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首页> 外文期刊>Transportation in Developing Economies >Database Development Approach and Survey Design for Travel Mode Shift Behavior Study with Respect to Mass Transit in a Metropolitan Context
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Database Development Approach and Survey Design for Travel Mode Shift Behavior Study with Respect to Mass Transit in a Metropolitan Context

机译：大城市背景下基于大众运输的出行方式转换行为研究的数据库开发方法和调查设计

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Travel behavior study is one of the most complexrnstudies as it governs multi-parameter evaluation at arntime and involves collection of huge data pertaining tornhuman behavior in variety of situations (Rastogi andrnKrishna Rao in Segmentation analysis of commutersrnaccessing transit: Mumbai study. J Transp Engrn135(8):506–515, 2009). Success of travel behavior studiesrnis mostly dependent on healthy data collection. Aim of thisrnpaper, is to evaluate and suggest the most suitable databaserndevelopment approach and research strategies which canrnbe adopted for travel behavior studies especially in contextrnof Indian metro cities. It also presents entire survey designrnprocedure adopted for travel mode shift study along withrnresearchers’ experience in execution of survey and difficultiesrnfaced by field teams. Three level filtration processesrnare adopted for selection of variables for development ofrnmode shift models. Initially the least important variablesrnwere omitted based on variable ranking results. Top rankedrnseven variables are identified for final survey design, out ofrnwhich travel time (including transit access time, waitingrntime and in-vehicle time), travel cost and number ofrntransfers are directly measurable variables while comfort,rnconvenience, safety and security are latent variables. Thernlatent variables are measured using psychometric scalernconstructed from various latent variable indicators and thenrnconverted into latent classes using principal componentrnanalysis. Research instrument developed and adopted forrnthe present study was tested through pilot survey andrncorrected as per data compatibility requirement. Finallyrn1017 valid responses out of 1500 distributed questionnairesrnare analyzed to evaluate the effects of enhanced level ofrnhypothetical public transport services on travel mode shiftrnbehavior through development of binary logistic regressionrnmodels. Changes in the model performance with inclusionrnof latent classes are also evaluated using various statisticalrntools. This paper includes original work based on primaryrndata from a field survey, findings of which are expected tornprovide a better understanding of entire database developmentrnprocess and effect of different variables on modernshift behavior in context of Indian metropolitan area.
机译：出行行为研究是最复杂的研究之一，因为它在学时控制着多参数评估，并且涉及在各种情况下收集与人类行为有关的海量数据（Rastogi和rnKrishna Rao在通勤者对过境的分段分析中：孟买研究。JTransp Engrn135（8） ）：506-515，2009）。旅行行为研究的成功主要取决于健康的数据收集。本文的目的是评估和建议最适合的数据库开发方法和研究策略，这些方法和方法可用于旅行行为研究，尤其是在印度大都市地区。它还介绍了用于出行方式转换研究的整个调查设计程序，以及研究人员在执行调查中的经验以及现场团队面临的困难。采用三级过滤过程来选择用于开发模式转移模型的变量。最初，基于变量排名结果，忽略了最不重要的变量。最终调查设计确定了排名最高的七个变量，其中旅行时间（包括中转通道时间，等候时间和上车时间），旅行成本和转乘次数是直接可测量的变量，而舒适性，便利性，安全性和安全性是潜在变量。潜在变量是使用由各种潜在变量指标构成的心理量表来衡量的，然后使用主成分分析将其转换为潜在类别。本研究开发和采用的研究工具通过试点调查进行了测试，并根据数据兼容性要求进行了更正。最后，通过开发二元逻辑回归模型，分析了1500份分布式问卷中的1017份有效回复，以评估虚拟公共交通服务水平的提高对出行方式转换行为的影响。还使用各种统计工具评估包含包含潜在类的模型性能的变化。本文包括基于实地调查的原始数据的原始工作，其发现有望更好地理解整个数据库的开发过程以及不同变量对印度大都市地区现代转变行为的影响。

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