TY - JOUR

T1 - An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival

AU - Schleibaum, Sören

AU - Müller, Jörg P.

AU - Sester, Monika

N1 - Funding Information: Tis work was supported by the Deutsche Forschungsgemeinschaft under grant 227198829/GRK1931. Te SocialCars Research Training Group focuses on future mobility concepts through cooperative approaches. Open Access funding was enabled and organized by Projekt DEAL.

PY - 2024/3/7

Y1 - 2024/3/7

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85188174640&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2203.09438

DO - 10.48550/arXiv.2203.09438

M3 - Article

AN - SCOPUS:85188174640

VL - 2024

JO - Journal of advanced transportation

JF - Journal of advanced transportation

SN - 0197-6729

M1 - 9301691

ER -