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Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM)

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The ridesharing economy is experiencing rapid growth and innovation. Companies such as Uber and Lyft are continuing to grow at a considerable pace while providing their platform as an organizing medium for ridesharing services, increasing consumer utility as well as employing thousands in part-time positions. However, many challenges remain in the modeling of ridesharing services, many of which are not currently under wide consideration. In this paper, an agent-based model is developed to simulate a ridesharing service in the Washington, D.C. metropolitan region. The model is used to examine levels of utility gained for both riders (customers) and drivers (service providers) of a generic ridesharing service. A description of the Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) is provided, as well as a description of a typical simulation run. We investigate the financial gains of drivers for a 24 hour period under two scenarios and two spatial movement behaviors. The two spatial behaviors were random movement and Voronoi movement, which we describe. Both movement behaviors were tested under a stationary run conditions scenario and a variable run conditions scenario. We find that Voronoi movement increased drivers’ utility gained but that emergence of this system property was only viable under variable scenario conditions. This result provides two important insights: The first is that driver movement decisions prior to passenger pickup can impact financial gain for the service and drivers, and consequently, rate of successful pickup for riders. The second is that this phenomenon is only evident under experimentation conditions where variability in passenger and driver arrival rates are administered.

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  • DOI: 10.1007/978-3-030-20309-2_7
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  1. 1.

    The author of this paper registered with one ridesharing service in order to gain insight into the natural behaviors of drivers and riders of the service. A total of 30 trips were carried out.

  2. 2.

    We will later explain our terminology in detail, but for now we define a driver as an agent who is picking up a rider from one location on our model’s spatial grid to another. Once a rider is “picked up” we will refer to him as a passenger. In our model a passenger is no longer an agent but is a data point in the driver agent’s attributes list.

  3. 3.

  4. 4.

    For a video of a typical model run, please visit

  5. 5.

    We assigned these cash variables based on a rough estimate of distance travelled versus fare/ride gained from observations and experiences with a ridesharing service.


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Correspondence to Joseph A. E. Shaheen .

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Shaheen, J.A.E. (2019). Simulating the Ridesharing Economy: The Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM). In: Carmichael, T., Collins, A., Hadžikadić, M. (eds) Complex Adaptive Systems. Understanding Complex Systems. Springer, Cham.

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