Abstract
Toward the common issue of quick urban sprawls of many cities in developing countries today, this research incorporates the expectation-maximization (EM) algorithm into the feedback application process of a newly developed feedback model to improve the modeling studies of the urban transport prediction and planning for the developments of the cities with their urban areas enlarged in the future. By utilizing the survey data obtained in Jabodetabek metropolitan region of Indonesia in 2002, the study results numerically confirm that the iteratively computational calibrations to the K-factors for the newly urbanized areas of a developing city by employing the EM algorithm in the feedback process can truly improve the validity of the proposed feedback model’s application to effectively predict the urban transport developments of developing cities in the future.
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References
Beijing transportation research center (BTRC). The outline of Beijing transportation development: Beijing transportation research center. Beijing Municipal Committee of Communication, 2004
Gakenheimer R. Urban mobility in the developing world. Transportation Research Part A, 1999, 33(7–8): 671–689
Soehodho S. Motorization in Indonesia and its impact to traffic accidents. International Association of Traffic and Safety Sciences (IATSS) Research, 2007, 31(2): 27–33
Steinberg F. Jakarta: environmental problems and sustainability. Habitat International, 2007, 31(3–4): 354–365
Mao B, Chen H, Chen S. Sustainability assessment of speed regulation of urban traffic. International Association of Traffic and Safety Sciences (IATSS) Research, 2002, 26(2): 18–24
Japan international cooperation agency (JICA), National development planning agency, Republic of Indonesia (BAPPENAS). The study on integrated transportation master plan for Jabodetabek (Phase II), final report & summary report: Pacific Consultants International Almec Corporation, 2004
Ding C. Impact analysis of spatial data aggregation on transportation forecasted demand: a GIS approach. In: Proceedings of URISA 1994 Annual Conference. Washington D.C.: Urban and Regional Information Systems Association. 1994, 362–375
Boyce D, Xiong C. Forecasting travel for very large cities: challenges and opportunities for China. Transportmetrica, 2007, 3(1): 1–19
McNally M G. The activity-based approach. Handbook of Transport Modelling. New York: Pergamon, 2000, 53–69
Pravinvongvuth S, Chen A. Adaptation of the paired combinatorial logit model to the route choice problem. Transportmetrica, 2005, 1(3): 223–240
Recker W, Duan J, Wang H. Development of an estimation procedure for an activity-based travel demand model. Computer-Aided Civil and Infrastructure Engineering, 2008, 23(7): 483–501
Anselin L. Spatial Econometrics: Methods and Models. Kluwer Academic, 1988
Miller H J. Modeling accessibility using space-time prism concepts within geographical information systems. International Journal of Geographical Information Systems, 1991, 5(3): 287–301
Fotheringham A S, Rogerson P A. GIS and spatial analytical problems. International Journal of Geographic Information Systems, 1993, 7(1): 3–19
Cressie N. Change of support and the modifiable areal unit problem. Geographical Systems, 1996, 3(2–3): 159–180
Getis A, Mur J, Zoller H G. Spatial econometrics and spatial statistics. Palgrave Macmillan, 2004
Cascetta E, Papola A. A trip distribution model involving spatial and dominance attributes. Computer-Aided Civil and Infrastructure Engineering, 2008, 23(2): 116–124
Boyce D. Is the sequential travel forecasting paradigm counterproductive? Journal of Urban Planning and Development, 2002, 128(4): 169–183
Zhao Y, Kockelman K M. The propagation of uncertainty through travel demand models: an exploratory analysis. Annals of Regional Science, 2002, 36(1): 145–163
McNally M G. The four-step model. Handbook of Transport Modelling. New York: Pergamon, 2000, 35–52
Boyce D, Zhang Y F, Lupa M R. Introducing “feedback” into fourstep travel forecasting procedure versus equilibrium solution of combined model. Transportation Research Record #1443. Washington D.C.: Transportation Research Board, 1994, 65–74
Siegel J D, De-Cea J, Fernandez J E, et al. Comparisons of urban travel forecasts prepared with the sequential procedure and a combined model. Networks and Spatial Economics, 2006, 6(2): 135–148
Kwana M P, Weber J. Scale and accessibility: implications for the analysis of land use-travel interaction. Applied Geography, 2008, 28(2): 110–123
Evans S P. Derivation and analysis of some models for combining trip distribution and assignment. Transportation Research, 1976, 10(1): 37–57
Safwat K N A, Magnanti T L. A combined trip generation, trip distribution, modal split, and trip assignment model. Transportation Science, 1988, 22(1): 14–30
Lam W H K, Huang H J. A combined trip distribution and assignment model for multiple user classes. Transportation Research Part B, 1992, 26(4): 275–287
Walker W, Peng H. Alternative methods to iterate a regional travel simulation model: computational practicality and accuracy. Transportation Research Record #1493. Washington D.C.: Transportation Research Board, 1995, 21–28
Lan C J, Menendez M, Gan A. Incorporating feedback loop into FSUTMS for model consistency, final report (Volume I). Contract No.BC-791, Prepared for Office of the State Transportation Planner, Systems Planning Office, State of Florida Department of Transportation, 2003
Boyce D, O’Neill C R, Scherr W. New computational results on solving the sequential travel forecasting procedure with feedback. In: Proceedings of the 11th Transportation Planning Applications Conference, Daytona Beach, FL, 2007
Levinson D, Kumar A. Integrating feedback into the transportation planning model: structure and application. Transportation Research Record #1413. Washington D.C.: Transportation Research Board, 1994, 70–77
Bar-Gera H, Boyce D. Solving a non-convex combined travel forecasting model by the method of successive averages with constant step sizes. Transportation Research Part B, 2006, 40(5): 351–367
Feng X, Zhang J, Fujiwara A, et al. Evaluating environmentally sustainable urban and transport policies for a developing city based on a travel demand model with feedback mechanisms. Journal of the Eastern Asia Society for Transportation Studies, 2007, 7(1881-1124): 751–765
Feng X, Zhang J, Fujiwara A, et al. Exploring sustainable urban form of Beijing with an integrated model. In: Proceedings of the 6th International Conference on Traffic and Transportation Studies. Reston: American Society of Civil Engineers, 2008, 111–120
Fotheringham A S, Wong D W S. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 1991, 23(7): 1025–1044
Amrhein C G. Searching for the elusive aggregation effect: evidence from statistical simulations. Environment and Planning A, 1995, 27(1): 105–119
Guo J Y, Bhat C R. Modifiable areal units: a problem or a matter of perception in the context of residential location choice modeling? Transportation Research Record #1898. Washington D.C.: Transportation Research Board, 2004, 138–147
McLachlan G J, Krishnan T. The EM Algorithm and Extensions. John Wiley & Sons, Inc., 1997
Hadiwinoto S, Leitmann J. Urban environmental profile: Jakarta. Cities, 1994, 11(3): 153–157
Cybriwsky R, Ford L R. City profile: Jakarta. Cities, 2001, 18(3): 199–210
Feng X, Zhang J, Fujiwara A. Analysis of feedback to obtain steady-state solutions in four-step modeling. Transportmetrica, 2009, 5(3): 215–227
Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society Series B, 1977, 39(1): 1–38
Little R J A, Rubin D B. Statistical Analysis with Missing Data. 2nd ed. John Wiley & Sons, Inc., 2002
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Feng, X., Zhang, J., Fujiwara, A. et al. Improved feedback modeling of transport in enlarging urban areas of developing countries. Front. Comput. Sci. China 4, 112–122 (2010). https://doi.org/10.1007/s11704-009-0069-4
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DOI: https://doi.org/10.1007/s11704-009-0069-4