Technical Papers
Sep 12, 2022

Investigating COVID-19 Induced Taxi and For-Hire Vehicle Ridership Disparities

Publication: Journal of Urban Planning and Development
Volume 148, Issue 4

Abstract

The impacts of COVID-19 on for-hire vehicle (FHV) (e.g., Uber/Lyft, often referred to as transportation network companies in other locations) and taxi use have been relatively understudied compared with transit and personal vehicles. This study analyzed and estimated the changes in ridership for taxis and FHVs in New York City during the COVID-19 pandemic to determine whether it had disproportional impacts on these competing modes, how these impacts varied over time and space, and the associated factors. Data supporting the analyses came from the Taxi and Limousine Commission, the COVID-19 Data Repository, Google's Community Mobility Reports, the American Community Survey, and the Primary Land Use Tax Lot Output. Temporal change was measured by the daily taxi/FHV ridership deviation from a defined baseline, which showed that COVID-19 more negatively impacted taxis than FHVs. Temporal moving average models were then employed, which showed that COVID-19 had different temporal impacts on taxis and FHVs in relation to the parameters’ significance, magnitude, and temporal correlation patterns. In general, taxi/FHV ridership dropped when people spent more time at home and the number of confirmed COVID-19 cases was greater. The spatial variation in taxi/FHV ridership was measured by the coefficient of variation. Spatial regression models indicated that the land use of a zone affected taxi/FHV ridership during the pandemic. In addition, a zone with more carless/car-free households, older persons, or more children enrolled in school was more likely to experience a decrease in taxi/FHV ridership. A zone with more workers who commuted by walking or taking transit (excluding taxis) in pre-COVID times was more likely to see a decrease in taxi/FHV ridership. A zone with more people working from home pre-COVID, was more likely to see an increase in FHV ridership. The models showed that COVID-19 had greater spatial impacts on taxis than FHVs. Based on these results, this study provides insights as to what factors affected ridership of the two competing travel modes and suggests actions that transportation authorities could take to reduce temporal and spatial impact disparities.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
The first author (Ruijie Bian) would like to thank the Louisiana Transportation Research Center for the financial support they provided. The second author (Pamela Murray-Tuite) would like to thank the National Science Foundation (Award CMMI-1822436). The third author (Jian Li) would like to thank the National Key R&D Programs of China (2018YFB1601100). However, any opinions, findings, and conclusions expressed are those of the authors and do not necessarily reflect the views of the sponsors.

References

Abdullah, M., C. Dias, D. Muley, and M. Shahin. 2020. “Exploring the impacts of COVID-19 on travel behavior and mode preferences.” Transp. Res. Interdiscip. Perspect. 8: 100255. https://doi.org/10.1016/j.trip.2020.100255.
Abreu, L., and A. Conway. 2021. “A qualitative assessment of the multimodal passenger transportation system response to COVID-19 in New York City.” Transp. Res. Rec. 1–13. https://doi.org/10.1177/03611981211027149.
Apple. 2020. “COVID-19 - Mobility trends reports - Apple.” Accessed August 14, 2020. https://covid19.apple.com/mobility.
Beck, M. J., and D. A. Hensher. 2020. “Insights into the impact of COVID-19 on household travel and activities in Australia – The early days of easing restrictions.” Transp. Policy 99: 95–119. https://doi.org/10.1016/j.tranpol.2020.08.004.
Beck, M. J., D. A. Hensher, and E. Wei. 2020. “Slowly coming out of COVID-19 restrictions in Australia: Implications for working from home and commuting trips by car and public transport.” J. Transp. Geogr. 88: 102846. https://doi.org/10.1016/j.jtrangeo.2020.102846.
Bhaduri, E., B. S. Manoj, Z. Wadud, A. K. Goswami, and C. F. Choudhury. 2020. “Modelling the effects of COVID-19 on travel mode choice behaviour in India.” Transp. Res. Interdiscip. Perspect. 8: 100273. https://doi.org/10.1016/j.trip.2020.100273.
Bian, R., C. G. Wilmot, and L. Wang. 2019. “Estimating spatio-temporal variations of taxi ridership caused by Hurricanes Irene and Sandy: A case study of New York City.” Transp. Res. Part D: Transp. Environ. 77: 627–638. https://doi.org/10.1016/j.trd.2019.10.009.
Bian, Z., F. Zuo, J. Gao, Y. Chen, S. S. C. Pavuluri Venkata, S. Duran Bernardes, K. Ozbay, X. Ban, and J. Wang. 2021. “Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle.” Transp. Res. Part A: Policy Pract. 145: 269–283. https://doi.org/10.1016/j.tra.2021.01.019.
Brownlee, J. 2020. “What is time series forecasting?” machinelearningmastery.com. Accessed February 4, 2022. https://machinelearningmastery.com/time-series-forecasting/.
Bucsky, P. 2020. “Modal share changes due to COVID-19: The case of Budapest.” Transp. Res. Interdiscip. Perspect. 8: 100141. https://doi.org/10.1016/j.trip.2020.100141.
Chen, C., T. Feng, C. Ding, B. Yu, and B. Yao. 2021. “Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model.” J. Transp. Geogr. 96: 103172. https://doi.org/10.1016/j.jtrangeo.2021.103172.
Chen, C., T. Feng, X. Gu, and B. Yao. 2022. “Investigating the effectiveness of COVID-19 pandemic countermeasures on the use of public transport: A case study of The Netherlands.” Transp. Policy 117: 98–107. https://doi.org/10.1016/j.tranpol.2022.01.005.
Contreras, S. D., and A. Paz. 2018. “The effects of ride-hailing companies on the taxicab industry in Las Vegas, Nevada.” Transp. Res. Part A: Policy Pract. 115: 63–70. https://doi.org/10.1016/j.tra.2017.11.008.
Conway, M. W., D. Id, D. A. Salon, and I. D. King. 2018. “Trends in taxi use and the advent of ridehailing, 1995–2017: Evidence from the US National Household Travel Survey.” Urban Sci. 2 (3): 79.
de Haas, M., R. Faber, and M. Hamersma. 2020. “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. 6: 100150. https://doi.org/10.1016/j.trip.2020.100150.
De Vos, J. 2020. “The effect of COVID-19 and subsequent social distancing on travel behavior.” Transp. Res. Interdiscip. Perspect. 5: 100121. https://doi.org/10.1016/j.trip.2020.100121.
Diao, M., H. Kong, and J. Zhao. 2021. “Impacts of transportation network companies on urban mobility.” Nat. Sustainability 4 (6): 494–500. https://doi.org/10.1038/s41893-020-00678-z.
Dong, E., H. Du, and L. Gardner. 2020. “An interactive web-based dashboard to track COVID-19 in real time.” Lancet Infect. Dis. 20 (5): 533–534.
Drummond, J., and M. S. Hasnine. 2022. “Short-term and long-term impact of COVID-19 on E-commerce, and transport modes: A synthesis based on recent literature.” In Transp. Res. Board 101st Annual Meeting. Washington, DC: National Academy of Sciences.
Faghih, S. S. 2019. “Understanding and modeling taxi demand using time series models.” Ph.D. thesis, Dept. of Civil Engineering, City College of New York.
FHWA (Federal Highway Administration). 2022. Moving to a complete streets design model: A report to congress on opportunities and challenges. Washington, DC: US Dept. of Transportation, FHWA.
Gao, J., et al. 2020. “Toward the ‘new normal’: A surge in speeding, new volume patterns, and recent trends in taxis/for-hire vehicles.” Preprint, submitted September 23, 2020, https://doi.org/10.48550/arXiv.2009.14018.
Gerte, R., K. C. Konduri, and N. Eluru. 2018. “Is there a limit to adoption of dynamic ridesharing systems? Evidence from analysis of uber demand data from New York City.” Transp. Res. Rec. 2672 (42): 127–136. https://doi.org/10.1177/0361198118788462.
Gonzales, E., C. Yang, E. F. Morgul, and K. Ozbay. 2014. Modeling taxi demand with GPS data from taxis and transit. San Jose, CA: Mineta National Transit Research Consortium.
Gonzalez, J. N., A. Camarero-Orive, N. González-Cancelas, and A. F. Guzman. 2022. “Impact of the COVID-19 pandemic on road freight transportation – A Colombian case study.” Res. Transp. Bus. Manage. 43: 100802. https://doi.org/10.1016/j.rtbm.2022.100802.
Google LLC. 2020. “Google COVID-19 community mobility reports.” Google LLC. Accessed May 10, 2020. https://www.google.com/covid19/mobility/data_documentation.html?hl=en#about-this-data.
Greene, W. H. 2002. Econometric analysis. Upper Saddle River, NJ: Prentice Hall.
Guse, C. 2020. “How Uber and Lyft doomed NYC’s yellow cab business.” New York Daily News. Accessed December 16, 2020. https://www.nydailynews.com/new-york/ny-medallion-foreclosures-taxi-bailout-plan-uber-lyft-20200130-s2mjkhjubzgptdxasoxddwdote-story.html.
Hawkins, A. J. 2020. “Uber’s response to COVID-19: face masks, selfies, and fewer people in the car.” The Verge. Accessed October 27, 2020. https://www.theverge.com/2020/5/13/21257432/uber-face-mask-driver-rider-require-selfies-maximum-passengers.
Hochmair, H. H. 2016. “Spatiotemporal pattern analysis of taxi trips in New York City.” Transp. Res. Rec. 2542: 45–56. https://doi.org/10.3141/2542-06.
Hyndman, R. J., and G. Athanasopoulos. 2018a. Forecasting: Principles and practice. Melbourne, Australia: OTexts.
Hyndman, R. J., and G. Athanasopoulos. 2018b. “12.7 Very long and very short time series.” In Forecasting: principles and practice. Melbourne, Australia: OTexts.
Hyndman, R. J., and Y. Khandakar. 2008. “Automatic time series forecasting: The forecast package for R.” J. Stat. Software 27 (3): 1–22. https://doi.org/10.18637/jss.v027.i03.
Jenelius, E., and M. Cebecauer. 2020. “Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts.” Transp. Res. Interdiscip. Perspect. 8: 100242. https://doi.org/10.1016/j.trip.2020.100242.
Jensen, K. L., J. Yenerall, X. Chen, and T. E. Yu. 2021. “US consumers’ online shopping behaviors and intentions during and after the COVID-19 pandemic.” J. Agric. Appl. Econ. 53 (3): 416–434. https://doi.org/10.1017/aae.2021.15.
JHU CSSE. 2020. “GitHub - CSSEGISandData/COVID-19: Novel coronavirus (COVID-19) cases.” Accessed July 17, 2020. https://github.com/CSSEGISandData/COVID-19.
Kamga, C., M. A. Yazici, and A. Singhal. 2015. “Analysis of taxi demand and supply in New York City: Implications of recent taxi regulations.” Transp. Plann. Technol. 38 (6): 601–625. https://doi.org/10.1080/03081060.2015.1048944.
Kim, T., S. Sharda, X. Zhou, and R. M. Pendyala. 2020. “A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service.” Transp. Res. Part C: Emerging Technol. 120: 102786. https://doi.org/10.1016/j.trc.2020.102786.
Lei, Y., and K. Ozbay. 2021. “A robust analysis of the impacts of the stay-at-home policy on taxi and Citi Bike usage: A case study of Manhattan.” Transp. Policy 110: 487–498. https://doi.org/10.1016/j.tranpol.2021.07.003.
Lepage, S., and C. Morency. 2021. “Impact of weather, activities, and service disruptions on transportation demand.” Transp. Res. Rec. 2675 (1): 294–304. https://doi.org/10.1177/0361198120966326.
LeSage, J. P. 1999. The theory and practice of spatial econometrics. Toledo, OH: Dept. of Economics. Univ. of Toledo.
Litman, T. 2021. Introduction to multi-modal transportation planning: Principles and practices. Victoria, BC, Canada: Victoria Transport Policy Institute.
Liu, Q., C. Ding, and P. Chen. 2020. “A panel analysis of the effect of the urban environment on the spatiotemporal pattern of taxi demand.” Travel Behav. Soc. 18: 29–36. https://doi.org/10.1016/j.tbs.2019.09.003.
Loa, P., S. Hossain, Y. Liu, and K. Nurul Habib. 2022. “How has the COVID-19 pandemic affected the use of ride-sourcing services? An empirical evidence-based investigation for the Greater Toronto Area.” Transp. Res. Part A: Policy Pract. 155: 46–62. https://doi.org/10.1016/j.tra.2021.11.013.
Lyu, T., P. Wang, Y. Gao, and Y. Wang. 2021. “Research on the big data of traditional taxi and online car-hailing: A systematic review.” J. Traffic Transp. Eng. 8 (1): 1–34. https://doi.org/10.1016/j.jtte.2021.01.001.
Mader, S. 2021. “Public transportation’s response to the covid-19 pandemic and how it shapes transit’s future.” Community Transportation Association of America. Accessed August 1, 2021. https://ctaa.org/wp-content/uploads/2021/07/CTAA_Vaccine_Transit.pdf.
Manley, E., S. Ross, and M. Zhuang. 2021. “Changing demand for New York yellow cabs during the COVID-19 pandemic.” Findings 5: 22158.
Medina, J., and R. Solymosi. 2019. “Chapter 9 Spatial regression models.” Crime mapping in R. .Accessed January 12, 2020. https://maczokni.github.io/crime_mapping_textbook/.
Mogaji, E., I. Adekunle, S. Aririguzoh, and A. Oginni. 2022. “Dealing with impact of COVID-19 on transportation in a developing country: Insights and policy recommendations.” Transp. Policy 116: 304–314. https://doi.org/10.1016/j.tranpol.2021.12.002.
NGA (National Governors Association). 2020. “Coronavirus state actions.” NGA. Accessed July 30, 2020. https://www.nga.org/coronavirus-state-actions-all/.
Nian, G., B. Peng, D. J. Sun, W. Ma, B. Peng, and T. Huang. 2020. “Impact of COVID-19 on urban mobility during post-epidemic period in megacities: From the perspectives of taxi travel and social vitality.” Sustainability (Switzerland) 12 (19): 7954.
Nie, Y. 2017. “How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China.” Transp. Res. Part C: Emerging Technol. 79: 242–256. https://doi.org/10.1016/j.trc.2017.03.017.
NYC Department of City Planning. 2020. “PLUTO and MapPLUTO.” Accessed September 15, 2020. https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page.
NYC.GOV. 2020. “NYC business reopening guide - NYC business.” nyc.gov. Accessed November 25, 2020. https://www1.nyc.gov/nycbusiness/article/reopening-guide#phasetwo.
NYCTLC (New York City Taxi and Limousine Commission). 2017. “Chapter 58 Medallion Taxicab Service.” http://www.nyc.gov/html/tlc/downloads/pdf/rule_book_current_chapter_58.pdf.
NYCTLC (New York City Taxi and Limousine Commission). 2018. 2018 TLC factbook. New York City: NYCTLC.
NYCTLC (New York City Taxi and Limousine Commission). 2020a. “–Trip record data.” Accessed August 15, 2020. https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page.
NYCTLC (New York City Taxi and Limousine Commission). 2020b. “E-Hail Providers - TLC.” Accessed December 14, 2020. https://www1.nyc.gov/site/tlc/businesses/e-hail-providers.page.
NY.GO. 2020. “Amid ongoing COVID-19 pandemic, governor cuomo issues executive order requiring all people in New York to wear masks or face coverings in public.” Governor’s Press Office. Accessed May 29, 2021. https://www.governor.ny.gov/news/amid-ongoing-covid-19-pandemic-governor-cuomo-issues-executive-order-requiring-all-people-new.
New York Times. 2022. “Covid in the U.S.: Latest map and case count.” nytimes.com. Accessed January 24, 2022. https://www.nytimes.com/interactive/2021/us/covid-cases.html.
Pan, R., H. Yang, K. Xie, and Y. Wen. 2020. “Exploring the equity of traditional and ride-hailing taxi services during peak hours.” Transp. Res. Rec. 2674 (9): 266–278. https://doi.org/10.1177/0361198120928338.
Parady, G., A. Taniguchi, and K. Takami. 2020. “Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing the effects of risk perception and social influence on going-out self-restriction.” Transp. Res. Interdiscip. Perspect. 7: 100181. https://doi.org/10.1016/j.trip.2020.100181.
Parker, M. E. G., M. Li, M. A. Bouzaghrane, H. Obeid, D. Hayes, K. T. Frick, D. A. Rodríguez, R. Sengupta, J. Walker, and D. G. Chatman. 2021. “Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period.” Transp. Policy 111: 53–62.
Parr, S. A., B. Wolshon, J. L. Renne, P. Murray-Tuite, and K. Kim. 2020. “Traffic impacts of the COVID-19 pandemic: Statewide analysis of social separation and activity restriction.” Nat. Hazard. Rev. 21 (3): 04020025.
Qian, X., and S. V. Ukkusuri. 2015. “Spatial variation of the urban taxi ridership using GPS data.” Appl. Geogr. 59: 31–42. https://doi.org/10.1016/j.apgeog.2015.02.011.
Qian, X., S. V. Ukkusuri, C. Yang, and F. Yan. 2017. “Forecasting short-term taxi demand using boosting-GCRF.” In Proc., 6th Int. Workshop on Urban Computing. New York: Association for Computing Machinery.
Qian, X., S. V. Ukkusuri, C. Yang, and F. Yan. 2022. “Short-term demand forecasting for on-demand mobility service.” IEEE Trans. Intell. Transp. Syst. 23 (2): 1019–1029.
Rayle, L., D. Dai, N. Chan, R. Cervero, and S. Shaheen. 2016. “Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco.” Transp. Policy 45: 168–178. https://doi.org/10.1016/j.tranpol.2015.10.004.
Safikhani, A., C. Kamga, S. Mudigonda, S. S. Faghih, and B. Moghimi. 2020. “Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models.” Int. J. Forecasting 36 (3): 1138–1148. https://doi.org/10.1016/j.ijforecast.2018.10.001.
Schaller Consulting. 2018. The new automobility: Lyft, Uber and the future of American cities. Brooklyn, NY: Schaller Consulting.
Shakibaei, S., G. C. de Jong, P. Alpkökin, and T. H. Rashidi. 2020. “Impact of the COVID-19 pandemic on travel behavior in Istanbul: A panel data analysis.” Sustainable Cities Soc. 65: 102619.
Shamshiripour, A., E. Rahimi, R. Shabanpour, and A. Mohammadian. 2020. “How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago.” Transp. Res. Interdiscip. Perspect. 7: 100216. https://doi.org/10.1016/j.trip.2020.100216.
Shen, H., F. Namdarpour, and J. Lin. 2022. “Investigation of online grocery shopping and delivery preference before, during, and after COVID-19.” Transp. Res. Interdiscip. Perspect. 14: 100580. https://doi.org/10.1016/j.trip.2022.100580.
Tang, J., F. Gao, C. Han, X. Cen, and Z. Li. 2021. “Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi.” J. Transp. Geogr. 95: 103134. https://doi.org/10.1016/j.jtrangeo.2021.103134.
Tang, J., F. Gao, F. Liu, W. Zhang, and Y. Qi. 2019. “Understanding spatio-temporal characteristics of urban travel demand based on the combination of GWR and GLM.” Sustainability 11 (19): 5525.
TLC. 2020. “NYC Taxi Zones.” NYC Open Data. Accessed November 25, 2020. https://data.cityofnewyork.us/Transportation/NYC-Taxi-Zones/d3c5-ddgc.
Toman, P., J. Zhang, N. Ravishanker, and K. C. Konduri. 2021. “Spatiotemporal analysis of ridesourcing and taxi usage by zones.” J. Indian Soc. Probab. Stat. 22 (1): 231–249. https://doi.org/10.1007/s41096-021-00102-5.
U.S. Census Bureau. 2019. “American FactFinder.” Accessed July 16, 2020. http://factfinder.census.gov.
U.S. Census Bureau. 2020. “Census Data.” Accessed November 24, 2020. https://data.census.gov/cedsci/.
Waheed, S., L. Herrera, and S. Ritoper. 2015. Ridesharing or ridestealing? Changes in taxi ridership and revenue in Los Angeles 2009–2014. Los Angeles: UCLA Labor Centre.
Walker, A. 2018. “In NYC, 139 prized yellow taxi medallions will hit the auction block.” Curbed NY. Accessed January 28, 2022. https://ny.curbed.com/2018/6/11/17450366/nyc-taxi-medallions-bankruptcy-auction.
Wallsten, S. 2015. The competitive effects of the sharing economy: How is Uber changing taxis?, 1–22. New York: Technological Policy Institute.
Wang, W., W. Miao, Y. Liu, Y. Deng, and Y. Cao. 2022. “The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China.” Transp. Res. Part A: Policy Pract. 155: 128–141. https://doi.org/10.1016/j.tra.2021.10.005.
Warren, M. S., and S. W. Skillman. 2020. “Mobility changes in response to COVID-19.” Preprint,submitted March 31, 2020. http://arxiv.org/abs/200314228.
Welch, T. F., S. R. Gehrke, and A. Widita. 2020. “Shared-use mobility competition: A trip-level analysis of taxi, bikeshare, and transit mode choice in Washington, DC.” Transportmetrica 16 (1): 43–55. https://doi.org/10.1080/23249935.2018.1523250.
Yang, C., and E. Gonzales. 2014. “Modeling taxi trip demand by time of day in New York City.” Transp. Res. Rec. 2429: 110–120. https://doi.org/10.3141/2429-12.
Yang, Z., M. L. Franz, S. Zhu, J. Mahmoudi, A. Nasri, and L. Zhang. 2018. “Analysis of Washington, DC taxi demand using GPS and land-use data.” J. Transp. Geogr. 66: 35–44. https://doi.org/10.1016/j.jtrangeo.2017.10.021.
Yu, J., N. Xie, J. Zhu, Y. Qian, S. Zheng, and X. (. Chen. 2022. “Exploring impacts of COVID-19 on city-wide taxi and ride-sourcing markets: Evidence from Ningbo, China.” Transp. Policy 115: 220–238. https://doi.org/10.1016/j.tranpol.2021.11.017.
Zander, G. 2017. “Predicting taxi passenger demand using artificial neural networks.” Master’s thesis, School of Computer Science and Communication, KTH Royal Institute of Technology.
Zhang, W., T. V. Le, S. V. Ukkusuri, and R. Li. 2020a. “Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems.” Transportation 47 (2): 971–996. https://doi.org/10.1007/s11116-018-9931-2.
Zhang, X., B. Huang, and S. Zhu. 2019. “Spatiotemporal influence of urban environment on taxi ridership using geographically and temporally weighted regression.” ISPRS Int. J. Geo-Inf. 8 (1): 23. https://doi.org/10.3390/ijgi8010023.
Zhang, X., B. Huang, and S. Zhu. 2020b. “Spatiotemporal varying effects of built environment on taxi and ride-hailing ridership in New York City.” ISPRS Int. J. Geo-Inf. 9 (8): 475. https://doi.org/10.3390/ijgi9080475.
Zheng, H., K. Zhang, and Y. Nie. 2021. “Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China.” Transp. Res. Part A: Policy Pract. 150: 349–366. https://doi.org/10.1016/j.tra.2021.06.012.

Information & Authors

Information

Published In

Go to Journal of Urban Planning and Development
Journal of Urban Planning and Development
Volume 148Issue 4December 2022

History

Received: Feb 4, 2022
Accepted: Jun 30, 2022
Published online: Sep 12, 2022
Published in print: Dec 1, 2022
Discussion open until: Feb 12, 2023

Permissions

Request permissions for this article.

Authors

Affiliations

Assistant Professor – Research, Louisiana Transportation Research Center, Louisiana State Univ., Baton Rouge, LA 70808. (corresponding author). ORCID: https://orcid.org/0000-0002-1781-1374. Email: [email protected]
Pamela Murray-Tuite, Ph.D., A.M.ASCE https://orcid.org/0000-0003-3079-289X [email protected]
Professor, Glenn Dept. of Civil Engineering, Clemson Univ., Clemson, SC 29634. ORCID: https://orcid.org/0000-0003-3079-289X. Email: [email protected]
Jian Li, Ph.D. [email protected]
Associate Professor, School of Transportation Engineering, Tongji Univ., Shanghai 201804, China. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share