Multimodal routing framework for urban environments considering real-time air quality and congestion

The travel choices of citizens can be based on distance, time, exposure, emissions, etc., but the current route planning systems are mostly based only on distance and time. This study develops a route planning framework by integrating the real-time congestion pattern and air pollution levels in the GraphHopper multi-modal routing engine. This study showcases the application for Delhi and Bangalore, India. However, it is transferable to any other city in the world. The route planning algorithms provide alternatives for the fastest, shortest, LEAP (least exposure to air pollution), and the balanced route, considering four travel modes: car, motorbike, bicycle, and foot (pedestrian). GraphHopper libraries involve custom weightings for each of the constraints for route choices, and these weightings are modified to find the optimal route as per users' requirements. The study's novelty lies in integrating real-time congestion patterns and air pollution levels in a multi-modal routing engine. The preference for low travel time and air pollution levels can be altered by adjusting factors in balanced route to suit travelers. Using different travel modes on a specific route found that the exposure reduction ranged from 42.73 % to 71.50 % for fastest to balanced routes and 64.52 % to 82.71 % for fastest to LEAP routes in Delhi. Higher exposure reduction percentages are observed in Bangalore. Using the routing engine can help travelers reduce their exposure by altering travel choices. The interface of the routing engine is made user-friendly and intuitive.