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Category Archives: R41

The Impact of Access to Public Transportation on Residential Property Value: A Comparative Analysis of American Cities

By Moses Snow Wayne

This paper develops a consistent model for analyzing the impact of access to public transportation on property value applied to the four cities of Atlanta, Boston, New York, and San Francisco. This study finds a negative relationship between increasing distance to public transit and property value. Additionally, the elicited effects in each city generally align with geographic features and the degree to which a city is monocentric. This study also demonstrates the salience of using actual map-generated distances as proximity measures and characteristics of public
transit systems in modeling the relationship between public transportation and residential property value.

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Advisors: Dr. Patrick Bayer and Kent Kimbrough | JEL Codes: C12, R14, R30, R41

The Toll of Commuting: The Effects of Commute Time on Well-Being

By M. Thomas Marshall Jr.

When deciding on housing location, people theoretically optimize for the best location given their commute time, housing cost, income, as well as other factors. Stutzer and Frey (2008) suggest that this is not true in some nations, such as in their investigation of Germany, with their results showing that the cost of an average commute is equivalent to 35.4% of the average income. This paper investigates the impact of commute time on the well-being of individuals in the United States, correcting for various other factors that determine housing choice such as race,
age, and whether they have a child living at home. The results of this study are clearly that the relationship found between commuting time and well-being cannot be proven to be statistically significant from zero, so there is not any evidence against optimization.

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Advisor: Kent Kimbrough | JEL Codes: D12, D61, R31, R41

High Occupancy Toll Lines: Do They Reduce Congestion?

By David Wang

In 2009, according to data from the American Community Survey, ninety percent of workers in the U.S. used a privately owned vehicle when commuting. For an average commuter, the annual traffic delay in urban areas has increased from below fifteen hours in 1982 to more than thirty-five hours in 2007 (Winston, 2013). Furthermore, the annual cost of congestion, including travel delays and fuel expenditures, exceeds $100 billion a year (Winston, 2013). From a welfare standpoint, these travel delays cause a total welfare cost of $45 billion a year (Langer, Winston, & Baum-Snow, 2008). Governments have considered a variety of solutions to combat this congestion, the most prevalent being high occupancy vehicle (HOV) lanes and congestion pricing, including high occupancy toll (HOT) lanes.
The federal government heavily encouraged the construction of HOV (high occupancy vehicle) lanes, passing the Intermodal Surface Transportation Efficiency Act of 1991. It was thought that the speed differential between HOV lanes and general-purpose (GP) lanes would lead drivers to switch to carpools, thereby reducing the number of vehicles on the roads and the amount of congestion. However, in practice, HOV lanes have not been very successful and single occupancy vehicle (SOV) users often complain about underutilized HOV lanes. These observations mirror transportation researchers’ criticisms of the ineffectiveness of HOV lanes in reducing congestion. Dahlgren (1998) argues that adding a GP lane to existing highways is more effective at lowering delay costs than adding an HOV lane.
Another strategy, congestion pricing, involves placing a price on using roadways to offset the social cost arising from such use. Under Vickrey’s theory, the charges should match the marginal social cost of each trip as closely as possible (Vickrey, 1963). In a standard highway with only GP lanes, the personal cost of traveling, in the form of the value of time, often does not equal the social cost imposed on other commuters on the highway. This scenario gives rise to a mismatch in incentives and to a tragedy of the commons. In actual application, few tolling schemes for congestion pricing exist. Since technology has made collecting tolls cheaper, the main challenge now is public resistance. Current implementations of congestion pricing include Singapore’s Electronic Road Pricing system and U.S. HOT lanes.
High occupancy toll (HOT) lanes have potential as a politically feasible policy to improve utilization of HOV lanes and generate revenue. HOT lanes give solo drivers the option to pay a toll for use of HOV lanes. The HOT lanes help address several issues, for example balancing the load and reducing congestion by shifting some solo drivers from GP lanes to HOV lanes, giving drivers the option of traveling on less congested lanes, and generating revenue for highway operators (Poole & Orski, 2000). Many studies of HOT lanes use California State Route 91 as an example of a HOT highway and seek to model any welfare gains from its implementation (Liu & McDonald, 1998; Small & Yan, 2001). Opened in 1995, the SR-91 serves as a good case study because it was one of the first HOT operations in the U.S. The literature also explores the distributional effects of congestion pricing over the Washington, D.C., metropolitan area, looking at a network of roads rather than a single one (Safirova et al., 2004). They emphasize that much of the benefit from HOT lanes comes from undoing the inefficiency created by existing HOV lanes. Safirova et al.’s study is one of few empirical ones in the literature. Nevertheless, theoretical models, such as that by Konishi and Mun (2010), could be used as a basis for future empirical studies. The economics literature covers welfare gains, but does not address empirically how the congestion reduction from HOV and HOT lanes has changed over time. Although theory may predict welfare gains in the short term, it is unclear what the long run effects may be. In this paper, we seek to examine the short-run effects of the conversion of HOV to HOT lanes on highway congestion.

Honors Thesis

Advisor: Charles Becker, Michelle Connolly | JEL Codes: R41, R48 | Tagged: Congestion Pricing, HOT lanes, HOV lanes, Tolls, Transportation economics

Questions?

Undergraduate Program Assistant
Jennifer Becker
dus_asst@econ.duke.edu

Director of the Honors Program
Michelle P. Connolly
michelle.connolly@duke.edu