SIMCal: A High-Performance Toolkit For Calibrating Traffic Simulation

Traffic simulators have many parameters that describe vehicle characteristics and driving behaviors. However, driving behaviors differ across urban, suburban, and rural areas. Even in the same area, driving behavior can be affected by the time of day or weather conditions. Therefore, it is difficult to get an accurate parameter set that is suitable for all scenarios. As a result, default parameters of simulators are usually determined only for a specific test case. To simulate a traffic scenario, researchers need to perform calibration to determine a suitable parameter set, which can provide more reliable simulated traffic than the default parameter set. A popular approach is manual calibration using human experience, but it is usually not effective due to the huge space of possible parameters. Although some studies proposed automated methods using evolutionary algorithms, implementing these methods is a time-consuming job. In this paper, we introduce a toolkit for researchers to easily conduct calibration for their own traffic scenarios. The toolkit supports several state-of-the-art algorithms and is designed to run in parallel to utilize the power of high performance computers. Moreover, by using this toolkit, we conduct in-depth experiments to understand which factors affect the calibration performance.