Recommender system for most relevant k pick-up points

Abstract

According to Microsoft, by 2025, 100% of new cars will be connected and by 2030, 15% of new cars will be autonomous, and will take care of sending, receiving and analyzing “large amounts of data”. Automobiles are becoming data centers on wheels. All these pieces of information can be used by many stakeholders (road authorities, leasing companies, municipalities, car manufacturers, insurers, workshops, emergency services, etc.) in the mobility space to improve processes. Taxi’s industry is a concrete example that is evolving rapidly. New competitors and technologies are changing the way traditional taxi services do business. In spite of this growth has created new effectualness, it has also created new challenges. Exploiting and understanding of the taxi supply and demand could increase the efficiency of the city’s taxi fleet management system. This paper describes a recommender system that recommends the most relevant pick-up points for a driver. It is based on spatiotemporal features, through data preprocessing and real-time recommendations. These recommendations could inform the drivers on where to position their taxi’s. We implemented a recommender system based on KMeans+Regression. Putting in the test with various features sets, we were able to achieve positive results. The effectiveness of our approach is demonstrated by the results, achieving approximately 98% accuracy in most cases.