Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent

Efficient public transport systems are crucial for sustainable urban development as cities face increasing mobility demands. Yet, many public transport networks struggle to meet diverse user needs due to historical development, urban constraints, and financial limitations. Traditionally, planning of transport network structure is often based on limited surveys, expert opinions, or partial usage statistics. This provides an incomplete basis for decision-making. We introduce an data-driven approach to public transport planning and optimization, calculating detailed accessibility measures at the individual housing level. Our visual analytics workflow combines population-group-based simulations with dynamic infrastructure analysis, utilizing a scenario-based model to simulate daily travel patterns of varied demographic groups, including schoolchildren, students, workers, and pensioners. These population groups, each with unique mobility requirements and routines, interact with the transport system under different scenarios traveling to and from Points of Interest (POI), assessed through travel time calculations. Results are visualized through heatmaps, density maps, and network overlays, as well as detailed statistics. Our system allows us to analyze both the underlying data and simulation results on multiple levels of granularity, delivering both broad insights and granular details. Case studies with the city of Konstanz, Germany reveal key areas where public transport does not meet specific needs, confirmed through an initial user study. Due to the high cost of changing legacy networks, our analysis facilitates the identification of strategic enhancements, such as optimized schedules or rerouting, and few targeted stop relocations, highlighting consequential variations in accessibility to pinpointing critical service gaps. Our research advances urban transport analytics by providing policymakers and citizens with a system that delivers both broad insights with granular detail into public transport services for a data-driven quality assessment at housing-level detail.