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155 The case examples described, as well as the experiences of other agencies and consult- ing fi rms, illustrate some of the opportunities and challenges to developing integrated dynamic model systems. These case examples also provide guidance regarding poten- tial next steps for parties who wish to pursue the advanced analytic capabilities that such models can provide. MIGRATION PATH It should be noted that there are a limited number of integrated dynamic model system development efforts that have been attempted. The case examples describe two fully integrated model systems. In these integrated model systems an advanced activity-based model system provides estimates of travel demand for input to a dynamic network assignment model, and the dynamic network assignment model in turn provides esti- mates of network performance for input to an activity-based model. The case exam- ples also include two partially integrated model systems, in which a travel demand model provides input to a quasiregionalâscale dynamic network assignment model but in which there is no feedback of network performance indicators from the assignment model to the demand model. Two other regions in the United States have recently initiated integrated dynamic model systems, but these efforts are still early in their development cycle. Of the four fully or partially integrated model systems, two primary migration paths are observed. In the fi rst migration path, an activity-based model system was implemented fi rst, and a regional-scale dynamic network assignment was implemented second. For the SHRP 2 C10A project, a regional-scale DTA model was implemented in a single effort, while for the SHRP 2 C10B and the SFCTA DTA Anyway projects, the DTA model was iteratively expanded geographically from a smaller regional subarea. In the second migration path, the regional (or quasiregional) dynamic network assign- ment was developed in advance of the availability of an activity-based model, although 7 CONCLUSIONS
156 Part 2: ISSUES IN ADOPTING INTEGRATED DYNAMIC MODELS SYSTEMS the intent is to ultimately integrate the activity-based model with the dynamic network assignment model. Because this is such a nascent field, it is not possible to draw any conclusions as to whether one path or the other offers advantages in terms of develop- ment time, costs, or capabilities. MODEL IMPLEMENTATION ISSUES The preceding sections identify and describe five primary types of integrated dynamic model implementation issues and present some potential next steps associated with each type of issue: ⢠Institutional awareness and capacity; ⢠Costs and schedule; ⢠Data; ⢠Methodology and software; and ⢠Application. Related to these types of implementation issues, a number of conclusions can be drawn from the lessons learned presented in each of the case studies. Some of these conclusions offer guidance about known challenges, while other conclusions identify issues where there are still significant uncertainties. Of course, there are also challenges and issues that have yet to be revealed, given the emerging nature of these tools. Development of base-year data does not represent a significant challenge to imple- menting an integrated dynamic model because of the availability of many tools and utilities provided by the software developers to facilitate data development. These tools can be used to help build activity-based model inputs and dynamic network assignment networks from existing geospatial data sources. Experience has shown that the use of actual, rather than synthesized, data is preferable in order to ensure that the baseline results are reasonable. However, developing future-year or alternative net- works is considerably more difficult than developing base-year data, primarily because of the complexities and fine-grained details required for the development of dynamic network assignment network assumptions. Model application is an area in which significant investigation needs to occur. Extensive efforts have been required to develop and calibrate the roadway and transit assignment models, and additional efforts are likely required to achieve a level of confidence required to support project evaluations. Applying the model was also com- plicated by the relatively long model system run times. Extensive testing of the model system was also necessary to determine the number of network assignment and model system iterations required to ensure that differences between alternative sce- nario model results were attributable to these policy and investments and not obscured by noise in the model system. This stochasticity, or simulation variation, is intrinsic to the simulation model as well as to the real world, and these efforts have revealed that additional investigation is required to understand how the models can be run and how the results can be applied, interpreted, and communicated to member agencies and to
157 Chapter 7: CONCLUSIONS the public. Model application will reveal additional issues that have likely not yet been identified by researchers and model developers. It should be noted that the more fully integrated model systems have been applied only to hypothetical policy and investment tests. Further work is required to under- stand their usefulness when subjected to the increased scrutiny of a real project. How- ever, the partially integrated model system in San Francisco has been used in a real project application context and was found to be useful because it generated more plausible results than a traditional static network model-based approach.
