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A hybrid of neuro-fuzzy inference system and hidden Markov Model for activity-based mobility modeling of cellphone users

机译：一种神经模糊推理系统和隐马尔可夫模型的手机用户的活动型杂交模型

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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.
机译：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功能的歧义。我们使用概率来模拟第一方面和模糊理论，以便与第二个方面进行治疗;因此，基于隐马尔可夫模型（HMM）和模糊推理系统（FIS）提出了一种混合模型，使得FIS用于HMM的发射模型。为了展示该模型的效率，我们将模型应用于Irancell运算符收集的数据，并使用四个不同的数据源验证结果;标记数据从志愿者收集，由专家标记的地面真理数据，基于活动的行为从/吸引到不同地区，并报告了高速公路的交通量。我们已经表明，在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信息时，可以获得模型的活动识别准确性，并且在比较模型生成的活动计划的统计数据时获得了5％的误差。生成的活动计划也通过Matsim仿真软件转换为运输网络链路上的流量卷，观察到有前景的R2值为0.83。

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