A Survey on Public Transport Optimization Strategies Leveraging Real-Time Data Analytics for Enhanced Service Efficiency

Abstract

The growth in urban dwellers and the changing demands in mobility requirements have created more demand on an efficient and sustainable mode of public transport system. Real-time analytics in data has emerged as a key driver to help in the optimization of the public transport service by delivering dynamic information on the position of vehicles, passenger movements, traffic, and service breakdown. In this survey article, a wide range of optimization methods, which utilize continuous data gathering through GPS devices, ticketing systems and intelligent transport systems (ITS), are investigated. Such strategies involve agile route design and planning, predictive maintenance to achieve better fleet reliability, and optimization of energy among electrification and smart routing. The fact of integration of innovative communication technologies, including the Internet of Vegetables (ISV), is used to interact between vehicles and infrastructure, supplementing operational control. Although they offer great promise, difficulties with data quality, standardization, privacy, and security issues prevent these analytics-based techniques from achieving their full potential. These barriers are discussed in the paper, while policy and technological aspects that should be considered to make the broad adoption possible are mentioned. This survey provides a synthesis of the recent researches and practice related to data analytics in the real time to provide the overview of the way, in which real-time data analytics can foster more flexible, reliable, and green public transport systems. Finally, it highlights future research directions focused on improving data interoperability, privacy-preserving analytics, and multi-sector collaboration