Data Collection, Archiving and Performance Measures: Why Should Freeway Operations Care?

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Mark Hallenbeck
Director
Washington State Transportation Center (TRAC)

(Last updated 3/15/03)

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At the recent annual meeting of the Transportation Research Board’s Freeway Operations Committee, Mark Hallenbeck, the Director of the Washington State Transportation Center (TRAC), gave a presentation titled, "Data Collection, Archiving and Performance Measure: Why Should Freeway Operations Care?" The talk was based on work conducted by the Washington State Department of Transportation. Below is a summary of the key points from that talk.

The Need For Accountability

In the current economy, nearly all states are struggling to find funding for basic services. Simultaneously, resistance to increasing taxes is considerable. As a result, a movement is growing nationwide for government, including state highway agencies, to account for how they spend tax money. The goal is to prove to both decision makers and the public that tax money is being spent wisely.

At the same time, highway agencies are under pressure to improve the management and operation of their road systems. Roadway use continues to rise, and congestion continues to grow. With funding tight and roadway expansion in urban areas becoming more difficult, the obvious choice is to maximize the use of existing roadway lanes through operational improvements. However, just because roadway management is "the obvious choice" does not mean that operations can be less accountable. In fact, because the push to fund operational improvements is happening at a time of limited funding for traditional capacity expansion, the need to defend operational expenditures is even greater.

The good news is that with a little work, the same data being collected to help manage the roadway system can be used to answer questions about why operational improvements are needed, the benefits to be gained, and why those expenditures are good public policy. These same data can also help highway agencies understand which operational approaches are "working" and which ones are not, thus guiding the application of operational plans.

Below are examples of how the Washington State Department of Transportation uses the data collected by its freeway surveillance system to answer key policy and operational questions. The surveillance system collects data on vehicle volumes and estimates of lane occupancy by location. These data are then converted into estimates of vehicle speed and travel time. An analysis process developed by TRAC produces facility performance information based on these data. This process also fuses the basic freeway surveillance data with independently collected transit ridership and car occupancy data to estimate person throughput.

ANSWERING Policy and Operating Questions

• Are HOV lanes being used?
• Should the operational rules for HOV be changed (e.g., should 24/7/365 rules be changed to some type of part-time operation)?
• Do travel time incentives increase HOV use?

Figure 1. Vehicle volumes (per lane) by time of day

Figure 1 allows WSDOT to examine vehicle volumes (per lane) by time of day in both the HOV and general purpose lanes. It is based on the average volume by time of day for all weekdays in 2000.

The same graphic can be extended to examine trends over time (see Figure 2).

Figure 2.Trends in vehicle volumes (per lane) by time of day

On the basis of these graphics, WSDOT can determine whether capacity exists in the general purpose lanes, whether sufficient demand exists for HOV lanes, and whether growth in HOV lane use is meeting public policy goals.

This basic volume-by-time-of-day graphic can be extended to illustrate when congestion occurs and its effect on vehicle speed and throughput. First, average speed is color coded to indicate how conditions routinely change by time of day. Then, because conditions vary considerably from day to day, reliability at this point in the roadway can be examined by defining "congestion" (in this case, the occurrence of LOS F conditions) and reporting on the frequency with which that congestion occurs. Graphically, it is possible to lay the "frequency of congestion" over the same graphic that illustrates vehicle volumes and average speeds. This is shown in Figure 3, again using all weekday data for 2000. (Read "Vehicle Volume Per Lane" on the left axis, and "Frequency of Congestion" on the right axis.)

Figure 3. Estimated frequency of congestion, volumes and speeds

This graphic can be used to show that this specific location experiences LOS F conditions more than 80 percent of all weekdays (four times a week). It is also possible to see the slight decrease in vehicle throughput, caused by congestion, which occurs in the heart of the morning peak period.

It is possible to overlay HOV volumes on this graphic (see Figure 4) to easily see that congestion drives HOV use. This provides further insight into when and how HOV lanes function.

Figure 4. Estimated frequency of congestion for GP lanes, with volumes for both general purpose and HOV lanes

In fact, in the Seattle metropolitan region, congestion in general purpose lanes can become bad enough, and HOV use high enough, that during the peak period HOV lane vehicle volumes exceed general purpose vehicle volumes (per lane) (see Figure 5).

Figure 5. Estimated frequency of congestion, volumes and speeds for general purpose and HOV lanes

Vehicle use is not the only measure of facility performance. By adding in car occupancy and transit ridership data, it is possible to show relative person throughput, a key statistic for responding to the public policy debate about the use of HOV lanes. Figures 6 and 7 show how HOV and general purpose lane use compare, both lane by lane and for the entire facility. (The graphics below represent one HOV lane and four general purpose lanes. Statistics are reported for the entire 3-hour morning peak period.)

Figure 6. Person and vehicle throughput per lane, general purpose and HOV lanes

Figure 7. Total person and vehicle throughput, four general purpose and one HOV lane

What does the congestion picture really look like?

What delays are the public experiencing?

Using vehicle speed data that can be obtained from the freeway surveillance system, it is possible to estimate vehicle travel times throughout the day. Again, by saving these data, it is possible to describe not only today’s travel times (excellent for measuring the effects of an incident) but an entire year’s travel times. Graphics like Figure 8 allow the analysis and reporting of travel conditions throughout the day.

Figure 8. Travel times (by time of day) for a specific route

The graphic illustrates the actual travel times experienced (by time of day) for a specific route of interest (in this case the northbound trip using the southern half of the I-405 corridor). The green line represents the average travel time for a trip starting at a given time. The red line illustrates the 90^th percentile trip. This is essentially the worst travel time a motorist could expect to experience once every two weeks. (Note that the Texas Transportation Institute has recommended the use of the "Buffer Index" as a measure of travel reliability. Changing the graphic to illustrate the 95^th percentile trip time would represent the TTI Buffer Index.)

Figure 8 also includes a measure of "congestion frequency." In this case, "congestion" is defined as the average speed for a trip of less than 35 mph. The blue histogram describes the frequency with which a motorist can expect to experience a trip that averages less than 35 mph for the entire trip duration.

Statistics such as the ones presented in the Figure 8, when tracked over time, allow freeway operations personnel to measure and present the broad, overall effects of the traffic control strategies they implement. These statistics also lead to more informed discussion of the travel conditions that exist (e.g., How bad is off-peak congestion? Is off-peak operation of the service patrol program necessary?), which in turn leads to more informed debate about the need for and relative merits of alternative operations strategies.

How big are the HOV travel time incentives?

Travel calculations can be estimated for HOV lanes just as they are estimated for general purpose lanes. By comparing travel times, it is possible to measure the differences in HOV and GP travel times by time of day. WSDOT has used the graphic in Figure 9 to explore these differences.

Figure 9. Differences in HOV and GP travel times by time of day

Is the HOV lane meeting its performance goals?

Travel times in HOV lanes are important in Seattle, in that the state has adopted a policy that HOV lanes should operate at 45 mph or faster, 90 percent of the time. (Failure to operate at that level triggers a study to determine the cause of failure and the development of recommendations for improving performance.) A graphic has been developed specifically to report on the 90^th percentile travel time and the frequency with which HOV lane performance falls below the 45 mph standard. This graphic is shown in Figure 10.

Figure 10. 90^th percentile travel time and the frequency with which HOV lane performance falls below the 45 mph standard

What improvements have ramps meters produced?

The analytical possibilities that arise from the use of surveillance data are varied. Any time significant operational changes are implemented within the surveillance area, the resulting changes in vehicle throughput and performance can be measured.

For example, WSDOT has operated ramp meters in the afternoon on SR 520 in Seattle for a number of years. Until recently, the ramp meters were not used in the morning. When morning metering was implemented, significant improvements in freeway performance occurred. Those improvements, illustrated in Figure 11, included an increase of over 170 vehicles per lane per hour and a decrease in the occurrence of LOS F conditions of one day per week. Did ramp meters "solve" the congestion problem? No. Did they make a considerable improvement? Yes.

Figure 11. The effect of ramp meters on vehicle volume (per lane) throughput and frequency of LOS F operations

What IS NECESSARY To PERFORM This Type of Analysis?

The good news is that ITS surveillance systems being built for traffic management purposes provide much of the data needed to perform these types of analyses; therefore, lots of "new" data are not necessary. Instead, the data already collected must be retained, analyzed, and reported.

Storing and analyzing the data are not free. However, a large number of potential users exist for the information that the surveillance system generates. The key is to work with potential users to fund the modest costs of storing, analyzing, and reporting the data already collected. The agency must also determine who will operate the database.

As this work gets under way, it is important to recognize that not all surveillance data are "good." Therefore, the analytical procedures must be able to identify and handle "unreliable" data. Mechanisms should also be in place to repair and calibrate unreliable sensors. (After all, unreliable data also hinder the operational control decisions that are based on those data.) Because most traffic management systems have limited equipment maintenance budgets, repair activities have to be prioritized. A key to consider when balancing cost versus data availability is that obtaining useful performance information does not require all detectors to be operating. (Does an agency really need to report volumes based on continuous data collection at 300 locations in the urban area, or will 12 to 20 sites spread strategically around the region reveal the important facts?) The reality is that necessary data can be obtained with a moderate amount of planning and cooperation.

When this cooperation occurs, it becomes truly possible to manage the roadway system. This is because an agency now has the data necessary to understand how the roads are actually performing and how that performance changes as a result of various management and operations activities.

Mark Hallenbeck can be reached at tracmark@u.washington.edu