Development of Transport Mode Choice Model by Using Adaptive Neuro-Fuzzy Inference System
Development of Transport Mode Choice Model by Using Adaptive Neuro-Fuzzy Inference System
复制标题
DOI：
10.1177/0361198106197700102
复制
发表时间：
2006
影响因子：
1.7
通讯作者：
Katia Andrade;K. Uchida;S. Kagaya
中科院分区：
文献类型：
--
作者：
Katia Andrade;K. Uchida;S. Kagaya
开发精确的出行行为模型对于估计交通需求以及规划交通系统非常重要。一项研究提出了一种混合模型，结合了随机模型与神经模糊推理系统。该模型适用于估计旅客行为的背景下，运输方式选择的问题。特别是，多项logit模型与神经模糊效用函数的开发，以调查购物旅客的偏好，关于公交车，地铁和汽车的方式。该模型进行评估，通过比较其结果与多项logit模型的结果。此外，通过应用这两个模型得到的选择一种运输方式的概率与实际的运输方式的选择进行了比较，这表明所提出的模型的性能更好。此外，该模型通过在验证过程中估计大量正确的选择，表现出良好的性能。灵敏度分析表明，时间变化的方式地铁上的概率选择一种运输方式的影响。分析突出了不同的效用函数所导致的模型的不同行为。结果表明，该模型可以描述出行者在交通方式选择时间上的不确定性。
Developing precise travel behavior models is important for estimating traffic demand and, consequently, for planning transportation systems. A study is presented that suggests a hybrid model that combines a stochastic model with a neuro-fuzzy inference system. The model is applied for estimating traveler behavior in the context of the problem of transport mode choice. Particularly, the multinomial logit model with neuro-fuzzy utility functions is developed to investigate shopping traveler preferences regarding the modes of bus, subway, and automobile. The model is evaluated by comparing its results with the results of a multinomial logit model. Moreover, the probabilities of selecting a transport mode obtained by applying the two models are compared with the actual transport mode choices, which show better performance of the proposed model. In addition, the model demonstrates good performance by estimating a large number of right choices during the validation process. A sensitivity analysis demonstrates the influence of time variations of mode subway on the probabilities of selecting a transport mode. The analysis highlights different behaviors of the models caused by the different utility functions. The results confirm that the proposed model can describe uncertainties regarding traveler decisions on the time of transport mode choice.