Passengers, Information, and Disruptions

Passengers traveling in public transport generate a detailed digital track record of their journey through using automated fare collection systems and carrying mobile devices. This information on passenger behavior has only recently become available to public transport operators. This thesis addresses the question how this new information can be used to improve passenger service in case of disruptions in public transportation. Major disruptions cause the current logistical schedule of the operator to be infeasible. Adjusting this schedule to the disruption is a complicated planning problem. Passengers will adjust their journeys to the new schedule, and may need to adjust their route choice due to the route choice of other passengers in case of capacity shortages. Therefore the passenger service results from a complex interaction between passengers themselves, and between passengers and the schedule. This thesis proposes new models for improving passenger service in case of major disruptions by adjusting the schedule while anticipating passenger’s reactions, and also by supporting passengers during disruptions through the provision of route advice. This research is combined with a study on passenger behavior based on the new data sources. The models are evaluated using data and case studies of the passenger rail network of Netherlands Railways and the urban rail network of the Massachusetts Bay Transportation Authority. It was found that indeed this new information, together with the option to provide route advice to passengers, could significantly improve service during major disruptions

Shuttle Planning for Link Closures in Urban Public Transport Networks

Urban Public Transport systems must periodically close certain links for main- tenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the link with a simple shuttle service. However, alternative shuttle services could reduce inconvenience at lower op- erating cost. This paper proposes a model to select shuttle lines and frequencies under budget constraints. A new formulation is proposed that allows a minimal frequency restriction on any line that is operated, and minimizes passenger inconvenience cost, including transfers and frequency-dependent waiting time. This model is applied to a shuttle design problem based on a real world case study of the MBTA network of Boston (USA). The results show that additional shuttle routes can reduce passenger delay in comparison to the standard industry practice, while also distributing delay more equally over passengers, at the same operating budget. The results are robust under different assumptions about passenger route choice behavior. Computational experiments show that the proposed formulation, coupled with a preprocessing step, can be solved faster than prior formulations

Subline frequency setting for autonomous minibusses under demand uncertainty

Over the last years, there have been initiated several pilots with autonomous minibusses. Unlike regular bus services, autonomous minibusses serve a limited number of stops and have more flexible schedules since they do not require bus drivers. This allows the operation of a line through a flexible combination of sublines, where a subline serves a subset of consecutive stops in the same order as the original line. This paper studies the subline frequency setting (SFS) problem under uncertain passenger demand. We present a frequency setting model that assigns autonomous minibusses to sublines in order to exploit the available resources as much as possible and minimize the operational and passenger waiting time costs. Passenger waiting time costs may depend on the combination of several lines whose frequencies cannot be perfectly aligned for each passenger journey. We present a new estimation of the expected waiting time for passengers to improve the accuracy of the passenger waiting time costs in the case of sublines. Our SFS model is originally formulated as a MINLP and reformulated as a MILP that can be solved to global optimality. Further, we explicitly consider the uncertainty of passenger demand in the optimization process by formulating a stochastic optimization model. The performances of our stochastic and deterministic models that assign minibusses to sublines are tested under various passenger demand scenarios in the 14-stop autonomous minibus line in Eberbach, Germany and a fictional bus line with 20 bus stops. Results show potential improvements in operational costs in the range of 10-40% depending on the passenger demand profile