Assessing the impact of interzonal migration on travel demand in Flanders using an Activity-based model

Eridona
Selita
  • Muhammad
    Adnan

Following the considerable attention of activity-based models in transportation planning and forecasting, and the necessity to understand and reveal the impacts of transportation policies, this paper aims to simulate and examine a population policy scenario. It explicitly uses FEATHERS, a demand-side activity-based model, operational for Flanders, Belgium. The ABM is required to estimate how the interzonal migration of Flanders’ population from small, rural towns into large, urbanised city areas closely affects the daily activity and travel choices of the individuals. The methodology consists of a classification of FEATHERS zoning system based on the urbanisation degree of Flanders, where the traffic analysis zones (TAZs) belonging to the Flemish Diamond, (comprising Brussels, Ghent, Antwerp, and Leuven) are categorised as urban and others as rural. This categorisation is used to spatially redistribute the population of Flanders from rural to urban zones by 2%, 5% and 10% with equal proportions.

Then, the FEATHERS simulation framework is exploited for executing a current baseline scenario and three migration scenarios. It simulates resident’s activity and travel decisions in a day unit and provides the results in the form of activity-travel diaries. They suggest a noticeable impact on the activity- travel related attributes of both zones after the implementation of the policy scenarios. There is a prominent modal shift toward car as the main mode of travel in urban zones and during peak hours, whereas a slight increase in public transport and car sharing is found in rural zones. Changes in the activity choice are deemed as well. There is an increase in the frequency of trips for home-based activities in urban areas and work-based activities in rural zones. Nevertheless, car remains the dominant mode for all types of activities. Last but certainly not least, the vehicle kilometres and hours travelled are scrutinised as well. Following the patterns of the modal shift and trip frequency analysed, there is an increment of VKT and VHT in urban zones whilst the opposite takes place in rural zones.

Intuitively, one could have expected the opposite to happen in both zones after the execution of the migration scenarios. Furthermore, the findings in the literature also indicate a reverse effect on the transportation demand of rural and urban zones when more individuals are relocated to large, urbanised zones. Therefore, many of the results from this research are indicative rather than conclusive. To ultimately improve the quality of this research and provide conclusive results of a greater extent, more cross-validation needs to be done by further validating the FEATHERS model calibration and output stability.

Bibliografie

Adnan, M., et al., (2016). Simmobility: A multi-scale integrated agent-based simulation platform. in 95th Annual Meeting of the Transportation Research Board Forthcoming in Transportation Research Record. 

 

Adnan. M., (2018). Demonstrator of a Cost/Utility Function Taking Activity-based Information into Account, deliverable 4.2,  iSCAPE project. (www.iscapeproject.eu)

 

Adnan, M., Outay, F., Ahmed, Sh., Brattich, E., di Sabatino, S. & Janssens, D. (2020). Integrated agent-based microsimulation framework for examining impacts of mobility-oriented policies. Personal and Ubiquitous Computing, 25, 205-217.

 

Adnan, M., Nahmias, B.B., Baburajan, V., Basak, K. & Ben-Akiva, M. (2020). Examining impacts of time-based pricing strategies in public transportation: A study of Singapore, Transportation Research Part A: Policy and Practice, 140, 127-141.



Arentze, T. & Timmermans, (2000). Albatross: a learning based transportation oriented simulation system.

 

Arentze, T.A. & Timmermans, H.J.P. (2004). A learning-based transportation oriented simulation system, Transportation Research Part B: Methodological,  38 (7), 613-633.

 

Arentze, T.A., Timmermans, H.J.P., Janssens, D., & Wets, G. (2008). Modeling short-term dynamics in activity-travel patterns: From Aurora to Feathers. Transportation Research Record Conference Proceedings, 42( 2),71- 77.

 

Auld, J. & Mohammadian, A. (2009). Framework for the development of the agent-based dynamic activity planning and travel scheduling (ADAPTS) model. Transportation Letters,1(3),.245-255.

 

Auld, J., et al., (2016). POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations. Transportation Research Part C: Emerging Technologies, 64,101-116.

 

Balac, M., Ciari, F. & Axhausen, K.W., (2017). Modeling the impact of parking price policy on free-floating carsharing: Case study for Zurich, Switzerland. Transportation Research Part C: Emerging Technologies, 207-225.

 

Bao, Q.  (2016). Activity-based Travel Demand Forecasting using FEATHERS: Model Extension, Evaluation, and Execution. PhD thesis, Hasselt University.

 

Balmer, M., Axhausen, K.  & Nagel, K. (2006). Agent-based demand-modelling framework for large-scale microsimulations. Transportation Research Record: Journal of the Transportation Research Board, 125-134.

 

Bellemans, T., Janssens, D., Wets, G., Arentze, T.A., & Timmermans, H.J.P. (2010). Implementation framework and development trajectory of Feathers activity-based simulation platform. Transportation Research Record: Journal of the Transportation Research Board, 2175, 111-119.

 

Ben-Akiva, M., Bowman, J. L., Ramming, S. & Walker, J.  (1998). Behavioural realism in urban transportation planning models. Paper presented at Transportation models in the policy making process: a symposium in memory of Greig Harvey, Asilomar Conference Center, California.

 

Ben-Akiva, M. E., & Bowman, J. L. (1998). Activity Based Travel Demand Model Systems. Equilibrium and Advanced Transportation Modelling, 27–46.

 

Berry N., Coffee N.T., Nolan R., Dollman J., Sugiyama T.  (2017)Neighbourhood Environmental Attributes Associated with Walking in South Australian Adults: Differences between Urban and Rural Areas. International  Journal of  Environnmental  Research and. Public Health, 14, 965.

 

Bhat, C., et al., (2004). Comprehensive econometric microsimulator for daily activity-travel patterns. Transportation Research Record: Journal of theTransportation Research Board, 57-66.

 

Bowman, J. &M. Bradley, (2005). Activity-based travel forecasting model for SACOG: Technical memos numbers 1-11. (http://jbowman.net)

 

Bradley, M.A., Portland Metro & Bowman, J. L., (1998). A system of activity-based models for Portland, Oregon in USDOT report number DOT-T-99-02, Washington, DC.

 

Bradley, M., et al (2001). Estimation of an activity-based micro-simulation model for San Francisco. 80th Annual Meeting of the Transportation Research Board,Washington, DC.

 

Bradley, M. & Bowman J., (2006). A summary of design features of activity-based microsimulation models for USMPOs. White Paper for the Conference on Innovations in Travel Demand Modelling, Austin, TX.

 

Cepolina, E.M. & Farina, A.  (2012). A new shared vehicle system for urban areas.Transportation Research Part C: Emerging Technologies, 21(1), 230-243.

 

Ciari, F., Balac, M. & Axhausen, K.W. (2016). Modeling Carsharing with the Agent-Based Simulation MATSim:State of the Art, Applications, and Future Developments. Transportation Research Record: Journal of the Transportation Research Board, 14-20.

 

Consult, P.B., (2005). The MORPC travel demand model: Validation and final report. Prepared for the Mid-Ohio Region Planning Commission.

 

Davidson, W., et al. (2010).  CT-RAMP family of activity-based models. In Proceedings of the 33rd Australasian Transport Research Forum (ATRF).

 

Delhoum, Y., Belaroussi, R., Dupin, F. & Zargayouna, M. (2020). Activity-Based Demand Modeling for a Future Urban District. Sustainability, MDPI, 12 (14).

 

De Vos, J., Ettema, D., & Witlox, F. (2018). Changing travel behaviour and attitudes following a residential relocation. Journal of Transport Geography73, 131–147.

 

Di Ciommo, F., Comendador, J., López-Lambas, M., E., Cherchi, E. & Ortúzar, J. (2014). Exploring the role of social capital influence variables on travel behaviour. Transportation Research Part A: Policy and Practice, 68, 46-55.

 

Dijst, M. & V. Vidakovic, (1997). Individual action space in the city. Activity-based approaches to travel analysis.

 

Eom, J.K.., Lee, K. S. & Seong, M.E. (2020). Development and application of the Activity-BAsed Traveler Analyzer (ABATA) system. Future Generation Computer Systems, 106, 135–153.

 

European Environment Agency (2016) Urban sprawl in Europe - Joint EEA-FOEN report, EEA Report 11/2016.

 

FPS Economy, SMEs, independent Professions & Energy, (2006). ECO-DATA, Land Cover.

 

Janssens, D., Wets, G., Timmermans, H.J.P. & Arentze, T.A., (2007). Modelling Short-Term Dynamics in Activity-Travel Patterns: Conceptual Framework of the Feathers Model. Presented at the 11th World Conference on Transport Research, Berkeley CA, USA

 

Jones, P., et al., (1983). Understanding Travel Behavior Gower Publishing Co. Ltd.Aldershot, UK Google Scholar,

 

Kakaš, A. & Gruber, E. (2016). Analysis of internal migration patterns. The example of Slovakia and Austria. Acta Geographica Universitas Comenianae, 60(2), 171-188.

 

Kochan, B., Bellemans,T., Janssens, D. & Wets, G., (2008). Assessing the Impact of Fuel Cost on Traffic Demand using Activity-Based Models. Travel Demand Management TDM, Vienna.

 

Kochan, B., Bellemans, T., Cools, M., Janssens, D. & Wets, G.  (2011). An estimation of total vehicle travel reduction in the case of telecommuting: Detailed analyses using an activity-based modeling approach. Presented at the European Transportation Conference, Glasgow,Scotland, UK.

 

Kochan, B. (2012). Implementation, validation, and application of an activity-based transportation model for Flanders. PhD thesis, Hasselt University.

 

Knapen, L., Adnan, M., Kochan, B., Bellemans, T., van der Tuin, M., Han, Z. & Snelder, M., (2021). An Activity Based integrated approach to model impacts of parking, hubs and new mobility concepts.  Procedia Computer Science, Elsevier, Warsaw, Poland.

 

Kwan, M., P., (1997). Gisicas: An Activity-based travel decision support system using a GIS-interfaced computational process model. Activity-based approaches to travel analysis.

 

Lenntorp, B., (1976). Paths in space-time environment. A time geographic study of possibilities of individuals. The Royal University of Lund, Department of Geography. Lund Studies in Geography, Series B. Human Geography, 44.

 

Liftango, (2020). What is the difference between Rural and Urban Transport?

 

Loris, I. & Pisman, A. (2017). A REAL CORP. Panta Rhei. A world in constant motion 12.-14. Proceedings of the 22nd International Conference on Urban Planning, Regional Development and Information Society, Vienna, Austria.  Proceedings, 209-217.

 

Lyu, H., Dong, Z., Roobavannan, M. et al. (2019). Rural unemployment

 

Mazzulla, G. (2009). An activity-based system of models for student mobility simulation. European Transport Research Review 1, 163–174.

 

Miller, E. & Roorda, M. (2003). Prototype model of household activity-travel scheduling. Transportation Research Record: Journal of the Transportation Research Board, 114-121.

 

Nagel, K., Beckman, R.L. &.Barrett. C.L., (1999). TRANSIMS for transportation planning. 6th International Conference on Computers in Urban Planning and Urban Management.

 

Pendyala, R., et al., (2005) Florida activity mobility simulator: overview and preliminary validation results. Transportation Research Record: Journal of the Transportation Researc hBoard, 123-130.

Petrik, O., Adnan,M., Basak, K. & Ben-Akiva, M. (2020). Uncertainty analysis of an activity-based microsimulation model for Singapore. Future Generation Computer Systems,110,350-363.

 

Pinjari, A.R., et al., (2006). Activity-based travel-demand analysis for metropolitan areas in Texas: CEMDAP Models,Framework, Software Architecture and Application Results.

 

Pirdavani, A., Kochan, B., Bellemans, T., Brijs, T.& Wets, G., (2012). Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modelling. Accident Analysis and Prevention, 50,186–195.



Pirdavani, A., Bellemans, T., Brijs, T., Kochan, B., & Wets, G. (2014). Assessing the road safety impacts of a teleworking policy by means of geographically weighted regression method. Journal of Transport Geography, 39, 96-110.

 

Poelmans, L., & Van Rompaey, A. (2009). Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flanders–Brussels region. Landscape and urban planning, 93(1), 10-19.

 

Ruimte Vlaanderen (2017). Witboek Beleidsplan Ruimte Vlaanderen. Brussel.

 

Sims, R. et al., (2014). Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

 

Shiftan, Y. & Suhrbier, J. (2002). The analysis of travel and emission impacts of travel demand management strategies using activity-based models.Transportation, 29(2),145-168.

 

Shiftan, Y. & Ben-Akiva, M (2010). A Practical Policy Sensitive Activity-Based Model. The Annals of Regional Science 47, 517–541 (2011).

 

Tajaddini, A. & Rose, G. & Kockelman, K. & Vu, H. (2020). Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability.

 

Virginia Department of Transportation (VDOT, 2009). Implementing activity-based models in Virginia. VTM Research Paper.

 

Vovsha, P., E. Petersen & Donnelly, R. (2002). Microsimulation in travel demand modeling: Lessons learned from the New York best practice model.Transportation Research Record:Journal of the Transportation Research Board, 68-77 .

 

Ziemke, D., Knapen, L., & Nagel, K. (2021). Expanding the analysis scope of a MATSim transport simulation by integrating the FEATHERS activity-based demand model. Procedia Computer Science, 184, 753-760.

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Universiteit of Hogeschool
Universiteit Hasselt
Thesis jaar
2022
Promotor(en)
Prof. dr.ir Tom Bellemans