Agent-based dynamic traffic simulation: Demand and supply calibration

This internship aims to develop an online calibration framework for agent–based dynamic traffic simulators in order to benefit from updated daily traffic data to deal with the demand and supply parameters for a given road network. The intern will work in the scenario team of the Congestion Impacts Reduction via CAV–in–the–loop Lagrangian Energy Smoothing (CIRCLES) project.

The main goals of this internship are to (i) build a calibrated simulation tool based on SUMO [2] for a given test case
based on the aggregated data; (ii) solve the calibration problem for dynamic traffic assignment (DTA), agents departure time and route choices, based on the existing approaches [3] and available data [4]; (iii) design an online calibration framework to be benefited by the data that will be collected by sensors and data collectors of the project.

Date de début souhaitée : dès que possible

Durée : 6 mois

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