Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services
The availability of various services for individual mobility is increasing, especially in urban areas. Dynamic ride-hailing services address these aspects and are gaining market share with providers such as MOIA, UberX Share, Sprinti or BerlKönig. To be able to offer competitive pricing for such a s...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
|
| Series: | EURO Journal on Transportation and Logistics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2192437623000018 |
| _version_ | 1811168514803761152 |
|---|---|
| author | R.-Julius O. Heitmann Ninja Soeffker Marlin W. Ulmer Dirk C. Mattfeld |
| author_facet | R.-Julius O. Heitmann Ninja Soeffker Marlin W. Ulmer Dirk C. Mattfeld |
| author_sort | R.-Julius O. Heitmann |
| collection | DOAJ |
| description | The availability of various services for individual mobility is increasing, especially in urban areas. Dynamic ride-hailing services address these aspects and are gaining market share with providers such as MOIA, UberX Share, Sprinti or BerlKönig. To be able to offer competitive pricing for such a service and at the same time provide a high service quality (e.g. fast response times), effective capacity management is needed. In order to reach this goal, two challenges have to be met by the service provider. On the one hand, a proper demand control has to be installed, which optimizes the responses to transportation requests from customers. On the other hand, suitable fleet control needs to be set in place to optimize the route of the fleet so that the demand can be met. Papers in the literature do solve both but typically focus on one of these two challenges. As an example, value function approximation (VFA) can be used to learn a service offering decision while anticipating future incoming requests. A typical example of a routing-focused method is the multiple scenario approach (MSA) creating a routing which anticipates future requests using a sampling method. In this paper, we combine VFA and MSA to address the two challenges in an effective way. The resulting method is called anticipatory-routing-and-service-offering (ARS). We find that the combined method significantly outperforms the individual components, improving not only the total reward but also the accepted requests. It is found that this performance is particularly high with a heavy workload and thus resources are relatively scarce. We analyse how and under which conditions the components together or individually are particularly important. |
| first_indexed | 2024-04-10T16:27:29Z |
| format | Article |
| id | doaj.art-f7440f94173642269648aab1de4d8880 |
| institution | Directory Open Access Journal |
| issn | 2192-4384 |
| language | English |
| last_indexed | 2024-04-10T16:27:29Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EURO Journal on Transportation and Logistics |
| spelling | doaj.art-f7440f94173642269648aab1de4d88802023-02-09T04:14:01ZengElsevierEURO Journal on Transportation and Logistics2192-43842023-01-0112100104Combining value function approximation and multiple scenario approach for the effective management of ride-hailing servicesR.-Julius O. Heitmann0Ninja Soeffker1Marlin W. Ulmer2Dirk C. Mattfeld3Technische Universität Braunschweig, Decision Support Group, Germany; Corresponding author.Universität Wien, Department of Business Decisions and Analytics, AustriaOtto von Guericke Universität Magdeburg, Chair of Management Science, GermanyTechnische Universität Braunschweig, Decision Support Group, GermanyThe availability of various services for individual mobility is increasing, especially in urban areas. Dynamic ride-hailing services address these aspects and are gaining market share with providers such as MOIA, UberX Share, Sprinti or BerlKönig. To be able to offer competitive pricing for such a service and at the same time provide a high service quality (e.g. fast response times), effective capacity management is needed. In order to reach this goal, two challenges have to be met by the service provider. On the one hand, a proper demand control has to be installed, which optimizes the responses to transportation requests from customers. On the other hand, suitable fleet control needs to be set in place to optimize the route of the fleet so that the demand can be met. Papers in the literature do solve both but typically focus on one of these two challenges. As an example, value function approximation (VFA) can be used to learn a service offering decision while anticipating future incoming requests. A typical example of a routing-focused method is the multiple scenario approach (MSA) creating a routing which anticipates future requests using a sampling method. In this paper, we combine VFA and MSA to address the two challenges in an effective way. The resulting method is called anticipatory-routing-and-service-offering (ARS). We find that the combined method significantly outperforms the individual components, improving not only the total reward but also the accepted requests. It is found that this performance is particularly high with a heavy workload and thus resources are relatively scarce. We analyse how and under which conditions the components together or individually are particularly important.http://www.sciencedirect.com/science/article/pii/S2192437623000018Ride-sharingDial-a-rideDynamic vehicle routingValue function approximationMultiple scenario approach |
| spellingShingle | R.-Julius O. Heitmann Ninja Soeffker Marlin W. Ulmer Dirk C. Mattfeld Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services EURO Journal on Transportation and Logistics Ride-sharing Dial-a-ride Dynamic vehicle routing Value function approximation Multiple scenario approach |
| title | Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services |
| title_full | Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services |
| title_fullStr | Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services |
| title_full_unstemmed | Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services |
| title_short | Combining value function approximation and multiple scenario approach for the effective management of ride-hailing services |
| title_sort | combining value function approximation and multiple scenario approach for the effective management of ride hailing services |
| topic | Ride-sharing Dial-a-ride Dynamic vehicle routing Value function approximation Multiple scenario approach |
| url | http://www.sciencedirect.com/science/article/pii/S2192437623000018 |
| work_keys_str_mv | AT rjuliusoheitmann combiningvaluefunctionapproximationandmultiplescenarioapproachfortheeffectivemanagementofridehailingservices AT ninjasoeffker combiningvaluefunctionapproximationandmultiplescenarioapproachfortheeffectivemanagementofridehailingservices AT marlinwulmer combiningvaluefunctionapproximationandmultiplescenarioapproachfortheeffectivemanagementofridehailingservices AT dirkcmattfeld combiningvaluefunctionapproximationandmultiplescenarioapproachfortheeffectivemanagementofridehailingservices |