Spatial Modelling of Modal Shift Due to COVID-19
Abstract
:1. Introduction
2. Literature Review
2.1. Reported Changes in Travel Behavior Because of COVID-19
2.2. Modelling Changes in Travel Behaviors
2.3. Mode Choice Models
3. Materials and Methods
3.1. Survey and AHP Calculation
3.2. Creation of GEO Dataset and OD Cost Matrix Calculation
3.3. Calculation of the Trade-Off
3.4. Examined Study Area
4. Results
4.1. Sociodemographic Characteristics of the Study Sample
4.2. Changes in Behavior Because of COVID-19
4.3. Comparison in Factors before and during the COVID-19 Situation
4.4. Preference Rate for Selected Alternatives
4.5. Mode Choice Map
5. Discussion
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Conflicts of Interest
References
- Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [Google Scholar] [CrossRef]
- Aloi, A.; Alonso, B.; Benavente, J.; Cordera, R.; Echániz, E.; González, F.; Ladisa, C.; Lezama-Romanelli, R.; López-Parra, Á.; Mazzei, V.; et al. Effects of the COVID-19 lockdown on urban mobil-ity: Empirical evidence from the city of santander. Sustainability 2020, 12, 3870. (In Spain) [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]
- Canitez, F.; Alpkokin, P.; Kiremitci, S.T. Sustainable urban mobility in istanbul: Challenges and prospects. Case Stud. Transp. Policy 2020, 8, 1148–1157. [Google Scholar] [CrossRef]
- Shakibaei, S.; de Jong, G.C.; Alpkökin, 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]
- Moslem, S.; Campisi, T.; Szmelter-Jarosz, A.; Duleba, S.; Nahiduzzaman, K.; Tesoriere, G. Best–worst method for modelling mobility choice after COVID-19: Evidence from Italy. Sustainability 2020, 12, 6824. [Google Scholar] [CrossRef]
- Marseglia, G.; Medaglia, C.M.; Ortega, F.A.; Mesa, J.A. Optimal alignments for designing urban transport systems: Application to seville. Sustainability 2019, 11, 5058. [Google Scholar] [CrossRef] [Green Version]
- Sultan, B.; Katar, I.; Al-Atroush, M. Towards sustainable pedestrian mobility in Riyadh city, Saudi Arabia: A case study. Sustain. Cities Soc. 2021, 69, 102831. [Google Scholar] [CrossRef]
- Paulley, N.; Balcombe, R.; Mackett, R.; Titheridge, H.; Preston, J.; Wardman, M.; Shires, J.; White, P. The demand for public transport: The effects of fares, quality of service, income and car ownership. Transp. Policy 2006, 13, 295–306. [Google Scholar] [CrossRef] [Green Version]
- Bucsky, P. Modal share changes due to COVID-19: The case of budapest. Transp. Res. Interdiscip. Perspect. 2020, 8, 100141. [Google Scholar] [CrossRef]
- Sirkeci, I.; Yucesahin, M.M. Coronavirus and migration: Analysis of human mobility and the spread of Covid-19. Migr. Lett. 2020, 17, 379–398. [Google Scholar] [CrossRef] [Green Version]
- Cartenì, A.; Di Francesco, L.; Martino, M. How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study. Sci. Total Environ. 2020, 741, 140489. [Google Scholar] [CrossRef] [PubMed]
- Falchetta, G.; Noussan, M. The Impact of COVID-19 on Transport Demand, Modal Choices, and Sectoral Energy Consumption in Europe. Available online: https://iaee.org/en/publications/newsletterdl.aspx?id=884 (accessed on 24 March 2021).
- Zhang, R.; Zhang, J. Long-term pathways to deep decarbonization of the transport sector in the post-COVID world. Transp. Policy 2021, 110, 28–36. [Google Scholar] [CrossRef]
- McKinsey & Company Automotive & Assembly. The Impact of COVID-19 on Future Mobility Solutions; McKinsey & Company: New York, NY, USA, 2020. [Google Scholar]
- Lee, M.; Zhao, J.; Sun, Q.; Pan, Y.; Zhou, W.; Xiong, C.; Zhang, L. Human mobility trends during the COVID-19 pandemic in the United States. arXiv 2005, arXiv:2005.01215. [Google Scholar]
- Bernhardt, J. Decline in Ridership, Adapted Timetables and Disinfection-Robots—The Impact of Corona/Covid-10 on Public Transport. Available online: https://www.urban-transport-magazine.com/en/decline-in-ridership-adapted-timetables-and-disinfection-robots-the-impact-of-corona-Covid-10-on-public-transport/ (accessed on 24 April 2021).
- Yıldırım, M.; Geçer, E.; Akgül, Ö. The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychol. Health Med. 2021, 26, 35–43. [Google Scholar] [CrossRef]
- Lai, S.; Leone, F.; Zoppi, C. Covid-19 and spatial planning. TeMA 2020. [Google Scholar] [CrossRef]
- Russo, R.; Camanho, R. Criteria in AHP: A systematic review of literature. Procedia Comput. Sci. 2015, 55, 1123–1132. [Google Scholar] [CrossRef] [Green Version]
- Saaty, T.L. The Analytic Hierarchy Process; McGraw-Hill: New York, NY, USA, 1980. [Google Scholar]
- Vienna, S. Vienna in Figures 2020. Available online: https://www.wien.gv.at/statistik/pdf/viennainfigures-2020.pdf (accessed on 25 March 2021).
- Vienna Public Transport. Available online: https://www.wien.gv.at/english/transportation-urbanplanning/public-transport/ (accessed on 20 April 2021).
- Šinko, S.; Rupnik, B.; Prah, K.; Kramberger, T. Spatial modelling of the transport mode choice: Application on the Vienna transport network. Transport 2021, in press. [Google Scholar]
- Abe, R. Preferences of urban rail users for first- and last-mile autonomous vehicles: Price and service elasticities of demand in a multimodal environment. Transp. Res. Part C Emerg. Technol. 2021, 126, 103105. [Google Scholar] [CrossRef]
- Rahman, F.I.; Bari, M.; Islam, M.; Joyanto, T.P. Analysis of mode choice behavior and value of time in Dhaka city, Bangladesh. Int. J. Traffic Transp. Eng. 2020, 10, 138–152. [Google Scholar] [CrossRef]
- Guo, J.; Susilo, Y.; Antoniou, C.; Pernestål, A. When and why do people choose automated buses over conventional buses? Results of a context-dependent stated choice experiment. Sustain. Cities Soc. 2021, 69, 102842. [Google Scholar] [CrossRef]
- Halawani, A.T.; Rehimi, F. Evaluation of the intention to shift to public transit in Saudi Arabia. Transp. Res. Part D Transp. Environ. 2021, 94, 102809. [Google Scholar] [CrossRef]
- Priya, G.N.; Mathew, S.; Subbaiyan, G. Household structure and the travel pattern of senior citizens for leisure trips. In Transportation Research; Springer: Singapore, 2019; pp. 325–340. [Google Scholar]
- Tran, Y.; Yamamoto, T.; Sato, H.; Miwa, T.; Morikawa, T. The analysis of influences of attitudes on mode choice under highly unbalanced mode share patterns. J. Choice Model. 2020, 36, 100227. [Google Scholar] [CrossRef]
- Tuan, V.A.; Huong, N.T.T. Analysis of mode choice behavior under adverse weather conditions using ra and sa surveys—A case study from Ho Chi Minh City (HCMC), Vietnam. Lect. Notes Civ. Eng. 2019, 959–964. [Google Scholar] [CrossRef]
- Zannat, K.E.; Islam, K.M.A.; Sunny, D.S.; Moury, T.; Das Tuli, R.; Dewan, A.; Adnan, M.S.G. Nonmotorized commuting behavior of middle-income working adults in a developing country. J. Urban Plan. Dev. 2021, 147, 1. [Google Scholar] [CrossRef]
- Duleba, S.; Moslem, S.; Esztergár-Kiss, D. Estimating commuting modal split by using the best–worst method. Eur. Transp. Res. Rev. 2021, 13, 1–12. [Google Scholar] [CrossRef]
- Carrese, S.; Cuneo, V.; Nigro, M.; Pizzuti, R.; Ardito, C.F.; Marseglia, G. Optimization of downstream fuel logistics based on road infrastructure conditions and exposure to accident events. Transp. Policy 2019. [Google Scholar] [CrossRef]
- Fan, S.; Zhang, Z. Predicting spatial distribution of plant functional traits in a forest-steppe zone. Pol. J. Ecol. 2021, 69, 1–13. [Google Scholar] [CrossRef]
- Bushira, K.M. Modeling the effectiveness of social distancing interventions on the epidemic curve of coronavirus disease in Ethiopia. Model. Earth Syst. Environ. 2021, 1–11. [Google Scholar] [CrossRef]
- Tusa, B.S.; Kebede, S.A.; Weldesenbet, A.B. Spatial distribution and determinant factors of anemia among adults aged 15–59 in Ethiopia; using mixed-effects ordinal logistic regression model. BMC Nutr. 2021, 7, 1–12. [Google Scholar] [CrossRef]
- Frassl, A. The Rules of Austria’s Third Lockdown. Metropole, 22 December 2020. Metropole. Available online: https://metropole.at/third-lockdown-in-austria/ (accessed on 23 March 2021).
- Hero, L. Vienna Coronavirus Updates. Available online: https://luggagehero.com/covid-19-information/vienna/ (accessed on 20 April 2021).
- König, A.; Dreßler, A. A mixed-methods analysis of mobility behavior changes in the COVID-19 era in a rural case study. Eur. Transp. Res. Rev. 2021, 13, 1–13. [Google Scholar] [CrossRef]
- 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, 1–13. [Google Scholar] [CrossRef]
- 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]
- EAM Association. Average Age of the EU Vehicle Fleet, by EU Country. Available online: https://www.acea.be/statistics/article/average-vehicle-age (accessed on 19 April 2021).
Article | Year | Country | Data Collecting | Method |
---|---|---|---|---|
Cartenì, Di Francesco, and Martino | 2020 | Italy | Data from Italian national census (ISTAT), Automatic sensors data | Multiple linear regression model |
Shakibaei, De Jong, Alpkökin, and Rashidi | 2021 | Istanbul | Panel survey during the epidemic (comparison of phase 1, 2, and 3) | Descriptive statistics |
Shamshiripour, Rahimi, R. Shabanpour, and Mohammadian | 2020 | Illinois (US) | Longitudinal analysis encompassing multiple survey waves Travel behaviour survey | Survey about habits, prior to and during the coronavirus Combining stated preference (SP) and revealed preference (RP) methods |
Engle, Stromme, and Zhou | 2020 | US | County-level panel data GPS data | Simple model that relates an individual’s travel decision to perceived disease prevalence |
Moslem, Campisi, Szmelter-Jarosz, Duleba, Nahiduzzaman, and Tesorier | 2020 | Southernmost Italy | Online survey prior to COVID outbreak (Nov and Dec 2021) and March and April 2020 | Best–worst method |
Shakibaei, De Jong, Alpkökin, and Rashidi | 2021 | Istanbul | Panel survey during the epidemic (comparison of phase 1, 2, and 3) | Descriptive statistics |
Anke, Francke, Schaefer, and Petzoldt | 2021 | Germany | Online survey about mobility habits before and during pandemic | Descriptive statistics |
Politis, Georgiadis, Papadopoulos et al. | 2021 | Greece | On-field survey before COVID (2019) and online survey after outbreak in 2021 | Descriptive statistics |
Used Methodology | Authors |
---|---|
Multinomial logit and mixed logit models | Abe, 2021; Guo, Susilo, Antoniou, and Penestal, 2021; Halawani and Rehimi, 2021; Priya, Mathew, and Subbaiyan, 2020; Rahman, Islam, and Joyanto, 2020 |
Binary logit model | Tuan and Thanh Huong, 2020 |
Multiple linear and binary logistic models | Zannat, Islam, Sunny, Moury, Da Tuli, Dewan, and Adnan, 2021 |
Hybrid models or integrated choice and latent variable (ICLV) | Tran, Yamamoto, Sato, Miwa, and Morikawa, 2020 |
Best–worst method | Duleba, Moslem, and Esztergár-Kiss, 2021 |
Sociodemographic Variable | Characteristics | n | % |
---|---|---|---|
Gender | Male | 100 | 50 |
Female | 100 | 50 | |
Age | 15–19 | 4 | 2 |
20–29 | 40 | 20 | |
30–39 | 56 | 28 | |
40–49 | 70 | 35 | |
50–59 | 30 | 15 | |
Daily outside activity | Job | 86 | 43 |
Education | 16 | 8 | |
Shopping | 58 | 29 | |
Sport | 22 | 11 | |
Other | 18 | 9 |
2019 | 2021 | |||
---|---|---|---|---|
Factor | Priority Weight | Rank | Priority Weight | Rank |
Convenience | 0.3626 | 1 | 0.3069 | 2 |
Ecological aspect | 0.3398 | 2 | 0.3885 | 1 |
Accessibility and interchanges | 0.2976 | 3 | 0.3046 | 3 |
2019 | 2021 | |||
---|---|---|---|---|
Alternative | Preference Rate | Rank | Preference Rate | Rank |
Walk | 0.2223 | 3 | 0.33977 | 1 |
Bicycle | 0.2361 | 2 | 0.24134 | 2 |
Automobile | 0.2047 | 4 | 0.22436 | 4 |
Public transport | 0.3369 | 1 | 0.23580 | 3 |
Change | % |
---|---|
Bike | +195.01 |
Automobile | +1390.57 |
Walk | 0 |
Public transport combined with walking | −72.68 |
Public transport combined with biking | −48.05 |
Shift in Transport Mode | n | % |
---|---|---|
Active–Active | 0 | 0 |
Active–Public | 0 | 0 |
Active–Automobile | 45 | 0.68 |
Public–Active | 827 | 12.44 |
Public–Automobile | 2903 | 43.67 |
Public–Public | 25 | 0.38 |
Automobile–Public | 0 | 0 |
Automobile–Active | 0 | 0 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 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
Šinko, S.; Prah, K.; Kramberger, T. Spatial Modelling of Modal Shift Due to COVID-19. Sustainability 2021, 13, 7116. https://doi.org/10.3390/su13137116
Šinko S, Prah K, Kramberger T. Spatial Modelling of Modal Shift Due to COVID-19. Sustainability. 2021; 13(13):7116. https://doi.org/10.3390/su13137116
Chicago/Turabian StyleŠinko, Simona, Klemen Prah, and Tomaž Kramberger. 2021. "Spatial Modelling of Modal Shift Due to COVID-19" Sustainability 13, no. 13: 7116. https://doi.org/10.3390/su13137116