A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/6/1021 |
| _version_ | 1797564744347942912 |
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| author | Michael Wittmann Lorenz Neuner Markus Lienkamp |
| author_facet | Michael Wittmann Lorenz Neuner Markus Lienkamp |
| author_sort | Michael Wittmann |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:02:14Z |
| format | Article |
| id | doaj.art-6e725f65360c4c3d9e1376f12e0eb0a5 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T19:02:14Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6e725f65360c4c3d9e1376f12e0eb0a52023-11-20T04:22:43ZengMDPI AGElectronics2079-92922020-06-0196102110.3390/electronics9061021A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in MunichMichael Wittmann0Lorenz Neuner1Markus Lienkamp2Chair of Automotive Technology, Technical University of Munich, 85748 Garching, GermanyChair of Automotive Technology, Technical University of Munich, 85748 Garching, GermanyChair of Automotive Technology, Technical University of Munich, 85748 Garching, GermanyThe 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.https://www.mdpi.com/2079-9292/9/6/1021mobility-on-demandfleet-managementtaxipredictive strategiesRHC |
| spellingShingle | Michael Wittmann Lorenz Neuner Markus Lienkamp A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich Electronics mobility-on-demand fleet-management taxi predictive strategies RHC |
| title | A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich |
| title_full | A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich |
| title_fullStr | A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich |
| title_full_unstemmed | A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich |
| title_short | A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich |
| title_sort | predictive fleet management strategy for on demand mobility services a case study in munich |
| topic | mobility-on-demand fleet-management taxi predictive strategies RHC |
| url | https://www.mdpi.com/2079-9292/9/6/1021 |
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