Hello,
I have a massive traffic dataset that contains noisy measurements of traffic in 15 minute bins on a few hundred road segments. My goal is to train a model on 80% of the segments and then predict the traffic on the other segments (their full timeseries). For this, it is fair to assume that the segments are very similar.
Although this is a timeseries forecasting question, it is different than normal forecasting models (like LSTM) because I am not using ground truth historical data to predict the future. Rather, I am using the sequences of other road segments to predict a new road segment.
My idea was to train a DNN including time of day, day of week, etc. as prediction columns to help incorporate the seasonality (daily, weekly, monthly) in the traffic predictions (along with my other noisy measurements). However, I wasn’t sure if this was the best option. Anyone have any ideas/suggestions? Would really appreciate any insight!