Calibration of a Microscopic Traffic Simulation in an Urban Scenario Using Loop Detector Data

A Case Study within the Digital Twin Munich




Induction Loop Detectors, Calibration, Travel Demand Estimation, Urban Digital Twin


Travel demand is an essential input for the creation of traffic models. However, estimating travel demand to accurately represent traffic behaviour usually requires the collection of extensive sets of data on traffic behaviour. Traffic counts are a comparably cost effective and reproducible source of information on travel demand. The utilisation of traffic counts to estimate demand is commonly found in the literature as the static and dynamic O-D estimation problem. A variety of approaches have been developed over recent decades to tackle this problem. Usually initial estimates of the O-D matrix are calibrated by utilising traffic counts and considering different assignment models. Other approaches for the estimation of travel demand solely based on traffic measurements can be found in the simulation software SUMO. The present work demonstrates the systematic development of a network model in SUMO in the inner city of Munich. In a sample network the estimation of travel demand through the tools flowrouter and routeSampler is tested by utilising flow measurements from induction loop detectors. The tests delivered unsatisfactory results, which is proven through observations of traffic flows in the resulting simulations as well as comparisons to historic traffic counts. The lack of sufficient detector data and the complexity of the sample network are discussed as the main reasons for the results. It is concluded that the applied tools should be tested in future studies with a more extensive dataset to perform a more comprehensive review of both tools. Therefore, we deliver specific requirements based on the network example of Munich.


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How to Cite

Keler, A., Kunz, A., Amini, S., & Bogenberger, K. (2023). Calibration of a Microscopic Traffic Simulation in an Urban Scenario Using Loop Detector Data: A Case Study within the Digital Twin Munich. SUMO Conference Proceedings, 4, 153.

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