摘要: |
In the United States, a significant number of individuals depend on the auto mode of transportation. The high auto dependency, in turn, has resulted in high auto travel demand on highways. The resulting traffic congestion levels, surging oil prices, the limited ability to address increased auto travel demand through building additional transportation infrastructure, and the emphasis on reducing greenhouse gas (GHG) emissions has led to the serious consideration and implementation of travel demand management (TDM) strategies in the past decade. Congestion pricing is a frequently considered TDM option to alleviate travel congestion in urban metropolitan regions. Congestion pricing might induce changes in activity location, travel route, departure time of day, and travel mode. The current study contributes toward understanding the influence of congestion pricing on commuter behavior by specifically examining what dimensions of commuter travel behavior are affected as a response to congestion pricing. Specifically, we formulate and estimate a joint disaggregate model of commute departure time and route choice drawing from the 2008 Chicago Regional Household Travel Inventory (CRHTI). The empirical analysis demonstrates the significance of individual and household socio-demographics on commuter behavior. The results also highlight how vehicle availability plays an important role in determining individual's sensitivity to travel time and travel cost. To demonstrate the applicability of the joint modeling framework to determine optimal toll fares, we compute value of travel time measures for different demographic groups. |