Discrete choice models help understand how people choose routes beyond just travel time. Logit-type models were tested on real data to improve traffic assignment accuracy. Two approaches were used: one estimates path utility without choice data, and the other considers travel time, cost, and income. The utility estimation significantly affects traffic flow and policy decisions, while model choice has less impact. This research highlights the importance of practical model calibration for better traffic predictions.