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Ride-sourcing services play an increasingly important role in meeting mobility needs in many metropolitan areas. Yet, aside from delivering passengers from their origins to destinations, ride-sourcing vehicles generate a significant number of vacant trips from the end of one customer delivery trip to the start of the next. These vacant trips create additional traffic demand and may worsen traffic conditions in urban networks. Capturing the congestion effect of these vacant trips poses a great challenge to the modeling practice of transportation planning agencies. With ride-sourcing services, vehicular trips are the outcome of the interactions between service providers and passengers, a missing ingredient in the current traffic assignment methodology. In this paper, we enhance the methodology by explicitly modeling those vacant trips, which include cruising for customers and deadheading for picking up them. Because of the similarity between taxi and ride-sourcing services, we first extend previous taxi network models to construct a base model, which assumes intranode matching between customers and idle ride-sourcing vehicles and thus, only considers cruising vacant trips. Considering spatial matching among multiple zones commonly practiced by ride-sourcing platforms, we further enhance the base model by encapsulating internode matching and considering both the cruising and deadheading vacant trips. A large set of empirical data from Didi Chuxing is applied to validate the proposed enhancement for internode matching. The extended model describes the equilibrium state that results from the interactions between background regular traffic and occupied, idle, and deadheading ride-sourcing vehicles. A solution algorithm is further proposed to solve the enhanced model effectively. Numerical examples are presented to demonstrate the model and solution algorithm. Although this study focuses on ride-sourcing services, the proposed modeling framework can be adapted to model other types of shared use mobility services.
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Off-Street Parking for TNC Vehicles to Reduce Cruising Traffic
This paper considers off-street parking for the cruising vehicles of transportation network companies (TNCs) to reduce the traffic congestion. We propose a novel business that integrates the shared parking service into the TNC platform. In the proposed model, the platform (a) provides interfaces that connect passengers, drivers and garage operators (commercial or private garages); (b) determines the ride fare, driver payment, and parking rates; (c) matches passengers to TNC vehicles for ride-hailing services; and (d) matches vacant TNC vehicles to unoccupied parking garages to reduce the cruising cost. A queuing-theoretic model is proposed to capture the matching process of passengers, drivers, and parking garages. A market-equilibrium model is developed to capture the incentives of the passengers, drivers, and garage operators. An optimization-based model is formulated to capture the optimal pricing of the TNC platform. Through a realistic case study, we show that the proposed business model will offer a Pareto improvement that benefits all stakeholders, which leads to higher passenger surplus, higher drivers surplus, higher garage operator surplus, higher platform profit, and reduced traffic congestion.
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- Award ID(s):
- 1839843
- PAR ID:
- 10220303
- Date Published:
- Journal Name:
- 2020 59th IEEE Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 2585 to 2590
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC42340.2020.9304277
