Using nlp to analyze accident report I want to use Natural Language Processing to analyze traffic accident reports and from the text determine two things: 


Direction of vehicle travel (just compass directions like north, southeast, etc.)
Vehicle movement descriptions (e.g. backing, turning left on a red, stopped in traffic, turning right, parked).


A fragment of an accident report narrative looks something like this:


  TU2 was parked and attended on the west side of Main St, facing west,
  engine turned off. TU1 was parked on the east side of Main St, facing
  east and exited the parking spot, driving backwards. TU1's rear
  collided with TU2's rear. TU2 was still parked with the engine off
  when rear ended by TU1.


The accident reports are in thousands of files, with all accident reports for one year in one city in a single file.  The "answers" (labels) to vehicle travel direction and movement descriptions are provided, along with the narrative of the accident report.  So I have a decent training dataset.

I was thinking about starting with an approach like an n-gram bag of words and a simple classifier for vehicle direction (north, southwest, etc.).  Would that be a good start?
Firstly, I hope that the label is either a short summary or words of varying length, not just one word direction. Because moving cars involved in an accident may have multiple directions, or one car could be just parked like the example.
Secondly, given that you are planning to predict varying length label, and given the example text, I am pretty sure that bag of words will not work well. You need the context. And you dont have million sized training set to train a transformer deep neural net. So try to utilize  pre-trained embeddings like USE, GloVe etc. If you are using the embedding, it provides you awesome feature engineering done. Just train some low complexity model like random forest/ xgboost on (label, embeddings). You can also explore pretrained summary generators like bertseq2seq, however the code for this didn't work for me yet.