Communities across the country are implementing policies to address their increasing commuter congestion. These policies are relatively new and vary from city to city, so not much is known about their full effects. To evaluate different congestion reduction policies, this project will develop a discrete choice structural model of the joint decision of individual residence and commuting mode, given the characteristics of the housing market and commuting options. The model is estimated for the Washington, D.C. metropolitan area using individual level, restricted-access data from the 1996–2013 American Community Surveys (ACS), which includes information on where individuals live and work, together with data on the structure of the transportation network, to map each individual’s optimal commute for each option in the individual’s choice set.
The mappings will create a dataset of commute options and characteristics that will be used to estimate the trade-offs that individuals make among consumption, housing amenities, and leisure when choosing a home and commuting mode pair. The model estimates will be used to simulate the effects of transportation policies that alter the financial and time costs of commuting. These policies include congestion pricing schemes, fuel or carbon taxes, and increased parking fees.
Christopher Clapp