final report

Traffic Congestion and Reliability: Linking Solutions to Problems

B. State of the Practice: Performance Measurement for Congestion and Operations

B.1 TRENDS IN THE USE OF CONGESTION AND OPERATIONS PERFORMANCE MEASUREMENT BY TRANSPORTATION AGENCIES

The use of performance measures has been growing in recent years, and ranges from site-specific operations analysis to corridor-level alternative investments analysis and to areawide planning and public information studies. In the past few years, the issue of performance monitoring has been elevated by transportation agencies to be responsive to the demands of the public and state legislatures and the Transportation Equity Act for the 21st Century's (TEA 21). Reauthorization of Federal highway activities is likely to continue this emphasis on performance monitoring, particularly with regard to congestion and system operations and management. Simultaneously, the deployment of intelligent transportation systems (ITS) technologies has the potential to make a vast amount of data available for analysis.

However, many challenges lie ahead before performance measurement becomes "standard practice" and is imbedded in the transportation decision-making process. These challenges include the following below.

The transportation profession is only beginning to define and measure performance in objective terms.
Based on what data are available, we can observe that congestion is growing in areas of every size.
Performance must be viewed from several perspectives; both the facility and the user perspectives are important, performance measures are useful in both planning and operations and homeland security issues must be addressed.
The concept of "reliability" is growing in importance. Measuring reliability requires continuous data, something that has been in short supply traditionally. While advances in performance concepts have been made, data limitations hamper their implementation.
In the short term, some combination of surveillance data, planning data, and modeling must be used to support performance measurement.
Communication of performance monitoring results also is crucial.
How performance measures are to be used in the transportation decision-making process is still evolving.

Recent research in congestion and operations performance monitoring⁴ suggests eight principles that should be addressed when developing a performance measurement program. These are listed below.

Principle 1 – Mobility performance measures must be based on the measurement of travel time.
Principle 2 – Multiple metrics should be used to report performance.
Principle 3 – Traditional Highway Capacity Manual-based performance measures (V/C ratio and level of service) should not be ignored but should serve as supplementary, not primary measures of performance in most cases.
Principle 4 – Both vehicle-based and person-based performance measures are useful and should be developed, depending on the application. Person-based measures provide a "mode-neutral" way of comparing alternatives.
Principle 5 – Both mobility (outcome) and efficiency (output) performance measures are required for performance monitoring. Efficiency measures should be chosen so that improvements in their values can be linked to positive changes in mobility measures.
Principle 6 – Customer satisfaction measures should be included with quantitative mobility measures for monitoring "outcomes."
Principle 7 – Three dimensions of congestion should be tracked with mobility performance measures: source of congestion, temporal aspects, and spatial detail.
Principle 8 – The measurement of reliability is a key aspect of performance measurement and reliability metrics should be developed and applied. Use of continuous data is the best method for developing reliability metrics, but abbreviated methods should also be explored.

Figure B.1⁵ depicts the relationship between mobility or congestion (outcome) measures and operations or efficiency (output) measures. The output measures related to operations, which is usually described as the traffic incident management process, are at the bottom of the chart. The graphic indicates that incident duration, whether nonrecurring or planned, has influence on the outcome measures of travel time and throughput shown at the top of the chart.

One concept that is clear is that performance must be viewed from several perspectives. A debate within the transportation profession has arisen over the proper perspective for measuring performance. With regard to mobility performance, some have suggested that it is the view of the user (traveler) that is the most appropriate while others argue it is the view from the facility that is the correct perspective. The authors have found this to be a phony argument: both perspectives are needed. The user perspective is important because that is how the system is experienced by transportation customers; this relates to characteristics of users' trips. The facility perspective is important because transportation professionals mainly manage facilities; trips are also managed by such strategies as traveler information and demand management but to a lesser degree than facilities.

Figure B.1 General Taxonomy of Mobility-Based Performance Measures

This figure shows a classification scheme for mobility-based performance measures. The primary measures are based upon travel time and throughput categories, with each category having sub-categories related to recurring congestion, nonrecurring congestion, and emergency response.

B.2 CHALLENGES AHEAD

B.2.1 Data

In order to gain a better understanding of the current state of congestion and trends over time, additional analysis is required. Some of the most significant challenges to this effort are related to the availability, coverage, quality, and consistency of traffic data across the nation. This section examines each of these challenges. Table B.1 describes what challenges are created by these issues. Subsequent sections address these challenges in more detail.

**Table B.1 Potential Challenges to Accurately Assessing Congestion**
Issue	Why Is It a Problem?
Availability	Continuous streams of data are not readily available in many regions. The snapshot nature of data availability makes it difficult to analyze conditions during unique events or over time.
Coverage	Data is only available for a portion of the transportation network. Therefore, it is difficult to accurately assess the entire impacts of congestion.
Quality	Data sets often contain erroneous data or have gaps of missing data. The data sets need significant cleaning before they can be used and accuracy is compromised.
Standards	Data is not consistently collected, analyzed, and stored across different regions, and often times within the same region. Standardization is needed to provide for the meaningful comparison of conditions in different regions.

Availability

Continuous streams of data, covering all periods and conditions, need to be made available to properly assess these conditions and allow for meaningful comparison of trends over time. However, data simply isn't available to conduct many analysis, and even when it is has been collected, there are often problems that make the data unsuitable.

Traffic data has historically been collected on a periodic basis providing snapshot views of congestion. Transportation planners have often planned data collection activities to avoid special events, inclement weather, and traffic incidents to provide information of conditions representative of a "normal" day. This provides an incomplete picture of the full range and characteristics of congestion.

Even in areas that have continuous data collection capabilities built into their traffic management programs, specific data may be difficult to obtain. Many traffic management centers simply "spool off" the collected data for storage, with no real data management plan. The large files that are created make the data difficult to work with or inaccessible in many cases.

A potential solution to this challenge is the development of formal Archived Data Management Systems (ADMSs), which are currently under development in many regions around the country. ADMSs take a more formal approach to archiving data and making them accessible to a variety of users.

Coverage

The limited coverage of performance measurement restricts the usefulness of the data. Data coverage in many areas is limited to particular jurisdictions or facilities. Often, monitoring coverage is limited to several freeway corridors. This requires the analyst to interpolate performance measures for parts of the system that are not covered which increases the possibility of introducing errors to the data, limiting its accuracy. This partial coverage does not provide a complete picture of the nature and impacts of congestion.

Greater data coverage is needed to provide a greater understanding of the full impacts of congestion. Fortunately, many initiatives are underway to increase the coverage by introducing performance monitoring to new jurisdictions, increasing the freeway coverage in existing jurisdictions, and expanding coverage to include signalized arterials and public transportation systems. The expansion of coverage of monitoring activities will increasingly provide a more accurate picture of the full nature of congestion.

Quality

The quality of data sets in many locals is often inadequate to perform meaningful assessments of congestion. If not corrected, these data errors can result in inaccurate measurement of congestion.

The errors in the data sets can be caused by a number of sources including improperly calibrated or poorly maintained field equipment, and the lack of formal data management systems and processes. There is often very limited funding and resources for these critical tasks.

These data quality problems can be alleviated or minimized through data cleaning and validation, increased data checking and quality control and the development of more formal data archiving and management programs. These activities will require that more resources and funding be provided to support these activities.

Standards

The lack of standards present problems for analyst attempting to compare different regions or identify trends. Different jurisdictions and agencies collect, analyze, and archive data differently based on their own needs. For example, traffic incident data in a region may be collected by a number of different agencies responsible for responding to traffic incidents (e.g., Fire Department, State Highway Patrol, Transportation Authority, or others). Each of these agencies may collect different data on the incidents to which they respond. This lack of standardization limits the meaningful comparison of the data between agencies.

Further, there is currently little consensus of the analysis methods and performance measures used to assess congestion on a national basis. Different regions often monitor and analyze different performance measures, and archive data in different formats than used in other regions. This creates difficulties in tracking trends and comparing performance between different regions.

Initiatives to develop standards for archived data are gaining momentum. The success of these initiatives in promoting the adoption of standardization will provide for more meaningful analysis, especially in the comparison of trends across different regions.

B.2.2 Modeling versus Measurement

Most transportation agencies utilize some sort of modeling to analyze congestion. These models may be used to enhance field data measurement by providing predicative capabilities, or may be used in place of field data measurement when data is unavailable due to the challenges presented earlier. Recent advances in data management technology has provided improvements in the accuracy, functionality, and usefulness of both modeling and measurement processes. Future advances will likely provide further opportunities for improvement and integration of these tools.

When Should They Be Used?

A common rule of thumb that is suggested in analyzing congestion is "Measure where you can, model everything else." This recognizes that measurement using operations data often represents the best combination of accuracy and detail. However, the use of measurement data is also not feasible due to lack of availability, coverage, quality, or standardization. In these situations, modeling may be the better option. In using one or both of the analysis processes it is important to understand that modeling and measurement each have their own relative strengths and weaknesses. In general:

Modeling provides an estimate of what would likely happen as a result of a particular change in the system assuming that individuals reacted similarly to past behaviors.
Measurement provides an accurate assessment of what has happened or what is happening (for real-time systems), but has less ability to draw conclusions about what will happen.

Table B.2 provides additional detail on the relative advantages and limitations of these two approaches to analyzing congestion.

**Table B.2 Comparison of the Relative Advantages and Limitations of Modeling Versus Measurement**
	Advantages	Limitations
Modeling	Provides predictive capabilities. Once developed, can provide rapid analysis of multiple scenarios. Can be developed to provide micro- or macro-level analysis. Technology advances in data management are providing for more advanced and accurate models.	Only as good as the data used to develop the models. Provides only a simulation of what is happening in the real-world. Results must be validated against observed data. Difficult to predict travelers reactions to unique conditions or innovative strategies. Can be costly to develop initial models. Measurement
Measurement	Provides a more accurate assessment of what is happening on the ground. Can be used to analyze traveler reactions to specific conditions or unique events. Technology advances in data collection and better data management are providing improved measurements.	Data availability and quality issues may limit usefulness of the data. Can be costly to implement extensive data collection programs or systems.

Since models are based on observed behaviors, they are most accurate when analyzing predictable conditions. Utilizing models to analyze extreme conditions, innovative operations strategies, or situations where traveler behaviors would be unpredictable is less advised. When the traffic conditions are extremely unpredictable, modeling should only be used if measurement is cost-prohibitive.

Figure B.2 shows the tradeoffs between the relative cost of the analysis and the conditions being analyzed, demonstrating the general areas of strength for both models and measurement.

Figure B.2 Modeling Versus Measurement – When Should They Be Used?

This figure compares traffic modeling to direct measurement based on the cost of data collection and the prevalent traffic conditions. The figure indicates that direct measurement should be used when traffic conditions are more unpredictable, but the subsequent cost will be higher. The figure shows that traffic modeling can be used when traffic conditions are more predictable, and at a lower cost.

Are Modeling and Measurement Processes Compatible?

Many agencies still view modeling and measurement as mutually exclusive processes with different end uses; however, many progressive agencies are increasingly integrating the processes to provide even more powerful tools for analyzing congestion.

Examples of the benefits that can be achieved through the integration of measurement and models include:

Data sets obtained through measurement can be used in the development and validation of models;
Models can be tied to real-time data measurement to add the capability to predict future conditions based on current real-world conditions;
Models can be used to extrapolate localized measurement data to a regional scale; and
Data generated by models can also be used to provide sensitivity testing as a reality check on measurement tools and data sets in order to help identify potentially erroneous data or alert personnel of inoperative data collection equipment.

B.3 WHAT WE HAVE LEARNED

The trends and lessons learned presented in this section were originally presented in the report, Congestion and System Performance Activities at FHWA: Trends, Lessons, and Future Direction.

Traffic Data Trends

Real-time traffic data collection and archiving processes have been developed independently in most of the cities and the details of these processes vary among the cities. As a general rule, TMCs at least have the capability to archive data from their surveillance systems. In a few cases, this capability is not used because of priorities elsewhere in the TMC, but it is clear that TMC software is being constructed with archiving as a function. However, the state of the practice in TMC archiving is still fairly primitive. The most common practice is to transfer the data to a storage device where they reside in simple file formats without an active information management system. Quality control is rarely performed at this level and access to the data is provided on a case-by-case basis without the benefit of a query or reporting structure — data are simply provided in whatever file formats are used to store them.

There are several process steps that are relatively common to nearly all cities. The data collection and archiving process typically includes the following steps:

Data are collected by traffic sensors and accumulated in roadside controllers. These field measurements are collected for each individual lane of traffic. At 20-second to two-minute intervals, the roadside controllers transmit the data to a central location, typically a TMC;
Some cities perform quality control on field-collected data, but this checking is simple and based on minimum- and maximum-range value thresholds;
Cities that use single inductance loop detectors as sensors can measure only volumes and lane occupancies directly. In these cases, speed estimation algorithms are used to compute speeds from volumes and lane occupancies. These speed estimation algorithms vary among cities;
Internal processes at the TMC aggregate the traffic data to specified time intervals for archival purposes. These time intervals vary from 20 seconds (no aggregation) to 15 minutes. In some cases, the data are also aggregated across all lanes in a given direction at a sensor location; and
The aggregated data are then stored in text files or databases unique to each TMC. CDs are routinely created at the TMCs to offload some of the storage burden and to satisfy outside requests for the data.

Calibration and maintenance of field equipment and communications are universal problems. The main impediment is lack of resources to devote to these tasks; TMC budgets are limited and must be used to address a multitude of issues. Calibration — at least to very tight tolerances — is not seen as a priority, given that operators focus on a broad range of operating conditions rather than precise volume and speed measurements. Or in some cases traffic managers may be willing to accept a certain level of data quality to satisfy only their current operations applications. This philosophy may be changing as a result of more stringent data requirements for traveler information purposes (e.g., travel time messages on variable message signs). However, we found the current data resolution used by TMCs to be quite coarse for supporting their traditional operations activities, such as traffic incident detection and ramp meter control.

Maintenance is a problem (due primarily to funding limitations) even when loops are known to be producing erroneous or no data. The problem is exacerbated where loops are used because most agencies are reluctant to shut down traffic on heavily traveled freeways just for loop repair. This is not to say that faulty loops are never repaired, but maintenance is often postponed to coincide with other roadway activities, which helps spread the cost burden as well.

Field checking of sensors is done periodically but no standardized procedures are used across all cities. If a detector is producing values that are clearly out of range, inspection and maintenance are usually performed. However, calibration to a known standard is rarely, if ever, performed. This means that more subtle errors may go undetected. Bearing in mind that TMCs typically do not require highly accurate data for most of their operations, this approach is reasonable and practical. Work zones exacerbate these problems and often contractors unknowingly sever communication lines or pave over inductance loops.

Traffic Incident Data and Other Event Data Trends

Archiving of traffic incident data is becoming more prevalent at TMCs. However, the nature of the data collected and the structure of the storage formats are extremely diverse. This is a larger problem than for traffic data, where the basic measurements are fairly well known and understood. By comparison, even the definition of an "incident" is subject to interpretation. The resulting inconsistency in reporting formats for traffic incidents limits analysis opportunities.

The UCR has gained some experience and insights using traffic incident, weather, and work zone event data. For example, the month of February 2002 featured a number of significant snowstorms in the northeast, affecting four of the 10 UCR cities (Pittsburgh, Philadelphia, Boston, and Cincinnati). The February storms featured large snowfall totals that rendered roadways largely impassable and suppressed travel demand for several days. The resulting UCR measures in these cities showed briefer periods of less intense congestion than on a typical workday in the days just following snowstorms. After the local roads had been cleared, however, and access to freeways opened, congestion was particularly long-lasting throughout the day and more intense than on a typical workday. The snowstorm example shows how weather and travel time data can be utilized jointly to provide insight on the mobility impacts of weather or other capacity-reducing events.

Data collected by UCR and MMP processes during traffic incident conditions tend to diminish the effect of the traffic incidents, while the UMS process incorporates a relatively simple estimate for the effect of traffic incidents. Just as in the snowstorm conditions, trips divert from the freeway mainlines when a traffic incident occurs. This diversion frequently results in traffic demand going to a major street that is not monitored by the data collection equipment. The effect of this diversion is to decrease the volume of traffic that appears to be affected by the traffic incident, and also decreases the amount of extra delay associated with the traffic incident. Until data is collected for more of the system, this problem will affect delay and traffic condition assessments.

Local Use of Congestion Performance Measures and Archived Data

As mentioned above, nearly all TMCs "spool off" their traffic data for storage, but formal data management rarely occurs. The resulting files are very large and relatively inaccessible, limiting the use of the data in many applications. However, several formal Archived Data Management Systems (ADMSs) are under development around the country. ADMSs take a more formal approach to archiving data and making them accessible to a variety of users. A variety of government and even private agencies are involved in ADMS development and operation. Universities are heavily represented in this category, but state DOTs and some metropolitan planning organizations (MPO) are also involved.

In addition to archiving ITS-generated data, many states and MPOs have embraced the concept of performance measurement. This trend is developed a substantial amount of inertia and can no longer be seen as theoretical — transportation agencies are imbedding performance measurement into their day-to-day activities. Examples include:

Arizona – Both the Arizona Department of Transportation (ADOT) and the Maricopa Association of Governments (MAG) are supporting performance monitoring programs. ADOT has folded the implementation of a scaled down CMS (based on the MMP's reliability index) into the Arizona state transportation plan (MoveAZ Plan).
Minnesota – The Minnesota Department of Transportation (MnDOT) is studying adoption of the primary performance measures in MMP, namely the travel time index as a mobility measure and the buffer time index as a reliability measure. The MMP team has worked with both MnDOT staff and their consultants as they have performed demonstration and feasibility projects for implementing these measures.
Oregon – The Oregon Department of Transportation (ODOT) Traffic Management Section is studying several of the measures used in the MMP reports. TTI is providing technical assistance as they conduct a demonstration project to study the travel time index, the buffer time index and other measures for local and statewide implementation. The goal is to use the archived data in combination with other, more widely available data to construct a method to evaluate operations on the entire roadway network.
California – The California Department of Transportation (Caltrans), with the technical support of the University of California-Berkeley, is in the process of developing and integrating a statewide data archive and performance monitoring system called PeMS (Freeway Performance Measurement System). The PeMS program has supplied data for Los Angeles for 2000 and 2001.
Virginia – The University of Virginia and the Virginia Transportation Research Council (VTRC), the research arm of Virginia DOT, have been designated the official data archive managers for the State of Virginia. Their staff has supplied data from Hampton Roads and Northern Virginia to the MMP team. Additionally, they have conducted several feasibility studies of the performance measures used in the MMP reports and are considering adoption of some of the mobility and reliability measures.
Washington – The Washington State DOT has a research and implementation effort to develop a set of mobility performance measures. The University of Washington, the primary analyst of archived data for WsDOT, is conducting the research and produces one of the premier annual congestion performance reports in the country.

⁴ NCHRP 3-68 (Guide for Effective Freeway Performance Measures) Tasks 1 and 2 Draft Report.

⁵ NCHRP 20-7(173) (Measuring and Communicating the Effects of Incident Management Improvements).

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