Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones
Tree-based ensembles such as the Random Forest are modern classics among statistical
learning methods. In particular, they are used for predicting univariate responses. In case of …
learning methods. In particular, they are used for predicting univariate responses. In case of …
Machine learning based short-term travel time prediction: Numerical results and comparative analyses
B Qiu, W Fan - Sustainability, 2021 - mdpi.com
Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction
(TTP) can be an important and useful tool for both travelers and traffic management …
(TTP) can be an important and useful tool for both travelers and traffic management …
Supervised learning for arrival time estimations in restaurant meal delivery
FD Hildebrandt, MW Ulmer - Transportation Science, 2022 - pubsonline.informs.org
Restaurant meal delivery companies have begun to provide customers with meal arrival
time estimations to inform the customers' selection. Accurate estimations increase customer …
time estimations to inform the customers' selection. Accurate estimations increase customer …
Synchronizing victim evacuation and debris removal: A data-driven robust prediction approach
This study introduces a new perspective in disaster management's response and post-
disaster phases to synchronize multiple vehicles for victim evacuation and debris removal …
disaster phases to synchronize multiple vehicles for victim evacuation and debris removal …
A flexible framework to coordinate debris clearance and relief distribution operations: a robust machine learning approach
B Vahdani - Expert Systems with Applications, 2023 - Elsevier
This study aims to offer an integrated, flexible action plan to bridge two vital operations in the
response phase of disaster management, namely debris clearance and relief items …
response phase of disaster management, namely debris clearance and relief items …
[HTML][HTML] Human vs. Machines: Who wins in semiconductor market forecasting?
L Steinmeister, M Pauly - Expert Systems with Applications, 2025 - Elsevier
Abstract “If you ask ten experts, you will get ten different opinions.” This common proverb
illustrates the common association of expert forecasts with personal bias and lack of …
illustrates the common association of expert forecasts with personal bias and lack of …
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?
Tree-based ensembles such as the Random Forest are modern classics among statistical
learning methods. In particular, they are used for predicting univariate responses. In case of …
learning methods. In particular, they are used for predicting univariate responses. In case of …
Probabilistic prediction of trip travel time and its variability using hierarchical Bayesian learning
This paper proposes a probabilistic machine learning methodology to predict travel time and
its variability for trips between locations in New York City. First, a hierarchical Bayesian …
its variability for trips between locations in New York City. First, a hierarchical Bayesian …
Adaptive forecast-driven repositioning for dynamic ride-sharing
In dynamic ride-sharing systems, intelligent repositioning of idle vehicles often improves the
overall performance with respect to vehicle utilization, request rejection rates, and customer …
overall performance with respect to vehicle utilization, request rejection rates, and customer …
Travel time prediction using hybridized deep feature Space and machine learning based heterogeneous ensemble
Travel Time Prediction (TTP) has become an essential service that people use in daily
commutes. With the precise TTP, individuals, logistic companies, and transport authorities …
commutes. With the precise TTP, individuals, logistic companies, and transport authorities …