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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16215

Title: Activity-based Travel Demand Forecasting using Micro-simulation: Stochastic Error Investigation of FEATHERS Framework
Authors: BAO, Qiong
WETS, Geert
Issue Date: 2014
Publisher: IGI Global
Citation: Data Science and Simulation in Transportation Research, p. 167-181
Series/Report: Advances in Data Mining and Database Management (ADMDM) Book Series
Abstract: Activity-based models of travel demand employ in most cases a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a result, running a transport micro-simulation model several times with the same input will generate different outputs. 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. In this chapter, systematic experiments are carried out by using the FEATHERS, an activity-based micro-simulation modeling framework currently implemented for Flanders (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 with respect to socio-demographic variables, transport mode alternatives, and activity types, are calculated by running the model 100 times. The results show that application of the FEATHERS at a highly aggregated level only requires limited model runs. However, when a 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), a larger number of model runs is needed to ensure confidence of a certain percentile of zones at this level to be stable. The values listed in this chapter can be consulted as a reference for those who plan to use the FEATHERS framework.
URI: http://hdl.handle.net/1942/16215
DOI: 10.4018/978-1-4666-4920-0.ch009
ISI #: 000364530200011
ISBN: 9781466649200
Category: B2
Type: Book Section
Validation: vabb, 2017
Appears in Collections: Research publications

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