隨著智慧型運輸系統（Intelligent Transportation Systems, ITS）的發展，先進旅行者資訊系統（Advanced Traveler Information Systems, ATIS），藉由提供即時路況資訊作為用路人進行相關旅運決策之參考，以減少用路人對於旅行時間的不確定性、舒緩交通壅塞的情形。然而，ATIS所提供的即時資訊品質，以及用路人在不確定環境下接收到交通資訊的反應行為，決定了資訊可靠度、資訊滲透率，以及資訊遵循率等資訊特性。對交通壅塞情形改善的實際效用則取決於用路人接收到資訊後確實能藉由減少旅行時間與不確定性。
傳統路徑選擇相關研究多探討路網特性與用路人行為特性對路徑選擇決策的影響，少有探討路況資訊的特性對用路人路徑選擇決策的影響。本研究以交通管理中心的角度探討隨機路網中，同時存在旅行時間變異性與用路人認知誤差，用路人透過ATIS提供交通資訊之下，根據本身風險傾向，透過學習及相關經驗的累積進行路徑選擇決策時，交通資訊對短程通勤旅次用路人路徑選擇的影響。
研究中利用情境分析模擬多種用路人路徑選擇情形，以運輸規劃應用軟體TransCAD進行交通指派，根據Cheu et al. （2008）所發展的Simpler Equivalent Link Disutility（Simpler ELD）計算旅行成本，並以Chen and Recker（2001）所建構的SN-SUE Model為用路人路徑選擇模型，加入資訊可靠度、資訊滲透率，以及資訊遵循率等反映交通資訊特性的重要參數，以“用路人認知負效用最小化”為決策準則，以反應隨機路網中短程通勤旅次用路人風險傾向的路徑選擇行為。

Researches about users’ route-choice behaviors under the influence of ATIS generally ignore the characteristics of information and the attributions of each user with the unrealistic assumptions of possessing perfect information, rationality and homogeneity. The utility of information is influenced by market penetration, compliance rate and reliability of information.
Since the uncertainty of traffic condition and individual’s heterogeneousness of information processing ability and perception, it is necessarily to review the route choice problem with consideration of variability of traffic flow and users’ preferences and perception error. Therefore, the research is aimed to study the context of ATIS under stochastic network and multi-class users with different characteristics.
Chen and Recker （2001）developed the SN-SUE model　by considering two realistic stochastic dimensions -travel time reliability and perception error. In this model, travel time reliability is considered as a risk, users with different risk-taking propensities will evaluate route cost based on the trade-off between travel time and travel time variability, and make route-choice decision by minimizing the equivalent link disutility (ELD) function. Recently, Cheu et al., (2008) derived a simply ELD function in the SN model, which is similar as the BPR function, and can account the link travel time and users’ respond to uncertainty to represent users’ risk-taking attribute without any assumption of the link travel time distribution and variance.
Since the objective of this paper is to predict multi-class users’ route choice behaviors with preferences and traffic information provided under stochastic network. This paper will combine traveler’s risk-taking attitude with the reaction to the traffic information, by using a simple ELD function as the form of route choice criterion.

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