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(Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories
Real-time estimation of destination and travel time for taxis is of grea...
09/17/2015 ∙ by Hoang Thanh Lam, et al. ∙
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Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compre
Within machine learning, the supervised learning field aims at modeling ...
04/26/2017 ∙ by Arnaud Joly, et al. ∙
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Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
Extremely preterm infants often require endotracheal intubation and mech...
08/24/2018 ∙ by Lara J. Kanbar, et al. ∙
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Comparing various regression methods on ensemble strategies in differential evolution
Differential evolution possesses a multitude of various strategies for g...
07/02/2013 ∙ by Iztok Fister Jr, et al. ∙
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Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting
Predicting traffic incident duration is a major challenge for many traff...
05/29/2019 ∙ by Adriana-Simona Mihaita, et al. ∙
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Efficient Destination Prediction Based on Route Choices with Transition Matrix Optimization
Destination prediction is an essential task in a variety of mobile appli...
11/08/2017 ∙ by Heli Sun, et al. ∙
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Artificial Neural Networks Applied to Taxi Destination Prediction
We describe our first-place solution to the ECML/PKDD discovery challeng...
07/31/2015 ∙ by Alexandre de Brébisson, et al. ∙
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Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic
10/12/2018 ∙ by Oleh Bodunov, et al. ∙
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In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97 mins) for the ETA prediction.
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