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首页> 外文会议>9th World conference on transport research (9th WCTR) >THE SPATIAL TRANSFERABILITY OF THEHELSINKI METROPOLITAN AREA MODECHOICE MODELS
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THE SPATIAL TRANSFERABILITY OF THEHELSINKI METROPOLITAN AREA MODECHOICE MODELS

机译：赫尔辛基大都市区模式选择模型的空间传递性

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The purpose of the research was to study the spatial transferability of mode choice models forother home-based trips using the mobility surveys conducted in the Helsinki Metropolitan Areain 1995 and in the Turku region in 1997. The Helsinki Metropolitan Area data base is used toestimate the models that are to be transferred. The data base in the Turku region represents theapplication context to which the estimated Helsinki Metropolitan Area models are transferred.The transfer procedures examined were the Bayesian updating, combined transfer estimation,transfer scaling, and joint context estimation. To explore the impact of sample size ontransferring performance, model transferability was tested using three to four different samplesizes. The model transferability was examined by comparing the transferred models to themodels estimated using the entire set of the data which can be regarded as the best estimaterepresenting “the real situation”.The results showed that the joint context estimation was generally the best method. Inparticular, the method was useful, if the transfer bias was large or only some of the modelcoefficients could be regarded as precise. The transfer scaling also gave rather good results.The problem with this method was the unpredictability of the quality of results. The scalefactor is usually estimated for travel time and the number of transfers. If the ratio of thesecoefficients does not stay constant, the model’s ability to predict the effects of changes intransportation system may become rather weak. The combined transfer estimation procedure
机译：该研究的目的是使用1995年在赫尔辛基都会区和1997年在图尔库地区进行的流动性调查来研究其他家庭出行的模式选择模型的空间可传递性。使用赫尔辛基都会区数据库来估算模型将被转移。土尔库地区的数据库表示将估计的赫尔辛基都会区模型转移到的应用程序上下文。所研究的转移程序是贝叶斯更新，联合转移估计，转移规模和联合上下文估计。为了探索样本量对转移性能的影响，使用三到四个不同的样本量测试了模型的转移能力。通过将转移的模型与使用整个数据集估计的模型进行比较来检验模型的可移植性，可以将其视为代表“真实情况”的最佳估计。结果表明，联合上下文估计通常是最佳方法。特别是，如果传递偏差很大或只有某些模型系数可以被认为是精确的，则该方法很有用。传递比例缩放也给出了很好的结果。此方法的问题是结果质量的不可预测性。通常估算比例系数来计算旅行时间和中转次数。如果这些系数的比率不保持恒定，则该模型预测运输系统变化影响的能力可能会变得很弱。组合转移估算程序

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