Advancements in Passenger Flow Optimization in Smart Transport: A Holistic Survey

Accurately predicting passenger flow is increasingly crucial, garnering significant attention in academia due to its benefits in managing crowds and potentially enhancing efficiency. This paper offers a comprehensive survey of contemporary developments in the field of passenger flow optimization. It focuses on three different approaches, namely ARIMA, traditional machine learning and deep learning. The study systematically compares and analyzes multiple research papers within the subtopics. By understanding the insights, identifying the trends and providing critical synthesis, this survey provides an overview of the state-of-art techniques in passenger flow optimization. The findings contribute to a deeper understanding of a broad field and offer valuable guidance for the researchers.