Human Aspects of Route Choice Behavior: Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior
Human Aspects of Route Choice Behavior: Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior
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路线选择行为的人为方面：将感知、学习趋势、潜在类别和人格特质纳入路线选择行为中驾驶员异质性的建模中
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Hesham A Rakha
中科院分区：
文献类型：
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作者：
Aly M. Tawfik;Hesham A Rakha
驾驶员出行行为的异质性在文献中多次被引用为需要解决的限制。在这项工作中，驱动程序的异质性是从四个不同的角度来解决。首先，驾驶员异质性是由驾驶员对行驶条件的感知模型来解决的：行驶距离、时间和速度。其次，从驾驶员学习趋势和驾驶员类型模型的角度进行了研究。驾驶员类型在运输工程的术语中并不常用。这是在这项工作中开发的一个术语，以反映驾驶员在路线切换行为中的侵略性。它可以被解释为类似于通常所知的人格类型，但适用于驾驶员行为。第三，司机异质性是通过潜在的类选择模型。最后，人格特质被发现在所有估计的模型显着。前三个采用的观点被建模为驾驶员人口统计学，人格特质和选择情况特征的变量的函数。这项工作是基于三个数据集：驾驶模拟器实验，在现实世界条件下的原位驾驶实验，和自然的现实生活中的驾驶实验。总的来说，结果是基于三个实验，109名司机，74个路线选择的情况下，和8,644路线选择。可以保证的是，发现所有三个实验的结果高度一致。网络导向的交通分配模型的预测和观察到的路线选择百分比之间的差异被确定，并将变量的驱动程序异质性，以提高路线选择模型的性能。从所有三个组的变量：司机人口统计学，人格特质，和选择的情况下的特点，被发现显着在所有考虑模型的司机异质性。然而，非常有趣的是，驾驶员人格特质的所有五个变量都被发现，一般来说，与旅行特征的变量一样重要，而且往往比旅行时间更重要。神经质、外向性和谨慎性会增加路径转换行为，开放性和随和性会减少路径转换行为。此外，正如预期的那样，旅行时间被认为是非常重要的模型开发。然而，出乎意料的是，还发现行进速度非常显著，并且行进距离没有预期的显著。这项工作的结果是非常有前途的未来的理解和人类出行行为的异质性建模，以及确定目标市场和智能交通系统的未来。
Driver heterogeneity in travel behavior has repeatedly been cited in the literature as a limitation that needs to be addressed. In this work, driver heterogeneity is addressed from four different perspectives. First, driver heterogeneity is addressed by models of driver perceptions of travel conditions: travel distance, time, and speed. Second, it is addressed from the perspective of driver learning trends and models of driver-types. Driver type is not commonly used in the vernacular of transportation engineering. It is a term that was developed in this work to reflect driver aggressiveness in route switching behavior. It may be interpreted as analogous to the commonly known personality-types, but applied to driver behavior. Third, driver heterogeneity is addressed via latent class choice models. Last, personality traits were found significant in all estimated models. The first three adopted perspectives were modeled as functions of variables of driver demographics, personality traits, and choice situation characteristics. The work is based on three datasets: a driving simulator experiment, an in situ driving experiment in real-world conditions, and a naturalistic real-life driving experiment. In total, the results are based on three experiments, 109 drivers, 74 route choice situations, and 8,644 route choices. It is assuring that results from all three experiments were found to be highly consistent. Discrepancies between predictions of network-oriented traffic assignment models and observed route choice percentages were identified and incorporating variables of driver heterogeneity were found to improve route choice model performance. Variables from all three groups: driver demographics, personality traits, and choice situation characteristics, were found significant in all considered models for driver heterogeneity. However, it is extremely interesting that all five variables of driver personality traits were found to be, in general, as significant as, and frequently more significant than, variables of trip characteristics – such as travel time. Neuroticism, extraversion and conscientiousness were found to increase route switching behavior, and openness to experience and agreeable were found to decrease route switching behavior. In addition, as expected, travel time was found to be highly significant in the models that were developed. However, unexpectedly, travel speed was also found to be highly significant, and travel distance was not as significant as expected. Results of this work are highly promising for the future of understanding and modeling of heterogeneity of human travel behavior, as well as for identifying target markets and the future of intelligent transportation systems.