16,111 research outputs found
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).Comment: Currently under review for journal publicatio
Ensemble Reinforcement Learning: A Survey
Reinforcement Learning (RL) has emerged as a highly effective technique for
addressing various scientific and applied problems. Despite its success,
certain complex tasks remain challenging to be addressed solely with a single
model and algorithm. In response, ensemble reinforcement learning (ERL), a
promising approach that combines the benefits of both RL and ensemble learning
(EL), has gained widespread popularity. ERL leverages multiple models or
training algorithms to comprehensively explore the problem space and possesses
strong generalization capabilities. In this study, we present a comprehensive
survey on ERL to provide readers with an overview of recent advances and
challenges in the field. First, we introduce the background and motivation for
ERL. Second, we analyze in detail the strategies that have been successfully
applied in ERL, including model averaging, model selection, and model
combination. Subsequently, we summarize the datasets and analyze algorithms
used in relevant studies. Finally, we outline several open questions and
discuss future research directions of ERL. By providing a guide for future
scientific research and engineering applications, this survey contributes to
the advancement of ERL.Comment: 42 page
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