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The stochastic user equilibrium (SUE) traffic assignment model integrates the concepts of random assignment and Wardrop equilibrium, and it makes the assignment results more reasonable. The conventional Logit-based SUE model is explicitly built upon path flow variables, which encumbers its large-scale applications and further theoretical analyses. In this paper, a link-based SUE traffic assignment model with link capacity constraints is developed by using the network equilibrium modeling approach. The solution equivalence and uniqueness of the proposed model are rigorously proved. An efficient solution algorithm is also proposed to obtain the equilibrium solution. Numerical results indicate that the proposed link-based SUE model can reflect the real route choice behavior of travelers more reasonable and also facilitate the algorithmic development and further theoretical analyses. KEYWORDS: stochastic user equilibrium; logit; link variables; link capacity constraints INTRDUCTIONTraffic management and road facilities can not solve the problem of traffic congestion as before, and series of traffic assignment models have been proposed to analyze the complexity of urban traffic problem. Wardrop(1952) proposed user equilibrium (UE)assignment principle that all used paths have smaller cost than the paths not used. Hence, the following UE models are too sensitive for traffic cost; all travelers will choose the small cost paths although the difference of the cost is little. Daganzo and Sheffi(1977) proposed stochastic user equilibrium(SUE) assignment;, Fisk(1980) is a general model that unifies Wardropian equilibrium and the concept of the stochastic assignment. They made the result of model more reasonable by joining a random "error term" to the cost to reduce the sensitivity. CICTP 2012

In this study, accurate global position system and geographic information system data were employed to reveal multiday routes people used and to study multiday route choice behavior for the same origin-destination trips, from home to work. A new way of thinking about route choice modeling is provided in this study. Travelers are classified into three kinds based on the deviation between actual routes and the shortest travel time paths. Based on the classification, a two-stage route choice process is proposed, in which the first step is to classify the travelers and the second one is to model route choice behavior. After analyzing the characteristics of different types of travelers, an artificial neural network was adopted to classify travelers and model route choice behavior. An empirical study using global position systems data collected in Minneapolis-St Paul metropolitan area was carried out. It finds that most travelers follow the same route during commute trips on successive days. And different types of travelers have a significant difference in route choice property. The modeling results indicate that neural network framework can classify travelers and model route choice well.

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