The aim of this paper is to develop an activity-based travel demand model by receiving cellular network data. Our contribution is to model the uncertainty of human behaviors and also the ambiguity in features affecting users’ activities. We used probabilities to model the first aspect and fuzzy theory to treat with the second; therefore, a hybrid model is proposed based on the Hidden Markov Model (HMM) and Fuzzy Inference System (FIS) such that FIS is used in the emission model of HMM. To show the efficiency of this model, we applied the model to the data collected by Irancell operator and validated the results with four different data sources; labeled data collected from volunteers, ground truth data labeled by an expert, activity-based number of trips generated from/attracted to different regions and reported traffic volume of highways. We have shown that the activity recognition accuracy of the model is 83% and an average error of 5% is obtained when comparing the statistics of the model generated activity plans and the corresponding statistics provided in reports. Generated activity plans are also converted to traffic volumes on transportation network links through MATSIM simulation software and the promising R² value of 0.83 is observed.

本文的目的是通过接收蜂窝网络数据来开发基于活动的旅行需求模型。我们的贡献是模拟人类行为的不确定性，以及影响用户活动的特征的模棱两可。我们使用概率对第一个方面进行建模，并使用模糊理论对第二个方面进行处理；因此，提出了基于隐马尔可夫模型（HMM）和模糊推理系统（FIS）的混合模型，从而将FIS用于HMM的排放模型。为了证明该模型的有效性，我们将该模型应用于Irancell运营商收集的数据，并使用四个不同的数据源验证了结果。从志愿者收集的标记数据，由专家标记的地面真相数据，从/吸引到不同区域的基于活动的出行次数以及所报告的高速公路通行量。我们已经显示，当比较模型生成的活动计划的统计数据和报告中提供的相应统计数据时，该模型的活动识别准确性为83％，并且平均误差为5％。生成的活动计划也可以通过MATSIM仿真软件和有前途的R转换为交通网络链接上的交通量观察到²值为0.83。