88 citations · 207 across the 31 of their papers we have counts for
5 papers · 1 filter
Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting
Weijiang Xiong, Robert Fonod, Nikolas Geroliminis
Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management. Current studies mainly focus on deterministic predic…
Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data
Weijiang Xiong, Robert Fonod, Alexandre Alahi +1
Traffic forecasting is a fundamental task in transportation research, however the scope of current research has mainly focused on a single data modality of loop detectors. Recently…
Deep Learning Methods for Adjusting Global MFD Speed Estimations to Local Link Configurations
Zhixiong Jin, Dimitrios Tsitsokas, Nikolas Geroliminis +1
In large-scale traffic optimization, models based on Macroscopic Fundamental Diagram (MFD) are recognized for their efficiency in broad network analyses. However, they fail to refl…
Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs
Semin Kwak, Nikolas Geroliminis, Pascal Frossard
Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes su…
Travel time prediction for congested freeways with a dynamic linear model
Semin Kwak, Nikolas Geroliminis
Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction…