Journal of Transport and Land Use

Spatiotemporal patterns of online food delivery services before the Covid-19 pandemic

Steven R. Gehrke

Northern Arizona University

https://orcid.org/0000-0001-9355-5571

Michael P. Huff

Northern Arizona University

Brendan J. Russo

Northern Arizona University

https://orcid.org/0000-0002-0606-7973

Edward J. Smaglik

Northern Arizona University

https://orcid.org/0000-0002-7034-6619

DOI: https://doi.org/10.5198/jtlu.2024.2310

Keywords: Food delivery services, Online shopping, Spatial Durbin Model, Built environment

---

Abstract

Public health concerns of the Covid-19 pandemic and the popularity of on-demand mobility services have led to a recent and prominent increase in online food delivery (OFD) service adoption. While app-based services that offer restaurant customers the convenience of a freshly prepared meal delivered to any location have existed, their present and future impacts to urban transportation networks and landscapes have become ever more apparent since the pandemic’s onset and subsequent restrictions on restaurant dining. By analyzing route-level data collected by a ridehailing driver assistant app between October 2015 and October 2019, this study informs a baseline understanding of where and when these on-demand food delivery services were used within the Phoenix metro area prior to the pandemic. This identification of the spatiotemporal patterns of OFD service is accompanied by the estimation of traditional and spatially lagged negative binomial models of delivery counts using a robust set of predictors of the built environment and socioeconomic context found at the trip destination. Study results indicate that on-demand food delivery services were popular during dinner hours corresponding with evening peak travel and in neighborhoods characterized by higher activity density, greater drinking establishment access, and increased shares of residents under 45 years old.

---

References

Ai, N., Zheng, J., Chen, X., & Kawamura, K. (2021). Neighborhood-specific traffic impact analysis of restaurant meal delivery trips: Planning implications and case studies in Chicago. Journal of Urban Planning and Development, 147(2), 05021013.

Allen, J., Piecyk, M., Cherrett, T., Juhari, M. N., McLeod, F., Piotrowska, M., ... & Wise, S. (2021). Understanding the transport and CO2 impacts of on-demand meal deliveries: A London case study. Cities, 108, 102973.

Anselin, L. (1988). Spatial econometrics: Methods and models. Berlin: Springer Science & Business Media.

Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.

Beck, M. J., & Hensher, D. A. (2020). Insights into the impact of COVID-19 on household travel and activities in Australia–The early days under restrictions. Transport Policy, 96, 76–93.

De Vos, J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior. Transportation Research Interdisciplinary Perspectives, 5, 100121.

Dias, F. F., Lavieri, P. S., Sharda, S., Khoeini, S., Bhat, C. R., Pendyala, R. M., ... & Srinivasan, K. K. (2020). A comparison of online and in-person activity engagement: The case of shopping and eating meals. Transportation Research Part C: Emerging Technologies, 114, 643–656.

Fowler, C. S., Frey, N., Folch, D. C., Nagle, N., & Spielman, S. (2020). Who are the people in my neighborhood? The “contextual fallacy” of measuring individual context with census geographies. Geographical Analysis, 52(2), 155–168.

Gehrke, S. R., & Welch, T. F. (2017). The built environment determinants of activity participation and walking near the workplace. Transportation, 44, 941–956.

Gehrke, S. R., & Huff, M. P. (2022). Spatial equity implications and neighborhood indicators of ridehailing trip frequency and vehicle miles traveled in the Phoenix metro region. Transportation, 51, 271–295.

Jiao, Y., Tang, X., Qin, Z., Li, S., Zhang, F., Zhu, H., & Ye, J. (2021). Real-world ride-hailing vehicle repositioning using deep reinforcement learning. Transportation Research Part C: Emerging Technologies, 130, 1–25.

Kaczmarski, M. (2023, October 9). Which company is winning the restaurant food delivery war? Bloomberg Second Measure. Retrieved from https://secondmeasure.com/datapoints/food-delivery-services-grubhub-uber-eats-doordash-postmates

Kim, W., & Wang, X. C. (2021). To be online or in-store: Analysis of retail, grocery, and food shopping in New York city. Transportation Research Part C: Emerging Technologies, 126, 103052.

LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Boca Raton, FL: Chapman and Hall/CRC.

Lezcano, M. S., Harper, C. D., Nock, D., Lowry, G. V., & Michalek, J. J. (2023). Online grocery delivery: Sustainable practice, or congestion generator and environmental burden? Transportation Research Part D: Transport and Environment, 119, 103722.

Liu, Y., & Li, S. (2023). An economic analysis of on-demand food delivery platforms: Impacts of regulations and integration with ride-sourcing platforms. Transportation Research Part E: Logistics and Transportation Review, 171, 103019.

Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.

Meena, P., & Kumar, G. (2022). Online food delivery companies’ performance and consumers expectations during Covid-19: An investigation using machine learning approach. Journal of Retailing and Consumer Services, 68, 103052.

Shamshiripour, A., Rahimi, E., Shabanpour, R., & Mohammadian, A. K. (2020). How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Research Interdisciplinary Perspectives, 7, 100216.

Spurlock, C. A., Todd-Blick, A., Wong-Parodi, G., & Walker, V. (2020). Children, income, and the impacts of home delivery on household shopping trips. Transportation Research Record, 2674(10), 335–350.

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.

Wang, Z., He, S. Y. (2021). Impacts of food accessibility and built environment on on-demand food delivery usage. Transportation Research Part D: Transport and Environment, 100, 103017.

Zhao, P., & Yu, B. (2006). On model selection consistency of Lasso. The Journal of Machine Learning Research, 7, 2541–2563.