Effective Topology

The efficiency and operation of on-demand ride-sharing depends on many factors including the layout of the street network and the distribution of requested rides on it. Results for binary request distributions suggest that these two factors could be combined into an effective topology, by weighing edges and/or nodes based on their request frequency. The task at hand is to analytically and in simulations derive effective topologies for general request distributions.

Pricing and Efficiency

The convenience and efficiency, as well as potentially the price of ride-pooling for the individual user changes with the request rate. We want to develop an agent-based model for individual mode choice decisions and analyze its behaviour with different parameters.

From taxi to bus - ride pooling as general transport paradigm

Ride-pooling bundles trips together, reducing the number of vehicles needed and thus the total distance driven. The exact amount of pooling happening, as well as how much of a delay the customers have to tolerate, however, depends on the fleet size, network topology and request density/distribution. It is clear that in the limit of few requests, the system increasingly operates like a taxi service. Simulation results suggest, that in the limit of high request densities, cyclic routes emerge, that can be interpreted as emergent line services. We want to study the nature of this transition and the properties of the emergent routes.