Traffic Simulation with SUMO – Simulation of Urban Mobility

Krajzewicz, Daniel · 2010 · OpenAlex-citations

DOI: 10.1007/978-1-4419-6142-6_7

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Summary

This doctoral thesis by Georg Hertkorn (2004) addresses the challenge of modeling time-dependent travel demand through a microscopic, activity-based approach. Traditional zone-based models often fail to capture the temporal dynamics and individual constraints of travel behavior, such as the sequential nature of daily activities and vehicle availability within households. The research aims to develop a model that simulates the trips of a synthetic population for a typical working day, determining departure times, origins, destinations, and modes of transport. This approach allows for a more granular analysis of traffic demand, accounting for how travel choices are derived from the need to perform specific activities at specific times and locations. The methodology relies on an activity-based framework where time allocation patterns are derived from empirical diary data from the Federal Statistical Office’s 1991/1992 time budget survey. These data undergo cluster analysis to classify time-use patterns, from which parameters for the variability of activity start times and durations are extracted. These patterns are then assigned to individuals in a synthetic population based on socio-demographic characteristics. Destination choice is modeled using the concept of intervening opportunities, dependent on travel times, while mode choice considers person type, trip purpose, and distance. Crucially, the model incorporates constraints such as household vehicle availability, ensuring a car is not used by multiple members simultaneously. To achieve consistency between demand and supply, the model establishes a feedback loop with an external traffic flow simulation, allowing travel times to influence destination and mode choices iteratively. The model was applied to the city of Cologne as a test case. The study compares characteristic quantities of the simulated travel demand against empirical findings and data from other modeling approaches. The analysis includes comparisons of trip distances and travel times, as well as spatial distributions across different areas of the city, such as the left and right banks of the Rhine. The results demonstrate that the microscopic model can reproduce realistic travel demand patterns, validating its ability to capture the complex interactions between individual activity schedules and network conditions. The significance of this work lies in its contribution to the field of transport planning by providing a tool that captures the dynamic and individualized nature of travel demand. By moving beyond aggregated zone-based models, the activity-based approach offers deeper insights into the causes of traffic demand and the potential impacts of policy changes, such as infrastructure modifications or public transport adjustments. The thesis highlights the importance of considering temporal constraints and household-level interactions in traffic simulation, offering a more transparent and detailed basis for evaluating transport policies and infrastructure investments.

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• mental model of traffic

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• Methodological Resource: tool software
• Theoretical Contribution: computational model