# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 1 Introduction
### Implementation of Intelligent Transport Systems via Simulation Model of Traffic Flow
It is essential to consider that the demand for different routes changes over time, showing clear patterns during peak and off-peak periods [21; 22; 23]. The ODMs require calibration to account for deviations over a more extended period [24; 25; 22]. Thus, SMTF with a calibrated ODM is crucial for accurately modeling PTS processes in VTS. Various software solutions are available to simplify the construction of a macroscopic (and mesoscopic) model (with ODM calibration capability). One notable example is the simulation of urban mobility v. 1.20.0 (SUMO) software, which is widely used in research fields, particularly in traffic management and transportation communications [26]. SUMO offers capabilities for constructing, adjusting, and modeling custom traffic flow architectures and supporting tools for importing network and ODMs of existing route sections. SUMO's versatility enables the study of traffic control strategies and vehicular communication systems [27]. The high-detail simulation in SUMO is essential for analyzing and optimizing traffic management strategies, evaluating the impact of infrastructure modifications, and examining vehicle interactions in complex urban environments [28]. Additionally, SUMO supports V2X technology modeling, facilitating the development and testing of interoperable automotive applications [29]. However, a significant drawback of such models is their challenge in adapting to dynamic changes in the PTS [30]. The accuracy of the simulated model data may differ significantly from that of real-life PTS data, since the simulation's source data rely on information from the OpenStreetMap project. Updating the OpenStreetMap database can be slow and dependent on the location and activity of volunteers in the relevant region [31]. The ODM and vehicle behavior parameters show the most significant differences from real-world traffic data in PTS. The ODM affects how traffic density is distributed over space and time, which can be monitored using data from ITS sensor systems [32]. To address discrepancies between the outdated ODM and current traffic conditions, adjustments can be made to the spatial and temporal distribution of traffic density in the SMTF through calibration of simulation models. Various calibration techniques can help bridge the gap between the simulated model and actual traffic conditions in PTS. These include direct calibration of the ODM [33; 34; 35; 36; 37], calibration of the macroscopic model based on physical processes [38; 39; 40], and the use of evolutionary algorithms to optimize calibration parameters [41; 42; 43]. Studies suggest that relying on a single traffic flow model may not capture all relevant phenomena accurately, potentially impacting simulation accuracy [13]. Hybrid traffic flow models, combining microscopic, mesoscopic, and macroscopic approaches [18; 44; 45; 46; 47], offer a more comprehensive representation of traffic dynamics with varying levels of scalability. Receiving transport system data from a PTS or VTS opens up the possibility of using machine learning (ML) algorithms for both SMTF calibration and applications that can be implemented in the VTS.