Machine Learning Frameworks in Carpooling

Abstract

Due to the development in human population and their requirements, the vehicular population on the globe is increasing day by day in the medium of public transportation. As a result, carpooling comes into play, with the fundamental notion being to share personal automobile space among persons travelling similar paths. Smart carpooling, car sharing, and ridesharing are other terms for the same thing. From a socioeconomic and environmental standpoint, the major task is to develop sustainable transportation. The success of carpooling should be measured in terms of cost, stress-free driving, traffic reduction, and air pollution reduction in the transportation solution system. The major challenge here is to assist vehicle users in gaining access to and picking an appropriate cost-effective transportation option based on their environmental footprint, matching his or her requirements, preferences, and legal limits, and determining the optimum route via specified areas.