Travel Demand Forecasting Using Activity‐Based Modeling Framework FEATHERS: An Extension

Activity‐based travel demand modeling is the approach with most relation and need for intelligent solutions as it directly aims at reproducing human decision making in daily life. Therefore, the way to implement the selected intelligent solution plays an important role in the successful application of the models. FEATHERS (the Forecasting 16 Evolutionary Activity‐Travel of Households and their Environmental RepercussionS) is an activity‐based microsimulation modeling framework used for transport demand forecasting. Currently, this framework is implemented for the Flanders region of Belgium and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2,386 virtual units with an average area of 5.8 km2. For the sake of more detailed travel demand forecasting, we investigated in this study the extension of the FEATHERS framework from the Subzone zoning system to a more disaggregated zoning system, i.e., Building block (BB), which is the most detailed geographical level currently applicable in Belgium consisting of 10,521 units with an average area of 1.3 km2. In this paper, we elaborated the data processing procedure to implement the FEATHERS framework under the BB zoning system. The observed as well as the predicted travel demand in Flanders based on the two zoning systems was compared. The extended modeling system was further applied to investigate the potential impact of light rail initiatives on travel demand at a local network in Flanders.

[1]  Pavlos S. Kanaroglou,et al.  Rediscovering light rail: assessing the potential impacts of a light rail transit line on transit oriented development and transit ridership , 2012 .

[2]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[3]  Bruno Kochan,et al.  Implementation, validation and application of an activity-based transportation model for Flanders , 2012 .

[4]  Davy Janssens,et al.  Investigating the Minimum Size of Study Area for an Activity-Based Travel Demand Forecasting Model , 2015 .

[5]  David A. Hensher,et al.  Handbook of Transport Modelling , 2000 .

[6]  Davy Janssens,et al.  Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework , 2015 .

[7]  Peter Jones,et al.  Developments in dynamic and activity-based approaches to travel analysis , 1990 .

[8]  F. Koppelman,et al.  Activity-Based Modeling of Travel Demand , 2003 .

[9]  Ram M. Pendyala,et al.  The Application of a Microsimulation Model System to the Analysis of a Light-Rail Corridor: Insights from a TRANSIMS Deployment , 2012 .

[10]  Craig R. Rindt,et al.  The Activity-Based Approach , 2008 .

[11]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .

[12]  Davy Janssens,et al.  Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand , 2010 .

[13]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[14]  Davy Janssens,et al.  Activity-Based Modeling to Predict Spatial and Temporal Power Demand of Electric Vehicles in Flanders, Belgium , 2012 .

[15]  Hongju Wang,et al.  Sensor Fault Tolerant Control of a Fast Steering Mirror System Using Adaptive PI-Based Sliding Mode Observer and Hardware Redundancy , 2015 .

[16]  Davy Janssens,et al.  Implementation Framework and Development Trajectory of FEATHERS Activity-Based Simulation Platform , 2010 .

[17]  Davy Janssens,et al.  Geographical Extension of the Activity-based Modeling Framework FEATHERS , 2014, ANT/SEIT.

[18]  Davy Janssens,et al.  Multi-agent simulation of individual mobility behavior in carpooling , 2014 .