Rico Krueger, Michel Bierlaire, Prateek Bansal
arXiv 5 Dec 2022 · Econometrics · publishedTransportation Research Part C Emerging Technologies (2023) · 16 citations (OpenAlex)
arXiv:2212.02178 · PDF · DOI · OpenAlex · Extracted main text
Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability. These models lack foundations in microeconomic theory, ignore competition of ride-sourcing with other travel modes, and cannot be seamlessly integrated into existing individual-level (disaggregate) activity-based models to evaluate system-level impacts of ride-sourcing services. In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources. We first construct a sample of trip-based mode choices in Chicago, USA by enriching household travel survey with publicly available ride-sourcing and taxi trip records. We then formulate a multivariate extreme value-based discrete choice with sampling and endogeneity corrections to account for the construction of the estimation sample from multiple data sources and endogeneity biases arising from supply-side constraints and surge pricing mechanisms in ride-sourcing systems. Our analysis of the constructed dataset reveals insights into the influence of various socio-economic, land use and built environment features on ride-sourcing demand. We also derive elasticities of ride-sourcing demand relative to travel cost and time. Finally, we illustrate how the developed model can be employed to quantify the welfare implications of ride-sourcing policies and regulations such as terminating certain types of services and introducing ride-sourcing taxes.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ghaffar, A., Mitra, S., and Hyland, M (2020) Modeling determinants of ridesourcing usage: A census tract-level analysis of chicago | 0.843 | 4 | 3 | 75% |
| 2 | Petrin, A. and Train, K (2010) A control function approach to endogeneity in consumer choice models | 0.843 | 3 | 3 | 100% |
| 3 | Lurkin, V., Garrow, L. A., Higgins, M. J., Newman, J. P., and Schyns… (2017) Accounting for price endogeneity in airline itinerary choice models: An application to continental us markets | 0.737 | 3 | 2 | 100% |
| 4 | Tirachini, A (2020) Ride-hailing, travel behaviour and sustainable mobility: an international review | 0.737 | 3 | 2 | 100% |
| 5 | Bierlaire, M. and Krueger, R (2020) Sampling and discrete choice self | 0.644 | 2 | 2 | 100% |
| 6 | Marquet, O (2020) Spatial distribution of ride-hailing trip demand and its association with walkability and neighborhood characteristics | 0.511 | 2 | 2 | 50% |
| 7 | McFadden, D (2012) Computing willingness–to–pay in random utility models | 0.511 | 2 | 2 | 50% |
| 8 | von Behren, S., Chlond, B., and Vortisch, P (2021) Exploring the role of individuals’ attitudes in the use of on-demand mobility services for commuting–a case study in eight chine… | 0.511 | 2 | 2 | 50% |
| 9 | Goletz, M. and Bahamonde-Birke, F. J (2021) The ride-sourcing industry: status-quo and outlook | 0.511 | 2 | 1 | 100% |
| 10 | Koppelman, F. S., Coldren, G. M., and Parker, R. A (2008) Schedule delay impacts on air-travel itinerary demand | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 73 scored citations.