Activity Based Travel Demand Model Systems

Stated simply, the motivation for activity based travel demand modeling is that travel decisions are activity based. Concerns about congestion, emissions and land use patterns have lead governments to consider policies aimed to aaect travel decisions. Examples of policies include employer based commute programs, single occupant vehicle regulation, road pricing, multimodal facilities and transit-oriented land development. Such policies aaect the problem indirectly through the behavior of individuals. Individuals, moreover, adjust their behavior in complex ways. Figure 1.1 illustrates this idea, representing the daily activity and travel pattern of one person who drove alone to work at 7:30 a.m., stopped to shop on the way, and returned home at 4:40 p.m. This traveler switched to transit in response to an employer-sponsored program ooering strong nancial incentives to commute via transit. The switch required him to begin the commute earlier, at 7:00 a.m., in order to arrive punctually. Because his preferred shopping destination was not on the transit path, he decided to come straight home after work and shop later. This response was rooted in demand for activity, and involved a complex adjustment to the entire day's pattern. A pattern with two stops on a single tour was exchanged for a pattern with two single-purpose tours. In this case, a conventional trip based model would probably fail to predict the compensating peak period auto trips induced by the transit incentive program. Travel demand models will be able to capture this kind of response accurately only if the models represent how people schedule their daily activities. A 1991 Boston area resident survey generated statistics that reveal some of the complexity and variety in peoples' activity and travel schedules. Examining rst the 1