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Fuzzy logic approach in mode choice modelling for education trips: a case study of indian metropolitan city

机译:教育出行方式选择建模中的模糊逻辑方法-以印度城市为例

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Urban population in India has increased significantly from 62 million in 1951 to 378 million in 2011 in six decades. It is estimated to reach 540 million by the year 2021. This reflects on likely pressure on urban transportation system. The situation necessarily calls plans for balanced personal and public transport system. Mandatory trips bear more importance in this regard owing to their higher share in urban trips. Mode share and their choice behaviour in estimation of such trips play vital role in analysing and boosting sustainable transportation. Logit modelling approach is the conventional method generally adopted for analysing mode choice behaviour, which is based on the principle of random utility maximization derived from econometric theory. However, such models cannot address uncertainity prevailing in the choice decisions. On the contrary, fuzzy logic bypasses the binary crisp derivations of the inputs and accepts multivalued inputs in linguistic expressions, which make possible to resemble the human behaviour closely. Therefore, the attempt here is to develop fuzzy logic based mode choice model for education trips, which constitutes a good share in mandatory trips by covering various income groups of Indian society.
机译:在六十年内,印度城市人口已从1951年的6200万大幅增加到2011年的3.78亿。估计到2021年将达到5.4亿。这反映了城市交通系统可能面临的压力。这种情况必然要求制定平衡个人和公共交通系统的计划。强制性旅行由于在城市旅行中所占的比例较高而在这方面更加重要。模式共享及其选择行为在此类行程的估计中,在分析和促进可持续交通方面起着至关重要的作用。 Logit建模方法是通常用于分析模式选择行为的常规方法,它基于从计量经济学理论中得出的随机效用最大化的原理。但是,此类模型无法解决选择决策中普遍存在的不确定性。相反,模糊逻辑绕过了输入的二进位明晰推导,并接受了语言表达式中的多值输入,这使得紧密模仿人类行为成为可能。因此,这里的尝试是为教育旅行开发基于模糊逻辑的模式选择模型,该模型通过涵盖印度社会的各个收入群体在强制性旅行中占了很大的份额。

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