Capacity Constraints in Public Transportation

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