| dc.description.abstracteng | Collective mobility and demand-driven transport systems are vital to proper,
efficient and sustainable functioning of biological, technical and social
systems.
They are relevant to mastering several major transitions human society is facing
today on a global scale, and they have been attracting considerable interest as
on-demand ride-sharing systems are projected to disrupt the individual mobility
and public transport sector.
In collective mobility systems and demand-driven transport systems alike,
vehicles or other discrete mobile units carry individual passengers, goods or
other discrete immobile loads.
These systems do so upon individual request for transport from individual
origins to individual destinations, within individual time windows.
Coordination functions in these systems include assigning requests to
transporters and routing the transporters within the underlying geometry.
When transporters carry multiple loads at the same time, another function of the
system is to bundle spatiotemporally overlapping requests.
Given both the need and the recent interest and implementation of collective
mobility and demand-driven transport systems, it is imperative to understand
their core structural and dynamical properties and how they relate to their
satisfactory and efficient functioning. Modelling and simulating such discrete-event systems involves untypical
technicalities that presumably have hindered progress in studying these systems
from the network dynamics and statistical physics perspective so far.
In order to unlock collective mobility and demand-driven transport systems for
studies in these fields, I devise a modular framework to model and simulate such
systems.
Furthermore, a fundamental steady-state performance measure is the transport
capacity of the system.
If overall demand exceeds capacity, the system congests and ceases to function.
Determining the capacity is henceforth crucial to inform system design for
optimized system efficiency and individual service quality.
Intriguingly, the brink to congestion constitutes a critical transition
reminiscent of percolation in time.
I develop a dynamic notion of criticality of such stochastic processes, mapping
the transition from stability to instabilty to a hybrid percolation phase
transition.
Overall, I anticipate this Thesis and the tools developed to be a starting point
for modelling and studying the dynamics of collective mobility and demand-driven
transport systems, and for understanding how the intricate
interplay of their structure and their dynamics governs their functioning. | de |