Determinants of last-mile travel mode choice under different COVID-19 alert levels: A case study of Batasan Hills, Quezon City, Philippines
##plugins.themes.bootstrap3.article.sidebar##
Published
Jun 24, 2024
##plugins.themes.bootstrap3.article.main##
Ma. Bernadeth B. Lim
Hector R. Lim Jr.
Abstract
The COVID-19 outbreak has led to remarkable changes in the transport sector and people’s travel behavior. The suspension of public transport leads to an increase in the number of private car users and the number of walking activities. The last mile, being one of the weakest links in the transport network, has become more challenging to manage with the imposition of different travel restrictions. Using the data collected from the households of Barangay Batasan Hills, Quezon City, Philippines, this study aimed to understand people’s travel behavior during the pandemic. Specifically, a binary logit model was used to determine the significant factors that affect the last-mile travel mode choice under different alert levels. Results showed that age during the pandemic, monthly household income, the purpose of travel, travel expense, travel time, departure time, origin, compliance with COVID-19 measures, and trip duration have significant factors in last-mile travel mode choice. In addition, risk perception on public transport was also a determinant of last-mile travel mode under alert levels 1 and 2. Analyzing travel behavior during the COVID-19 pandemic is deemed beneficial in devising strategies and interventions that will help mitigate the spread of the virus while still allowing economic activity and the movement of people to happen.
How to Cite
Lim MBB, Lim Jr. HR, Anabo JML, Ramos JD. 2024. Determinants of last-mile travel mode choice under different COVID-19 alert levels: A case study of Batasan Hills, Quezon City, Philippines. The Palawan Scientist. 16(2):1–9. https://doi.org/10.69721/TPS.J.2024.16.2.01.
Downloads
Download data is not yet available.
##plugins.themes.bootstrap3.article.details##
Keywords
alert levels, COVID-19, last mile, mode choice, travel behavior
References
Aaditya B and Rahul T. 2021. Psychological impacts of COVID-19 pandemic on the mode choice behaviour: A hybrid choice modelling approach. Transport Policy, 108: 47-58. https://doi.org/10.1016/j.tranpol.2021.05.003
Abdullah M, Ali N, Bilal Aslam A, Ashraf Javid M and Arif Hussain S. 2022. Factors affecting the mode choice behavior before and during COVID-19 pandemic in Pakistan. International Journal of Transportation Science and Technology, 11(1): 174-186. https://doi.org/10.1016/j.ijtst.2021.06.005
Abdullah M, Dias C, Muley D and Shahin M. 2020. Exploring the impacts of COVID-19 on travel behavior and mode preferences. Transportation Research Interdisciplinary Perspectives, 8: 100255. https://doi.org/10.1016/j.trip.2020.100255
Ancheta D, Tani R and Uchida K. 2023. The relationship of social vulnerability and travel behavior with COVID-19 in Metro Manila, Philippines. Asian Transport Studies, 9: 100093. https://doi.org/10.1016/j.eastsj.2022.100093
Baclig C. 2022. EXPLAINER: The Philippines’ COVID-19 alert level system. Inquirer. https://newsinfo.inquirer.net/1535963/explainer-the-philippines-covid-19-alert-level-system. Accessed on 09 November 2022.
Batasan Hills. undated. Batasan Hills. https://placeandsee.com/wiki/batasan-hills. Accessed on 09 November 2022.
Ben-Akiva ME and Lerman SR. 1985. Discrete choice analysis: theory and application to travel demand (Vol. 9). MIT press.
Bhaduri E, Manoj B, Wadud Z, Goswami AK and Choudhury CF. 2020. Modelling the effects of COVID-19 on travel mode choice behaviour in India. Transportation Research Interdisciplinary Perspectives, 8: 100273. https://doi.org/10.1016/j.trip.2020.100273
Borkowski P, Jażdżewska-Gutta M and Szmelter-Jarosz A. 2021. Lockdowned: Everyday mobility changes in response to COVID-19. Journal of Transport Geography, 90: 102906. https://doi.org/10.1016/j.jtrangeo.2020.102906
Chen S, Yan X, Pan H and Deal B. 2021. Using big data for last mile performance evaluation: An accessibility-based approach. Travel Behaviour and Society, 25: 153-163. https://doi.org/10.1016/j.tbs.2021.06.003
Co NJ, Dimaculangan K and Peralta MH. 2023. Effects of Covid-19 pandemic on mode choice behavior of working Filipinos in Metro Manila. Asian Transport Studies, 9: 100101. https://doi.org/10.1016/j.eastsj.2023.100101
de Haas M, Faber R and Hamersma M. 2020. How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, 6: 100150. https://doi.org/10.1016/j.trip.2020.100150
DOH (Department of Health). 2020. DOH confirms first 2019-NCOV case in the country; Assures public of intensified containment measures. https://doh.gov.ph/doh-press-release/doh-confirms-first-2019-nCoV-case-in-the-country. Accessed on 09 November 2022.
DOH (Department of Health). 2021. Guidelines on the nationwide implementation of alert level system for COVID-19 response. https://doh.gov.ph/sites/default/files/health-update/Guidelines-Alert-Level-System-for-COVID-19-Response.pdf. Accessed on 09 November 2022.
Guo Y, Yang L, Huang W and Guo Y. 2020. Traffic Safety Perception, Attitude, and Feeder Mode Choice of Metro Commute: Evidence from Shenzhen. International Journal of Environmental Research and Public Health, 17(24): 9402. https://doi.org/10.3390/ijerph17249402
Hasnine MS, Lin T, Weiss A and Habib KN. 2018. Determinants of travel mode choices of post-secondary students in a large metropolitan area: The case of the city of Toronto. Journal of Transport Geography, 70: 161-171. https://doi.org/10.1016/j.jtrangeo.2018.06.003
Hasselwander M, Tamagusko T, Bigotte J, Ferreira A, Mejia A and Ferranti E. 2021. Building back better: The COVID-19 pandemic and transport policy implications for a developing megacity. Sustainable Cities and Society, 69: 102864. https://doi.org/10.1016/j.scs.2021.102864
Hensher DA. 1994. Stated preference analysis of travel choices: the state of practice. Transportation, 21: 107-133.
Lee S, Park S, Yang S, Park J and Lee J. 2022. Contributing factors to the change in travel mode choice after COVID-19 in Korea using bivariate probit model. International Journal of Sustainable Building Technology and Urban Development, 13(2): 184-197. https://doi.org/10.22712/susb.20220016
Lu Y, Prato CG, Sipe N, Kimpton A and Corcoran J. 2022. The role of household modality style in first and last mile travel mode choice. Transportation Research Part A: Policy and Practice, 158: 95-109. https://doi.org/10.1016/j.tra.2022.02.003
Mack EA, Agrawal S and Wang S. 2021. The impacts of the COVID-19 pandemic on transportation employment: A comparative analysis. Transportation Research Interdisciplinary Perspectives, 12: 100470. https://doi.org/10.1016/j.trip.2021.100470
Mao Z, Ettema D and Dijst M. 2018. Analysis of travel time and mode choice shift for non-work stops in commuting: case study of Beijing, China. Transportation, 45: 751–766. https://doi.org/10.1007/s11116-016-9749-8
Massaccesi C, Chiappini E, Paracampo R and Korb S. 2021. Large Gatherings? No, Thank You. Devaluation of Crowded Social Scenes During the COVID-19 Pandemic. Frontiers in Psychology, 12: 689162 https://doi.org/10.3389/fpsyg.2021.689162
McFadden D. 1997. Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments. Cowles Foundation Discussion Papers.
Meng M, Koh P and Wong Y. 2016. Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice. Journal of Public Transportation, 19(2): 38-54. https://doi.org/10.5038/2375-0901.19.2.3
Mo B, Shen Y and Zhao J. 2018. Impact of Built Environment on First- and Last-Mile Travel Mode Choice. Transportation Research Record: Journal of the Transportation Research Board, 2672(6): 1-12 https://doi.org/10.1177/0361198118788423
Mohd Ali NF, Mohd Sadullah AF, Abdul Majeed AP, Mohd Razman MA and Musa RM. 2022. The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier: An evaluation for active commuting behavior. Journal of Transport & Health, 25: 101362. https://doi.org/10.1016/j.jth.2022.101362
Monmousseau P, Marzuoli A, Feron E and Delahaye D. 2020. Impact of Covid-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US Air Transportation System to sleep. Transportation Research Interdisciplinary Perspectives, 7: 100179. https://doi.org/10.1016/j.trip.2020.100179
Philippine News Agency. 2022. IATF approves amended guidelines for Alert Level 1. https://www.pna.gov.ph/articles/1168637. Accessed on 09 November 2022.
Patil GR, Basu R and Rashidi TH. 2020. Mode choice modeling using adaptive data collection for different trip purposes in Mumbai metropolitan region. Transportation in Developing Economies, 6(1): 1-10. https://doi.org/10.1007/s40890-020-0099-z
Paul T, Chakraborty R, Afia Ratri S and Debnath M. 2022. Impact of COVID-19 on mode choice behavior: A case study for Dhaka, Bangladesh. Transportation Research Interdisciplinary Perspectives, 15: 100665. https://doi.org/10.1016/j.trip.2022.100665
Pawar DS, Yadav AK, Akolekar N and Velaga NR. 2020. Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown. Transportation Research Interdisciplinary Perspectives, 7: 100203. https://doi.org/10.1016/j.trip.2020.100203
Philippine News Agency. 2022. DOTr to keep 70% capacity in all rail lines under Alert Level 3. https://www.pna.gov.ph/articles/1164313. Accessed on 09 November 2022.
Shakibaei S, de Jong GC, Alpkökin P and Rashidi TH. 2021. Impact of the COVID-19 pandemic on travel behavior in Istanbul: A panel data analysis. Sustainable Cities and Society, 65: 102619. https://doi.org/10.1016/j.scs.2020.102619
Stam B, van Oort N and Hoogendoorn SP. 2021. Travellers’ preferences towards existing and emerging means of first/last mile transport: a case study for the Almere centrum railway station in the Netherlands. European Transport Research Review, 13(1): 1-14. https://doi.org/10.1186/s12544-021-00514-1
Tantuco V. 2021. IATF completes nationwide implementation of COVID-19 alert level system. Rappler. https://www.rappler.com/nation/iatf-completes-nationwide-implementation-covid19-alert-level-system/. Accessed on 09 November 2022.
Tight M, Rajé F and Timms P. 2016. Car-Free Urban Areas: A Radical Solution to the Last Mile Problem or a Step Too Far? Built Environment 42(4), 603–616. http://dx.doi.org/10.2148/benv.42.4.603
Wambua J, Loedy N and Jarvis CI. 2023. The influence of COVID-19 risk perception and vaccination status on the number of social contacts across Europe: insights from the CoMix study. BMC Public Health 23, 1350. https://doi.org/10.1186/s12889-023-16252-z
Washington SP, Karlaftis MG and Mannering FL. 2011. Statistical and econometric methods for transportation data analysis (2nd ed.) [Electronic version]. New York: Chapman and Hall/CRC.
Yang Y, Wang C, Liu W and Zhou P. 2018. Understanding the determinants of travel mode choice of residents and its carbon mitigation potential. Energy Policy, 115: 486-493. https://doi.org/10.1016/j.enpol.2018.01.033
Zhang J, Hayashi Y and Frank LD. 2021. COVID-19 and transport: Findings from a world-wide expert survey. Transport Policy, 103: 68-85. https://doi.org/10.1016/j.tranpol.2021.01.011
Zubair H, Karoonsoontawong A and Kanitpong K. 2022. Effects of COVID-19 on Travel Behavior and Mode Choice: A Case Study for the Bangkok Metropolitan Area. Sustainability, 14(15): 9326. https://doi.org/10.3390/su14159326
Abdullah M, Ali N, Bilal Aslam A, Ashraf Javid M and Arif Hussain S. 2022. Factors affecting the mode choice behavior before and during COVID-19 pandemic in Pakistan. International Journal of Transportation Science and Technology, 11(1): 174-186. https://doi.org/10.1016/j.ijtst.2021.06.005
Abdullah M, Dias C, Muley D and Shahin M. 2020. Exploring the impacts of COVID-19 on travel behavior and mode preferences. Transportation Research Interdisciplinary Perspectives, 8: 100255. https://doi.org/10.1016/j.trip.2020.100255
Ancheta D, Tani R and Uchida K. 2023. The relationship of social vulnerability and travel behavior with COVID-19 in Metro Manila, Philippines. Asian Transport Studies, 9: 100093. https://doi.org/10.1016/j.eastsj.2022.100093
Baclig C. 2022. EXPLAINER: The Philippines’ COVID-19 alert level system. Inquirer. https://newsinfo.inquirer.net/1535963/explainer-the-philippines-covid-19-alert-level-system. Accessed on 09 November 2022.
Batasan Hills. undated. Batasan Hills. https://placeandsee.com/wiki/batasan-hills. Accessed on 09 November 2022.
Ben-Akiva ME and Lerman SR. 1985. Discrete choice analysis: theory and application to travel demand (Vol. 9). MIT press.
Bhaduri E, Manoj B, Wadud Z, Goswami AK and Choudhury CF. 2020. Modelling the effects of COVID-19 on travel mode choice behaviour in India. Transportation Research Interdisciplinary Perspectives, 8: 100273. https://doi.org/10.1016/j.trip.2020.100273
Borkowski P, Jażdżewska-Gutta M and Szmelter-Jarosz A. 2021. Lockdowned: Everyday mobility changes in response to COVID-19. Journal of Transport Geography, 90: 102906. https://doi.org/10.1016/j.jtrangeo.2020.102906
Chen S, Yan X, Pan H and Deal B. 2021. Using big data for last mile performance evaluation: An accessibility-based approach. Travel Behaviour and Society, 25: 153-163. https://doi.org/10.1016/j.tbs.2021.06.003
Co NJ, Dimaculangan K and Peralta MH. 2023. Effects of Covid-19 pandemic on mode choice behavior of working Filipinos in Metro Manila. Asian Transport Studies, 9: 100101. https://doi.org/10.1016/j.eastsj.2023.100101
de Haas M, Faber R and Hamersma M. 2020. How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Research Interdisciplinary Perspectives, 6: 100150. https://doi.org/10.1016/j.trip.2020.100150
DOH (Department of Health). 2020. DOH confirms first 2019-NCOV case in the country; Assures public of intensified containment measures. https://doh.gov.ph/doh-press-release/doh-confirms-first-2019-nCoV-case-in-the-country. Accessed on 09 November 2022.
DOH (Department of Health). 2021. Guidelines on the nationwide implementation of alert level system for COVID-19 response. https://doh.gov.ph/sites/default/files/health-update/Guidelines-Alert-Level-System-for-COVID-19-Response.pdf. Accessed on 09 November 2022.
Guo Y, Yang L, Huang W and Guo Y. 2020. Traffic Safety Perception, Attitude, and Feeder Mode Choice of Metro Commute: Evidence from Shenzhen. International Journal of Environmental Research and Public Health, 17(24): 9402. https://doi.org/10.3390/ijerph17249402
Hasnine MS, Lin T, Weiss A and Habib KN. 2018. Determinants of travel mode choices of post-secondary students in a large metropolitan area: The case of the city of Toronto. Journal of Transport Geography, 70: 161-171. https://doi.org/10.1016/j.jtrangeo.2018.06.003
Hasselwander M, Tamagusko T, Bigotte J, Ferreira A, Mejia A and Ferranti E. 2021. Building back better: The COVID-19 pandemic and transport policy implications for a developing megacity. Sustainable Cities and Society, 69: 102864. https://doi.org/10.1016/j.scs.2021.102864
Hensher DA. 1994. Stated preference analysis of travel choices: the state of practice. Transportation, 21: 107-133.
Lee S, Park S, Yang S, Park J and Lee J. 2022. Contributing factors to the change in travel mode choice after COVID-19 in Korea using bivariate probit model. International Journal of Sustainable Building Technology and Urban Development, 13(2): 184-197. https://doi.org/10.22712/susb.20220016
Lu Y, Prato CG, Sipe N, Kimpton A and Corcoran J. 2022. The role of household modality style in first and last mile travel mode choice. Transportation Research Part A: Policy and Practice, 158: 95-109. https://doi.org/10.1016/j.tra.2022.02.003
Mack EA, Agrawal S and Wang S. 2021. The impacts of the COVID-19 pandemic on transportation employment: A comparative analysis. Transportation Research Interdisciplinary Perspectives, 12: 100470. https://doi.org/10.1016/j.trip.2021.100470
Mao Z, Ettema D and Dijst M. 2018. Analysis of travel time and mode choice shift for non-work stops in commuting: case study of Beijing, China. Transportation, 45: 751–766. https://doi.org/10.1007/s11116-016-9749-8
Massaccesi C, Chiappini E, Paracampo R and Korb S. 2021. Large Gatherings? No, Thank You. Devaluation of Crowded Social Scenes During the COVID-19 Pandemic. Frontiers in Psychology, 12: 689162 https://doi.org/10.3389/fpsyg.2021.689162
McFadden D. 1997. Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments. Cowles Foundation Discussion Papers.
Meng M, Koh P and Wong Y. 2016. Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice. Journal of Public Transportation, 19(2): 38-54. https://doi.org/10.5038/2375-0901.19.2.3
Mo B, Shen Y and Zhao J. 2018. Impact of Built Environment on First- and Last-Mile Travel Mode Choice. Transportation Research Record: Journal of the Transportation Research Board, 2672(6): 1-12 https://doi.org/10.1177/0361198118788423
Mohd Ali NF, Mohd Sadullah AF, Abdul Majeed AP, Mohd Razman MA and Musa RM. 2022. The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier: An evaluation for active commuting behavior. Journal of Transport & Health, 25: 101362. https://doi.org/10.1016/j.jth.2022.101362
Monmousseau P, Marzuoli A, Feron E and Delahaye D. 2020. Impact of Covid-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US Air Transportation System to sleep. Transportation Research Interdisciplinary Perspectives, 7: 100179. https://doi.org/10.1016/j.trip.2020.100179
Philippine News Agency. 2022. IATF approves amended guidelines for Alert Level 1. https://www.pna.gov.ph/articles/1168637. Accessed on 09 November 2022.
Patil GR, Basu R and Rashidi TH. 2020. Mode choice modeling using adaptive data collection for different trip purposes in Mumbai metropolitan region. Transportation in Developing Economies, 6(1): 1-10. https://doi.org/10.1007/s40890-020-0099-z
Paul T, Chakraborty R, Afia Ratri S and Debnath M. 2022. Impact of COVID-19 on mode choice behavior: A case study for Dhaka, Bangladesh. Transportation Research Interdisciplinary Perspectives, 15: 100665. https://doi.org/10.1016/j.trip.2022.100665
Pawar DS, Yadav AK, Akolekar N and Velaga NR. 2020. Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown. Transportation Research Interdisciplinary Perspectives, 7: 100203. https://doi.org/10.1016/j.trip.2020.100203
Philippine News Agency. 2022. DOTr to keep 70% capacity in all rail lines under Alert Level 3. https://www.pna.gov.ph/articles/1164313. Accessed on 09 November 2022.
Shakibaei S, de Jong GC, Alpkökin P and Rashidi TH. 2021. Impact of the COVID-19 pandemic on travel behavior in Istanbul: A panel data analysis. Sustainable Cities and Society, 65: 102619. https://doi.org/10.1016/j.scs.2020.102619
Stam B, van Oort N and Hoogendoorn SP. 2021. Travellers’ preferences towards existing and emerging means of first/last mile transport: a case study for the Almere centrum railway station in the Netherlands. European Transport Research Review, 13(1): 1-14. https://doi.org/10.1186/s12544-021-00514-1
Tantuco V. 2021. IATF completes nationwide implementation of COVID-19 alert level system. Rappler. https://www.rappler.com/nation/iatf-completes-nationwide-implementation-covid19-alert-level-system/. Accessed on 09 November 2022.
Tight M, Rajé F and Timms P. 2016. Car-Free Urban Areas: A Radical Solution to the Last Mile Problem or a Step Too Far? Built Environment 42(4), 603–616. http://dx.doi.org/10.2148/benv.42.4.603
Wambua J, Loedy N and Jarvis CI. 2023. The influence of COVID-19 risk perception and vaccination status on the number of social contacts across Europe: insights from the CoMix study. BMC Public Health 23, 1350. https://doi.org/10.1186/s12889-023-16252-z
Washington SP, Karlaftis MG and Mannering FL. 2011. Statistical and econometric methods for transportation data analysis (2nd ed.) [Electronic version]. New York: Chapman and Hall/CRC.
Yang Y, Wang C, Liu W and Zhou P. 2018. Understanding the determinants of travel mode choice of residents and its carbon mitigation potential. Energy Policy, 115: 486-493. https://doi.org/10.1016/j.enpol.2018.01.033
Zhang J, Hayashi Y and Frank LD. 2021. COVID-19 and transport: Findings from a world-wide expert survey. Transport Policy, 103: 68-85. https://doi.org/10.1016/j.tranpol.2021.01.011
Zubair H, Karoonsoontawong A and Kanitpong K. 2022. Effects of COVID-19 on Travel Behavior and Mode Choice: A Case Study for the Bangkok Metropolitan Area. Sustainability, 14(15): 9326. https://doi.org/10.3390/su14159326
Section
Original Article
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.