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Modelling Trip Distribution Using Fuzzy Logic Approach

机译:使用模糊逻辑方法对行程分布建模

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摘要

Fuzzy logic is a soft computing technique, used for handling subjective, ambiguous and imprecise data. The decision to travel and choice of destination resulted from human are affected by socio-economic characteristics and residential locations and places of urban activities of the commuters. The subjective pattern of decision makers can be captured using a fuzzy logic system. The present study focuses the second stage of conventional travel demand modelling i.e. Trip distribution. Fast developing metropolitan city, Surat in South Gujarat has been considered as the study area. Southwest zone administratively delineated by Surat Municipal Corporation is selected as a typical subject area covering nearly 3.69 lakhs population to demonstrate model building on Fuzzy Logic. Subzones eight in number form the trip distribution matrix for the area in developing trip distribution model. The sub-zonal productions, attractions and the distance between the sub-zones are the major inputs for the Fuzzy Logic Trip Distribution Model (FLoTDM). MATLAB FIS toolbox is used for developing the model. The fuzzy model results are compared with the outputs of the traditional Gravity model. Results show that the Fuzzy Logic Trip Distribution Model is giving lesser error compared to the conventional Gravity Model. Hence, Fuzzy Logic approach can be used as a non-traditional trip distribution model.
机译:模糊逻辑是一种软计算技术,用于处理主观,模棱两可和不精确的数据。人为出行和目的地选择的决定会受到社会经济特征以及通勤者居住地和城市活动场所的影响。可以使用模糊逻辑系统捕获决策者的主观模式。本研究的重点是常规出行需求建模的第二阶段,即出行分布。快速发展的大都市,位于南古吉拉特邦的苏拉特被视为研究区域。苏拉特市政公司在行政区域划定的西南区域被选为涵盖近36.9万人口的典型主题区域,以演示基于模糊逻辑的模型构建。在开发行程分布模型中,八个分区组成了该区域的行程分布矩阵。分区生产,景点和分区之间的距离是模糊逻辑行程分配模型(FLoTDM)的主要输入。 MATLAB FIS工具箱用于开发模型。将模糊模型的结果与传统重力模型的输出进行比较。结果表明,与传统的重力模型相比,模糊逻辑跳闸分布模型给出的误差较小。因此,模糊逻辑方法可以用作非传统行程分配模型。

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