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Identification of Measures of Effectiveness (MOEs) for developing Pedestrian Level of Service (PLOS)

机译:确定发展行人服务水平(PLOS)的有效措施(MOE)

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A wide range of literature is available about assessing Pedestrian Level of Service (PLOS), which use different approaches and different Measures of Effectiveness (MOEs) — or attributes — to characterise the PLOS models. In recent years, there has been a growing consensus of capturing three different constructs in the PLOS model — flow characteristics of the pedestrian traffic, the built walking environment and the user's perception. Existing PLOS literature has been capturing these broad constructs, but not in a combined fashion. This paper explores the MOEs responsible for developing such a PLOS and records expert opinion surveys on a Fuzzy-Likert (FL) scale. Three established rating data techniques —TOPSIS, RIDIT are GRA are then utilised to get a ranking of the MOEs that could be further used to develop the said PLOS model. It is seen from these rankings that of the top 10 MOEs preferred by the experts, nine belong to the broad construct categories of design (built walking environment) and the user's perception, and only one belongs to the broad construct of flow characteristics. This result reinforces the fact that the PLOS has to be created using all the three broad constructs and not separately — or in pairs — as had been done so far. This study also deals with the effectiveness of using an FL scale compared to a Likert scale as a response measurement tool and found that an FL scale is 13.08% more accurate than a Likert scale in measuring ordinal responses.
机译:关于评估行人服务水平(PLOS)的文献很多,它们使用不同的方法和不同的有效性度量(MOE)或属性来表征PLOS模型。近年来,在PLOS模型中捕获三种不同的构造(行人交通的流量特性,建成的步行环境和用户的感知)的共识日益增长。现有的PLOS文献一直在捕获这些广泛的结构,但没有以结合的方式进行。本文探讨了负责制定此类PLOS的MOE,并以Fuzzy-Likert(FL)规模记录专家意见调查。然后利用三种已建立的评级数据技术-TOPSIS,RIDIT和GRA来获得MOE的排名,该排名可进一步用于开发所述PLOS模型。从这些排名中可以看出,在专家首选的前10个MOE中,有9个属于设计(建筑步行环境)和用户感知的广泛构造类别,只有一个属于流动特性的广泛构造。这一结果加强了这样一个事实,即必须使用所有三个广泛的结构来创建PLOS,而不是像到目前为止那样单独或成对地创建PLOS。这项研究还探讨了将FL量表与Likert量表相比用作响应测量工具的有效性,发现FL量表在测量序数响应方面比Likert量表的准确度高13.08%。

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