The effects of traffic congestion on travel behavior are complex and multidimensional because they are related to various factors such as density, land use patterns, network connectivity, and individual preferences. Traffic congestion is a phenomenon that not only affects transportation systems but also influences commuters’ quality of life and population mobility. The present research aims to analyze the effects of traffic congestion on individuals’ travel behaviors, addressing both direct and indirect effects of congestion on vehicle miles traveled (VMT) per driver by implementing structural equation modeling (SEM) techniques. In addition to the causal analysis between traffic congestion and VMT, this study examined the complex relationship between an individual’s socioeconomic characteristics, the built environment, congestion, and VMT. Measuring local congestion at a national level is also a key contribution of this research. This study used the same methodology as the Texas A&M Transportation Institute to compute a road congestion index and quantify local congestion for 93,769 drivers within 337 metropolitan areas. Our findings suggest that congestion is the main driver of VMT reduction. The findings also confirm that residents in compact development regions have lower daily VMTs because of the proximity of origins and destinations in denser areas with higher job–population balances. Therefore, rather than expanding highway networks, public transit investment might address traffic congestion more efficiently—not only by providing residents with more equitable and sustainable means of transportation, but also by encouraging people to reside in more compact and location-efficient areas.
Get full access to this article
View all access options for this article.
References
1.
BentoA. M.CropperM. L.MobarakA. M.VinhaK.The Effects of Urban Spatial Structure on Travel Demand in the United States. Review of Economics and Statistics, Vol. 87, No. 3, 2005, pp. 466–478. https://doi.org/10.1162/0034653054638292
2.
BhatC. R.GuoJ. Y.A Comprehensive Analysis of Built Environment Characteristics on Household Residential Choice and Auto Ownership Levels. Transportation Research Part B: Methodological, Vol. 41, No. 5, 2007, pp. 506–526. https://doi.org/10.1016/j.trb.2005.12.005
3.
CerveroR.KockelmanK.Travel Demand and the 3Ds: Density, Diversity, and Design. Transportation Research Part D: Transport and Environment, Vol. 2, No. 3, 1997, pp. 199–219. https://doi.org/10.1016/S1361-9209(97)00009-6
4.
CerveroR.MurakamiJ.Effects of Built Environments on Vehicle Miles Traveled: Evidence from 370 US Urbanized Areas. Environment and Planning A, Vol. 42, No. 2, 2010, pp. 400–418. https://doi.org/10.1068/a4236
5.
EwingR.HamidiS.GallivanF.NelsonA.GraceJ.Combined Effects of Compact Development, Transportation Investments, and Road User Pricing on Vehicle Miles Traveled in Urbanized Areas. Transportation Research Record: Journal of the Transportation Research Board, 2013. 2397: 117–124.
6.
SarzynskiA.WolmanH. L.GalsterG.HansonR.Testing the Conventional Wisdom About Land Use and Traffic Congestion: The More We Sprawl, the Less We Move?Urban Studies, Vol. 43, No. 3, 2006, pp. 601–626. https://doi.org/10.1080/00420980500452441
7.
EwingR.CerveroR.Travel and the Built Environment: A Synthesis. Transportation Research Record: Journal of the Transportation Research Board, 2001. 1780: 87–114.
8.
HandyS.LeeR.MaissR.WaltersJ.ParkerT.Improved Data and Tools for Integrated Land Use-Transportation Planning in California: Appendix A—Annotated Literature Review on Land Use-Transportation Relationships, 2012. http://ultrans.its.ucdavis.edu. Accessed Nov. 15, 2015.
HechtA.The Journey-to-Work Distance in Relation to the Socio-Economic Characteristics of Workers. Canadian Geographer, Vol. 18, No. 4, 1974, pp. 367–378. https://doi.org/10.1111/j.1541-0064.1974.tb00210.x
11.
ManaughK.Miranda-MorenoL. F.El-GeneidyA. M.The Effect of Neighbourhood Characteristics, Accessibility, Home–Work Location, and Demographics on Commuting Distances. Transportation, Vol. 37, No. 4, 2010, pp. 627–646. https://doi.org/10.1007/s11116-010-9275-z
12.
MauchM.TaylorB.Gender, Race, and Travel Behavior: Analysis of Household-Serving Travel and Commuting in San Francisco Bay Area. Transportation Research Record: Journal of the Transportation Research Board, 1997. 1607: 147–153.
13.
TrowbridgeM. J.McDonaldN. C.Urban Sprawl and Miles Driven Daily by Teenagers in the United States. American Journal of Preventive Medicine, Vol. 34, No. 3, 2008, pp. 202–206. https://doi.org/10.1016/j.amepre.2007.11.013
Ben-AkivaM. E.LermanS. R.Discrete Choice Analysis: Theory and Application to Travel Demand, Vol. 9. MIT Press, Cambridge, MA., 1985.
16.
BoarnetM. G.CraneR.Travel by Design: The Influence of Urban Form on Travel. Oxford University Press on Demand, New York, 2000.
17.
HandyS.Understanding the Link Between Urban Form and Nonwork Travel Behavior. Journal of Planning Education and Research, Vol. 15, No. 3, 1996, pp. 183–198. https://doi.org/10.1177/0739456X9601500303
18.
MiD.FerreiraJ.Jr.Vehicle Miles Traveled and the Built Environment: Evidence from Vehicle Safety Inspection Data. Environment and Planning A, 2014. 1831: 2991–3009.
19.
AndersonW.KanaroglouP.MillerE.Urban Form, Energy and the Environment: A Review of Issues, Evidence and Policy. Urban Studies, Vol. 33, No. 1, 1996, pp. 7–35. https://doi.org/10.1080/00420989650012095
20.
BagleyM. N.MokhtarianP. L.The Impact of Residential Neighborhood Type on Travel Behavior: A Structural Equations Modeling Approach. The Annals of Regional Science, Vol. 36, No. 2, 2002, pp. 279–297. https://doi.org/10.1007/s001680200083
21.
CerveroR.Built Environments and Mode Choice: Toward a Normative Framework. Transportation Research Part D: Transport and Environment, Vol. 7, No. 4, 2002, pp. 265–284. https://doi.org/10.1016/S1361-9209(01)00024-4
22.
ChatmanD.How Density and Mixed Uses at the Workplace Affect Personal Commercial Travel and Commute Mode Choice. Transportation Research Record: Journal of the Transportation Research Board, 2003. 1831: 193–201.
23.
GiulianoG.SmallK. A.Is the Journey to Work Explained by Urban Structure?Urban Studies, Vol. 30, No. 9, 1993, pp. 1485–1500. https://doi.org/10.1080/00420989320081461
HoltzclawJ.ClearR.DittmarH.GoldsteinD.HaasP.Location Efficiency: Neighborhood and Socio-Economic Characteristics Determine Auto Ownership and Use: Studies in Chicago, Los Angeles and San Francisco. Transportation Planning and Technology, Vol. 25, No. 1, 2002, pp. 1–27. https://doi.org/10.1080/03081060290032033
DavisA. F.The Impact of Traffic Congestion on Household Behavior: Three Essays on the Role of Heterogeneity. Ph.D. dissertation. North Carolina State University, Raleigh, 2004.
28.
SweetM. N.ChenM.Does Regional Travel Time Unreliability Influence Mode Choice?Transportation, Vol. 38, No. 4, 2011, pp. 625–642.
29.
KuzmyakJ. Richard.Land Use and Traffic Congestion. Arizona Department of Transportation Research Center, Phoenix, AZ., 2012.
30.
EwingR.TianG.LyonsT.TerzanoK.Does Compact Development Increase or Reduce Traffic Congestion?, 2017. https://doi.org/10.3141/2397-14.
31.
LomaxT.SchrankD.EiseleW.EiseleThe Keys to Estimating Mobility in Urban Areas. The Texas A&M University, College Station, TX., 2005.
32.
e SilvaJ. D. A.MorencyC.GouliasK. G.Using Structural Equations Modeling to Unravel the Influence of Land Use Patterns on Travel Behavior of Workers in Montreal. Transportation Research Part A: Policy and Practice, Vol. 46, No. 8, 2012, pp. 1252–1264.
33.
GraceJ. B.Structural Equation Modeling and Natural Systems. Cambridge University Press, Cambridge, 2006.
34.
U.S. Department of Transportation, Federal Highway Administration. Methodology: Local Area Transportation Characteristics for Households. Bureau of Transportation Statistics, 2009.
35.
LeiP. W.WuQ.Introduction to Structural Equation Modeling: Issues and Practical Considerations. Educational Measurement: Issues and Practice, Vol. 26, No. 3, 2007, pp. 33–43.
36.
HoxJ. J.BechgerT. M.An Introduction to Structural Equation Modeling. Family Science Review, Vol. 11, No. 4, 2007, pp. 354–373.
37.
U.S. Department of Transportation. Federal Highway Administration. National Household Travel Survey. 2009. www.nhts.ornl.gov. Accessed December 12, 2016.
38.
HamidiS.EwingR.A longitudinal study of changes in urban sprawl between 2000 and 2010 in the United States. Landscape and Urban Planning, Vol. 128, 2014, pp. 72-82. https://doi.org/10.1016/j.landurbplan.2014.04.021.
39.
United States Census Bureau. American FactFinder, 2010. www.factfinder2.census.gov. Accessed November 2016.
40.
United States Census Bureau. Longitudinal Employer-Household Dynamics (LEHD), 2010, www.lehd.ces.census.gov. Accessed December 2016.
41.
U.S Department of Transportation. Office of Highway Policy Information,. Highway Performance Monitoring System (HPMS) Public Release of Geospatial Data in Shapefile Format, 2010. https://www.fhwa.dot.gov/policyinformation/hpms/shapefiles.cfm Accessed December 2016.
42.
Environmental Systems Research Institute (ESRI). ArcGIS Business Analyst Database. 2012, www.esri.com. Accessed January 2017.
43.
Center for Neighborhood Technology. The National Transit Oriented Developmet Database, 2010. www.toddata.cnt.org. Accessed December 2016.
44.
MansonS.SchroederJ.Van RiperD.RugglesS.IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota, 2017. http://doi.org/10.18128/D050.V12.0. Accessed January 2017.
