Integrering och utvärdering av en datadriven tidsuppdelad OD-matris i trafikmodeller för strategisk trafikplanering

This study evaluates the integration of two data-driven, time-sliced OD-matrices, representing the morning and afternoon peak hours, into an existing 24-hour-based macroscopic traffic model used for traffic planning in the municipality of Norrköping. The purpose is to examine how GPS data and traffic measurements can be utilized for calibration and validation, and how simulations using time-sliced OD-matrices compare to those using a traditional 24-hour OD-matrix estimation. The background of the study lies in recent advancements in the collection of mobility data through the mobile network and GPS, as explored in the CODE PROBE and CODE FLOW projects. The work is also placed within the context of the project Störningsfri Stad, which aims to support strategic traffic and logistics planning during urban development and large-scale construction projects such as the relocation of Norrköping’s central station. The methodology follows a process involving calibration, validation, and analysis of simulation results using both types of OD-matrices. The analysis focuses on identifying differences in model output and potential impacts on decision making, particularly for peak hour traffic and in case study scenarios. The results show that time-sliced OD-matrices offer valuable additional perspectives by capturing temporal variations in travel demand that are not visible in daily aggregates. This enables a more detailed assessment of traffic impacts and supports improved strategic traffic planning. However, the study also identifies certain limitations in the time-sliced models, primarily related to the integration and calibration of the time-sliced OD-matrices. Although the time-sliced OD-matrices captured general travel patterns, they both overestimated and underestimated demand in specific areas, which in turn affected route choice realism during simulation. In several cases, the routes deviated from expected travel paths, revealing inconsistencies between the OD-matrix structure and the observed travel times. Although adjustments of the capacity and volume delay function (VDF) in the models improved travel time accuracy and flow alignment, some remaining discrepancies point to the model’s sensitivity to how the OD-matrices are constructed and how well they reflect the transport network characteristics. These issues, especially noticeable in case study scenarios with substantial traffic pressure during peak hours and potentially misclassified road segments for the analysis, highlight the need to refine OD-matrix estimation methods and broaden the scope of calibration in future work.