Neural Network: One model per user or (one-hot) variable with one model?

I have about 120 users with a total of 4500 data points. The minimum user has about 5 data points and the maximum has about 100 data points. I would like to build a model that will make predictions for each user.

What is the optimal approach? Do I create a single model for each user or do I create a single model with a categorical variable to specify the user?

I would imagine the single model approach would leverage the correlation between users, but the model per user approach might suffer from not enough data to generalize well.

I would consider a pooling method described here: possible duplicate, but there is not enough information in the independent variables to distinguish the users from one another, which is why I would have to create a categorical variable to distinguish users. Meaning, there are many users with the same input variables, but systematically different outputs.

The input variables include: arrival time, day of week, and temperature. The output variables include departure time and miles charged.