Defining the Resolution of a Network for Transportation Analyses: a New Method to Improve Transportation Planning Decisions

Travel demand models are important tools used in the analysis of transportation plans, projects, and policies. The modeling results are useful for transportation planners making transportation decisions and for policy makers developing transportation policies. Defining the level of detail (i.e., the number of roads) of the transport network in consistency with the travel demand model’s zone system is crucial to the accuracy of modeling results. However, travel demand modelers have not had tools to determine how much detail is needed in a transport network for a travel demand model. This dissertation seeks to fill this knowledge gap by (1) providing methodology to define an appropriate level of detail for a transport network in a given travel demand model; (2) implementing this methodology in a travel demand model in the Baltimore area; and (3) identifying how this methodology improves the modeling accuracy. All analyses identify the spatial resolution of the transport network has great impacts on the modeling results. For example, when compared to the observed traffic data, a very detailed network underestimates traffic congestion in the Baltimore area, while a network developed by this dissertation provides a more accurate modeling result of the traffic conditions. Through the evaluation of the impacts a new transportation project has on both networks, the differences in their analysis results point out the importance of having an appropriate level of network detail for making improved planning decisions. The results corroborate a suggested guideline concerning the development of a transport network in consistency with the travel demand model’s zone system. To conclude this dissertation, limitations are identified in data sources and methodology, based on which a plan of future studies is laid out.

Behavioral Response to Environmental Taxation: Evidence from the Transportation Sector

This dissertation analyzes how individuals respond to the introduction of taxation aimed to reduce vehicle pollution, greenhouse gases and traffic. The first chapter analyzes a vehicle registration tax based on emissions of carbon dioxide (CO2), a major greenhouse gas, adopted in the UK in 2001 and subject to major changes in the following years. I identify the impact of the policy on new vehicle registrations and carbon emissions, compared to alternative measures. Results show that consumers respond to the tax by purchasing cleaner cars, but a carbon tax generating the same revenue would further reduce carbon emissions. The second chapter looks at a pollution charge (polluting vehicles pay to enter the city) and a congestion charge (all vehicles pay) adopted in 2008 and 2011 in Milan, Italy, and how they affected the concentration of nitrogen dioxides (NOx). I use data from pollution monitoring stations to measure the change between areas adopting the tax and other areas. Results show that in the first quarter of their introduction, both policies decreased NOx concentration in a range of -8% and -5%, but the effect declines over time, especially in the case of the pollution charge. The third chapter examines a trial conducted in 2005 in the Seattle, WA, area, in which vehicle trips by 276 volunteer households were recorded with a GPS device installed in their vehicles. Households received a monetary endowment which they used to pay a toll for each mile traveled: the toll varied with the time of the day, the day of the week and the type of road used. Using information on driving behavior, I show that in the first week a $0.10 toll per mile reduces the number of miles driven by around 7%, but the effect lasts only few weeks at most. The effect is mainly driven by a reduction in highway miles during trips from work to home, and it is strongly influenced by past driving behavior, income, the size of the initial endowment and the number of children in the household.