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

TL;DR

This paper presents a data-driven, visual analytics approach for analyzing and optimizing public transport infrastructure at the individual level, considering diverse demographic needs and scenarios to identify service gaps and inform strategic improvements.

Contribution

It introduces a novel simulation and visualization framework that assesses public transport accessibility for different population groups, enabling targeted infrastructure enhancements.

Findings

Identified key service gaps in Konstanz's public transport network.

Demonstrated the effectiveness of scenario-based simulations for infrastructure planning.

Provided insights into optimizing schedules and rerouting to improve accessibility.

Abstract

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 a formative 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.