Will Millennials Drive Less as the Economy Recovers: A Postrecession Analysis of Automobile Travel Patterns

Personal vehicle miles traveled (VMT) in the United States has seen a dip from 2004 to 2014, owing to the recent economic recession. News reports and academic articles relate this change to Millennials’ (those born in the last two decades of the twentieth century) travel patterns. Some researchers argue that the existing mobility patterns of Millennials may not persist as the U.S. economy fully recovers from the Great Recession. This study examines the changes in automobile travel patterns across generations. The empirical study builds on three most recent nationwide travel surveys. Research results reveal that residential location choice and life course events have stronger associations with Millennials’ automobility travel patterns than those of Gen Xers. We also find as personal wealth increases, the Millennials generation will not increase auto mileage as much as the preceding generations. Analyzing the driving distances by trip purposes while accounting for the differences between weekdays and weekends offers timely insights on formulating effective policy provisions for specific periods or planning targets.

Pricing and Reliability Enhancements in the San Diego, California, Activity-Based Travel Model

The estimation of demand for priced highway lanes is becoming increasingly important to agencies seeking to improve mobility and find alternative revenue sources for the provision of transportation infrastructure. However, many modeling tools fall short of what is required for robust estimates of demand with respect to toll and managed lanes in two key areas: the value of time is often aggregate and not consistently defined throughout the model system, and the reliability of transport infrastructure is rarely considered. This paper describes an effort that implemented recommendations of the Strategic Highway Research Program on pricing and reliability within a regional activity-based modeling system for the San Diego, California, region. The implemented recommendations included distributed travel time sensitivities across the synthetic population and special travel markets, continuous cost sensitivity on the basis of income, and multiple value of time bins in highway skimming and assignment. The work also included innovative research related to the analysis of travel time variability on the basis of a temporally disaggregate (1-min interval) data set of automobile travel speeds for most automobile links in the San Diego network for the month of October 2012. Regression equations that related the travel time reliability to link characteristics, incorporated reliability in automobile travel skims, incorporated those skims in the travel demand model system, and calculated toll elasticity on toll roads in San Diego County were estimated. The enhanced model matched observed toll demand better than the original model. Resulting elasticity values were generally found to be in the ranges reported in the literature.

Welfare Measures to Reflect Home Location Options When Transportation Systems are Modified

Transportation system improvements do not provide simply travel time savings, for a fixed trip table; they affect trip destinations, modes, times of day, and, ultimately, home and business location choices. This paper examines the welfare (or willingness-to-pay) impacts of system changes by bringing residential location choice into a three-layer nested logit model to more holistically anticipate the regional welfare impacts of various system shifts using logsum differences (which quantify changes in consumer surplus). Here, home value is a function of home price, size, and accessibility; and accessibility is a function of travel times and costs, vis-à-vis all mode and destination options. The model is applied to a sample of 60 Austin, Texas, zones to estimate home buyers’ welfare impacts across various scenarios, with different transit fares, automobile operating costs, travel times, and home prices. Results suggest that new locators’ choice probabilities for rural and suburban zones are more sensitive to changing regional access, while urban and central business zone choice probabilities are more impacted by home price shifts. Automobile costs play a more important role in residential location choices in these simulations than those of transit, as expected in a typical U.S. setting (where automobile travel dominates). When generalized costs of automobile travel are simulated to rise 20%, 40%, and 60% (throughout the region), estimated welfare impacts (using normalized differences in logit logsum measures) for the typical new home buying household (with $70,000 in annual income and 2.4 household members) are estimated to be quite negative, at -$56,000, -$99,000, and -$132,000, respectively. In contrast, when auto’s generalized costs fall everywhere (by 20%, 40%, and then 60%), welfare impacts are very positive (+$74,000, $172,500, and $320,000, respectively). Such findings are meaningful for policymakers, planners, and others when anticipating the economic impacts of evolving transportation systems, in the face of new investments, rising travel demands, distance-based tolls, self-driving vehicles, and other changes.