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Publication Number:  FHWA-HRT-15-071    Date:  January 2016
Publication Number: FHWA-HRT-15-071
Date: January 2016


The Use of Data in Planning for Operations: State-Of-The-Practice Review

Chapter 3. Evaluating M&O Strategies in Planning

M&O Strategies Currently Evaluated

In general, it is not yet common practice among MPOs to quantitatively evaluate and forecast the potential impacts of M&O strategies on transportation system performance, safety, and other measures. The majority of cases that were examined for this review did not forecast impacts of M&O strategies as part of the planning process. The examples of M&O strategy evaluation were primarily performed as part of a Federal initiative, such as an integrated corridor management (ICM) AMS study or an FHWA AMS data integration project. A notable exception was the Bay Area Metropolitan Transportation Council (MTC), which used an activity-based model to evaluate express lanes, congestion pricing, intelligent transportation systems (ITS) improvements, HOV and bus rapid transit, and contraflow lanes. In addition, MTC was involved in the evaluation of signal optimization, freeway service patrols, and transit and travel demand management (TDM) improvements.

Through an FHWA AMS data integration project, the Portland, OR, MPO (Metro) evaluated ITS improvements, signal systems, ramp meters, and transit and truck priority along a corridor.(7) The San Diego Association of Governments (SANDAG) is continuing to use the modeling approach from the ICM AMS work to evaluate proposed operations improvements on other corridors.

Overview of Data Needs for Analysis Tools and Methods

Many different tools and methods are available to support the quantitative analysis of M&O strategies in planning and programming, and most of the MPOs interviewed for this state-of-the-practice review reported that they were initiating or advancing their use of tools for evaluating M&O strategies to support planning and investment decisions.

Using the categories established in FHWA’s Applying Analysis Tools in Planning for Operations brochure, the following tools can be used for analyzing M&O strategies:(8)

In addition, archived operations data can be used to analyze M&O strategies. More advanced analysis methods are being studied and integrated into operations modeling and analysis, including the following:

Currently, only multiresolution or multiscenario analysis methods have the capability to analyze all of the M&O strategies commonly recognized.(9) The M&O strategies that may be analyzed by a specific tool or methodology are shown in figure 2 from the 2012 FHWA Operations Benefit/Cost Analysis Desk Reference, which provides additional information on each of the tools listed in the figure.(9)

Figure 2. Chart. Capability of analysis tools/methodologies to address M&O strategies. 9
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Figure 2. Chart. Capability of analysis tools/methodologies to address M&O strategies.(9)

The FHWA Operations Benefit/Cost Analysis Desk Reference and the Strategic Highway Research Program 2 (SHRP2)—L05 Incorporating Reliability Performance Measures into the Transportation Planning and Programming Processes: Technical Reference both contain the same three broad categories of analysis tools and methods: sketch planning methods, post-processing methods, and simulation (or multiresolution and multiscenario) methods.(9,3) The SHRP2—L05 report also contains a fourth category, monitoring and management tools and methods, which is not included in this discussion because those tools and methods primarily focus on assessing past conditions. Sketch planning methods have fairly limited data requirements, whereas post-processing methods require more specific data, and simulation (or multiresolution and multiscenario) methods have more comprehensive data requirements.

The SHRP2—L05 report, Incorporating Reliability Performance Measures into the Transportation Planning and Programming Processes: Technical Reference, provides a summary of the input data needed for each major type of analysis tool or method.(3) The descriptions of the general needs are followed by examples that illustrate the specific use of data for analyzing M&O strategies as part of the planning, programming, or project development phase prior to implementation of the strategies.

Sketch Planning Methods: Input Data

Most sketch-planning methods can be used when only limited data is available. They require segment free-flow speed and distance at the most basic level. Average travel time is input as the next step, and the travel time can be obtained through measuring it in the field, extracting it from a model, or estimating it based on segment volume and capacity.(3)

Postprocessing Methods: Input Data

The input data needed for model postprocessing methods includes link-level data available from most regional travel demand models or simulation models. These data include basic facility capacities, basic geometric data, and loaded roadway volumes that may represent peak hour, peak period, or daily analysis. Information on the probability of various weather conditions (e.g., number of days or year of rain) and road work is needed for assigning weights to scenarios if multiscenario approaches are used.(3)

Simulation or Multiresolution and Multiscenario Methods: Input Data

At a minimum, simulation or multiresolution and multiscenario methods require a regional travel demand model as well as the data needed to develop and calibrate a simulation model. The data required for a simulation model generally include detailed roadway geometry, traffic signal timing, and discrete data on travel speeds and volumes. A condition occurrence distribution for a multiscenario analysis would require archived data on demand, incidents, and weather as well as the distribution of the likelihood of each scenario.(3)

Data Needs and Use in AMS of Specific M&O Methods or Strategies

There are a few national transportation publications that have recently been issued or drafted that address AMS techniques for more specific M&O strategies or transportation management methods, including FHWA’s Analysis, Modeling, and Simulation for Traffic Incident Management Application; the U.S. Department of Transportation’s (USDOT) Traffic Analysis Toolbox Volume XIII: Integrated Corridor Management Analysis, Modeling, and Simulation Guide; USDOT ITS Joint Program Office’s Use of Mobile Data for Weather-Responsive Traffic Management Models; and FHWA’s Assessing the Effectiveness of Transportation Management Plan (TMP) Strategies. (See references 6, 10, 11, and 12.) The following sections provide information on the data needs for analyzing the potential impacts of TIM strategies and ICM.

Data Needs in AMS of TIM

The FHWA report Analysis, Modeling, and Simulation for Traffic Incident Management Application provides pertinent information regarding data needs for evaluating TIM strategies using several types of models: sketch planning models, deterministic (HCM-type macroscopic) models, mesoscopic simulation models, and microscopic simulation models, as shown in
figure 3.(6) Again, the sketch planning models have fairly low data requirements, whereas the mesoscopic and microscopic models are much more data intensive. One of the primary improvements in TIM AMS applications recommended by the authors is the need for a data dictionary and guidelines to ensure consistent data collection and archiving of TIM data to enable comparisons and data integration across agencies.(6)

Figure 3. Chart. Data needs for each model type to analyze TIM strategies.(6)
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Figure 3. Chart. Data needs for each model type to analyze TIM strategies.(6)

Data Needs and Use in AMS of ICM

Recently, USDOT developed an AMS methodology for use in evaluating and forecasting the impact of ICM on several key integrated corridor performance measures: delay, travel time reliability, and throughput. This methodology has been demonstrated by three ICM sites in the United States: San Diego, CA; Dallas, TX; and Minneapolis, MN. ICM integrates transportation management techniques across facilities and modes along a corridor to balance demand, coordinate management strategies, reduce congestion, and improve the overall operational performance of the corridor. The methodology required to assess the effects of ICM is significantly more complex than traditional transportation investments. The assessment of ICM typically includes freeways and arterials as well as multiple modes and potentially road or parking pricing strategies. In addition, the impacts of ICM need to be evaluated in several operations scenarios, including incidents, weather, special events, and other non-recurring events that disrupt normal travel conditions. The ICM AMS methodology is flexible and requires significant tailoring to meet the needs of individual corridors. The methodology integrates up to three classes of modeling tools (microscopic, mesoscopic, and macroscopic) as well a mode shift model and a transit travel time estimation model, interfaces between the tools, and a performance measurement or benefit-cost module.(10)

The ICM AMS methodology has substantial requirements for data that is high-quality, reliable, and collected continuously for at least 6 to 12 mo. The data should be collected from all sources during the same time period. Archived data sources are generally preferred over manually collected data, and data collected during different operational conditions allow greater opportunity to model the impacts of ICM during different operational scenarios. Long-term archived data enables congestion patterns over many days to be analyzed.(10)

USDOT’s Traffic Analysis Toolbox Volume XIII: Integrated Corridor Management Analysis, Modeling, and Simulation Guide contains an extensive list of sample data requirements for conducting AMS for ICM, as seen in figure 4.(10)

Figure 4. Chart. Sample data requirements for AMS for ICM.(10)
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  • These data must be provided for all links in the corridor study area.
  • These data must be provided for a consistent analysis time period, including the same data from all facilities in the corridor area.
  • To facilitate the assessment of variability in the traffic volumes and speeds, data must be provide for multiple days of the week and months of the year for all facilities in the study corridor.
Figure 4. Chart. Sample data requirements for AMS for ICM.(10)


Dallas Area Rapid Transit (DART) and U.S. 75 Dallas ICM Team conducted an AMS assessment of the effects of several ICM strategies on the performance of the U.S. 75 corridor as documented in a 2010 report from USDOT.(13) The U.S. 75 corridor in Dallas is a critical, regional corridor in which freeway and arterial expansion is not possible. As outlined in “Integrated Corridor Management,” the corridor includes the following:(14)

The AMS approach used for U.S. 75 included using the North Central Texas Council of Governments’ TransCAD travel demand model, a mesoscopic simulation model (DIRECT), a time of departure choice element, and an analysis of mode shift and transit. The ICM strategies that were assessed for the U.S. 75 corridor include the following:(13)

Several data elements were used as input or for calibration and validation of the various modeling components used in the assessment. The North Central Texas Council of Governments’ travel demand model provided the primary source of vehicular trip tables and networks that were used as input to DIRECT. The DART on-board survey provided the data to estimate the transit O-D trip table also used as input to DIRECT.

The following data were used for the validation and calibration of the model:(13)

Queue observations were made along critical segments of the ICM Corridor freeway and arterial components. An extensive AMS method was also applied to the I-394 corridor in Minneapolis an east-west commuter route that connects the Minneapolis Central Business District to the western suburbs. This assessment focused on the impacts of the following ICM strategies:(15)

The AMS for the corridor relied on a combination of the region’s travel demand model in TP+, travel demand forecasting software package, and a mesoscopic simulation model, DynusT, developed by the University of Arizona. DynasT includes a mode shift model. As with the Dallas pilot, the Twin Cities regional travel demand model developed in TP+ was used to extract networks and trip tables for the analysis for the corridor. Additional data entered into the model included signal timing plans and intersection lane geometry configuration. The traffic flow model was calibrated using the speed-density relationship in the field data, and the O-D tables were calibrated using link count data. Detector data were also used to develop time-dependent
O-D tables. Collected travel time validated the calibrated model. Automated passenger count and route ridership data were used to create the transit O-D tables.(15)

The following data were used for model validation:

In San Diego, the ICM AMS was performed on the I-15 corridor, which is a primary artery for the movement of commuters and goods from northern San Diego County to downtown. It is an 8- to 10-lane freeway with two reversible managed (high-occupancy toll (HOT)) lanes. The AMS tested several ICM strategies, including the following:(16)

The AMS approach used by the San Diego team consisted of a TransCAD-based regional travel demand model and a TransModeler-based corridor microsimulation model. As input to the microsimulation model, the roadway network, and an a.m. peak period, O-D trip table for the
I-15 corridor study area was extracted from the regional travel demand model. The microsimulation model was calibrated using a three-step strategy to calibrate on capacity, route choice, and system performance. The California Department of Transportation (Caltrans) Performance Measurement System (PeMS) database provided much of the data for the calibration and validation, including speed data. As outlined in Annex 3. ICM AMS Results for the I-15 Corridor, the set of data used to calibrate and validate the microsimulation model included the following:(16)

Assessing Impact of Implemented M&O Strategies


This section addresses another key component of a performance-based approach to planning for operations that requires data: assessing the impact of M&O strategies post-implementation. This review did not systematically identify the data needed to evaluate each M&O strategy post-implementation but instead identified examples during conversations with MPOs.

There are several more examples of M&O strategies that were evaluated post-implementation than were found for evaluation during planning. Traffic signal coordination or retiming projects were frequently mentioned by MPOs as an M&O strategy that is evaluated for improvements via the collection of travel time data. Other examples include a cost-benefit analysis of the Houston, TX, area TIM program and the use of traffic count data to conduct before-and-after studies of implemented M&O strategies by the Southwestern Pennsylvania Commission (SPC). In addition, the Houston TranStar partnership (including Houston-Galveston Area Council (H-GAC)) conducts public surveys to assess the use and impacts of the TranStar traveler information
Web site.


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