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A simultaneous two-dimensionally constraint disaggregate trip generation, distribution and mode choice model - Theory and application for a Swiss national model

机译:同时二维约束分解行程生成,分布和模式选择模型 - 瑞士国家模型的理论和应用

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摘要

The Swiss federal government has asked the IVT, ETH Zufcrich in collaboration with the TU Dresden and Emch+Berger, Zufcrich to estimate origin-destination matrices by mode and purpose for the year 2000. The zoning system employing about 3'000 zones of very uneven size required a solution algorithm which is fast, but also able to model generation, distribution and mode choice simultaneously, while addressing the different data availability for traffic within, destined for and passing through the country. The EVA algorithm developed by Lohse (1997) was adapted for this purpose. The key proper-ties of the algorithm are its disaggregate description of demand, its use of appropriate logit-type models for the demand distribution, while maintaining the known marginal distributions of the matrices generated. This last point is of particular importance in a large scale planning applica-tion such as the one at hand. The algorithm calculates trip production and attractions by zone using activity pairs. The 17 ac-tivity pairs distinguished are the combinations of two activities, such as home-work or work-leisure. The relevant daily rates are derived for each of the 17 activity pairs from the 2000 Swiss National Travel Survey (Bundesamt fufcr Statistik and Bundesamt fufcr Raumentwicklung, 2001). The zonal attractivity is defined separately for each trip purpose. In addition to the common variables, such as employment or population, detailed descriptions of education places, shop-ping or leisure facilities, overnight accommodations, shopping centres etc. are employed (see Tschopp, Keller and Axhausen, 2003 for the data). The combined destination and mode choice models estimated for the different traveller types and activity pairs are based on the Swiss National Travel survey (RP data), but incorporates re-sults from a prior SP study on mode and route choice (Vrtic and Axhausen, 2004). The different zone sizes and the different levels of data available required the formulation of new additional models for the transit traffic passing through Switzerland and the traffic originat-ing outside, respectively leaving the country The matching network models for public transport and road traffic were implemented using VISUM 9.0 of PTV AG, Karlsruhe. The timetable based assignment considers all scheduled train services plus the relevant interurban bus services, in particular in rural areas. The paper has three main parts: the first main part derives and describes for the first time the EVA algorithm in English, including the solution method used. The second part summarizes the results of choice model estimation using the generalised cost elasticities of demand by purpose and traveller type. The third part assesses the quality of the results. These assessments are based on two independently derived matrices, which are available for rail-travel from on board - counts and for commuters from the 2000 national census. In addition, we compare the assign-ment results with the available cross section counts. The conclusions discuss computing times, accuracy and issues for further research.
机译:瑞士联邦政府已要求IVT,ETH Zufcrich与TU Dresden和Emch + Berger Zuffrich合作,按模式和目的估算2000年的原点目的地矩阵。该分区系统使用了大约3000个分区大小非常不均匀的区域需要一种快速的解决方案算法,而且能够同时对生成,分发和模式选择进行建模,同时解决针对国内,目的地和通过该国的流量的不同数据可用性。为此,Lohse(1997)开发了EVA算法。该算法的关键特性是其对需求的分类描述,对需求分布使用适当的logit型模型,同时保持了所生成矩阵的已知边际分布。最后一点在诸如手头的大规模规划应用中特别重要。该算法使用活动对按区域计算出行次数和景点。区分出的17个活动对是两项活动的组合,例如家庭作业或工作休闲。从2000年瑞士国家旅行调查得出的17个活动对中的每个活动的相关每日费率(Bundesamt f ufcr Statistik和Bundesamt f ufcr Raumentwicklung,2001)。为每个行程目的分别定义了区域吸引力。除就业或人口等常见变量外,还采用了对教育场所,购物或休闲设施,过夜住宿,购物中心等的详细说明(有关数据,请参见Tschopp,Keller和Axhausen,2003年)。针对不同旅行者类型和活动对估算的目的地和方式选择组合模型基于瑞士国家旅行调查(RP数据),但结合了先前SP对方式和路线选择进行的SP研究的结果(Vrtic和Axhausen,2004年) )。不同的区域大小和可用的不同数据水平,需要分别为通过瑞士和来自国外的过境交通量制定新的附加模型。卡尔斯鲁厄PTV AG的VISUM 9.0。基于时间表的分配考虑了所有预定的火车服务以及相关的城际巴士服务,尤其是在农村地区。本文包括三个主要部分:第一个主要部分首次衍生和描述了英文EVA算法,包括所使用的求解方法。第二部分总结了使用目的和旅行者类型的需求的广义成本弹性对选择模型进行估计的结果。第三部分评估结果的质量。这些评估基于两个独立得出的矩阵,这些矩阵可用于车上的铁路旅行(计数)和2000年全国人口普查的通勤者。此外,我们将分配结果与可用的横截面数量进行比较。结论讨论了计算时间,准确性和有待进一步研究的问题。

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