On the inefficiency of ride-sourcing services towards urban congestion
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Abstract (arXiv)
The advent of shared-economy and smartphones made on-demand transportation services possible, which created additional opportunities, but also more complexity to urban mobility. Companies that offer these services are called Transportation Network Companies (TNCs) due to their internet-based nature. Although ride-sourcing is the most notorious service TNCs provide, little is known about to what degree its operations can interfere in traffic conditions, while replacing other transportation modes, or when a large number of idle vehicles is cruising for passengers. We experimentally analyze the efficiency of TNCs using taxi trip data from a Chinese megacity and a agent-based simulation with a trip-based MFD model for determining the speed. We investigate the effect of expanding fleet sizes for TNCs, passengers' inclination towards sharing rides, and strategies to alleviate urban congestion. We show that the lack of coordination of objectives between TNCs and society can create 37% longer travel times and significant congestion. Moreover, allowing shared rides is not capable of decreasing total distance traveled due to higher empty kilometers traveled. Elegant parking management strategies can prevent idle vehicles from cruising without assigned passengers and lower to 7% the impacts of the absence of coordination.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2022Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approachcited by 4
- 2024Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurationscited by 1
- 2026A Grid-Based Framework for E-Scooter Demand Representation and Temporal Input Design for Deep Learning: Evidence from Austin, Texascited by 0
- 2026A Grid-Based Framework for E-Scooter Demand Representation and Temporal Input Design for Deep Learning: Evidence from Austin, Texas13 Mar 2026
- 2024Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations23 May 2024
- 2022Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach5 Jun 2022