ETA Prediction with Graph Neural Networks in Google Maps[C]. CIKM, 2021. Link Golovnin O, Perevozchikov N. E-STGCN: Enhanced Spatial-Temporal Graph Convolutional Network for Road Traffic Forecasting[C]//2021 International Conference on Information Technology and Nanotechnology (ITNT). IEEE, ...
get_closest_idx.m -- Returns the closest K sensors to a query sensor. plot_google_map.m -- Helper function for plotting using Google Maps. fastAUC -- fast computation of AUC. Might need recompiling -- read the documentation within.About...
Compared with the existing traffic information perception network (Vehicle-to-Everything, Google Maps, Waze, Apple Map public transportation information systems and traffic management center, etc.), the main features of the comprehensive traffic information perception system are embodied in the physical ...
on dayj, α is the regression intercept,β1is the effect of bus stop/bus type (X1) on Y,β2is the effect of the first confounder ofnconfounders,biis the random effect for propulsion/stop type andεijis the random error. Effects of influential points were assessed by calculating Cook’s...
The test showed that the P-value of each predictor and the whole predictors simultaneously were greater than 0.05 which indicated that there were no time-varying variables in the model and the proportional hazard assumption was fitted (p = 0.1274) (Annex- V). The model’s fitness test ...
In absence of real-time traffic information services, time- and location-specific historical traffic data can be used as a baseline predictor (Wan et al., 2018). Traffic speed can be imposed as a spatio-temporally varying upper bound on the CAV speed (Asadi et al., 2010). Speed limit, ...
Natural soundscapes commonly experienced in parks are increasingly valued as an important cultural ecosystem service with the potential to promote greater mental well-being for people. Yet the quality of urban park soundscapes can differ, containing varying proportions of natural sounds, such as bird ...
Lastly, a temporal predictor is designed to capture short-term and long-term dependencies of air quality, utilizing deep LSTM (Long Short Term Memory) networks. These solutions are only based on sensor data. In the study, distinct sub-models are employed to discern spatial correlations among ...
python scripts/train.py --dataset_path <path-to-lyft-data-directory> --config_name l5_agent_predictor --debug nuScenes dataset: First train a spatial planner: python scripts/train.py --dataset_path <path-to-nuScenes-data-directory> --config_name nusc_spatial_planner --debug Then train ...
The remainder of this section discusses models 1 and 2, used as stepping stones to achieve the better model 3, and a test of the effectiveness of road class as a predictor of motorized flow. Model 1 is the initial attempt to use road class to predict cycling, and used for calibration pu...