FABILUT: The Flexible Agent-Based Integrated Land Use/Transport Model

Dominik Ziemke

Technische Universität Dresden, Technische Universität Berlin

Nico Kuehnel

Technical University of Munich

Carlos Llorca

Technical University of Munich

Rolf Moeckel

Technical University of Munich

Kai Nagel

Technische Universität Berlin

DOI: https://doi.org/10.5198/jtlu.2022.2126

Keywords: integrated land use/transport model, agent-based model, land use/transport interaction, land use/transport feedback cycle


Abstract

Integrated land-use transport models are often accused of being too complex, too coarse or too slow. We tightly couple the microscopic land use model SILO (Simple Integrated Land Use Orchestrator) with the agent-based transport simulation model MATSim (Multi-Agent Transport Simulation). The integration of the two models is person-centric. It means, firstly, that travel demand is generated microscopically. Secondly, SILO agents can query individualized travel information to search for housing or jobs (and to choose among available modes). Consequently, travel time matrices (skim matrices) are not needed anymore. Travel time queries can be done for any time of the day (instead of for one or few time periods), any x/y coordinate (instead of a limited number of zones) and take into account properties of the individual. This way, we avoid aggregation issues (e.g., large zones that disguise local differences) and we can account for individual constraints (e.g., nighttime workers who cannot commute by public transport for lack of service). Therefore, the behavior of agents is represented realistically, which allows us to simulate their reaction to novel policies (e.g., emission-class-based vehicle restrictions) and to extract system-wide effects. The model is applied in two study areas: a toy scenario and the metropolitan region of Munich. We simulate various transport and land use policies to test the model capabilities, including public transport extensions, zones restricted for private cars and land use development regulations. The results demonstrate that the increase of the model resolution and model expressiveness facilitates the simulation of such policies and the interpretation of the results.


References

Acheampong, R. and E. Silva. 2015. Land-use transport interaction modeling: A review of the literature and future research directions. Journal of Transport and Land Use, 8(3):1–28.

Alonso, W. 1964. Location and Land Use: Toward a General eory of Land Rent. Harvard University Press.

Beckmann, K. J., U. Brüggemann, J. Gräfe, F. Huber, H. Meiners, P. Mieth, R. Moeckel, H. Mühlhans, G. Rindsfüser, H. Schaub, R. Schrader, C. Schürmann, B. Schwarze, K. Spiekermann, D. Strauch, M. Spahn, P. Wagner, and M. Wegener. 2007. ILUMASS integrated land-use modelling and transportation system simulation. Final report.

Bischoff, J., M. Maciejewski, and K. Nagel. 2017. City-wide shared taxis: A simulation study in Berlin. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE. doi: 10.1109/itsc.2017.8317926.

Blanchard, S. and P. Waddell. 2017. UrbanAccess: Generalized methodology for measuring regional accessibility with an integrated pedestrian and transit network. Transportation Research Record, 2653:35–44.

Broaddus, A., T. Litman, and G. Menin. 2009. Trasportation demand management. Technical report, Gesellscha für technische Zusammenarbeit.

Brosi, P. 2019. gtfs.de. https://www.gtfs.de.

Chang, K.-T., Z. Khatib, and Y. Ou. 2002. Effects of zoning structure and network detail on traffic demand modeling. Environment and Planning B: Planning and Design, 29(1):37–52. doi: 10.1068/b2742.

Creutzig, F., R. Mühlhoff, and J. Römer. 2012. Decarbonizing urban transport in european cities: four cases show possibly high co-benefits. Environmental Research Letters, 7(4):044042. doi: 10.1088/1748-9326/7/4/044042.

Davidson, W., R. Donnelly, P. Vovsha, J. Freedman, S. Ruegg, J. Hicks, J. Castiglione, and R. Picado. 2007. Synthesis of first practices and operational research approaches in activity-based travel demand modeling. Transportation Research Part A: Policy and Practice, 41(5):464–488. ISSN 09658564. doi: 10.1016/j.tra.2006.09.003.

de la Barra, T. 1989. Integrated Land Use and Transport Modelling: Decision Chains and Hierarchies. Number 12 in Cambridge urban and architectural studies. Cambridge; New York: Cambridge University Press.

de Palma, A., M. Bierlaire, R. Hurtubia, and P. Waddell. 2015. Future challenges in transport and land use modeling. In M. Bierlaire, A. de Palma, R. Hurtubia, and P. Waddell, eds., Integrated Transport & Land Use Modeling for Sustainable Cities, chapter 22, pp. 513–529. Lausanne: EPFL Press, 1 edition.

Echenique, M. H., D. Crowther, and W. Lindsay. 1969. A spatial model of urban stock and activity. Regional Studies, 3(3):218–312.

Fotheringham, A. S., M. Batty, and P. A. Longley. 1989. Diffucsion-limited aggregation and the fractal nature of urban growth. Papers in Regional Science, 67(1):55–69. doi: 10.1111/j.1435- 5597.1989.tb01182.x.

Gerber, P., G. Caruso, E. Cornelis, and C. M. de Chardon. 2018. A multi-scale fine-grained LUTI model to simulate land-use scenarios in luxembourg. Journal of Transport and Land Use, 11(1):255– 272. doi: 10.5198/jtlu.2018.1187.

Hilgert, T., M. Heilig, M. Kagerbauer, and P. Vortisch. 2017. Modeling week activity schedules for travel demand models. Transportation Research Record, 2666:69–77. doi: 10.3141/2666-08.

Horni, A., K. Nagel, and K. W. Axhausen, eds. 2016. e Multi-Agent Transport Simulation MATSim. Ubiquity, London. doi: 10.5334/baw.

Hunt, J. D. and J. E. Abraham. 2003. Design and application of the PECAS land use modelling system. In 8th Conference on Computers in Urban planning and urban management (CUPUM). Sendai, Japan.

Kaddoura, I., L. Kröger, and K. Nagel. 2017. An activity-based and dynamic approach to calculate road traffic noise damages. Transportation Research Part D: Transport and Environment, 54:335– 347. doi: 10.1016/j.trd.2017.06.005.

Kaddoura, I., J. Laudan, D. Ziemke, and K. Nagel. 2019. Verkehrsmodellierung für das Ruhrgebiet: Simulationsbasierte Szenariountersuchung und Wirkungsanalyse einer verbesserten regionalen Fahrradinfrastruktur. VSP Working Paper 19-10, TU Berlin, Transport Systems Planning and Transport Telematics. URL http://www.vsp.tu-berlin.de/publications.

Kickhöfer, B. and J. Kern. 2015. Pricing local emission exposure of road traffic: An agent-based approach. Transportation Research Part D: Transport and Environment, 37(1):14–28. ISSN 1361- 9209. doi: 10.1016/j.trd.2015.04.019.

Konduri, K. C., D. You, V. M. Garikapati, and R. M. Pendyala. 2016. Enhanced synthetic population generator that accommodates control variables at multiple geographic resolutions. Transportation Research Record: Journal of the Transportation Research Board, 2563(1):40–50. ISSN 0361-1981. doi: 10.3141/2563-08.

Kuehnel, N., D. Ziemke, and R. Moeckel. 2021. Traffic noise feedback in agent-based integrated landuse/ transport models. Journal of Transport and Land Use, 14(1). doi: 10.5198/jtlu.2021.1852. URL https://doi.org/10.5198/jtlu.2021.1852.

Kuehnel, N., D. Ziemke, R. Moeckel, and K. Nagel. 2020. e end of travel time matrices: Individual travel times in integrated land use/transport models. Journal of Transport Geography, 88. doi: 10.1016/j.jtrangeo.2020.102862.

Lee, D. B. 1973. Requiem for large-scale models. Journal of the American Institute of Planners, 39(3):163–178. doi: 10.1080/01944367308977851.

Llorca, C. and R. Moeckel. 2019. Effects of scaling down the population for agent-based traffic simulations. Procedia Computer Science, 151:782–787. ISSN 18770509. doi: 10.1016/j.procs.2019.04.106.

Lowry, I. 1964. A model of Metropolis. Technical report, Rand Corporation. Martínez, F. 1996. MUSSA: Land use model for Santiago City. Transportation Research Record, 1552:126–134.

Miller, E. J. 2018. e case for microsimulation frameworks for integrated urban models. Journal of Transport and Land Use, 11(1). doi: 10.5198/jtlu.2018.1257.

Miller, E. J. and P. A. Salvini. 2001. e integrated land use, transportation, environment (ILUTE) microsimulation modelling system: Description and current status. In D. Hensher, ed., Travel Behaviour Research: e Leading Edge, pp. 711–724. Amsterdam: Pergamon.

Moeckel, R. 2016. Constraints in household relocation: Modeling land-use/transport interactions that respect time and monetary budgets. Journal of Transport and Land Use, 10(2):1–18. ISSN 19387849. doi: 10.5198/jtlu.2015.810.

Moeckel, R. 2018. Integrated Transportation and Land Use Models. Transportation Research Board. doi: 10.17226/25194.

Moeckel, R., N. Kuehnel, C. Llorca, A. T. Moreno, and H. Rayaprolu. 2019. Microscopic travel demand modeling: Using the agility of agent-based modeling without the complexity of activity-based models. In Annual Meeting of the Transportation Research Board. Washington, DC.

Moeckel, R. and K. Nagel. 2016. Maintaining mobility in substantial urban growth futures. Transportation Research Procedia, 19:70–80. doi: 10.1016/j.trpro.2016.12.069.

Molloy, J. and R. Moeckel. 2017. Automated design of gradual zone systems. Open Geospatial Data, Soware and Standards, 2(1):19. ISSN 2363-7501. doi: 10.1186/s40965-017-0032-5.

Moreno, A. and R. Moeckel. 2018. Population synthesis handling three geographical resolutions. ISPRS International Journal of Geo-Information, 7(5):174. ISSN 2220-9964. doi: 10.3390/ijgi7050174.

Mueller, N., D. Rojas-Rueda, H. Khreis, M. Cirach, D. Andrés, J. Ballester, X. Bartoll, C. Daher, A. Deluca, C. Echave, C. Milà, S. Márquez, J. Palou, K. Pérez, C. Tonne, M. Stevenson, S. Rueda, and M. Nieuwenhuijsen. 2020. Changing the urban design of cities for health: e superblock model. Environment International, 134:105132. doi: 10.1016/j.envint.2019.105132.

Muller, P. 2004. Transportation and urban form: Stages in the spatial evolution of the American metropolis. In S. Hanson and G. Giuliano, eds., e Geography of Urban Transportation, chapter 3, pp. 59–85. e Guilford Press, 3rd edition.

Nicolai, T. W. and K. Nagel. 2015. Integration of agent-based transport and land use models. In M. Bierlaire, A. de Palma, R. Hurtubia, and P. Waddell, eds., Integrated Transport and Land Use Modeling for Sustainable Cities, chapter 17, pp. 333–354. Lausanne: EPFL press. ISBN 978-2- 940222-72-8.

Openshaw, S. 1977. A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling. Transactions of the Institute of British Geographers, 2(4):459. ISSN 00202754. doi: 10.2307/622300.

OpenStreetMap. 2020. OpenStreetMap. https://www.openstreetmap.org.

Poletti, F., P. M. Bösch, F. Ciari, and K. W. Axhausen. 2017. Public transit route mapping for largescale multimodal networks. ISPRS International Journal of Geo-Information, 6(9):268.

Rienstra, S., P. Rietveld, and E. Verhoef. 1999. e social support for policy measures in passenger transport: A statistical analysis for the netherlands. Transportation Research Part D: Transport and Environment, 4(3):181–200. doi: 10.1016/s1361-9209(99)00005-x.

Schwanen, T., M. Dijst, and F. M. Dieleman. 2004. Policies for urban form and their impact on travel: e netherlands experience. Urban Studies, 41(3):579–603. doi: 10.1080/0042098042000178690.

Simmonds, D. 1999. e design of the DELTA land-use modelling package. Environment and Planning B, 26(5):665–684. Simmonds, D. and D. Coombe. 2000. Sustainable Urban Form, chapter e Transport Implications of Alternative Urban Forms, pp. 121–138. Routledge.

Spiekermann, K. and M. Wegener. 2000. Freedom from the tyranny of zones: towards new gis-based models. In A. Fotheringham and M. Wegener, eds., Spatial Models and GIS. New Potential and New Models, pp. 45–61. London: Taylor & Francis Group.

Spiekermann, K. and M. Wegener. 2008. Environmental feedback in urban models. International Journal of Sustainable Transportation, 2(1):41–57. ISSN 15568334. doi: 10.1080/15568310701517034.

Spiekermann, K. and M. Wegener. 2018. Multi-level urban models: Integration across space, time and policies. Journal of Transport and Land Use, 11(1):67–81. ISSN 19387849. doi: 10.5198/jtlu.2018.1185.

Strauch, D., R. Moeckel, M. Wegener, J. Gräfe, H. Mühlhans, G. Rindsfüser, and K.-J. Beckmann. 2005. Linking transport and land use planning: e microscopic dynamic simulation model ILUMASS. In P. Atkinson, G. Foody, S. Darby, and F. Wu, eds., Geodynamics, chapter 20, pp. 295–311. Boca Raton, Florida: CRC Press.

omas, I., C. Cotteels, J. Jones, A. P. Bala, and D. Peeters. 2015. Spatial challenges in the estimations of luti models: Some lessons from the sustaincity project. In M. Bierlaire, A. de Palma, R. Hurtubia, and P. Waddell, eds., Integrated Transport & Land Use Modeling for Sustainable Cities, chapter 4, pp. 55–74. Lausanne: EPFL Press.

Timmermans, H. J. P. 2007. e saga of integrated land use-transport modeling: How many more dreams before we wake up? In K. W. Axhausen, ed., Moving through nets: e physical and social dimensions of travel, pp. 219–248. Elsevier.

Waddell, P. 2002. Urbansim: Modeling urban development for land use, transportation and environmental planning. Journal of the American Planning Association, 68:297–314.

Waddell, P., A. Borning, M. Noth, N. Freier, M. Becke, and G. Ulfarsson. 2003. Microsimulation of urban development and location choices: Design and implementation of UrbanSim. Networks and Spatial Economics, 3(1):43–67.

Wagner, P. and M. Wegener. 2007. Urban land use, transport and environment models, experiences with an integrated microscopic approach. disP, 170:45–56.

Wegener, M. 1982. Modeling urban decline: A multilevel economic-demographic model for the Dortmund region. International Regional Science Review, 7:217–241.

Wegener, M. 1994. Operational urban models: State of the art. Journal of the American Planning Association, 60(2):17–29.

Wegener, M. 2014. Land-use transport interaction models. In M. Fischer and P. Nijkamp, eds., Handbook of Regional Science, pp. 741–758. Berlin, Heidelberg: Springer.

Wegener, M. and K. Spiekermann. 2009. From macro to micro – how much micro is too much? Published in Transport Reviews, 31(2):14–16. URL http://spiekermann-wegener.de/pub/pdf/MW_Amsterdam_151009.pdf.

Ziemke, D., B. Charlton, S. Hörl, and K. Nagel. 2021. An efficient approach to create agent-based transport simulation scenarios based on ubiquitous Big Data and a new, aspatial activity-scheduling model. Transportation Research Procedia, 52:613–620. doi: https://doi.org/10.1016/j.trpro.2021.01.073.

Ziemke, D., J. W. Joubert, and K. Nagel. 2017. Accessibility in a post-Apartheid city: Comparison of two approaches for accessibility computations. Networks and Spatial Economics, 18:241–271. doi: 10.1007/s11067-017-9360-3.

Ziemke, D., K. Nagel, and R. Moeckel. 2016. Towards an agent-based, integrated land-use transport modeling system. Procedia Computer Science, 83:958–963. doi: 10.1016/j.procs.2016.04.192.

Zilske, M., A. Neumann, and K. Nagel. 2011. OpenStreetMap for traffic simulation. In M. Schmidt and G. Gartner, eds., 1st European State of the Map – OpenStreetMap conference, 11-10, pp. 126–134. Vienna. URL 2011.sotm-eu.org/userfiles/proceedings_sotmEU2011.pdf.