Considering the sprawl of cities, conventional public transport with fixed route and fixed schedule becomes less efficient and desirable every day. However, emerging technologies in computation and communication are facilitating more adaptive types of public transport systems, such as demand responsive transport that operates according to real-time demand. It is crucial to study the feasibility and advantages of these novel systems before implementation to prevent failure and financial loss. In this work, an extensive comparison of demand responsive transport and conventional public transport is provided by incorporating a dynamic routing algorithm into an agent-based traffic simulation. The results show that replacing conventional public transport with demand responsive transport will improve the mobility by decreasing the perceived travel time by passengers without any extra cost under certain circumstances. The simulation results are confirmed for different forms of networks, including a real-world network proving the potential of demand responsive transport to solve the challenge of underutilised conventional public transport in suburban areas with low transport demand.

Unlike their nonspatial counterparts, spatial multi-sided platforms are matchmaking platforms with an additional layer of complexity: their customers expect to meet in space, not only virtually. This additional challenge will be studied in this paper in the context of a two-sided ride-sharing platform, which serves drivers and passengers. As with any two-sided platform, there is an interdependence between both groups of customers: More drivers are more attractive for passengers, and vice versa. This interdependence creates the old chicken-and-egg problem, only that here drivers and passengers need to be matched not for a virtual transaction, but by their ability to meet physically and travel jointly. We argue, and illustrate by simulations, that in spatial multi-sided markets there is not a single critical mass frontier that needs to be reached in order to make the system self-sustained (as in nonspatial markets), and that this frontier is varying from one location to the next, depending on the density and distribution of the demand and supply over space and time. Identification of the critical mass frontier will allow for better evaluation of implementation policies and regulations.

No abstract

No abstract

No abstract