Abstract
The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach.
Highlights
The global mobility market is undergoing considerable changes, primarily due to developments in connectivity, autonomous driving, electrification, and shared mobility
The objective of this paper is to demonstrate the benefits of a predictive fleet management strategy on operational efficiency, with the example of a taxi fleet in Munich
None of the existing predictive approaches were tested in conjunction with a dynamic, time-varying fleet size
Summary
The global mobility market is undergoing considerable changes, primarily due to developments in connectivity, autonomous driving, electrification, and shared mobility. Besides these four major technological advances, trends in population growth, shifting consumer behavior, and sustainability serve to promote these disruptive changes, especially in urban areas. These developments are set to bring about multiple challenges in terms of sustainable traffic flow management and an increased demand for transport, which future megacities will have to face. The demand for mobility as a service (MaaS) is expected to grow disproportionately in urban environments. The rapid emergence of such new entrants as car or ride sharing companies, as well as software producers for mobility purposes serves to confirm this development
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