TR2023-012
Travel-time prediction using neural-network-based mixture models
-
- , "Travel-time prediction using neural-network-based mixture models", International Workshop on Statistical Methods and Artificial Intelligence, DOI: 10.1016/j.procs.2023.03.144, March 2023.BibTeX TR2023-012 PDF
- @inproceedings{Sharma2023mar,
- author = {Sharma, Abhishek and Zhang, Jing and Nikovski, Daniel and Doshi-Velez, Finale},
- title = {Travel-time prediction using neural-network-based mixture models},
- booktitle = {International Workshop on Statistical Methods and Artificial Intelligence},
- year = 2023,
- month = mar,
- doi = {10.1016/j.procs.2023.03.144},
- url = {https://www.merl.com/publications/TR2023-012}
- }
- , "Travel-time prediction using neural-network-based mixture models", International Workshop on Statistical Methods and Artificial Intelligence, DOI: 10.1016/j.procs.2023.03.144, March 2023.
-
MERL Contact:
-
Research Areas:
Abstract:
Accurate estimation of travel times is an important step in smart transportation and smart building systems. Poor estimation of travel times results in both frustrated users and wasted resources. Current methods that estimate travel times usually only return point estimates, losing important distributional information necessary for accurate decision-making. We propose using neural network-based mixture distributions to predict a user’s travel times given their origin and destination coordinates. We show that our method correctly estimates the travel time distribution, maximizes utility in a downstream elevator scheduling task, and is easy to retrain—making it a versatile and an inexpensive-to-maintain module when deployed in smart crowd management systems.