A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Bayesian neural networks with physics‐aware regularization for probabilistic travel time modeling
The integration of data‐driven models such as neural networks for high‐consequence decision making has been largely hindered by their lack of predictive power away from training data and their inability to quantify uncertainties often prevalent in engineering applications. This article presents an ensembling method with function‐space regularization, which allows to integrate prior information about the function of interest, thus improving generalization performance, while enabling quantification of aleatory and epistemic uncertainties. This framework is applied to build a probabilistic ambulance travel time predictor, leveraging historical ambulance data provided by the Fire Department of New York City. Results show that the integration of a non‐Gaussian likelihood and prior information from a road network analysis yields appropriate probabilistic predictions of travel times, which could be further leveraged for emergency medical service (EMS) decision making.
Bayesian neural networks with physics‐aware regularization for probabilistic travel time modeling
The integration of data‐driven models such as neural networks for high‐consequence decision making has been largely hindered by their lack of predictive power away from training data and their inability to quantify uncertainties often prevalent in engineering applications. This article presents an ensembling method with function‐space regularization, which allows to integrate prior information about the function of interest, thus improving generalization performance, while enabling quantification of aleatory and epistemic uncertainties. This framework is applied to build a probabilistic ambulance travel time predictor, leveraging historical ambulance data provided by the Fire Department of New York City. Results show that the integration of a non‐Gaussian likelihood and prior information from a road network analysis yields appropriate probabilistic predictions of travel times, which could be further leveraged for emergency medical service (EMS) decision making.
Bayesian neural networks with physics‐aware regularization for probabilistic travel time modeling
Olivier, Audrey (author) / Mohammadi, Sevin (author) / Smyth, Andrew W. (author) / Adams, Matt (author)
Computer‐Aided Civil and Infrastructure Engineering ; 38 ; 2614-2631
2023-12-01
18 pages
Article (Journal)
Electronic Resource
English
Travel Mode Choice Modeling: A Comparison of Bayesian Networks and Neural Networks
| British Library Conference Proceedings | 2012