IIGPTS: IoT-Based Framework for Intelligent Green Public Transportation System

Today, growing urbanization coupled with the high demands of daily commute for working professionals has increased the popularity of public transport systems (PTS). The traditional PTS is in high demand in urban cities and heavily contributes to air pollution, traffic accidents, road congestion, increase of green house gases like oxides of carbon (OC), methane \({({\text {CH}}_4)}\), and oxides of nitrogen \({{\text {(NO}}_x)}\) emissions. PTS also suffers from the limitations of preset routes, privacy, crowd, and less space for passengers. Some PTS are densely crowded in few routes, whereas in other routes they are not crowded at all. Thus, the aforementioned limitations of toxic emissions coupled with load management and balancing in PTS are a critical issue. The paper proposes IIGPTS: IoT-based framework for Intelligent Green Public Transportation System that addresses the mentioned issues by measuring emission sensor readings with respect to varying parameters like passenger density (total carrying load), fuel consumption, and routing paths. The performance of IIGPTS is analyzed at an indicated time slot to measure emissions and load on the system. Then, the simulation is performed based on dynamic routes by increasing passenger load and measuring the service time for user traffic as requests, also considering the request drops. The results obtained by IIGPTS framework indicate a delay of 104 s for 10,000 requests spread across an entire day, which is negligible considering the load. Thus, IIGPTS can intelligently handle varying capacity loads at varying routes, with fewer emissions, making the realization of green eco-friendly PTS systems a reality.