Real-time Taxi Demand and Supply prediction based on Specific Geo locations using Machine Learning - A Systematic Literature Review

Abstract

With the increasing popularity of ride-hailing services, the accurate prediction of taxi demand has become a crucial task for service providers. In recent years, the availability of large-scale geospatial data and the development of machine learning algorithms have led to significant advancements in taxi demand prediction.The aim of systematic literature review is to analyze the techniques and approaches for taxi demand and supply prediction using geospatial data and machine learning algorithms.A total of 21 research papers published between 2017 and 2023 were selected based on inclusion and exclusion criteria. The papers were analyzed based on their research objectives,methodology, datasets and evaluation metrics.The result of the literature review indicate that the accuracy of taxi demand prediction models depends on the quality and quantity of the data, the selection of learning algorithms, and the feature engineering techniques used.The systematic literature review highlights the potential of using geospatial data and machine learning algorithms for accurate taxi demand prediction and need for more standardized evaluation metrics and further research to address the challenges. Machine learning algorithms, such as linear regression, decision trees, and artificial neural networks, clustering have been widely used for prediction tasks, focusing on factors like real-time population data.Through a comprehensive analysis, it is determined that clustering emerges as the most suitable technique for the research.