Learning Towards Transportation Network Equilibrium: A Model Comparison Study

As an interdisciplinary topic, human travel-choice behavior has attracted the interests of transportation managers, theoretical computer science researchers and economists. Recent studies on tacit coordination in iterated route choice games (i.e., a large number of subjects could achieve the transportation network equilibrium in limited rounds) have been driven by two questions. (1) Will learning behavior promote tacit coordination in route choice games? (2) Which learning model can best account for these choices/behaviors? To answer the first question, we choose a set of learning models and conduct extensive simulations to determine their success in accounting for major behavioral patterns. To answer the second question, we compare these models to one another by competitively testing their predictions on four different datasets. Although all the selected models account reasonably well for the slow convergence of the mean route choice to equilibrium, they account only moderately well for the mean frequencies of the round-to-round switches from one route to another and fail to appropriately account for substantial individual differences. The implications of these findings for model construction and testing are briefly discussed.