Holding has been extensively investigated as a strategy to mitigate the inherently stochastic nature of public transport operations. Holding focuses on either regulating vehicle headways using a rule-based approach or minimizing passenger travel cost by employing optimization models. This paper introduces a holding decision rule that explicitly addresses passenger travel cost. The decision to hold relies on the passenger demand distribution along the line. The passenger cost holding rule is tested using simulation for a high frequency bus line in Stockholm, Sweden and is compared with a nocontrol scheme and the currently used headway-based strategy. The results indicate that the new decision rule results in relatively minor reductions of passenger cost compared to the currently adopted strategy, and that it allocates the greatest share of holding time at the beginning of the route.

In this paper, we propose a conceptual modeling framework, as well as derive mathematical submodels for route choice on motorways and urban networks. The models convey the most relevant aspects that play a role in route choice, among other things learning, risk attitude under uncertainty, habit and the impacts of ATIS on route choice and learning. To get insight into the relative importance of the different aspects and processes constituting route choice behavior and as such support the proposed conceptual framework, the models have been estimated using data from two experiments that have been carried out using a so-called interactive travel simulator. The latter is a new research laboratory that combines the advantages of both SP and RP research. Many relevant contributions on the aforementioned aspects that play a role in route choice can be found in literature, but a simultaneous consideration of all is lacking. Based on these contributions from literature, a conceptual framework is developed that integrates these aspects. The results from the laboratory experiments show that people perform best under the most elaborate information scenario and that habit and inertia together with en-route information play a major role in the route choice. Learning about the route attributes is especially important during the first days, but hereafter plays a smaller role than the provided information and the developed habit. Finally, the way the information is presented in the experiments proves to have a large impact on the route choice.

Much effort has been spent in developing more efficient control systems for signalized intersections to adapt the capacity of the network to the variability of the demand. This variability is partly due to time-dependent factors but also to the stochastic nature of the demand itself. The available formulae can still be successfully adopted in undersaturated conditions, where the stochastic effects fade into one green phase, or in highly oversaturated cases, where the queues aren't comparable with their portions caused by these uncertainties. In cases where the value of the degree of saturation is close to one the stochastic effect plays a relevant role. This paper provides a novel heuristic formula able to model the transition phase when the study involves a variable demand. It particularly solves the queuing behavior of an oversaturated period on the successive undersaturated ones. The delay experienced by the users is modeled using a Markov Chain process. Based on this data the novel model is calibrated. The new model for queues is further applied in two case studies. The first involves a two arms intersection where users of the two OD pairs can only choose between different times of departure. The second involves one OD pair and two parallel routes, one faster but ending with a traffic light while the other is unsignalised. The paper discusses the change in users' decisions with respect to the travel times and the delays they perceive at signalized intersections both from the route choice and the departure time choice point of view. In this sense a Dynamic Traffic Assignment problem in an extended network can be applied and solved. From this research we gained a deeper knowledge of the relevant role played by the random nature of the queue evolution in time for the delay experienced by the users. By acquiring this knowledge a time-dependent queue function has been provided. It is now possible to model the queue as a continuous function with more accurate results than the ones provided by the simple deterministic method.