transp. Res. Interdiscip. Perspect. 2021, 9, 100288. [Google Scholar] [CrossRef]
- Zhang, N.; Jia, W.; Wang, P.; Dung, C.H.; Zhao, P.; Leung, K.; Su, B.; Cheng, R.; Li, Y. Changes in local travel behaviour before and during the COVID-19 pandemic in Hong Kong. Cities 2021, 112, 103139. [Google Scholar] [CrossRef]
- Zhang, Y.; Fricker, J.D. Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model. Transp. Policy 2021, 103, 11–20. [Google Scholar] [CrossRef]
- Bucsky, P. Modal share changes due to COVID-19: The case of Budapest. Transp. Res. Interdiscip. Perspect. 2020, 8, 100141. [Google Scholar] [CrossRef] [PubMed]
- Jenelius, E.; Cebecauer, M. Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts. Transp. Res. Interdiscip. Perspect. 2020, 8, 100242. [Google Scholar] [CrossRef] [PubMed]
- Teixeira, J.F.; Lopes, M. The link between bike sharing and subway use during the COVID-19 pandemic: The case-study of New York’s Citi Bike. Transp. Res. Interdiscip. Perspect. 2020, 6, 100166. [Google Scholar] [CrossRef]
- Brough, R.; Freedman, M.; Phillips, D.C. Understanding socioeconomic disparities in travel behavior during the COVID-19 pandemic. J. Reg. Sci. 2021, 61, 753–774. [Google Scholar] [CrossRef]
- Naveen, B.R.; Gurtoo, A. Public transport strategy and epidemic prevention framework in the Context of Covid-19. Transp. Policy 2022, 116, 165–174. [Google Scholar] [CrossRef]
- de Haas, M.; Faber, R.; Hamersma, M. How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transp. Res. Interdiscip. Perspect. 2020, 6, 100150. [Google Scholar] [CrossRef]
- Downey, L.; Fonzone, A.; Fountas, G.; Semple, T. The impact of COVID-19 on future public transport use in Scotland. Transp. Res. Part A Policy Pract. 2022, 163, 338–352. [Google Scholar] [CrossRef]
- Palm, M.; Allen, J.; Zhang, Y.; Tiznado-Aitken, I.; Batomen, B.; Farber, S.; Widener, M. Facing the future of transit ridership: Shifting attitudes towards public transit and auto ownership among transit riders during COVID-19. Transportation, 2022; 1–27, in press. [Google Scholar] [CrossRef]
- Shakibaei, S.; de Jong, G.C.; Alpkokin, P.; Rashidi, T.H. Impact of the COVID-19 pandemic on travel behavior in Istanbul: A panel data analysis. Sustain. Cities Soc. 2021, 65, 102619. [Google Scholar] [CrossRef]
- Bhaduri, E.; Manoj, B.S.; Wadud, Z.; Goswami, A.K.; Choudhury, C.F. Modelling the effects of COVID-19 on travel mode choice behaviour in India. Transp. Res. Interdiscip. Perspect. 2020, 8, 100273. [Google Scholar] [CrossRef]
- Zhang, J.; Lee, J. Interactive effects between travel behaviour and COVID-19: A questionnaire study. Transp. Saf. Environ. 2021, 3, 166–177. [Google Scholar] [CrossRef]
- Greene, W.H. Econometric Analysis, 8th ed.; Pearson Education International: Upper Saddle River, NJ, USA, 2017. [Google Scholar]
- Anastasopoulos, P.C.; Karlaftis, M.G.; Haddock, J.E.; Mannering, F.L. Household Automobile and Motorcycle Ownership Analyzed with Random Parameters Bivariate Ordered Probit Model. Transp. Res. Rec. 2012, 2279, 12–20. [Google Scholar] [CrossRef]
- Lin, H.; Guo, C.; Hu, Y.; Liang, H.; Shen, W.; Mao, W.; He, N. COVID-19 control strategies in Taizhou city, China. Bull. World Health Organ. 2020, 98, 632–637. [Google Scholar] [CrossRef] [PubMed]
- Liu, W.; Yue, G.; Tchounwou, P.B. Response to the COVID-19 Epidemic: The Chinese Experience and Implications for Other Countries. Int. J. Environ. Res. Public Health 2020, 17, 2304. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Baig, F.; Kirytopoulos, K.; Lee, J.; Tsamilis, E.; Mao, R.; Ntzeremes, P. Changes in People’s Mobility Behavior in Greece after the COVID-19 Outbreak. Sustainability 2022, 14, 3567. [Google Scholar] [CrossRef]
- Lee, J.; Baig, F.; Pervez, A. Impacts of COVID-19 on individuals’ mobility behavior in Pakistan based on self-reported responses. J. Transp. Health 2021, 22, 101228. [Google Scholar] [CrossRef]
- Irawan, M.Z.; Belgiawan, P.F.; Joewono, T.B.; Bastarianto, F.F.; Rizki, M.; Ilahi, A. Exploring activity-travel behavior changes during the beginning of COVID-19 pandemic in Indonesia. Transportation 2022, 49, 529–553. [Google Scholar] [CrossRef]
- Nurse, A.; Dunning, R. Is COVID-19 a turning point for active travel in cities? Cities Health 2020, 5, S174–S176. [Google Scholar] [CrossRef]
- Costa, C.S.; Pitombo, C.S.; de Souza, F.L.U. Travel Behavior before and during the COVID-19 Pandemic in Brazil: Mobility Changes and Transport Policies for a Sustainable Transportation System in the Post-Pandemic Period. Sustainability 2022, 14, 4573. [Google Scholar] [CrossRef]
- Aydin, N.; Kuşakcı, A.O.; Deveci, M. The impacts of COVID-19 on travel behavior and initial perception of public transport measures in Istanbul. Decis. Anal. J. 2022, 2, 100029. [Google Scholar] [CrossRef]
- DiDi Annual Financial Report. 2021. Available online: https://ir.didiglobal.com/financials/annual-reports/default.aspx (accessed on 5 February 2023).
- Fan, X.; Lu, J.; Qiu, M.; Xiao, X. Changes in travel behaviors and intentions during the COVID-19 pandemic and recovery period: A case study of China. J. Outdoor Recreat. Tour. 2022, 100522. [Google Scholar] [CrossRef]
- Zhao, P.; Gao, Y. Public transit travel choice in the post COVID-19 pandemic era: An application of the extended Theory of Planned behavior. Travel Behav. Soc. 2022, 28, 181–195. [Google Scholar] [CrossRef]
- WHO. WHO Director-General’s Opening Remarks at the MEDIA Briefing on COVID-19—13 April 2020; WHO: Geneva, Switzerland, 2020. [Google Scholar]
- Sui, W.; Prapavessis, H. COVID-19 has Created More Cyclists: How Cities can Keep Them on Their Bikes. 2020. Available online: https://theconversation.com/covid-19-has-created-more-cyclists-how-cities-can-keep-them-on-their-bikes-137545 (accessed on 5 February 2023).
- Liu, H.; Han, J.; Fu, Y.; Zhou, J.; Lu, X.; Xiong, H. Multi-modal transportation recommendation with unified route representation learning. Proc. VLDB Endow. 2021, 14, 342–350. [Google Scholar] [CrossRef]
- Liu, H.; Tong, Y.; Zhang, P.; Lu, X.; Duan, J.; Xiong, H. Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019. [Google Scholar] [CrossRef]
- Liu, H.; Li, T.; Hu, R.; Fu, Y.; Gu, J.; Xiong, H. Joint Representation Learning for Multi-Modal Transportation Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 29–31 January 2019. [Google Scholar] [CrossRef] [Green Version]
- Morar, C.; Tiba, A.; Jovanovic, T.; Valjarević, A.; Ripp, M.; Vujičić, M.D.; Stankov, U.; Basarin, B.; Ratković, R.; Popović, M.; et al. Supporting Tourism by Assessing the Predictors of COVID-19 Vaccination for Travel Reasons. Int. J. Environ. Res. Public Health 2022, 19, 918. [Google Scholar] [CrossRef]
Independent Variables | Categories | Frequency | Percent |
---|---|---|---|
Gender | Male | 218 | 38.4% |
Female | 350 | 61.6% | |
Age | 16–24 years | 294 | 51.8% |
>24 years | 274 | 48.2% | |
Residence 1 | First-tier cities | 105 | 18.5% |
New first-tier cities | 89 | 15.6% | |
Second-tier cities | 55 | 9.7% | |
Third-tier cities | 222 | 39.1% | |
Rural | 97 | 17.1% | |
Ownership of a transportation mode 2 | Car | 184 | 32.4% |
Scooter | 253 | 44.5% | |
Bicycle | 165 | 29.0% | |
None | 143 | 25.2% | |
Occupation | Student | 232 | 40.8% |
Office worker | 148 | 26.1% | |
Non-office worker | 64 | 11.3% | |
Self-employed | 34 | 6.0% | |
Freelancer | 39 | 6.9% | |
Retired/unemployed | 51 | 9.0% |
Reduction Rate (before Peak)/before 1 | Recovery Rate after/before 1 | |
---|---|---|
Bus and subway | 81.3% | 73.5% |
Taxi and ride sharing | 72.2% | 252.0% |
Private car | 40.7% | 92.3% |
Scooter | 61.1% | 86.6% |
Bicycle | 57.6% | 86.5% |
Walking | 52.5% | 79.2% |
Categories | Definition of Dependent Variables | |||
---|---|---|---|---|
before (B) → Peak (P) 1 | Peak (P) → after (A) 1 | |||
Bus and subway | Decrease 2 | P-B < 0: y = 1 | Increase | A-P > 0: y = 1 |
P-B ≥ 0: y = 0 | A-P ≤ 0: y = 0 | |||
Private car | Increase | P-B > 0: y = 1 | Decrease | A-P < 0: y = 1 |
P-B ≤ 0: y = 0 | A-P ≥ 0: y = 0 | |||
Walking | Increase | P-B > 0: y = 1 | Decrease | A-P < 0: y = 1 |
P-B ≤ 0: y = 0 | A-P ≥ 0: y = 0 | |||
Two-wheelers | Increase | P-B > 0: y = 1 | Decrease | A-P < 0: y = 1 |
P-B ≤ 0: y = 0 | A-P ≥ 0: y = 0 |
Variables | Bus and Subway | Private Car | Two-Wheelers | Walking | ||||
---|---|---|---|---|---|---|---|---|
B-P (Decrease) | P-A (Increase) | B-P (Decrease) | P-A (Increase) | B-P (Decrease) | P-A (Increase) | B-P (Decrease) | P-A (Increase) | |
Intercept | 0.560 *** | 0.147 | −0.441 *** | −0.575 *** | −1.038 *** | −1.294 *** | 0.381 *** | 0.124 |
Standard deviation | 0.103 ** | 0.811 *** | 0.137 *** | 0.149 *** | 0.475 *** | 0.206 *** | ||
Gender: male | 0.195 *** | |||||||
Standard deviation | 0.464 *** | |||||||
Age: >24 years | −0.467 *** | −0.051 *** | ||||||
Standard deviation | 0.319 *** | |||||||
Ownership | Reference: none | |||||||
Two-wheelers | 0.164 | −0.031 | −0.046 | 0.684 *** | 0.783 *** | 0.039 | 0.263 ** | |
Standard deviation | 0.360 *** | 0.166 *** | 0.507 *** | |||||
Car | −0.204 * | 0.505 *** | 0.585 *** | −0.182 | −0.174 | −0.260 ** | −0.179 | |
Standard deviation | 0.470 *** | 0.424 *** | 0.003 *** | 0.708 *** | ||||
Occupation | Reference: student | |||||||
Office worker and non-office worker | −0.583 ** | |||||||
Self-employed and freelancer | −0.888 *** | |||||||
Retired and no job | −0.525 ** | |||||||
Standard deviation | 1.478 *** | |||||||
Residence | Reference: others | |||||||
First-tier and new first-tier cities | 0.569 *** | 0.350 *** | 0.226 * | 0.288 ** | ||||
Standard deviation | 0.624 *** | 0.177 ** | 0.081 *** | |||||
Correlation between the errors (ρ) | 0.891 *** | 0.99994 *** | 0.982 *** | 0.994 *** | ||||
Log-likelihood | −535.618 | −460.812 | −431.029 | −534.561 | ||||
K (number of parameters) | 16 | 16 | 11 | 12 | ||||
Comparison between Bivariate model and two independent univariate models | ||||||||
Log-likelihood (univariate) | −289.93 | −318.092 | −314.266 | −305.428 | −263.779 | −245.177 | −315.185 | −316.661 |
K (number of parameters) | 7 | 3 | 4 | 5 | 3 | 3 | 3 | 4 |
Likelihood ratio test | 144.808 (d.f. = 6) | 317.764 (d.f. = 7, p < 0.001) | 155.855 (d.f. = 5, p < 0.001) | 194.568 (d.f. = 5, p < 0.001) |
Period | Time Duration | The COVID-19 Situation | Restrictions [38,39] |
---|---|---|---|
Before | Before 27 December 2019 | No cases. | No restrictions |
Peak | From 20 January to 17 March 2020 | The outbreak began in Wuhan, and quickly spread across the country. | Lockdown Stay-at-home order Screening and quarantine Traffic entrance management: establish checkpoints to inspect people entering the city, community and village Epidemiological investigations and prevention measures Mask-wearing requirement The “health code” regulation: Through big data and communication technologies, a quick response (QR) code was used to show the probability of a person having COVID-19 by displaying green, yellow, or red to indicate their health status Metro was stopped (Wuhan) Negative PCR test result when entering public places Social distancing School closures Teleworking Avoid/limit large gatherings and close public venues Non-essential businesses closure |
After | From 18 March to the date of the survey (17 August) | Within the territory of the whole sporadic distribution, local areas appear small clusters of outbreaks. | Lockdown: only in areas with small clusters of outbreaks Stay-at-home order gradually lifted Traffic entrance management Metro reopens Public venues gradually reopen Schools gradually reopen Resume work gradually Non-essential businesses gradually reopen (Other restrictions remain the same as the peak period) |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, H.; Lee, J. Contributing Factors to the Changes in Public and Private Transportation Mode Choice after the COVID-19 Outbreak in Urban Areas of China. Sustainability 2023, 15, 5048. https://doi.org/10.3390/su15065048
Liu H, Lee J. Contributing Factors to the Changes in Public and Private Transportation Mode Choice after the COVID-19 Outbreak in Urban Areas of China. Sustainability. 2023; 15(6):5048. https://doi.org/10.3390/su15065048
Chicago/Turabian StyleLiu, Haiyan, and Jaeyoung Lee. 2023. "Contributing Factors to the Changes in Public and Private Transportation Mode Choice after the COVID-19 Outbreak in Urban Areas of China" Sustainability 15, no. 6: 5048. https://doi.org/10.3390/su15065048