ML-Based Optimized Route Planner for Safe and Green Virtual Bike Lane Navigation

In several metropolitan cities, the availability of exclusive cycling lanes is severely limited. In addition, the intricate urban design and the diverse road conditions, encompassing dangerous steep roads, busy crossroads, heavy traffic, and varying weather conditions, combine to make it very challenging for cyclists. A multi-criterion route planner system for green and safe navigation was previously proposed. The system is established in Cairo and provides the best possible path according to the rider’s comfort, health, and safety. While this previous work is prominently functional and serves its purpose, it lacks a tolerable response time to output the greenest safest path from the input start location to the desired destination. Consequently, this results in a tedious experience for the user. This chapter provides an optimization technique to the previous work by employing multiple machine learning techniques to gain insights and knowledge to learn from previous patterns and improve the less-than-prompt response time.