Thema der Dissertation:
Swarm-Based Trajectory Planning for Autonomous Cars Thema der Disputation:
ETA Prediction with Graph Neural Networks in Google Maps
Swarm-Based Trajectory Planning for Autonomous Cars Thema der Disputation:
ETA Prediction with Graph Neural Networks in Google Maps
Abstract: Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike. Further, such a task requires accounting for complex spatiotemporal interactions (modelling both the topological properties of the road network and anticipating events - such as rush hours - that may occur in the future). Hence, it is an ideal target for graph representation learning at scale.
This talk will discuss the graph neural network estimator for estimated time of arrival (ETA) which was presented in the paper below and has been deployed in production at Google Maps. Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo,
X., Wiltshire, B., et al. (2021). ETA Prediction with Graph Neural Networks in Google Maps.
https://doi.org/10.1145/3459637.3481916
This talk will discuss the graph neural network estimator for estimated time of arrival (ETA) which was presented in the paper below and has been deployed in production at Google Maps. Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo,
X., Wiltshire, B., et al. (2021). ETA Prediction with Graph Neural Networks in Google Maps.
https://doi.org/10.1145/3459637.3481916
Zeit & Ort
03.06.2022 | 14:00
Raum 031
(Fachbereich Mathematik und Informatik, Arnimallee 7, 14195 Berlin)