Conference Agenda

The issue of the last mile in public transportation is crucial to its attractiveness and utilization, and thus represents one of the most significant challenges of the mobility transition in rural and urban areas. This contribution introduces a mathematical model represented as a directed and weighted graph to simulate public transit networks. The model is enhanced by incorporating edges that represent the last mile connections between transit stops and final destinations, addressing a critical gap in accessibility. These edges are assigned weights that vary based on the travel resistance, as evaluated using the established methodology for the standardized evaluation of transport infrastructure investments.

The methodology specifies a modal split proportion for public transit on a given route and computing the maximum allowable travel resistance as the objective function under specified constraints. This framework addresses the fundamental question of how the travel resistance of the last mile should be in order to achieve a desired modal shift towards public transit. The model can also be employed to investigate the acceleration of routes by omitting stops, which increases the last-mile problem for some customers, but reduces the travel time of the route. Using this framework, it is possible to estimate which public transport services, such as bike and scooter sharing or automated ridepooling, are required, which close certain gaps in comprehensive public transport coverage. By integrating last-mile connectivity, this model offers insights into the design of public transportation systems that promote higher usage while considering constraints of urban environments.

Public transportation systems are a sustainable alternative to driving for commuters. In order to make these systems more attractive, local authorities need to prioritize the minimization of commute times in their planning process.

In this contribution, we present an approach to identify delays within the routes of public transport systems. This approach uses data from an Intermodal Transport Control System (ITCS) and OpenStreetMap to analyze the operation of public transport systems. Within each individual trip, avoidable and unavoidable obstacles like curves and traffic lights are identified, located, and classified as a basis for constructing the best-case scenario of the trip. This best-case scenario is constructed for public transport systems using acceleration and deceleration functions of the vehicle and takes into account unavoidable speed restrictions such as curves. If the best-case scenario offers time savings compared to the original trip, these savings are distributed source-specific among the identified causes via a dynamic weighting system. The computation results in the identification of the length, location and cause of avoidable delays within the transportation system operation. This algorithm can also be used to calculate the time savings from excluding stops in the route planning process.

This approach has been successfully used to identify the most time-consuming traffic light systems within the operation of the local tram system in the city of Krefeld, Germany. This research result of increasing the mean velocity is presented in this contribution.