Game Theoretic Systematic Approach for Transportation Quality Assessment

The public transportation system of every nation is the most important aspect of its overall infrastructure. Transport management officials are under enormous pressure to meet the needs of the general public in light of the increasing traffic, especially in densely populated regions. Conspicuously, evaluating the quality of transportation services offered by government officials has become essential. In this research, the transportation quality of services is examined. An Internet of Things (IoT) architecture is used to collect real-time ambient information in smart public vehicles. The probabilistic Bayesian Belief Model is used to categorize data using a quantitative measure of the Probability of Transportation Grade (PoTG) for service quality. In addition, the temporal data abstraction is used to compute the Transportation Quality Index (TQI), a numerical measure for the temporally cumulative quality assessment. Moreover, 2-player game-theoretic decision-making is then used to evaluate quality in a time-sensitive way. A simulated environment with 236,436 data segments is used to test the proposed architecture. Enhanced in terms of Sensitivity(94.43%), Accuracy(94.46%), Specificity(94.44%), F-measure(94.78%), Temporal Delay(26.79 seconds), and Reliability (92.69%) are registered comparative to state-of-the-art methodologies for estimating the performance enhancement of the proposed framework.