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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_052.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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potentially one of the biggest advantages of a tour- based model. On average, each person in the Denver region makes 4.4 trips per day and 1.6 tours per day, for an average of 2.7 trips per tour. Of all tours, 38% include three or more trips, and 55% of trips are on tours with three or more trips. These results show that a large fraction of travel includes some trip chaining, and developing a model that properly accounts for this phenomenon could have a significant influence on the model’s performance. Finally, notice the difference in mode shares between trips and tours. These differences result from the priori- tization scheme used to define the primary mode of the tour. The shared- ride mode was defined as a lower pri- ority than the drive- alone mode, such that if any trip on a driving tour is a drive- alone trip, the primary mode would be drive alone. Drive alone takes a higher prior- ity because it requires the exclusive use of a vehicle, and the tour coding of the modes correctly captures that the driver at some point needs use of a vehicle. Conversely, transit is a high priority in defining the primary tour mode, and the transit mode share is 50% higher for tours than for trips. This result indicates a substantial level of trip chaining on transit tours, in which travelers may stop to shop at some point during their transit trip. CONCLUSIONS In total, the survey data present a reasonable picture of travel behavior and one that is both more interesting and more intuitive than traditional trip- based statistics. They make plain the degree of trip chaining at which the trip- based statistics hint and show primary destination– purpose statistics that make sense given most people’s perception of their primary daily activities (work for older adults and school for children and young adults). DRCOG’s experience with the use of its home- interview survey in the development of tour codes strongly suggests that complex, advanced activity- based surveys are not necessary to develop reasonable tour codes to support the development of tour- based models. The place survey conducted by DRCOG was not notice- ably more complex than a traditional trip survey, and our experience with the data suggests that it would also be possible to develop tour codes by using a trip- based survey. These results suggest that many metropolitan planning organizations may already possess the data they need to develop tour- based models. Finally, one specific lesson learned from DRCOG’s experience is that there is no substitute for a robust onboard transit survey. DRCOG used a brief onboard survey to recruit transit riders to participate in its home- interview survey. While this transit oversample provided extremely useful data, the sample size was too small to provide a complete picture of the use of the transit sys- tem, and a full onboard survey would provide a nice complement to the oversample. REFERENCES 1. Lawton, K. Activity and Time Use Data for Activity- Based Forecasting. Proc., Activity- Based Travel Forecasting Con- ference: Summary, Recommendations and Compendium of Papers, June 2–5, 1996, Texas Transportation Institute, Arlington, 1997. 2. PB Consult and Gallup Corporation. The Integrated Regional Model Project: Vision Phase Final Report. Den- ver Regional Council of Governments, Denver, Colo., March 2005. 3. Bradley, M., M. Outwater, N. Jonnalagadda, and E. Ruiter. Estimation of an Activity- Based Microsimulation Model for San Francisco. Presented at 80th Annual Meeting of the Transportation Research Board, Washington, D.C., 2001. 4. Jonnalagadda, N., J. Freedman, W. A. Davidson, and J. D. Hunt. Development of Microsimulation Activity- Based Model for San Francisco: Destination and Mode Choice Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1777, TRB, National Research Council, Washington, D.C., 2001, pp. 25–35. 5. Bradley, M. A., J. L. Bowman, Y. Shiftan, K. Lawton, and M. E. Ben- Akiva. A System of Activity- Based Models for 52 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2 TABLE 3 Comparison of Tour Purpose and Trip Purpose for Trips Trip Purpose HBW HBNW NHB Total Tour Expanded Row Expanded Row Expanded Row Expanded Row Purpose Trips (%) Trips (%) Trips (%) Trips (%) Work 1,497,387 50 495,971 16 1,012,793 34 3,006,151 100 School 180 0 1,017,667 77 299,596 23 1,317,443 100 Shopping 1,591 0 689,607 62 424,438 38 1,115,635 100 Social–recreational 653 0 560,063 71 227,082 29 787,797 100 Drop off and pick up 248 0 528,673 85 90,260 15 619,180 100 Other 5,626 0 1,152,088 61 734,116 39 1,891,829 100 Total 1,505,685 17 4,444,067 51 2,788,283 32 8,738,035 100

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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