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Publication Number:  FHWA-HRT-13-036    Date:  August 2013
Publication Number: FHWA-HRT-13-036
Date: August 2013


The Effective Integration of Analysis, Modeling, and Simulation Tools


The increasing complexity and interrelationship of transportation issues such as congestion, safety, emissions, accessibility, and mobility intensifies the need for practitioners to produce modeling results at multiple levels of resolution across multiple domains. Yet, no single AMS tool exists to answer the complex, multifaceted problems facing agency managers and elected officials. Often, gaps between data needs and capabilities prevent agencies from confidently addressing the problem at hand. Transportation Research Board’s Special Report 288 highlights the importance of applying the right tool for the right job by stating, "Travel forecasting tools...should be appropriate for the nature of the questions being posed by its constituent jurisdictions and the types of analysis being conducted."(pg. 3)(1)

Given the multi-resolution nature of U.S. transportation problems, there remains an outstanding need to develop tools, guidelines, and approaches to effectively integrate a range of AMS tools. Planners and engineers have, at some level, integrated AMS tools for many years through the transcription of inputs and outputs, either manually or through customized utility programs. For example, traditional four-step regional TDM results are often postprocessed to develop turning movement counts which are then manually input into a deterministic Highway Capacity Manual (HCM)-method based model to arrive at intersection level performance measures such as level of service or volume-to-capacity (V/C) ratio.(2)

Drawbacks with this manual approach to integrated modeling include the following:

AMS tools primarily exist to address a single resolution or domain, such as solving large-scale transportation problems with coarse resolution or solving small-scale problems with fine details.(3) The integration of these models has historically been ad hoc in nature, offering data transfers within or between models or model resolutions in response to specific project needs. The problem with this approach is the lack of a framework or guidance to allow smaller integration efforts to fit together in a larger, collective body of work. Consistency and simplicity in model integration would enhance practitioner understanding and ease of use when integrating models.


The effective integration of AMS tools requires enhanced, reliable data sources, as well as informed well-trained users with a clear analytic objective typically defined by policy or decisionmakers to complement the multi-resolution models. Figure 2 illustrates a concept for integrating multiple AMS models and their relationships with data sources and decisionmakers.

This flowchart shows the implementation of a conceptual integrated analysis, modeling, and simulation (AMS) tool. It is divided into data, models, and decisionmakers. The flow generally moves from left to right, with some elements providing feedback to the element that originally provided input into them. Data elements include point sensor data, automatic vehicle identification data, and Global Positioning System probe data, all of which feed into a model calibration engine that straddles the line between data and models. From the model calibration engine, information flows via a virtual cloud computing or parallel computing environment into a multi-scale traffic network/
State database, which in turn flows information down into macro-analysis instances, meso-assignment instances, and micro-simulation instances, which also flow information back into the multi-scale traffic network/State database. From these instances, the flow of information proceeds by exiting the virtual cloud or parallel computing environment as it enters the cross-domain visualization interface, which straddles the line between models and decisionmakers. From here, information flows into traffic management and planning organizations. At this point, information also flows back into the traffic control/advanced traveler information systems  scenario interface, which also straddles the line between decisionmakers and model. From here, information flows back into the cloud or parallel computing environment as it makes its final way to the multi-scale traffic network/State database, feeding into the model process all over again.
Figure 2. Flowchart. Implementation of a conceptual integrated AMS tool.

It is important to note that effective integration of AMS tools requires more than seamlessly and accurately linking multiple model resolutions/domains together. Effective integration will require the following:(4)


Demand for integrated modeling across multiple domains and resolutions are growing. Currently, software vendors, universities, and public agencies are developing approaches to accomplish model linkages and data transfers, but many are ad hoc in nature. A survey conducted as part of NCHRP 8-36 Task 90 indicated that the majority of users associated with integrated corridor management had already linked or were planning to link in the near future their regional demand planning model to a network simulation tool to perform time-dependent traffic assignments.(4) A one-way link between the coarser four-step TDM and a mesoscopic DTA or microsimulation model was by far the most common linkage cited within the surveyed agencies. Respondents who did not plan to integrate their planning and simulation models in the near future were discouraged by the difficulties in designing the data exchange mechanism.

Surveyed agencies identified the following common challenges to effectively integrate their planning (regional demand) models and network simulation tools (DTA or microsimulation):(4)

NCHRP 8-36 Task 90 mainly focuses on system and congestion management as well as identifying capacity improvement projects through the lens of traffic operations.(4) It is important to recognize that effective integration of AMS tools must branch across multiple types of transportation disciplines. The need for linking operational performance measures with land use, environmental/emissions, safety, cost-benefit, and human factors models is paramount to supporting increasingly complex policy decisions within surface transportation.