Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent (2407.10791v2)

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.

• The paper presents a data-driven methodology that uses agent-based simulations to model diverse demographic travel behaviors for urban transit evaluation.
• It leverages open data and GTFS standards to generate detailed visual outputs like heatmaps and density maps at the household level.
• The study identifies cost-effective network improvements by suggesting strategic modifications such as stop relocations and optimized bus schedules.

Analyzing Urban Public Transport through Mobility Profile Simulations

The paper "Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent" presents a data-driven methodology to improve public transport planning and analysis. The authors developed a visual analytics framework combining agent-based simulations, enriched with open data sources, to assess and optimize urban transport networks. This framework focuses on delivering detailed accessibility metrics at the household level, providing insights into diverse demographic rider needs.

Methodological Approach

Central to the framework is a scenario-based model which simulates the daily travel behaviors of various demographic categories—such as schoolchildren, students, workers, and pensioners. These groups interact within the simulated public transport system with unique mobility schedules, calculated using Points of Interest (POI) and travel time metrics. The visual outputs include heatmaps, density mappings, and detailed statistic overlays, allowing nuanced exploration at various granularities.

The model relies on standard tools for public transport schedules and open street maps to craft a realistic simulation environment reflective of the local urban landscape, using the city of Konstanz, Germany, as a primary case paper. Housing data, bus stop locations, and transport routes are integrated with timetable data in the General Transit Feed Specification (GTFS) format to ensure a structured, scalable approach to transport network analysis.

Results

Simulations reveal critical service gaps in the transport system by contrasting expected with real-time accessibility measures. The paper noted areas with poor transport services, validated through user studies, and identified potential network enhancement strategies, such as stop relocations and optimized bus schedules. The emphasis on minimal but strategic infrastructure modifications, such as minor route changes or additional stops, underscores the system's utility in suggesting cost-effective improvements.

Implications and Future Directions

The framework offers practical and theoretical implications for the field of urban transport analysis. Practically, policymakers receive valuable data-driven insights on public transport service deficiencies in real-time, facilitating informed decision-making and promoting transport equity. The system's visualization capabilities can engage a broader audience, including the public and advocacy groups, in transport planning discussions.

Theoretically, this work builds upon existing agent-based modeling and transport simulation, pushing the envelope in terms of detail and granularity by focusing on household-level analysis. Future research could extend the model to incorporate multi-modal transport solutions, providing a more comprehensive picture of urban mobility behaviors and needs. Additionally, expanding the system's application to international cities would test its adaptability and robustness, offering comparative analyses across urban contexts.

In conclusion, this paper presents an adept synthesis of data-driven analytics and agent-based modeling for public transport infrastructure. It sets the groundwork for more responsive and inclusive urban transport networks, highlighting the potential of open-data frameworks to transform current public transport planning paradigms.