Intelligent passenger demand prediction-based rerouting for comfort perception in public bus transport systems

Predicting the passenger count and rerouting the busses for the demanding routes to reduce the waiting time of passengers is considered the major challenge in the modern public transportation system. Meanwhile, the transportation sector has been revolutionized in recent years due to big data. In this proposed method, the deep learning and fuzzy logic concept is introduced to face such challenges. In this proposed method, the deep attributes extraction and prediction of passenger demand are accomplished using a deep sailfish network (DSFN). Then, the rerouting concept for demanding routes is carried out using the fuzzy logic-based rerouting process, which reduces the waiting time of passengers. This prediction-based rerouting framework enhances the efficiency of bus scheduling by satisfying the passenger demand is performed. It increases the quality of transportation with greater comfort for passengers. Meanwhile, the usage of public transportation is also getting increases. Four different routes from Gujarat (India) are considered in this work analysis, they are Railway station terminal (RAST), Sahara Darwaja (SAHD), Katargam Darwaja BRT (KADB), Udhana Darwaja BRTS, and Adajan GSRTC (ADGS). The proposed approach is implemented with Matlab, and the performance is evaluated using standard metrics such as mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results prove that the suggested method provides prediction results with high accurate values than other existing methods in terms of MAPE and RMSE.