| dc.description.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. | en_US |
