A Proposed Predictive Analytics Model for ITDSS

Although relying on Predictive Analytics (PA) in our digital transformation age becomes an important way to achieve success, the public enterprise sector companies specialized in passenger intercity transportation in Egypt still rely on descriptive reports and unsupported opinions in transportation planning. Therefore, these companies need to rely on PA in the transportation planning and decision-making process. Moving toward PA is a clear path to become an intelligent organization. The use of PA does not only drive cost-saving and revenue growth but also provides more accurate and timely information to develop the decision-making process and achieve various strategic objectives. Ridership prediction is one of the most important prediction studies that could be performed in intercity public transportation companies. It is considered at the heart of transportation policymaking and the success of transportation systems because it affects the revenue of the company. The main goal of this book is to build a predictive analytics machine learning ridership model to aid Upper Egypt Company (UE Co.) planners depend on analytics to replace unsupported opinions with data-driven conclusions.