Investigating and analyzing the management of the process of paying traffic fines using process mining

This research examines the effectiveness of using process mining in the management of traffic fines. The main purpose of this study is to investigate the effectiveness of this method in improving the performance of fine collection processes and a better understanding of drivers' behavior and improving traffic laws and regulations.

In this research, first, using process mining, process control flow is built based on information extracted from incident reports. Then, process mining techniques are used to model the process of traffic fines due to violations. Directed graphs and diagrams are used to build information process models, and the PM4PY library is used in the Python programming environment to perform process mining.

The findings of this research show that the use of process mining, taking into account the activities in the relevant detectors, provides useful information in identifying cases. This approach is able to directly track the behavior of offenders in the follow-up process and emphasizes the importance of process analysis in improving the efficiency of fine collection processes and reducing related costs.

According to the findings of this research, the importance of efficient management of traffic fines to maintain road safety and enforce traffic laws is clearly defined. The use of process mining methods can lead to the improvement of performance and more effective implementation of fine collection processes, as well as to a better understanding of the behavior of drivers and the improvement of traffic laws and regulations.