Proactive route choice refers to a driver’s taking into account future diversion possibilities enabled by real-time information in a network with random travel times. Route choice experiments were conducted in three types of networks with increasing complexity, where the simplest one has no diversion possibilities and is used to gauge risk attitude. Two apparatuses with different cognitive load, a driving simulator and PC were used. Aggregate analyses show that network complexity negatively affects subjects’ ratio of choosing the risky option on a driving simulator; cognitive load negatively affects subject’s ratio of choosing the risky option in the simplest network, but not in more complex networks. A mixed Logit model with two latent classes, proactive and myopic, is specified and estimated. Results show that subjects learn to be more proactive over time. The impact of network complexity or cognitive load on being proactive is not statistically significant. Cognitive load however increases myopic subjects’ risk aversion.

主动路线选择是指驾驶员考虑到具有随机行驶时间的网络中的实时信息所支持的未来改道可能性。路线选择实验在复杂性不断增加的三种类型的网络中进行，其中最简单的一种没有改道的可能性，用于衡量风险态度。使用了两种具有不同认知负荷的装置，驾驶模拟器和 PC。综合分析表明，网络复杂性会对受试者在驾驶模拟器上选择有风险选项的比率产生负面影响；认知负荷会对受试者在最简单网络中选择风险选项的比率产生负面影响，但在更复杂的网络中则不会。指定并估计了具有两个潜在类别（前瞻性和近视性）的混合 Logit 模型。结果表明，随着时间的推移，受试者会变得更加积极主动。网络复杂性或认知负荷对主动性的影响在统计上并不显着。然而，认知负荷增加了近视受试者的风险厌恶。