The capacities of public transportation systems are limited in several ways:
Among other limitations, there exist only a finite number of vehicles, space inside
the vehicles is limited, and space inside the stations is limited. In this thesis a transit
assignment model is used, where vehicle capacities are explicitly taken into account
in the strategy choice model. The basic assumption of the model is that passengers
know in advance, which parts of the network will be congested. Passengers take the
possibility of failure to board a vehicle into account before they start their journey.
In the model passengers use strategies instead of routes. A framework for
strategy costs is developed, which is based on random variables. This way it is
possible for the first time to take into account the passenger's averseness to travel
time variability in a public transport assignment model. Furthermore, strategy cost
functions are developed that reflect limited information and bounded rationality of
passengers. Finally, cost functions that reflect the use of portable journey planners
are analyzed.
The assignment model is analyzed in detail on a small bottleneck network. The
results show that the model reacts as expected in all cases. In the model the peak of
passenger arrival times on the origin stop is earlier if there is more demand, which
is a result that is hard to reproduce in models that do not have explicit capacity
constraints. An improved method to model demand is developed. Instead of the
original demand model, which is based on grouping passengers into groups before
the strategy choice is executed, strategy costs are calculated first, and then strategy
choice is executed. As opposed to the original model this method does not suffer from a discretization error and leads to stable results.