This is a ridesharing model (Uber/Lyft) of the larger Washington DC metro area. The model can be modified (Netlogo 6.x) relatively easily and be adapted to any metro area. Please cite generously (this was a lot of work) and please cite the paper, not the comses model.

Link to the paper published in “Complex Adaptive Systems” here: https://link.springer.com/chapter/10.1007/978-3-030-20309-2_7

Citation: 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. https://doi.org/10.1007/978-3-030-20309-2_7

The agent-based simulation is set to work on information that is either (a) functional, (b) pseudo-functional, (c) dysfunctional, or (d) irrelevant. The idea is that a judgment on whether information falls into one of the four categories is based on the agent and its network. In other words, it is the agents who interprets a particular information as being (a), (b), (c), or (d). It is a decision based on an exchange with co-workers. This makes the judgment a socially-grounded cognitive exercise. The uFUNK 1.0.2 Model is set on an organization where agent-employee work on agent-tasks.

The model generates disaggregated traffic flows of pedestrians, simulating their daily mobility behaviour represented as probabilistic rules. Various parameters of physical infrastructure and travel behaviour can be altered and tested. This allows predicting potential shifts in traffic dynamics in a simulated setting. Moreover, assumptions in decision-making processes are general for mid-sized cities and can be applied to similar areas.

Together with the model files, there is the ODD protocol with the detailed description of model’s structure. Check the associated publication for results and evaluation of the model.

Installation
Download GAMA-platform (GAMA1.8.2 with JDK version) from https://gama-platform.github.io/. The platform requires a minimum of 4 GB of RAM.
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