An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival
Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ET...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/9301691 |
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| author | Sören Schleibaum Jörg P. Müller Monika Sester |
| author_facet | Sören Schleibaum Jörg P. Müller Monika Sester |
| author_sort | Sören Schleibaum |
| collection | DOAJ |
| description | Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction. |
| format | Article |
| id | doaj-art-54b1e8b01b8e460ab905ba3f41c3c414 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-54b1e8b01b8e460ab905ba3f41c3c4142025-02-03T06:14:54ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/9301691An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of ArrivalSören Schleibaum0Jörg P. Müller1Monika Sester2Clausthal University of TechnologyClausthal University of TechnologyLeibniz University HannoverSustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction.http://dx.doi.org/10.1155/2024/9301691 |
| spellingShingle | Sören Schleibaum Jörg P. Müller Monika Sester An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival Journal of Advanced Transportation |
| title | An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival |
| title_full | An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival |
| title_fullStr | An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival |
| title_full_unstemmed | An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival |
| title_short | An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival |
| title_sort | explainable stacked ensemble model for static route free estimation of time of arrival |
| url | http://dx.doi.org/10.1155/2024/9301691 |
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