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

TL;DR

This paper presents a data-driven framework to analyze and optimize public transport by computing housing-level accessibility through mobility profiles for multiple demographic groups. It integrates open data sources and a scenario-based routing workflow with a visual analytics interface to simulate and compare infrastructure changes. The main contributions are an end-to-end data pipeline, an adaptable mobility profile model, and interactive tools for planners and citizens demonstrated on Konstanz case studies and an initial user study. The approach enables targeted, economically viable improvements and supports scalable, participatory urban transport planning towards more sustainable mobility.

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.

Figures (7)

• Figure 1: The user interface of our approach, enabling the exploration and interactive simulation of Mobility Profiles: It consists of two main visual elements: a map view with a legend (a) and a sidebar (b-f) with several control components. The background information (b), as well as particular network and POI information, can flexibly be toggled. The scenarios reflecting the mobility of a set of demographics can be modified (d), compared to different assumptions (c), and interactively partly selected (e). Further general settings are available (f).
• Figure 2: The Scenario Editor to modify the mobility profiles of different demographic groups (B). For each demographic, different categories of interest (A, i.e., one or more different POI types) are modeled with a probability of visit (C) for a particular time of day within a prototypical week. The inset (D) shows a more detailed example.
• Figure 3: A more detailed Mobility Profile card example for a group Elderly
• Figure 4: Data Exploration Mode: Map layers to inspect underlying information like transport lines, stop locations, points of interest, and housing data.
• Figure 5: The application allows a comparison of travel times for different demographics, based on generated mobility profiles. We can identify neighborhoods that are especially suited or unsuited for different demographic groups. This indicated the potential for targeted modifications.