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Enriched travel demand estimation by including zonal and traveler characteristics and their relationships

机译:通过包括区域和旅行者特征及其关系来丰富旅行需求估计

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Current procedure in travel demand estimation models is to separately deal with attraction, production and trip distribution, where the latter typically assumes inverse distance proportionality. We show that this procedure leads to errors in the demand estimation, particularly when dealing with very specific zones and heterogeneous travel behavior. We argue that this traditional procedure is rooted in traditional ways of data collection, while new (big) data sources allow direct observation of travel demand patterns. Using such data, we propose an enriched travel demand estimation method in which zonal and traveler characteristics and their relationships are consistently carried over from the empirical data into the demand model. This can improve both the validity and richness of demand estimations.
机译:出行需求估计模型中的当前程序是分别处理吸引力,生产和出行分布,后者通常假设距离成反比。我们表明,此过程导致需求估计中的错误,尤其是在处理非常特定的区域和异构旅行行为时。我们认为,这种传统程序植根于传统的数据收集方式,而新的(大)数据源则可以直接观察旅行需求模式。利用这些数据,我们提出了一种丰富的旅行需求估计方法,其中区域和旅行者特征及其关系一直从经验数据中延续到需求模型中。这可以提高需求估计的有效性和丰富性。

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