Check that road segments are not unique and shared by trips (and ideally overlapping routes as well).
pull in the shape file
group by shape_id, this breaks the dataframe into small dfs for each shape_id
do an adjacent pairwise iteration over all the lat/lngs
take pairwise into 4 tuple of origin dest lat/longs, put into hash table and see how often they show up. Should probably be > than 1 since a given route is probably driven more than once a day.
build a histogram of usage so we can wee which road segments that are not termini are best suited have en-route charger. This may be difficult to do with lots of overlapping termini and tight road segments
If the road segments are shared, count the number of buses that pass through.
shape_id