Segev Shlomov, Avi Yaeli
CHI 2024
We propose a new method for predicting the travel-time along an arbitrary path between two locations on a map. Unlike traditional approaches, which focus only on particular links with heavy traffic, our method allows probabilistic prediction for arbitrary paths including links having no traffic sensors. We introduce two new ideas: to use string kernels for the similarity between paths, and to use Gaussian process regression for probabilisticprediction. We test our approach using traffic data generated by an agent-based traffic simulator.
Segev Shlomov, Avi Yaeli
CHI 2024
Imran Nasim, Melanie Weber
SCML 2024
Arnold.L. Rosenberg
Journal of the ACM
Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022