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


The Effective Integration of Analysis, Modeling, and Simulation Tools



In today’s practice, integrated modeling generally functions less like a system and more like a series of independent activities. A key objective of the AMS data hub is to develop a systematic approach for integrating AMS tools to enable the continuous flow of data which, in turn, allows users to dedicate more time to performing modeling functions such as calibration, alternatives and sensitivity analyses, and performance evaluation.

The AMS data hub has the following four primary components, as illustrated in figure 4:

This report provides a high-level overview of a recommended database schema for unifying modeling data across commonly used AMS tools. Figure 4 provides an overview on the proposed software architecture by illustrating the information data flow procedures between different components.

The following sections describe each component in detail, including its primary function, internal relationship with other components, key characteristics, and operation environment.

This complex system diagram depicts a series of connections with close-up view of two main elements showcasing the analysis, modeling, and simulation (AMS) data hub architecture. Across the top are a list of example system end users from left to right, including agency/metropolitan planning organization (MPO) planner/analyst, consultant practitioners, agency system operator, university researcher, software programmer/developer, and agency manager. Moving from left to right, the schematic depicts federates, their interactive relationship with the Network EXplorer for Traffic Analysis (NeXTA) graphic user interface (i.e., the data hub), the cloud interface, and visualization. Federates include transportation-related AMS tools, comprising land use, emissions, safety, geographic information system (GIS), and transit. AMS tools including the travel demand/activity-based model, dynamic traffic assignment, microsimulation, and deterministic tools. Field data inputs comprise probes, detectors, counts, signal timing, and survey inputs. The AMS tools and transportation-related AMS tools are linked together as federates, but the field data inputs are not linked to the other federates. All federates send inputs to and take outputs from the NeXTA graphic user interface or the data hub via the data conversion toolbox. The data conversion toolbox in turn sends inputs to and takes outputs from the database schema. The data conversion toolbox features a blowup showing that network data flow is used to compile the subarea cut, demand data flow is used for origin-destination (O-D) matrix estimation, signal control data flow is used for signal timing estimation, and performance measurement data flow is used for field-to-model data matching. The database schema also features a close-up view of a box that is divided into zones and configurations. Within the zones, the elements that are interlinked include links, demand, nodes, measures of effectiveness (MOEs), and scenarios. Links connect to trajectory under configuration, and nodes connect to signals, also under configuration. Both signals and trajectory are labeled as meso and micro. The database schema sends output to and receives input from the cloud interface, which is comprised of Google Fusion Tables®. The final element, visualization, incorporates Google Maps® and NeXTA visualization. While the Google Fusion Tables® feed directly into the Google Maps® element, the data hub area generally flows directly into NeXTA visualization.
Figure 4. Illustration. AMS data hub architecture.


This component includes analysis tools and data that can be integrated to yield higher analysis fidelity. To facilitate description and presentation, analysis tools are categorized using the traditional (albeit sometimes misleading) resolution descriptions of macroscopic, mesoscopic, microscopic, and HCM to distinguish the basic levels of information required by transportation AMS tools. The descriptions are as follows:

Field data inputs include existing traffic counts, traffic information, geometric information, signal controller settings, travel time runs, etc., which can be integrated with analysis tools to facilitate data input or calibration and validation. Field data inputs include the following:

Note that this report does not provide detailed information on transportation-related analysis tools such as land use, emissions, and safety models. The AMS data hub concept can be extended in the future to include other related models.


The conversion toolbox is one of the core components of the AMS data hub. Within the current state of the practice, many conversions are required to transfer data among analysis tools. Over time, it is likely that the conversion toolbox will become less and less critical as analysis tools adopt the unified data structure and have built-in capability to import/export AMS data hub compatible format. However, for near-term applications, the conversion toolbox is essential. This section provides data hub users with a better understanding of underlying multi-resolution modeling elements, automated processes of disaggregating and aggregating data across different resolutions, and value added and data mining support tools such as subarea O-D demand matrix updating and sensor data management.

Table 4 provides a summary of typical modeling components used at different resolutions of transportation modeling and simulation tools.

Table 4. Summary of typical transportation AMS data types.

Data Type

Data Resolution

Macroscopic Regional Planning Models (Including Agent-Based Demand and Land Use Models)

Mesoscopic DTA

Microscopic Traffic Simulation, Travel Demand, and Highway Capacity Analysis Tools


Coordinates turning movement permission and restriction

Movement-specific capacity

Turning volume and signal timing plan


Upstream node, downstream node, length, link capacity, and number of lanes

Number of left-turn and right-turn bays and length of bays

Lane-to-lane connectors at intersections and detailed geometry of lanes

Vehicle demand

Household, population, employment of traffic analysis zones, parcel-level household data for activity origins and destinations, and peak hour O-D demand matrix by different trip purposes and vehicle types

Time-dependent departure time profile and vehicle paths under different traveler information provision strategies

Node-specific turning movement and vehicle routing plan in the subarea

Transit demand

Transit network with connectors from zone centroids

Number of transit vehicles on network

Individual transit stops with walk access connectors from land parcels

Link measure of effectiveness (MOE) output from simulators/ models

Peak hour end-to-end travel time and accessibility, link speed, and flow rate

Link-based time-dependent flow rates and queue evolution

Second-by-second lane-by-lane vehicle trajectory

Observed sensor measurements

Peak hour link count and end-to-end travel time

Time-dependent loop detector data such as speed, flow, occupancy, and time-
dependent travel time

High-fidelity vehicle trajectory data (e.g., Next Generation Simulation)

To facilitate the seamless cross resolution modeling practice and improve the modeling accuracy of simulation and planning models, the AMS data hub should consider the following guiding principles:

Table 5. Data flow for information propagation and exchange in cross domain applications.

Data Type

Data Resolution

Macroscopic Representation

Mesoscopic Representation

Microscopic Representation

Domain application

Safety impact evaluation

Travel time reliability analysis

Emission impact studies

Output through data hub

Link volume (average annual daily traffic (AADT)) and congestion level

Time-dependent path flow pattern and link capacity variations under recurring conditions

Second-by-second lane-by-lane vehicle trajectory

Additional information from specific domain applications

AADT-based crash prediction formulas for different facility types

Link volume, capacity, and demand variations due to incidents, work zone, and severe weather conditions

Vehicle-specific power-to-emission conversion table

Additional MOEs

Peak hour and daily crash rates and capacity reduction

End-to-end travel time reliability measures under recurring and non-recurring conditions

Regional- and project-level emissions estimates and sustainability analysis

Network EXplorer for Traffic Analysis (NeXTA) version 3 is the prototype implementation of the AMS data hub that was developed based on the guiding principles highlighted in this section. It houses various conversion tools and provides a visualization as well as connection with the cloud storage. The following conversion tools are currently embedded in NeXTA:

Other functions provided by NeXTA include the following:


This section describes a unified data structure that facilitates input/output data conversion in the short term and promotes data consistency and exchange in the long term.

The component diagram shown in figure 4 illustrates the relationship of the series of tables in the proposed database schema: zones, nodes, links, demands, transit, household travel surveys, travel model outputs, MOEs, scenarios, signal control, vehicle trajectories, and configuration.

A few key points regarding the organization of the data hub tables are as follows:

Advantages of the database schema are as follows:

One disadvantage of such an overarching unified data schema is its size, which has an adverse effect on computational speed and efficiency. The data structure is understandably large in order to accommodate analysis models in various resolutions (i.e., a single intersection analysis using the HCM method would utilize only a small portion of the data structure).

A description of each of the components of the database schema is provided in appendix B of this report. The description is general in nature, and the database schema can be implemented in multiple ways. Regardless of the format, the AMS data hub must accommodate a wide variety of data types, some of which are quite complex (see table 6). Examples of data types are as follows:

Simple data types:

Complex data types:

The data type definitions in table 6 are proposed in the database schema.

Table 6. Data types.








Signed 32-bit integer value



Other possible integer types (which are less often used): byte (8 bits), short int (16 bits), and long int (64 bits)


Double precision floating point number



Used for most inputs that can take on any value, including fractional values


Takes on one of several prespecified values



Used for categorical variables (e.g., facility type = freeway, arterial, ramp, etc.)


Boolean value:
true or false





String of unicode characters




Extensible Markup Language (XML)

XML formatted field



Can alternately be defined as a string variable, but some DBMSs provide XML field definitions with special handling (e.g., the user may have to specify a validating schema)


n-dimensional array



An array of any simple type


Date and time value





Geometric object



Can be a point, line segment, path, or polygon


User-defined data object to hold complex data types



Some DBMSs allow user-defined objects, but these usually require an additional handling code to be written by the user

N/A = Not applicable.
n = Number of dimensions in the subject array.

NeXTA version 3 is used for the test applications conducted for the AMS data hub. Its data structure is loosely based on the proposed data schema. To allow the most flexibility, readability, and compatibility with the open-source concept, NeXTA’s tables are currently stored in a series of comma-separated values (CSV) files. As a compromise, it lacks advanced capabilities such as table linkage, shared access, entry validation, etc., that are typically inherent in a database.


Google Fusion Tables® is employed to demonstrate the benefits of cloud storage. Data uploaded to Google Fusion Tables® are stored on the Google® cloud server. The tables can be shared with many users. Google Fusion Tables® is free and provides many functions to work with and manipulate data. Since it is open source, there is a growing number of add-in tools developed by others and distributed freely. Figure 5 shows link and node data from a sample network that has been exported from NeXTA and uploaded to Google Fusion Tables® . Similarly, the network data can be downloaded from Google Fusion Tables® in CSV format and imported into NeXTA or other software.

This figure shows a screenshot of Google Fusion Table® data exported from Network EXplorer for Traffic Analysis (NeXTA).
©2012 Google Fusion Tables®
Figure 5. Screenshot. Google Fusion Tables® data exported from NeXTA.(6)


Visualization is an increasingly important element of transportation analysis and should be an integral part of any AMS data hub. Analysts can apply several readily available visualization functions with Google Fusion Tables® . Figure 6 and Figure 7 illustrate two examples.

This figure snows a network of an area west of Portland, OR, plotted using Google Maps®.
©2013 Google Maps®
Figure 6. Illustration. Example network plotted using Google Maps® .(7)

This graph shows an example scatter plot from Google Fusion Tables®. Lane capacity in vehicles per hour is on the
©2012 Google Fusion Tables®
Figure 7. Graph. Example scatter plot using Google Fusion Tables® .(8)

The NeXTA interface also has many useful visualization functions. Visual display of commonly used MOEs such as V/C ratio, speed, and queuing are readily available from one of the toolbars (see figure 8).

This figure shows a screen capture of NeXTA toolbar visualization options, which include buttons for demand, scenario, simulation, network, animation, density, volume, speed, and queue.
Figure 8. Illustration. NeXTA toolbar visualization options.

V/C Visualization

The V/C visualization view shows time-dependent V/C ratios for each link in the network. The color coding is user definable and allows for quantifying locations and durations of congestion in a network, as shown in figure 9.

This figure shows a screen capture of a Network EXplorer for Traffic Analysis map illustrating volume-to-capacity (V/C) ratio performance by link. In this example, one link shown in yellow is operating with a V/C ratio of greater than 0.75. The remaining links shown in green are operating with V/C ratios less than 0.75.
Figure 9. Illustration. V/C ratio performance by link.

Speed Visualization

The travel speeds at the link level are displayed in a time-dependent manner very similar to the V/C visualization. The speed MOE used is percentage of speed limit (or designated link speed) and is illustrated in figure 10. Again, the user may define the color coding and thresholds
for display.

This figure shows a screen capture of a Network EXplorer for Traffic Analysis map illustrating time-dependent speed performance by link. Links shown in green operate at a speed that is 90 to 100 percent of the speed limit. Links in yellow operate with speeds between 50 and 70 percent and 70 and 90 percent of the speed limit.
Figure 10. Illustration. Speed performance by link.

Queuing Visualization

The queue is visually represented on the link using both line width and color. Links without queue are drawn with thin gray lines. When a queue is present, the portion of the link which is occupied with queued vehicles is drawn as a red line with increased line thickness. The distance over which these link visualization changes are applied represents the percentage of the link that is occupied with queued vehicles. The length of the queue on the link changes dynamically over time, corresponding to the time-dependent queue length. The numerical values are shown in terms of the percentage of the link occupied with queued vehicles. Figure 11 provides an example of the queuing visualization tool in NeXTA.

This figure shows a screen capture of a Network EXplorer for Traffic Analysis map illustrating link performance. Two red-colored links represent the percentage of the link that is occupied with queued vehicles. Intersections are represented by black circles with identification numbers. Directional links are represented by lines connecting the intersections.
Figure 11. Illustration. Queuing performance by link.

NeXTA’s visualization tools also provide additional MOEs, some of which are more advanced and in the forefront of the transportation analysis practice. One of them is the path travel time reliability analysis tool, which is an advanced feature for investigating travel time reliability and sources of unreliability.



The research team conducted two test applications using the AMS data hub prototype. The objective of the test application is to create seamless linkages between AMS tools and multiple resolutions. The test applications aim to replicate real-world analyses and enable alternatives analyses and scenario evaluations.

The two networks selected for testing are located in Tucson, AZ, and Portland, OR. These networks were chosen due to agency interest, availability of field data, and availability of AMS models. Additionally, the two networks collectively represent a freeway (Tucson, AZ) and arterial (Portland, OR) network.


Tucson, AZ, Test Network

The I-10 Casa Grande Tucson Highway traverses the northwestern and eastern portions of Pima County and is an important corridor serving Tucson commuters as well as interstate traffic. The Arizona Department of Transportation (ADOT), in conjunction with the Federal Highway Administration, has identified the need to reconstruct I-10 from milepost 247.5 to milepost 253.0 to increase the roadway capacity and to improve operational efficiency. As part of the planning process, a detailed traffic study is required to establish year 2040 traffic demands and capacity needs. The study should also recommend a construction sequencing strategy with the least impact to road users. This test application presents the application of the AMS data hub to address some of the project’s traffic questions.

Objectives of the Tucson, AZ, test application are as follows:

Figure 12 depicts the study area, the I-10 corridor, and its interchanges (TIs). As shown, there are four existing interchanges within the study corridor. The I-10 mainline currently goes over the crossroads at the Ina Road, Sunset Road, and Ruthrauff Road TIs. The reconstruction will place the crossroads over I-10 and thus require complete closure of those three TIs.

This figure shows a map of the I-10 corridor in Tucson, AZ, with close-up views of the four interchanges within the study area.
Figure 12. Illustration. Tucson, AZ, test network and intersection geometry.

Input data for the Tucson, AZ, test application are summarized in table 7.

Table 7. Source data for Tucson, AZ, test application.

Source Data Type

AMS Tool


Regional TDM



24-h segment counts, intersection turning movement counts, I-10 mainline speed data


Collected by quality counts for ADOT

Regional Synchro® models



N/A = Not applicable.

Portland, OR, Test Network

NW 185th Avenue is located in the center of Portland’s western suburbs and bisects Beaverton and Hillsboro. The population of both communities is rising and outpacing growth in the greater Portland metro area. NW 185th Avenue is one of the longest continuous north-south major arterials in western suburban Portland. This important link has largely commercial and residential adjacent land uses and carries an average daily traffic of 30,000 across a mostly five-lane cross section.

Figure 13 shows the limits of the NW 185th Avenue corridor and locations of traffic signals. Projected future vehicular volumes warrant widening NW 185th Avenue to seven lanes or more, but regional MPO livability policy dictates no roadway widening beyond five lanes. Thus, a time-dependent, capacity constrained evaluation is needed at the link level to provide an informed decision about the likely congestion impacts and route diversion to occur if NW 185th Avenue arterial is left at five lanes or if it is widened.

This illustration shows the NW 185th Avenue arterial test network area. The corridor is aligned in a north-south direction and includes 18 intersections with traffic signals. It also includes three locations where Bluetooth® data collection readers were placed along the corridor for data collection.
Figure 13. Illustration. NW 185th Avenue arterial test network area.

NW 185th Avenue has the following notable features that make it an interesting test network for the AMS data hub test application:

This photo shows a signalized intersection located in advance of a rail crossing with a light rail train traversing the crossing. The signal is red, and the crossing is closed to traffic, with queued vehicles waiting to proceed over the crossing.
Photo Credit: Shaun Quayle, Kittelson & Associates
Figure 14. Photo. Light rail crossing on NW 185th Avenue.

For the purposes of this test application, a DTA evaluation is necessary at a larger subarea level to evaluate alternative routes due to sizing of the NW 185th Avenue arterial. For the deterministic or microsimulation evaluations, the team focused on the most congested portion of the network near the US 26 interchange, which is adjacent to the Tanasbourne regional shopping center where ramp meter spillback is most pronounced.

The source data used in this test application are summarized in table 8.

Table 8. Source data for Portland, OR, test application.

Source Data Type

AMS Tool


Region TDM

PTV Visum®

Portland Metro

Field-measured 24-h link volumes and speeds


Washington County

Peak period turning movement counts (morning, midday, afternoon, and weekend)


Washington County

Signal timing plans

Timing sheets and Synchro®

Washington County

Bluetooth® travel time data


Washington County

N/A = Not applicable.

Figure 15 and Figure 16 show traffic and congestion on the Portland, OR, test network.

This photo shows traffic and congestion on the Portland, OR, test network.
Photo credit: Shaun Quayle, Kittelson & Associates
Figure 15. Photo. NW 185th Avenue arterial test corridor traffic.

This photo shows a second view of traffic and congestion on the Portland, OR, test network.
Photo credit: Shaun Quayle, Kittelson & Associates
Figure 16. Photo. Second view of NW 185th Avenue arterial test corridor traffic.

Objectives of the Portland test application are as follows:



This section describes the steps that were conducted to build a DTA subarea network to analyze the I-10 corridor and subsequently export the subarea to PTV Vissim® for microsimulation. For illustration purposes, the test application only focused on the morning peak conditions.

Step 1—Export Network from Regional TDM

PAG maintains the Tucson regional TDM in TransCAD® . This network needs to be converted to a set of shape files before importing them into NeXTA. This was accomplished by using the shape file export function in TransCAD® . The resulting shape files depict the node and link layers in the network. TransCAD® also exported the demand matrix (for morning peak period) to CSV files.

Intermediate Step—Change Map Projection to the Word Geodetic System (WGS84)

The original TransCAD® network was in the North American Datum (NAD83) coordinate system and thus required conversion to the WGS84 system for compatibility with NeXTA. This process was completed using projection tools in the Economical and Social Research Institute (ESRI® ) ArcGIS software to modify the coordinate system of the shape files.

Step 2—Import Network from Regional TDM

The second step in the network conversion process was to use NeXTA’s network import tool to convert the network shape files. In order for NeXTA to interpret the shape files for conversion, a configuration initialization (INI) file was prepared to map field names between the shape files and the NeXTA format (which includes a series of CSV files).

The network import process is divided into the following three internal steps:

1. Prepare INI Configuration File and Attribute Files for Conversion

The INI configuration file is divided into different sections depending on the type of data to be imported. The first section describes general model attributes and import options. The remaining sections are used to describe the different types of network objects that can be imported. Separate sections are used to import links and nodes, with optional sections for importing zones, zone centroids, and zonal connectors. A few key entries are shown in figure 17, and a detailed description of all entries in the INI files is included in appendix A.

This photo shows a screenshot of a segment of the initialization (INI) configuration file.
Figure 17. Screenshot. Sample INI configuration file.

In figure 17, the variables on the left side of the equal sign are NeXTA’s field names, while the variables in quotation marks are field names in the TransCAD® shape files. Some of the fields imported from TransCAD® into NeXTA were from_node, to_node, street name, segment length, capacity, and speed limit.

Two additional attribute files that required preparation were the input_link_type.csv and input_node_control_type.csv. The input_link_type maps the link types in TransCAD® with the link types in NeXTA. Since the Tucson TDM does not contain traffic control data, NeXTA applied its default control types during conversion.

2. Use NeXTA’s Import Network Tool to Convert the Network

Starting with a new empty network project in NeXTA, the conversion process was initiated by selecting the INI file. After the successful conversion process, NeXTA displayed a file loading status window as shown in figure 18.

This figure shows a screenshot of a file loading status window depicting a list of imported elements after import completion.
Figure 18. Screenshot. File loading status window showing import results after completion.

For the Tucson network, NeXTA imported 12,184 links rather than the 12,230 links in the TDM network. Duplicate links and extra nodes were the primary cause of discrepancy. These were corrected in the shape files, and the import process was repeated.

The final imported network in NeXTA is shown in figure 19. It should be noted that because the regional TransCAD® model does not contain traffic control information, NeXTA used its internal logic to add signal control to intersections of major arterial streets.

This figure shows a screenshot of the compiled model of the Tucson regional network as depicted by Network EXplorer for Traffic Analysis (NeXTA).
Figure 19. Illustration. Tucson regional network imported into NeXTA.

3. Save the New Network As a New Project File

The network was saved as a new transportation network project (.tnp) file.

Step 3—Read Demand Data from Regional TDM

Similar to the INI file, the input_demand_meta_data.csv file is used by NeXTA to find and read the O-D tables exported from TDMs. This metadata file requires several entries, but the relevant entries include the following:

Step 4—Run Assignment with DTALite to Equilibrium

Before a subarea was created for more detailed analyses, DTA was performed with DTALiteto ensure equilibrium network path flows and thus reasonable trips entering the subarea. Running the DTALite assignment engine requires editing the simulation settings in the input_scenario_settings.csv file and initiating the assignment engine (e.g., selecting simulation from one of the NeXTA toolbars).

DTALite is fairly efficient. For the Tucson regional network with 10 simulation runs for about 310,000 vehicles, DTALite took 5 min 22 s of computational time on an Intel® Core 2 Duo T7500 (2.2 GHz) with 3 GB RAM. It was determined that the average travel time was 14.67 min with an average trip length of 7.55 mi within the Tucson network.

Step 5—Cut a Subarea Within the Larger Model for More Detailed Analysis

To focus on the I-10 corridor from the Ina Road interchange to the Ruthrauff Road interchange, select link analyses were conducted, and it was determined that the study subarea needed to include one additional interchange to the north and three interchanges to the south. NeXTA simplifies the subarea creation process by automatically handling extraction of necessary nodes, links, zones, and O-D tables.

The subarea creation process is divided into the following four internal steps:

1. Create a Subarea Boundary in NeXTA

Using the create subarea tool in NeXTA, a subarea boundary was drawn around the I-10 study area (see figure 20). The links and nodes within the boundary are highlighted, which allows a visual assessment of the boundary so that adjustments can be made if needed.

This figure shows a screenshot of the Tucson I-10 study area’s subarea boundary selection highlighting the links and nodes within the boundary.
Figure 20. Illustration. Subarea boundary selection for Tucson I-10 study area.

2. Use NeXTA’s Subarea Cut Tool to Clip the Network

The subarea cut tool in NeXTA automatically removed all of the network objects (nodes and links) outside of the subarea boundary and extracted links, nodes, zones, O-D pairs, and subarea path records. The resulting subarea network is shown in figure 21.

This figure shows a screenshot of the Tucson I-10 study area with the network objects (nodes and links) outside of the subarea boundary and extracted links, nodes, zones, origin-destination pairs, and subarea path records cropped out.
Figure 21. Illustration. Clipped subarea for the Tucson I-10 study area.

3. Convert Zonal Connectors to Side Streets Within the Subarea

The generate physical zone centroids on road network tool in NeXTA converts the zonal connectors to side streets within the network. This tool replaces zone centroids with additional nodes so that no paths can be routed through a zone centroid. While DTALite cannot use paths through zone centroids, other AMS software tools such as Synchro® and PTV Vissim® do not make such distinctions. Executing this command ensures that the resulting network is compatible with Synchro® and PTV Vissim® .

4. Save the New Subarea Network as a New Project File

The last step is to save the new subarea network as a new project file.

Step 6—Prepare Field Data for ODME

ODME is a part of the calibration process that matches link counts to simulated volumes. DTALite’s ODME model reads field data from the input_sensor.csv file, which the user must prepare before executing the ODME process. This input file uses a flexible format for reading multiple types of observed data in the network including link volume, occupancy, speed, and travel time field data for specific locations and time periods, allowing for time-dependent ODME applications. The Tucson I-10 subarea field data were prepared from link volume counts collected at 37 locations on freeways and arterials in the subarea model with hourly and 15-min link volume counts. Their locations are represented as green squares in figure 22.

This figure shows a screenshot of the Tucson I-10 subarea field data sensor locations for origin-destination matrix estimation (ODME) in Dynamic traffic assignment (DTA)Lite. Sensors are represented as green squares on the display.
Figure 22. Illustration. Subarea field data sensor locations for ODME in DTALite.

Step 7—Run ODME Using Field Data for Calibration

To enable ODME mode in DTALite, the user must set up the input_scenario_settings.csv file and the ODME_Settings.txt file. These files specify the number of iterations, the amount of adjustment allowed per iteration, the calibration time period which can be a portion of the simulation period, and weight on historical O-Ds.

The plots in figure 23 and figure 24 compare the observed and simulated link volumes/counts at the subarea sensor locations. The initial equilibrium assignment (before ODME) produced link volumes that were relatively similar to the observed link volumes with R2 = 0.74, although under- and over-estimation was observed at multiple locations. After running ODME for 10 iterations, the under- and over-estimation was significantly reduced, and the R2 value improved to 0.89 over all observations.

This graph shows observed and simulated link volumes/counts at the subarea sensor locations before origin-destination matrix estimation (ODME). Simulated link count is on the y-axis from 0 to 1,800, and observed link count is on the x-axis from 0 to 2,000. The R-squared value is 0.74, and the y value is 0.9832x.
Figure 23. Graph. Results before ODME for Tucson subarea.

This graph shows link counts after running origin-destination matrix estimation (ODME) for 10 iterations. Simulated link count is on the y-axis from 0 to 1,800, and observed link count is on the x-axis from 0 to 2,000. The R-squared value is 0.89, and the y value is 0.9973x.
Figure 24. Graph. Results after ODME for Tucson subarea.

Step 8—Export to Synchro® /PTV Vissim® for Signal Optimization and/or Microscopic Analysis

After the ODME process, the I-10 subarea was employed to assess operations and impacts of adding new links to the network. It was also exported to Synchro® and PTV Vissim® for further analysis. Since a typical TDM does not contain signal information, NeXTA can approximate signal phasing and timing using HCM’s QEM. This approach was used in this test application before exporting the network to Synchro® . The procedure for exporting a subarea network for microscopic analysis is as follows:

1. Use QEM to Estimate Initial Signal Phasing and Timing

An automated QEM spreadsheet is used to generate initial signal phasing and timing for the subarea network. NeXTA writes the geometry and volume information to the spreadsheet, the spreadsheet calculates appropriate phasing and timing data, and then NeXTA reads that phasing and timing data back into its files.

2. Export to Synchro® Using Universal Traffic Data Format (UTDF) CSV Format

NeXTA is capable of writing its network data in UTDF that is compatible with Synchro® . Figure 25 shows the I-10 study area after it was imported into Synchro® . The Synchro® model was used to optimize the signal operation and produce traditional HCM-based delays and levels of service for intersections and arterials.

This figure shows a screen capture of the Tucson I-10 subarea network as depicted in Synchro®. It represents a subarea network that includes I-10 and adjacent arterials. Each intersection is shown with turning movement volumes. A close-up view is provided for two sample intersections to display intersection-specific lane geometries and turning movement volumes.
©Trafficware® LLC
Figure 25. Illustration. Tucson I-10 subarea network exported in Synchro® .

3. Export to PTV Vissim® Using Animation (ANM) Format

The I-10 study area was also exported into PTV Vissim® via the ANM format. ANM is a text-based format developed by PTV Group® to allow the linkage between PTV Vissim ® and other software. NeXTA is capable of generating the .anm and .anmroute files that allow PTV Vissim® to replicate the NeXTA network and vehicle path flows. The imported network in PTV Vissim® is shown in figure 26.

This figure shows a screen capture of the Tucson I-10 subarea network as depicted in PTV Vissim®.
©PTV Group®
Figure 26. Illustration. Tucson I-10 subarea network in PTV Vissim® .


The Tucson test application demonstrated the integration of TDM, DTA, and Synchro® / PTV Vissim® as well as the successful application of NeXTA to a real-world project. Some of the positive features of the AMS data hub that were noted during the test application include the following:

This figure shows a screen capture of the Tucson I-10 subarea measure of effectiveness (MOE) network with a color-coded display. Line widths increase with increasing volume, and the line color varies by the volume-to-capacity ratio (red = high and green = low).
©2013 Google Maps®
Figure 27. Illustration. Tucson network MOE visualization.(9)

In summary, the AMS data hub achieved its primary goals of creating significant time savings by producing automatic linkages across models through the use of an open source data management tool (NeXTA). The NeXTA prototype overcomes many of the challenges associated with integrated modeling applications; however, the current prototype requires familiarity with the data schema and the ability to set up initial linkages between models. This can be overcome through training and through the development of a more robust relational or object-oriented database system.

United States Department of Transportation - Federal Highway Administration