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

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Suggested Citation:"T57054 txt_180.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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ment. In addition, the market segmentation is limited to a single variable (usually income) in conventional mod- els whereas all characteristics of the simulated individu- als can be retained in the activity- based approach. Another way in which transportation project analysis is improved compared with the use of conventional mod- els is that the effects of new projects on travel demand (i.e., induced travel) can be modeled directly. Conven- tional trip- generation models consider only demographic variables and do not consider transportation level of ser- vice. The magnitude of the effects of improved trans- portation level of service stemming from new projects on the amount of travel demand can be estimated through the incorporation of level- of- service variables in all steps of the demand modeling process. The use of logsum vari- ables from subsequent model steps provides a way to do this while maintaining consistency among the level- of- service data for all model components. It is worth briefly discussing some of the ways in which the proposed activity- based modeling approach fails to address some of the planning analysis needs. One of the most significant is that a conventional static traf- fic assignment process will be used. Although it would be desirable to consider traffic microsimulation or dynamic traffic assignment (DTA) procedures, the ability to implement and validate such procedures when they are applied at a regional level (at least in a region as large as the Denver metropolitan area) has not yet been proven. Lack of a fully disaggregate or at least DTA pro- cedure will limit the model’s ability to analyze the effects of queuing of traffic and to examine variations in traffic flow within peak periods. This inhibits the full explo- ration of the effects of tolling options and other highway operations analyses. Another issue is that, despite its use of microsimula- tion of individuals, the model will still have some aggre- gate elements. The region will still be divided into analysis zones, which will be used as the basis for high- way travel time and some other level of service network skims. This means that aggregation error will still exist in the model (although to a lesser extent than in a con- ventional model). However, current model design antici- pates storing each household and job at the point level, mitigating some aggregation errors by allowing detailed calculation of walk skims. IMPROVING THE MODELING PROCESS In conclusion, it is clear that existing modeling tools come up short in their ability to address the planning analysis needs of the Denver region. While the proposed activity- based approach is not a panacea for all of the shortcom- ings, it does provide many improvements to the modeling process that specifically address some of the issues. These include the ability to introduce distributions for the value of travel time in road pricing analyses, the use of a more accurate tour- based time- of day- modeling procedure in road pricing and other analyses, the use of additional seg- mentation variables in such analyses as the development of urban centers and transit- oriented development, the ability to directly model trip chaining, and the use of transportation level- of- service variables in all steps of the model to estimate the effects of induced travel demand. These advantages led DRCOG to begin development of an activity- based model as the main travel demand esti- mation tool for future planning analyses. REFERENCE 1. Cambridge Systematics, Inc., M. Bradley, and J. Bowman. Model Design Plan for the DRCOG Activity- Based Model. January 2006. 180 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2

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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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