An agent- and GIS-based virtual city creator: A case study of Beijing, China
Chengxiang Zhuge
Department of Geography, University of Cambridge
Chunfu Shao
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University
Shuling Wang
Beijing Transport institute
Ying Hu
Beijing Transport institute; MOE Key Laboratory for Transportation Complex Systems Theory and Technology, Beijing Jiaotong University
DOI: https://doi.org/10.5198/jtlu.2018.1270
Abstract
Many agent-based integrated urban models have been developed to investigate urban issues, considering the dynamics and feedbacks in complex urban systems. The lack of disaggregate data, however, has become one of the main barriers to the application of these models, though a number of data synthesis methods have been applied. To generate a complete dataset that contains full disaggregate input data for model initialization, this paper develops a virtual city creator as a key component of an agent-based land-use and transport model, SelfSim. The creator is a set of disaggregate data synthesis methods, including a genetic algorithm (GA)-based population synthesizer, a transport facility synthesizer, an activity facility synthesizer and a daily plan generator, which use the household travel survey data as the main input. Finally, the capital of China, Beijing, was used as a case study. The creator was applied to generate an agent- and Geographic Information System (GIS)-based virtual Beijing containing individuals, households, transport and activity facilities, as well as their attributes and linkages.References
Abraham, J. E., Stefan, K. J., & Hunt, J. (2012). Population synthesis using combinatorial optimization at multiple levels. Paper presented at the Transportation Research Board 91st Annual Meeting, Washington, DC.
Bar-Gera, H., Konduri, K. C., Sana, B., Ye, X., & Pendyala, R. M. (2009). Estimating survey weights with multiple constraints using entropy optimization methods. Paper presented at the Transportation Research Board 88th Annual Meeting, Washington, DC.
Barthelemy, J., & Toint, P. L. (2013). Synthetic population generation without a sample. Transportation Science, 47(2), 266–279.
Beckman, R. J., Baggerly, K. A., & McKay, M. D. (1996). Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice, 30(6), 415–429.
BMCCM. (2009). Planning for the development of refueling stations from 2009 to 2015. Beijing: Beijing Municipal Commission of City Management (BMCCM).
Chen, J., & Hao, Q. (2008). The impacts of distance to CBD on housing prices in Shanghai: A hedonic analysis. Journal of Chinese Economic and Business Studies, 6(3), 291–302.
Chen, T. D., Kockelman, K. M., & Khan, M. (2013). The electric vehicle charging station location problem: A parking-based assignment method for Seattle. Paper presented at the Transportation Research Board 92nd Annual Meeting, Washington, DC.
Chu, Z., Cheng, L., & Chen, H. (2012). A review of activity-based travel demand modeling. Paper presented at the The Twelfth COTA International Conference of Transportation Professionals, Beijing, China.
Dong, C., Shao, C., Ma, Z., Zhuge, C., & Li, Y. (2012). Temporal-spacial characteristic of urban expressway under jam flow condition. Jiaotong Yunshu Gongcheng Xuebao, 12(3), 73–79.
Ettema, D. (2011). A multi-agent model of urban processes: Modelling relocation processes and price setting in housing markets. Computers, Environment and Urban Systems, 35(1), 1–11.
Farooq, B., Bierlaire, M., Hurtubia, R., & Flötteröd, G. (2013). Simulation based population synthesis. Transportation Research Part B: Methodological, 58, 243–263.
Filatova, T., Parker, D., & Van der Veen, A. (2009). Agent-based urban land markets: agent's pricing behavior, land prices and urban land use change. Journal of Artificial Societies and Social Simulation, 12(1), 3.
Filatova, T., Parker, D. C., & van der Veen, A. (2007). Agent-based land markets: heterogeneous agents, land prices and urban land use change. Paper presented at the Proceedings of the 4th Conference of the European Social Simulation Association (ESSA'07), Toulouse, France.
Frade, I., Ribeiro, A., Gonçalves, G., & Antunes, A. (2011). Optimal location of charging stations for electric vehicles in a neighborhood in Lisbon, Portugal. Transportation Research Record: Journal of the Transportation Research Board, 2252, 91–98.
Gao, J., Zhao, P., Zhuge, C., & Zhang, H. (2012). Research on public transit network hierarchy based on residential transit trip distance. Discrete Dynamics in Nature and Society, 2012. doi: 10.1155/2012/390128
Gargiulo, F., Ternes, S., Huet, S., & Deffuant, G. (2010). An iterative approach for generating statistically realistic populations of households. PloS One, 5(1), e8828.
Ge, Y., Meng, R., Cao, Z., Qiu, X., & Huang, K. (2014). Virtual city: An individual-based digital environment for human mobility and interactive behavior. Simulation, 90(8), 917–935.
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.
Hodgson, M. J. (1990). A flow—capturing location— allocation model. Geographical Analysis, 22(3), 270–279.
Hodgson, M. J., & Rosing, K. E. (1992). A network location-allocation model trading off flow capturing and p-median objectives. Annals of Operations Research, 40(1), 247–260.
Horni, A., Nagel, K., & Axhausen, K. W. (2016). The multi-agent transport simulation MATSim. London: Ubiquity.
Huang, Q., Parker, D. C., Filatova, T., & Sun, S. (2014). A review of urban residential choice models using agent-based modeling. Environment and Planning B Planning and Design, 41(4), 661–689.
Kim, J.-G., & Kuby, M. (2012). The deviation-flow refueling location model for optimizing a network of refueling stations. International Journal of Hydrogen Energy, 37(6), 5406–5420.
Kuby, M., & Lim, S. (2005). The flow-refueling location problem for alternative-fuel vehicles. Socio-Economic Planning Sciences, 39(2), 125–145.
Kuby, M., Lines, L., Schultz, R., Xie, Z., Kim, J., & Lim, S. (2009). Optimization of hydrogen stations in Florida using the flow-refueling location model. International Journal of Hydrogen Energy, 34(15), 6045–6064.
Lau, K. M., & Li, S.-M. (2006). Commercial housing affordability in Beijing, 1992–2002. Habitat International, 30(3), 614–627.
Li, S., & Huang, Y. (2015). Optimal design of electric vehicle charging infrastructure network–A case study of South Carolina. Paper presented at the Transportation Research Board 94th Annual Meeting, Washington, DC.
Li, X., Zhuge, C., Zhang, X., Gao, J., & Zhang, H. (2014). Multiobjective optimization model of residential spatial distribution. Mathematical Problems in Engineering, 2014, 1-9. doi: http://dx.doi.org/10.1155/2014/167495.
Lim, S., & Kuby, M. (2010). Heuristic algorithms for siting alternative-fuel stations using the flow-refueling location model. European Journal of Operational Research, 204(1), 51–61.
Liu, X., & Long, Y. (2016). Automated identification and characterization of parcels with OpenStreetMap and points of interest. Environment and Planning B: Planning and Design, 43(2), 341–360.
Ma, L., & Srinivasan, S. (2015). Synthetic population generation with multilevel controls: A fitness-based synthesis approach and validations. Computer-Aided Civil and Infrastructure Engineering, 30(2), 135–150.
Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.
Magliocca, N., Safirova, E., McConnell, V., & Walls, M. (2011). An economic agent-based model of coupled housing and land markets (CHALMS). Computers, Environment and Urban Systems, 35(3), 183–191.
McMillen, D. P. (2002). The center restored: Chicago's residential price gradient reemerges. Economic Perspectives — Federal Reserve Bank of Chicago, 26(2), 2–11.
Müller, K., & Axhausen, K. W. (2010). Population synthesis for microsimulation: State of the art. Zürich: ETH Zürich, Institut für Verkehrsplanung, Transporttechnik, Strassen-und Eisenbahnbau (IVT).
Müller, K., & Axhausen, K. W. (2011). Hierarchical IPF: Generating a synthetic population for Switzerland. Zürich: Eidgenössische Technische Hochschule Zürich, IVT.
Parker, D. C., & Filatova, T. (2008). A conceptual design for a bilateral agent-based land market with heterogeneous economic agents. Computers, Environment and Urban Systems, 32(6), 454–463.
Pinjari, A. R., & Bhat, C. R. (2011). Activity-based travel demand analysis. A Handbook of Transport Economics, 10, 213–248.
ReVelle, C. S., & Swain, R. W. (1970). Central facilities location. Geographical Analysis, 2(1), 30–42.
Salvini, P., & Miller, E. J. (2005). ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and Spatial Economics, 5(2), 217–234.
Twomey, P., & Cadman, R. (2002). Agent-based modelling of customer behavior in the telecoms and media markets. Info, 4(1), 56–63.
Upchurch, C., & Kuby, M. (2010). Comparing the p-median and flow-refueling models for locating alternative-fuel stations. Journal of Transport Geography, 18(6), 750–758.
van Bergeijk, P. A. (2012). The relation between land price and distance to CBD in Bekasi. Master’s thesis. Erasmus University, Rotterdam.
Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314.
Wang, Y.-W., & Lin, C.-C. (2009). Locating road-vehicle refueling stations. Transportation Research Part E: Logistics and Transportation Review, 45(5), 821–829.
Ye, X., Konduri, K., Pendyala, R. M., Sana, B., & Waddell, P. (2009). A methodology to match distributions of both household and person attributes in the generation of synthetic populations. Paper presented at the 88th Annual Meeting of the Transportation Research Board, Washington, DC.
Zhang, H., Shi, B., Yu, X., Mou, Z., Li, M., Wang, L., & Zhuge, C. (2018). Transfer stability of urban subway network with passenger flow: Evidence in Beijing. International Journal of Modern Physics B, 32(14), 1850174.
Zhuge, C., Li, X., Ku, C.-A., Gao, J., & Zhang, H. (2016). A heuristic-based population synthesis method for micro-simulation in transportation. KSCE Journal of Civil Engineering, 21(6), 2373–2383. doi:10.1007/s12205-016-0704-1
Zhuge, C., & Shao, C. (2018a). Agent-based modelling of locating public transport facilities for conventional and electric vehicles. Networks and Spatial Economics. doi:10.1007/s11067-018-9412-3
Zhuge, C., & Shao, C. (2018b). Agent-based modelling of purchasing, renting and investing behavior in dynamic housing markets. Journal of Computational Science, 27, 130–146.
Zhuge, C., Shao, C., Gao, J., Dong, C., & Zhang, H. (2016). Agent-based joint model of residential location choice and real estate price for land use and transport model. Computers, Environment and Urban Systems, 57, 93–105.
Zhuge, C., Shao, C., Gao, J., Meng, M., & Ji, X. (2014). Evolution prospect and structure of urban transportation and land use system. Journal of Transportation Systems Engineering and Information Technology, 14(2), 19–24.
Zhuge, C., Shao, C., Gao, J., Meng, M., & Xu, W. (2014). An initial implementation of multiagent simulation of travel behavior for a medium-sized city in China. Mathematical Problems in Engineering, 2014, 1–11. doi: http://dx.doi.org/10.1155/2014/980623.
Zhuge, C., Shao, C., Li, X., Gao, J., & Zhang, H. (2016). A genetic algorithm based population synthesizer. Paper presented at the 14th World Conference on Transport Research, Shanghai, China.
Zhuge, C., Shao, C., Wang, S., & Hu, Y. (2017). Sensitivity analysis of integrated activity-based model: Using MATSim as an example. Transportation Letters, 2017, 1–11. doi:10.1080/19427867.2017.1286772.
Zilske, M., Neumann, A., & Nagel, K. (2011). OpenStreetMap for traffic simulation. Retrieved from https://depositonce.tu-berlin.de/bitstream/11303/4976/2/zilske_neumann_nagel.pdf.
