Data-Driven Multi-Criteria Assessment Framework for Analyzing the Reliability of Bus Services

Abstract

Intelligent systems have been extensively used to improve the reliability of transport services as a result of technological advances. Despite the technical and methodological achievements, public transportation companies are still facing excessive challenges in assessing the performance and reliability of the system. This study establishes a data-driven multi-criteria decision-making model for prioritizing bus routes that illustrates both operator and consumer views on bus routes. The multi-criteria fuzzy outranking process is handled by ELECTRE III and Condorcet methods. The developed model utilizes alternative indices of bus travel-time reliability to fully capture the uncertain nature of the input data. The reliability assessment framework is based on automatic vehicle location (AVL) data which works as an effective evaluation system for enhanced service reliability on different routes network-wide. Using this model, bus transport companies can set a benchmark and a reliable ranking system for their bus routes. This hybrid prioritization framework is used for characterizing and enhancing transport network efficiency. The effectiveness of the model is examined by quantifying the reliability of eight bus routes controlled by the Qazvin public transportation system, in Iran. A wide range of AVL data sources is employed within an in-depth statistical analysis based on both user and operator preferences. According to the concordance matrix results, line 18 has been found to be superior to other bus routes, and the possibility of identifying less efficient bus routes has been fulfilled.