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single households, an increase in dual- worker house- holds, and more women entering the workforce. ⢠From 1980 to 1992, vehicle miles of travel (VMT) in the region increased by approximately 80%, while employment grew by 40%, and population increased by less than 5%. Thus, growth in VMT far outpaced growth in population and employment. Part of the increase in VMT was due to changing demographics and part was probably due to the available capacity on many freeways and roadways in the Puget Sound Region in the early 1980s. Much of the Interstate system in the area was constructed in the 1960s and 1970s. By the late 1980s and early 1990s, traffic congestion was becoming a problem, however. ⢠At the time, some policy makers and other groups focused on using this percentage growth in VMT in plan- ning future project needs. If 1992 is used as the base year for VMT increases, however, a different picture emerges. From 1992 to 2004, VMT increased by approximately 22%, while employment grew by 20% and population increased by about 19%. Over the same period, lane miles increased by approximately 12%. ⢠Tracking the population in Washington State by gen- der and age since the late 1800s highlights some interesting trends. In 1880, the population was fairly small. Washing- ton became a state in 1889 and the gold rush in Alaska, which contributed to the growth in Seattle, occurred in the 1890s. By 1890, the population of Washington was increasing due to immigration. The increase was primarily in males between the ages of 24 to 39. These growth trends continued to 1910, along with an increase in females in the same age groups. From the 1920s through the 1940s, the population remained relatively stable in number with an aging trend. In the 1940s, Washington experienced popu- lation increases due to the military bases in the state. In 1950, the number of births increased significantly, reflect- ing the start of the baby boom. The population in 1960 reflected a further increase in births, as well as continued immigration. The trends from 1960 through 2000 reflected the aging of the baby boom generation, the continued immigration, and more people living longer. ⢠The aging of the baby boom generation raises inter- esting questions concerning potential similarities to and differences from the travel behavior of individuals cur- rently in older age brackets. The panel survey data were examined to provide insights into the travel behavior of baby boomers as they retire or near retirement. ⢠A cluster analysis was conducted using the 2-day travel diaries of individuals 50 years of age and older. Cluster 1 comprised individuals who made close to 5.6 trips on each of the 2 days, with approximately 7 hours a day out of the home. About half of these trips were made with family members. Cluster 2 included individuals who made a little over four trips each day, with 8.4 hours a day spent out of the home and only 0.02 of the trips made with relatives. Cluster 3 included individuals who made about three trips each day, spent about 1.9 hours each day out of the home, and made 0.7 of the trips with family members. Cluster 4 included individuals who made 3.9 trips on Day 1, but no trips on Day 2. These individuals spent 3.7 hours out of the house on Day 1 and 1.6 trips were made with family members. Individuals in Cluster 5 made no trips on Day 1 and 3.9 trips on Day 2, with 3.4 hours spent out of the house and 1.6 of the trips made with family members. Cluster 6 included individuals who made no trips on Day 1 or Day 2. ⢠Clusters 4, 5, and 6, which were the clusters with infrequent trip makers and no trip makers, were exam- ined for possible weekly patterns. Cluster transitions from 1999 to 2000 were also explored. Approximately 70% of the individuals in Clusters 1 and 2 in 1999 stayed in the same clusters in 2000. In comparison, only 32% of the individuals in Cluster 3 in 1999 stayed in Cluster 3 in 2000 and 47% of the individuals in Clusters 4, 5, and 6 in 1999 stayed in the same cluster in 2000. These results indicate that 5-day or 2-week travel diaries are needed to capture the behavior patterns of individuals 50 years of age and older. ⢠The most important factor correlating to a transi- tion among clusters was a change in employment status. This change might include retirement, working part- time, and volunteering. Other factors that correlated with clus- ter transitions were a change in driverâs license status, a change in available vehicles, and a change in the number of children in the household. Factors that did not corre- late well with a transition in clusters were gender, land use mix, and a change in home location. One implication for travel demand modeling is that it is important to first forecast the employment status of the baby boom gener- ation before forecasting their trip making. ⢠The number of trips and miles traveled per day cor- related with changes in the number of automobiles in the household, the number of drivers in the household, chil- dren becoming adults, older adults moving into the household, and household location. These results indi- cate that to forecast baby boomers at the household level, information on available vehicles, number of driv- ers, demographic shifts with different travel require- ments, and the relocation of households is needed. NEW SURVEY ITEMS FOR A FULLER DESCRIPTION OF TRAVELER BEHAVIOR: BIOGRAPHIES AND SOCIAL NETWORKS Kay Axhausen Kay Axhausen discussed research related to travel sur- veys in Switzerland. He described elements that may influence travel behavior, factors to consider in defining 60 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 1
