In this paper, a new land use classification method is explored for its utility in explaining travel behavior and as a new dimension in population synthesis for travel demand forecasting. This method is based on latent profile analysis applied to 17 business establishment indicators for each of the more than 20,000 block groups in California. The method reproduces the four types of land use environments (urban, suburban, exurban, and rural) identified in a previous paper, and improves our ability to create a finer-grain geographic classification based on land use. It also offers similar indications about the difference between urban dwellers (that make more trips but travel shorter distances) and rural residents (that make fewer trips but with more vehicle miles traveled).
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References
1.
McBrideE. C.DavisA. W.LeeJ. H.GouliasK. G.Incorporating Land Use in Synthetic Population Generation Methods and Transfer of Behavioral Data. Transportation Research Record: Journal of the Transportation Research Board, 2017. 2668: 11–20.
2.
BeckmanR. J.BaggerlyK. A.McKayM. D.Creating Synthetic Baseline Populations. Transportation Research Part A: Policy and Practice, Vol. 30, No. 6, 1996, pp. 415–429.
3.
AdigaA.AgasheA.ArifuzzamanS.BarrettC. L.BeckmanR.BissetK.ChenJ.ChungbaekY.EubankS.GuptaS.KhanM.KuhlmanC. J.LofgrenE.LewisB.MaratheA.MaratheM. VMortveitH. S.NordbergE.RiversC.StretzP.SwarupS.WilsonA.XieD.Generating a Synthetic Population of the United States. Technical Report NDSSL 15-009. Network Dynamics and Simulation Science Laboratory, Virginia Tech, Blacksburg, VA, 2015.
4.
AuldJ.MohammadianA.Efficient Methodology for Generating Synthetic Populations with Multiple Control Levels. Transportation Research Record: Journal of the Transportation Research Board, 2010. 2175: 138–147.
KonduriK. C.YouD.GarikapatiV. M.PendyalaR. M.Enhanced Synthetic Population Generator That Accommodates Control Variables at Multiple Geographic Resolutions. Transportation Research Record: Journal of the Transportation Research Board, 2016. 2563: 40–50.
7.
ZhuY.FerreiraJ.Jr.Synthetic Population Generation at Disaggregated Spatial Scales for Land Use and Transportation Microsimulation. Transportation Research Record: Journal of the Transportation Research Board, 2014. 2429: 168–177.
8.
CasatiD.MüllerK.FourieP. J.ErathA.AxhausenK. W.Synthetic Population Generation by Combining a Hierarchical, Simulation-Based Approach with Reweighting by Generalized Raking. Transportation Research Record: Journal of the Transportation Research Board, 2015. 2493: 107–116.
9.
MaL.SrinivasanS.Synthetic Population Generation with Multilevel Controls: A Fitness-Based Synthesis Approach and Validations. Computer-Aided Civil and Infrastructure Engineering, Vol. 30, No. 2, 2015, pp. 135–150.
10.
BallasD.ClarkeG.DorlingD.EyreH.ThomasB.RossiterD.SimBritain: A Spatial Microsimulation Approach to Population Dynamics. Population, Space and Place, Vol. 11, No. 1, 2005, pp. 13–34.
California Department of Transportation. 2010–2012 California Household Travel Survey Final Report Appendix. California Department of Transportation, Sacramento, CA, 2013.
15.
YeX.KonduriK. C.PendyalaR. M.SanaB.WaddellP.A Methodology to Match Distributions of Both Household and Person Attributes in the Generation of Synthetic Populations. Presented at 88th Annual Meeting of the Transportation Research Board, Washington, D.C., 2009.
16.
Bar-GeraH.KonduriK. C.SanaB.YeX.PendyalaR. M.Technical Report 09-1354: Estimating Survey Weights with Multiple Constraints using Entropy Optimization Methods. Transportation Research Board of the National Academies, Washington, D.C., 2008.
17.
Mobility Analytics Research Group. PopGen: Synthetic Population Generator [Online]. Mobility Analytics Research Group. http://www.mobilityanalytics.org/popgen.html. Accessed October 1, 2018.
18.
GouliasK. G.DavisA. W.McBrideE. C.JanowiczK.ZhuR.Long Distance Travel in CHTS and Social Media Augmentation. Final Report, Contract Number: 65A0529, Task Order 047. University of California, Santa Barbara. Department of Geography, GeoTrans and STKO Laboratories, California Department of Transportation, Sacramento, CA, 2017.
19.
MasynK. E.Latent Class Analysis and Finite Mixture Modeling. In T. D. Little (ed.), The Oxford Handbook of Quantitative Methods: Statistical Analysis, Vol. 2.Oxford University Press, New York, NY, 2013, pp. 551–611.
20.
Environmental Systems Research Institute. ArcGIS Desktop: Release 10.2. Environmental Systems Research Institute, Redlands, CA, 2014.
MuthénL.MuthénB.Mplus User’s Guide, 7th ed.Muthén & Muthén, Los Angeles, CA, 1998.
23.
NylundK. L.AsparouhovT.MuthenB. O.Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal, Vol. 14, No. 4, 2007, pp. 535–569.
24.
AsparouhovT.MuthénB. O.Using Mplus TECH11 and TECH14 to Test the Number of Latent Classes. Mplus Web Notes, No. 14, 2012, pp. 1–17.
25.
CeleuxG.SoromenhoG.An Entropy Criterion for Assessing the Number of Clusters in a Mixture Model. Journal of Classification, Vol. 13, No. 2, 1996, pp. 195–212.
26.
TurnerT.NiemeierD.Travel to Work and Household Responsibility: New Evidence. Transportation, Vol. 24, No. 4, 1997, pp. 397–419.
27.
KwanM.-P.Space-Time and Integral Measures of Individual Accessibility: A Comparative Analysis using a Point-Based Framework. Geographical Analysis, Vol. 30, 1998, pp. 191–216.
28.
ChenY.RavulaparthyS.DeutschK.DalalP.YoonS. Y.LeiT.GouliasK. G.PendyalaR. M.BhatC. R.HuH.-H.Development of Indicators of Opportunity-Based Accessibility. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2255: 58–68.
