Highlights Modeling dynamic congestion in ride-sharing systems with a trip-based MFD model. Investigating how willingness to share influences shared rides and dynamic congestion. Understanding quantitatively the effect of fleet size in congestion. Developing elegant parking management strategies to prevent idle vehicles from cruising. Analyzing day-to-day dynamics for drivers’ participation in the service.
Abstract 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 an 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 observe that, although a larger fleet size reduces waiting time, it also intensifies congestion, which, in turn, prolongs the total travel time. Such congestion effect is so significant that it is nearly insensitive to passengers’ willingness to share and flexible supply. Finally, parking management strategies can prevent idle vehicles from cruising without assigned passengers, mitigating the negative impacts of ride-sourcing over congestion, and improving the service quality.
On the inefficiency of ride-sourcing services towards urban congestion
2020-11-19
Article (Journal)
Electronic Resource
English
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