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A hybrid modeling approach for parking and traffic prediction in urban simulations

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Abstract

Urban simulations are an important tool for analyzing many policy questions relating to the usage of public space, roads, and communal transportation; they can be used to predict the long-term impact of new construction projects, traffic restrictions, and zoning laws. However, it is unwise to rely upon predictions from a single model since each technique possesses different strengths and weaknesses and can be highly sensitive to the choice of parameters and initial conditions. In this article, we describe a hybrid approach for combining agent-based and stochastic simulations (Markov chain Monte Carlo, MCMC) to improve the accuracy and reduce the variance of long-term predictions. In our proposed approach, the agent-based model is used to bootstrap the proposal distribution for the MCMC estimator. To demonstrate the applicability of our modeling technique, this article presents a case study describing the usage of our hybrid simulation method for forecasting transportation patterns and parking lot utilization on a large university campus. A comparison of our simulation results against an independently collected dataset reveals that our hybrid approach accurately predicts parking lot usage and performs significantly better than other comparable modeling techniques. Developing novel architectures for combining the predictions of agent-based models can produce insights that are different than simply selecting the best model.

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Notes

  1. The parable of the blind men and the elephant appears in a number of religions originating from the Indian subcontinent.

  2. http://www.iroffice.ucf.edu/character/current.html.

  3. http://map.ucf.edu/printable/.

  4. The complete code of this model can be accessed at this link: http://code.google.com/p/ucf-abm/.

References

  • Andrieu C, Moulines É (2006) On the ergodicity properties of some adaptive MCMC algorithms. Ann Appl Probab 16(3):1462–1505

    Article  MathSciNet  Google Scholar 

  • Andrieu C, De Freitas N, Doucet A, Jordan M (2003) An introduction to MCMC for machine learning. Mach Learn 50(1):5–43

    Article  Google Scholar 

  • Axtell R (2003) Economics as distributed computation. In: Meeting the challenge of social problems via agent-based simulation. Springer, Japan, pp 3–23

  • Balbi S, Giupponi C (2009) Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability. University Ca’Foscari of Venice, Dept of Economics Research Paper Series (15_09), Working Paper No. 15/WP/2009

  • Barreteau O, Sauquet E, Riaux J, Gailliard N, Barbier R (2012) Agent based simulation of drought management policy in practice. In: International Workshop on Agent-based Modeling for Policy Engineering (AMPLE 2012), European Conference on AI, pp 29–44

  • Beheshti R, Sukthankar G (2012) Extracting agent-based models of human transportation patterns. In: Proceedings of the ASE/IEEE international conference on social informatics, Washington, pp 157–164

  • Benenson I, Torrens P, Europe W, Portugali J (2004) Geosimulation: automata-based modeling of urban phenomena. Environ Plan B Plan Design 31(4):589–613

    Article  Google Scholar 

  • Brown D, Riolo R, Robinson D, North M, Rand W (2005) Spatial process and data models: toward integration of agent-based models and GIS. J Geogr Syst 7(1):25–47

    Article  Google Scholar 

  • Cauchemez S, Carrat F, Viboud C, Valleron AJ, Bolle PY (2004) A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data. Stat Med 23(22):3469–3487

    Article  Google Scholar 

  • Chen B, Cheng HH (2010) A review of the applications of agent technology in traffic and transportation systems. IEEE Trans Intell Trans Syst. 11(2):485–497

    Article  Google Scholar 

  • Chen X (2003) Agent-based simulation of evacuation strategies under different road network structures. University Consortium of Geographic Information Science

  • De Freitas N, Højen-Sørensen P, Jordan M, Russell S (2001) Variational MCMC. In: Proceedings of the conference on uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., pp 120–127

  • Dia H (2002) An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Trans Res Part C Emerg Technol 10(5-6):331–349

    Article  Google Scholar 

  • Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pervasive Ubiquitious Comput 10:255–368

    Article  Google Scholar 

  • Eaton D, Murphy K (2007) Bayesian structure learning using dynamic programming and MCMC. In: Proceedings of the conference on uncertainty in artificial intelligence (UAI2007), pp 101–108

  • Edwards P (1999) Global climate science, uncertainty, and politics: Data-laden models, model-filtered data. Sci Cult 8:437–472

    Article  Google Scholar 

  • Floyd RW (1962) Algorithm 97: shortest path. Commun ACM 5(6):345

    Article  Google Scholar 

  • Gailliard N, Olivier B, Audrey RF (2012) A conceptual model of participatory policy making in practice: water governance and boundary workers. In: International workshop on agent-based modeling for policy engineering (AMPLE 2012), European Conference on AI, pp 90–104

  • Garlick M, Chli M (2009) The effect of social influence and curfews on civil violence. In: Proceedings of the international conference on autonomous agents and multiagent systems, pp 1335–1336

  • Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell (6):721–741

  • Gerst M, Wang P, Roventini A, Fagiolo G, Dosi G, Howarth R, Borsuk M (2012) Agent-based modeling of climate policy: An introduction to the ENGAGE multi-level model framework. Environ Modell Softw

  • Gilks W, Richardson S, Spiegelhalter D (1995) Markov Chain Monte Carlo in practice: interdisciplinary statistics, vol 2. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  • Gimblett H (2002) Integrating geographic information systems and agent-based modeling techniques for simulating social and ecological processes. Oxford University Press, Oxford

    Google Scholar 

  • Hailegiorgis AB, Kennedy WG, Roleau M, Bassett J, Coletti M, Balan G, Gulden T (2010) An agent based model of climate change and conflict among pastoralists in east Africa. In: Proceedings of the international congress on environmental modelling and software

  • Hinkelmann F, Murrugarra D, Jarrah AS, Laubenbacher R (2011) A mathematical framework for agent based models of complex biological networks. Bull Math Biol 73(7):1583–1602

    Article  MathSciNet  Google Scholar 

  • Ilachinski A (2012) Modelling insurgent and terrorist networks as self-organised complex adaptive systems. Int J Parallel Emergent Distrib Syst 27(1):45–77

    Article  MathSciNet  Google Scholar 

  • Jin X, Jie L (2012) A study of multi-agent based models for urban intelligent transport systems. Int J Adv Comput Technol 4(6):126–134

    Google Scholar 

  • Jin X, White R (2012) An agent-based model of the influence of neighbourhood design on daily trip patterns. Comput Environ Urban Syst 36(5):398 – 411

    Article  Google Scholar 

  • Jordan R, Birkin M, Evans A (2012) Agent-based modelling of residential mobility, housing choice and regeneration. In: Agent-based models of geographical systems, Springer, Netherlands, pp 511–524

  • Klügl F, Bazzan AL (2012) Agent-based modeling and simulation. AI Magaz 33(3):29

    Google Scholar 

  • Kohler TA, Bocinsky RK, Cockburn D, Crabtree SA, Varien MD, Kolm KE, Smith S, Ortman SG, Kobti Z (2012) Modelling prehispanic Pueblo societies in their ecosystems. Ecol Modell 241:30–41

    Article  Google Scholar 

  • Laine M (2013) MCMC toolbox for Matlab, Finnish meteorological institute. Retrieved from: http://helios.fmi.fi/lainema/mcmc/

  • Liu R, Tao J, Shi N, He X (2011) Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling. Comput Stat Data Anal 55(3):1498–1508

    Article  MathSciNet  Google Scholar 

  • Liu Y, Wang Q, Liu J, Wark T (2012) MCMC-based indoor localization with a smart phone and sparse WiFi access points. In: IEEE international conference on pervasive computing and communications workshops, pp 247–252

  • López-Paredes A, Saurí D, Galán JM (2005) Urban water management with artificial societies of agents: The FIRMABAR simulator. Simulation 81(3):189–199

    Article  Google Scholar 

  • Maghami M, Sukthankar G (2012) Identifying influential agents for advertising in multi-agent markets. In: Proceedings of international conference on autonomous agents and multi-agent systems, Valencia, Spain, pp 687–694

  • Mengersen K, Tweedie R (1996) Rates of convergence of the Hastings and Metropolis algorithms. Ann Stat 24(1):101–121

    Article  MathSciNet  Google Scholar 

  • Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1093

    Article  Google Scholar 

  • Niazi MA, Hussain A, Kolberg M (2009) Verification & validation of agent based simulations using the VOMAS (Virtual Overlay Multi-agent System) approach. In: MAS&S at Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW)

  • Oakes J (2008) Invited commentary: rescuing Robinson Crusoe. Am J Epidemiol 8(1):9–12

    Article  Google Scholar 

  • Pan X, Han CS, Dauber K, Law KH (2007) A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI Soc 22(2):113–132

    Article  Google Scholar 

  • Press W, Teukolsky S, Vetterling W, Flannery B (2007) Numerical recipes 3rd Edition: the art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  • Streit RE, Borenstein D (2009) An agent-based simulation model for analyzing the governance of the Brazilian financial system. Expert Syst Appl 36(9):11,489–11,501

    Article  Google Scholar 

  • Verella J, Wardak A (2008) Modeling public opinion and voting as a complex system with agent-based simulations. In: IEEE Syst Inform Eng Design Symp, pp 261–266

  • Ward MD, Gleditsch KS (2002) Location, location, location: An MCMC approach to modeling the spatial context of war and peace. Polit Anal 10(3):244–260

    Article  Google Scholar 

  • Wilensky U (1999) NetLogo. Evanston, IL: Center for connected learning and computer-based modeling, Northwestern University. Retrieved from: http://ccl.northwestern.edu/netlogo/

  • Wilensky U, Rand W (2007) Making models match: replicating an agent-based model. J Artif Soc Soc Simulation 10(4):2

    Google Scholar 

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Acknowledgments

This research was supported in part by National Science Foundation award IIS-0845159.

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Authors and Affiliations

  1. Department of EECS, University of Central Florida, Orlando, FL, 32816, USA

    Rahmatollah Beheshti & Gita Sukthankar

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  1. Rahmatollah Beheshti
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  2. Gita Sukthankar
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Correspondence to Rahmatollah Beheshti.

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Beheshti, R., Sukthankar, G. A hybrid modeling approach for parking and traffic prediction in urban simulations. AI & Soc 30, 333–344 (2015). https://doi.org/10.1007/s00146-013-0530-7

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