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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18448

Title: Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework
Authors: BAO, Qiong
WETS, Geert
Issue Date: 2015
Citation: Transportation planning and technology, 38 (4), p. 425-441
Abstract: Activity-based models of travel demand have received considerable attention in transportation planning and forecasting over the last decades. However, they use in most cases micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a consequence, running a transport microsimulation model several times with the same input will generate different outputs, which to a great extent baffles practitioners in applying such a model and in interpreting the results. In order to take the variation of outputs in each model run into account, a common approach is to run the model multiple times and to use the average value of the results. The question then becomes: what is the minimum number of model runs required to reach a stable result (i.e., with a certain level of confidence that the obtained average value can only vary within an acceptable interval). In this study, systematic experiments are carried out by using the FEATHERS, an activity-based micro-simulation modeling framework currently implemented for the Flanders region of Belgium. Six levels of geographic detail are taken into account, which are Building block level, Subzone level, Zone level, Superzone level, Province level, and the whole Flanders. Three travel indices, i.e., the average daily number of activities per person, the average daily number of trips per person, and the average daily distance travelled per person, as well as their corresponding segmentations are calculated by running the model 100 times. The results show that the more disaggregated level is considered (the degree of the aggregation not only refers to the size of the geographical scale, but also to the detailed extent of the index), the larger the number of model runs is needed to ensure confidence of a certain percentile of zones at this level to be stable. Furthermore, based on the time-dependent origin- destination table derived from the model output, traffic assignment is performed by loading it onto the Flemish road network, and the total vehicle kilometres travelled in the whole Flanders are computed subsequently. The stable results at the Flanders level provides model users with confidence that application of the FEATHERS at an aggregated level only requires limited model runs.
Notes: Bao, Q (reprint author), Hasselt Univ, Transportat Res Inst IMOB, Wetenschapspk 5 Bus 6, B-3590 Diepenbeek, Belgium. qiong.bao@uhasselt.be
URI: http://hdl.handle.net/1942/18448
DOI: 10.1080/03081060.2015.1026102
ISI #: 000353773200004
ISSN: 0308-1060
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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