# Research on the Impact of Flexible Working Hours on Reducing Traffic Delays in the City
## 2 Materials and Methods  
### Introduction to Traffic Modeling  
Traffic modeling represents a fundamental tool for the efficient management of high volumes of automobile traffic on major roadways and the optimization of complex road infrastructure. In recent years, this approach has become indispensable for the analysis, simulation, and prediction of traffic behavior at various levels of complexity, from congested urban areas to rural environments, utilizing macro, meso, and microscopic modeling levels [41]. Microsimulation models focus on detailed tracking of individual vehicles and their dynamics, such as lane-changing maneuvers, providing a comprehensive view of vehicle behavior in traffic [42]. These models provide detailed results and accuracy in monitoring individual driver behaviors. Macrosimulation models, on the other hand, deal with traffic at a higher level of abstraction, where key parameters are traffic density and road link capacity. These models do not account for individual driver behaviors but provide an overall picture of traffic flow and enable the analysis of road network capacity needs [43]  
Mesosimulation models, which represent a compromise between macro and microsimulation approaches, allow for dynamic simulation of traffic entities with less emphasis on detail compared to microsimulation models [44]. These models are suitable for analyzing broader trends in traffic behavior without the need for high detail. Another modern approach in microscopic traffic modeling is agent-based modeling, which is gaining popularity in urban logistics [45]. These models incorporate individual actor behaviors, allowing for the simulation of their decision-making in various scenarios, such as policy measures [46]. The overall use of traffic modeling, whether macro, meso, or microsimulation approaches, contributes to a better understanding and management of traffic systems, which is essential for the effective administration of urban and rural transportation and the improvement of quality of life in these areas. Traffic behavior is the volume, speed, and density of traffic flow, estimated based on available historical or real-time data. In recent years, with the rapid increase in intelligent transportation services, traffic monitoring applications, and the development of the Internet of Things (IoT), traffic modeling has gained significant attention in the research community. Most research in traffic modeling focuses on designing models that analyze and predict traffic congestion issues and simulating traffic flow in various road environments, such as highways, intersections, and urban streets [47; 48; 49]. The availability of these models is crucial for traffic engineering and the performance evaluation of traffic facilities. These models examine spatial and temporal interactions within traffic situations to capture their significant impact on traffic behavior. The temporal factor in these models identifies directional movement within a time series, while the spatial factor considers geographic space to create accurate predictions. The combination of temporal and spatial characteristics in traffic prediction models significantly improves the prediction accuracy [50]. Numerous studies have demonstrated that machine learning models can effectively model spatiotemporal correlations in continuous traffic data. On the other hand, traffic simulation models are used to simulate vehicle movements and realistically depict traffic flow. These models are essential for optimizing road design and evaluating the impact of road network design on vehicle movement. Simulation models provide a realistic geographic environment for planning driver behavior scenarios on various types of roads, including urban and highway networks. They use mathematical models to simulate real-time vehicle movement, following theories that describe automobile behavior. The difference between traffic prediction models and traffic simulation models lies in their goals and usage.