As @Christopher Lauden mentioned above, time-series analysis is most appropriate for this sort of thing. If, however, you wished to do a more traditional "machine learning approach", something that I have done in the past is to block up your data into overlapping windows of time as features, then use it to predict the next days (or weeks) traffic.
Your feature matrix would be something like:
t1 | t2 | ... | tN
t2 | t3 | ... | tN+1
t3 | t4 | ... | tN+2
tW | tW+1 | ... |tN+W
tI is the traffic on day
I. The feature you'll be predicting is the traffic on the day after the last column. In essence, use a window of traffic to predict the next day's traffic.
Any sort of ML model would work for this.
In response to the question, "can you elaborate on how you use this feature matrix":
The feature matrix has values indicating past traffic over a period of time (for instance, hourly traffic over 1 week), and we use this to predict traffic for some specified time period in the future. We take our historic data and build a feature matrix of historic traffic and label this with the traffic at some period in the future (e.g. 2 days after the window in the feature). Using some sort of regression machine learning model, we can take historic traffic data, and try and build a model that can predict how traffic moved in our historic data set. The presumption is that future traffic will resemble past traffic.