1. Determinants of Congestion: A Time Series Analysis (1982 – 2009)
Yue Ke
Graduate Student, Lyles School of Civil Engineering
NB s
NB s
Results (Cont.)
Effects of Congestion
Data (Cont.)
Model 2 (r2 = )
Increase travel time/delays
Productivity losses
Higher transportation costs
Safety
Environmental externalities
Estimated (2020) National Highway System Peak Period Congestion. Image source: FHWA.
Mitigation Strategies
Build highway lanes to increase capacity (spoiler: did not work)
Hypothesis Testing
Listing and Locations of 87 MSAs included in data. Image source: TTI.
Methodology
Mixed: Negative coefficients for recession dummies but reverse is not supported (positive coefficient but not significant)
Recession and Boom
Increase in construction jobs cause decrease in congestion, perhaps construction sites not in CBD
Construction Job Share
Positive but statistically insignificant coefficient, thus no discernable effect
Retail Job Share
Congestion increases when incomes increase: households may be moving to the suburbs and increasing commute distances
Income Effect
No significant effect on congestion: Perhaps due to induced demand, similar to how/why freeway expansion had no long run effects
Transit Effect
5 hypotheses to test; two models needed
Model 1 is random effects GLS transformation with AR(1) error distribution—H1, H4, H5
Model 2 is first-differenced, with three standard error structures (based on clustering unit)—H2, H3
Los Angeles, peak hour congestion. Image source: AP News.
Regression Results
Model 1 (r2 = )
Build light rail and increase public transit services
Upgrade facilities for bike and pedestrian use
Increase high-density development within urban areas (e.g. TODs)
Data
Further Work
Panel of 87 MSAs between 1982 and 2009
Industry Mix of each MSA
Transit effect seems counterintuitive, warrants additional study
Underlying causes of macroeconomic boom/bust cycles may vary and have more nuanced effects on congestion in particular MSAs
Initial expectation of rise in fuel cost would lead to lower congestion not supported by model results
Examination of why the proportion of public and private sector jobs significantly impact congestion NB
References
Star McMullen and Nathan Eckstein. Determinants of VMT in Urban Areas: A Panel Study of 87 US Urban Areas Journal of the Transportation Research Forum, 52(3): 5-24, 2013.
William S. Vickrey. Congestion Theory and Transport Investment. The American Economic Review, 59(2): , 1969.
Robert M. Solow. Congestion Cost and the Use of Land for Streets. The Bell Journal of Economics and Management Science, 4(2): , 1973.
Lourens Broersma and Jan Oosterhaven. Regional Labor Productivity in the Netherlands: Evidence of Agglomeration and Congestion Effects. Journal of Regional Science, 49(3): , 2009.
Alex Anas and Rong Xu. Congestion, Land Use, and Job Dispersion: A General Equilibrium Model. Journal of Urban Economics, 45: , 1999.
William C. Wheaton. Commuting, Congestion, and Employment Dispersal in Cities with Mixed Land Use. Journal of Urban Economics, 55: , 2004.
K. Small and V. Dender. Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect. Energy Journal, 28(1):25-51, 2007.
Todd Litman. Generated Traffic and Induced Demand: Implications for Transport Planning
Clifford Winston and Ashley Langer. The Effect of Government Highway Spending on Road Users’ Congestion Costs. Journal of Urban Economics, 60: , 2006.
Kent Hymel. Does Traffic Congestion Reduce Employment Growth? Journal of Urban Economics, 65: , 2009.
Chad Shirley and Clifford Winston. Firm Inventory Behavior and the Returns From Highway Infrastructure Investments. Journal of Urban Economics, 55: , 2004.
David Brownstone. Key Relationships Between the Built Environment and VMT
Joe Cortright. Measuring Urban Transportation Performance: A Critique of Mobility Measures and a Synthesis