I have created an Origin-Destination cost matrix showing the distance in feet between several thousand home addresses (origins) and about 24 rental vehicles (destinations) at various points throughout the city. Each address point that was input into the OD matrix analysis also originally contained information on the number of times the resident at each address had reserved a particular vehicle.
My goal is to create a table that will show, for all home addresses, the distance from that address to each of the 24 rental cars and the number of times the resident reserved each of the 24 cars. I could then say, for example, "40% of reservations made by residents of Neighborhood A are for cars within 1/2 mile" or "Residents who reserved car X lived an average of 2000 feet away".
Here is the problem. In conducting the OD matrix analysis, it seems that all the unique identifiers of the data that I input have been obliterated. It would appear that there is no way to associate the distances between origins and destinations back to the other attributes associated with the home address points except for some sort of spatial join. Yet, between the inaccuracies introduced by geocoding thousands of addresses and the fact that many home locations are apartments with multiple residents in the same building, it is impossible to distinguish which resident made which trips by using a spatial join.
How have others typically handled the origin-destination problem when origin points overlap and still need to be associated back to other attributes?