This project examines an event log from Italy's road-traffic fines management system. The objective is to uncover insights that improve fine payment completion rates, reduce management costs, and detect process issues early. Through dataset exploration, noise filtering, process discovery, and summarization, this study aims to offer practical strategies for enhancing operational efficiency and achieving better process outcomes.
This project employs process mining techniques to optimize the management of road traffic fines. It begins by analyzing event logs from the police information system to understand statistical properties distinguishing paid and unpaid cases. The objectives include discovering the current process (AS-IS), deriving an optimal process (TO-BE), and analyzing discrepancies, particularly in unpaid cases.
- Increase Payment Completion Probability: Focus on filtering, summarizing, and analyzing data to improve payment rates promptly after notification.
- Reduce Process Management Costs: Streamline fine management by filtering out irrelevant cases and analyzing cost-related data to minimize overhead.
- Early Detection of Process Issues: Identify anomalies in process execution times and appeal outcomes to enhance process efficiency and address potential issues promptly.
The Knowledge Uplift Trail (KUT) is a structured approach to transform raw data into valuable insights. It involves several key steps:
- Data Cleaning: Standardizes data formats, fills in missing values, and converts categorical data into numerical formats, ensuring the data is ready for analysis.
- Data Filtering: Filters the dataset based on specific criteria, such as keeping only cases where the final activity was a payment, to focus on relevant information.
- Descriptive Analysis: Utilizes statistical tools to analyze the distribution and characteristics of the data, providing a clear understanding of its properties.
- Process Mining: Applies process mining techniques to uncover the underlying process from the event log data, revealing how activities are typically performed.
- Conformance Checking: Identifies any deviations from the expected sequence of events within the process, highlighting potential inefficiencies or irregularities.
- Intervention Strategies: Based on insights gained, strategies are developed to optimize processes. These strategies are continuously refined through ongoing monitoring and measurement to improve outcomes.
The KUT methodology ensures that data is systematically processed and analyzed to derive actionable knowledge, guiding informed decision-making and process improvements.
The dataset used in this study focuses on road-traffic fines managed by the Italian police. It is documented in an event log to support process management containing over 500,000 logs.
- Dataset link: Road Traffic Fine Management Data