TEMPORARY LOSSES OF HIGHWAY CAPACITY AND IMPACTS ON PERFORMANCE: PHASE 2

Title | Contents | Acknowledgements | Exec. Summary
1. Intro | 2. Approach | 3. Crashes | 4. Breakdowns | 5. Work Zones | 6. Weather | 7. Signal Timing
8. RR Crossings | 9. Toll Facilities | 10. PUD | 11. Results Summary | 12. Next Steps | 13. References

Executive Summary

ES-1.  Background

Traffic congestion and its impacts significantly affect the nation's economic performance and the public's quality of life.  In most urban areas, travel demand routinely exceeds highway capacity during peak periods.  In addition, events such as crashes, vehicle breakdowns, work zones, adverse weather, railroad crossings, large trucks loading/unloading in urban areas, and other factors such as toll collection facilities and sub-optimal signal timing cause temporary capacity losses, often worsening the conditions on already congested highway networks.  The impacts of these temporary capacity losses include delay, reduced mobility, and reduced reliability of the highway system.  They can also cause drivers to re-route or re-schedule trips. Such information is vital to formulating sound public policies for the highway infrastructure and its operation.

In response to this need, Oak Ridge National Laboratory, sponsored by the Federal Highway Administration (FHWA), made an initial attempt to provide nationwide estimates of the capacity losses and delay caused by temporary capacity-reducing events (Chin et al. 2002). This study, called the Temporary Loss of Capacity (TLC) study, estimated capacity loss and delay on freeways and principal arterials resulting from fatal and non-fatal crashes, vehicle breakdowns, and adverse weather, including snow, ice, and fog. In addition, it estimated capacity loss and delay caused by sub-optimal signal timing at intersections on principal arterials. It also included rough estimates of capacity loss and delay on Interstates due to highway construction and maintenance work zones. Capacity loss and delay were estimated for calendar year 1999, except for work zone estimates, which were estimated for May 2001 to May 2002 due to data availability limitations. Prior to the first phase of this study, which was completed in May of 2002, no nationwide estimates of temporary losses of highway capacity by type of capacity-reducing event had been made.

This report describes the second phase of the TLC study (TLC2). TLC2 improves upon the first study by expanding the scope to include delays from rain, toll collection facilities, railroad crossings, and commercial truck pickup and delivery (PUD) activities in urban areas. It includes estimates of work zone capacity loss and delay for all freeways and principal arterials, rather than for Interstates only. It also includes improved estimates of delays caused by fog, snow, and ice, which are based on data not available during the initial phase of the study. Finally, computational errors involving crash and breakdown delay in the original TLC report are corrected.

ES-2.  Objective and Scope

The TLC2 study develops estimates of highway capacity losses and delay caused by temporary capacity-reducing events. The scope of TLC2 includes all urban and rural freeways and principal arterials in the nation's highway system for 1999—delays on minor arterials, collectors, and local roads are not included.  The highways within the scope of TLC2 accounted for about 54 percent of the highway vehicle-miles of travel (VMT) in 1999.

TLC2 attempts to quantify the extent of temporary capacity losses due to the following events:

• Crashes (fatal and non-fatal)
• Breakdowns
• Work zones
• Adverse weather (rain, snow, fog, ice)
• Sub-optimal signal timing (urban principal arterials only)
• Railroad crossings (urban and rural principal arterials)
• Toll collection facilities
• Commercial truck pickup and delivery (PUD) activities (urban principal arterials only)

These events can cause impacts such as capacity reduction, delays, trip rescheduling, rerouting, reduced mobility, and reduced reliability. This study focuses on the reduction of capacity and resulting delays caused by the temporary events mentioned above.  Impacts other than capacity losses and delay, such as re-routing, re-scheduling, reduced mobility, and reduced reliability, are not covered in this phase of research.

It should also be noted that the study does not attempt to estimate capacity losses and delays due to events that occur simultaneously, such as a crash that takes place during a snowstorm or a breakdown that takes place in a work zone. Such coinciding events can often cause more capacity loss and delay than they might have cause if they had occurred separately. The interaction of capacity-reducing events is an area of interest and may be addressed in future research. However, due to time and funding constraints of the initial phases of the study, this interaction was not modeled.

Finally, due to funding and time constraints, a thorough evaluation of TLC and TLC2 results was not possible. Therefore, at the end of each chapter, a brief discussion of the limitations of the methodology, data, and assumptions used to generate the capacity loss and delay for that event type is provided. The potential effects of these limitations on the final estimates are also discussed.

ES-3.  Approach

The study uses traffic engineering modeling methods, the best available data, and engineering judgment to derive estimates of capacity losses and delays.  Because direct measurements are scarce and available data are generally incomplete, the validity of the estimates is dependent on the reasonableness of a number of critical assumptions.  The philosophy followed is to rely on published peer-reviewed studies whenever possible and, when assumptions must be based solely on the researchers' judgment, to err on the side of underestimating losses of capacity and delay.  There is one general exception to this rule, which is discussed below.

A critical distinction is made between the loss of capacity and its impacts.  Capacity is a measure of potential:  it describes the maximum sustainable throughput at a point on a highway.  As such, it is independent of the highway's actual level of use.  Impacts, however, depend not only on the loss of capacity, but on the volume of traffic on the highway at the time the loss occurs.  A crash occurring on an Interstate highway in the middle of the night will cause far less delay than, but possibly the same loss of capacity as, the same crash occurring during rush hour.  Delay is measured in vehicle-hours, which can be converted to person-hours by multiplying by a suitable vehicle occupancy.  Capacity loss, on the other hand, is a loss of potential throughput (measured in vehicles per lane per hour), integrated over time and a length of roadway.  While capacity for a given point on a highway at a given amount of time is measured in vehicles per lane per hour (vplph), the general methodology used in this study attempts to estimate capacity reductions over a finite period of time and along a finite length of roadway with a given number of lanes affected. Therefore, in this study, estimated capacity loss is measured in vehicles.

In the course of this study, methods were developed for estimating the impacts of temporary events on the loss of capacity and delay, but not for estimating the impacts of temporary capacity losses on re-scheduling, re-routing, reduced mobility, and reliability (the four Rs).  Thus, the impacts of events on traffic volumes were not predicted.  For example, a heavy snowstorm might reduce traffic volumes drastically due to travelers re-scheduling or canceling planned travel.  On the other hand, because normal traffic volumes are assumed, delay will be overestimated. Thus, in general, the delay estimates presented here reflect, to an unknown degree, the other negative impacts of temporary capacity losses on the four Rs. A high priority for future analysis should be to develop methods for analyzing all five types of impacts.

ES-4.  Results

Total Effects

Temporary capacity losses due to work zones, crashes, breakdowns, adverse weather, sub-optimal signal timing, toll facilities, and railroad crossings caused over three and a half billion estimated vehicle-hours of delay on U.S. freeways and principal arterials in 1999 (Table ES-1).  Assuming an average vehicle occupancy of 1.6 persons, this translates into nearly six billion person-hours of delay.  Assuming an average value of time of $15 per hour for each person impacted, temporary capacity losses produced approximately $55 billion in lost time alone in 1999.  Because conservative assumptions have been used throughout this analysis, and because several significant sources of delay have not been included, these estimates are believed to be a lower bound on the actual impacts of TLC.

Crashes and breakdowns account for over half the delay attributed to TLC events, and work zones account for about one quarter of the delay (Fig. ES-1).  Over 85 percent of the delay from TLC events occurs in the off peak or on uncongested segments in the peak period of weekday recurring congestion (Table ES-2).  Americans lose 2.5 hours for every 1,000 miles of travel due to delay from TLC events: delay is over 4 hours per 1,000 miles of travel in very large urban areas, about 3 hours and 45 minutes in large urban areas, over 2 hours in small and medium areas, and 45 minutes in rural areas (Table ES-3).

The TLC2 estimates were compared to two sets of delay estimates for 1999 by Texas Transportation Institute (TTI), including estimates for 85 urban areas in the 2004 Urban Mobility Study (Schrank and Lomax 2004) and unpublished estimates for all urban areas for FHWA.  The TLC2 study estimates of total crash and breakdown delay are slightly higher than the TTI estimates for incident delay (Fig. ES-2).

Figure ES-1. Most of the delay attributed to TLC events was caused by crashes and work zone activities.

Figure ES-2. A comparison of TLC2 estimates with those from two studies by TTI: one that includes 85 urbanized areas and another that includes all urbanized areas.

Crashes

Crashes on freeways and principle arterials caused an estimated 1.7 billion vehicle-hours (approximately 2.7 billion person-hours) of delay in 1999.  Estimated delays on freeways amounted to 1.2 billion vehicle-hours, almost three times the delays from crashes on principal arterials.  Non-fatal crashes were the primary source of delay due to their greater occurrence rate. The TLC2 study estimates that crashes were responsible for over 45 percent of delay caused by TLC events.

Several areas for improving crash impact estimates have been identified:  (1) developing methods to estimate delays on transverse arterials (neglected here), (2) developing improved methods for geo-locating non-fatal accidents, and (3) developing means of validating the Monte Carlo methods used in conjunction with the GES crash data.

Breakdowns

It is estimated that vehicle breakdowns caused approximately 440 million vehicle-hours (0.7 billion person-hours) of delay in 1999, accounting for 12 percent of delays from TLC events. Information about breakdowns is scarce, making the associated capacity loss and delay estimates one of the weakest of all impacts estimated.  Better data are needed on virtually all aspects of breakdowns.  In particular, information regarding the total number of breakdowns or vehicle breakdown rates would improve estimates greatly, as would case studies of the impacts of breakdowns under a variety of circumstances.

Work Zones

Work zones on freeways and principal arterials caused an estimated 889 million vehicle-hours (1.4 billion person-hours) of delay in 1999. The majority of delay (90 percent) is associated with the transition area of the work zone rather than the activity area. Work zones accounted for about a quarter of all estimated delay from TLC sources within the scope of TLC2.

Work zone impact estimates are believed to be low for two principal reasons.  First, the estimates presented in this report are based on freeway and principal arterial construction only. Delay from construction and maintenance on local arterials was not estimated. Second, the research team believes the Rand McNally database under-represents construction projects scheduled for more than four months in the future.

Weather

Major events of fog, rain, snow, and ice combined to cause an estimated 330 million vehicle-hours (0.5 billion person-hours) of delay on freeways and principal arterials in 1999. These adverse weather events accounted for 9 percent of delay from TLC sources. Rain was estimated to be the most significant weather factor, accounting for 71 percent of the estimated weather delay. 

The methods used to estimate capacity reductions and delays due to weather have several shortcomings. First, as for other events in the study, the impacts of weather events on traffic volumes were beyond the study's scope and were not considered.  Thus, the impacts of major snowstorms and ice are estimated assuming normal traffic volumes.  In reality, a substantial fraction of the impacts of such conditions is likely to be in re-scheduled or reduced travel, and these impacts were not estimated.  As a result, the delay impacts, per se, have likely been overestimated, while the total impacts may be underestimated. Second, the duration of weather impacts is undoubtedly underestimated due to data limitations. Weather data includes the duration of weather events (such as precipitation falling as snow), but there is no data on how long roadways are actually affected by these events. For example, while the visibility impacts of fog last only as long as the event itself, a six-hour snowstorm may reduce capacity for several hours (or days in some areas) after it stops snowing. In order to be conservative in estimating delays, in the absence of impact duration data, the study assumes that the duration of capacity reduction coincides with the duration of the weather event rather than the duration of its impact. Third, capacity reductions and delays from several weather-related events are not included in the study. This includes capacity reductions caused by reduced visibility from solar glare, dust storms, and forest fires, as well as reduced capacity caused by strong winds on high-profile vehicles. Likewise, the impacts of catastrophic events such as hurricanes, floods, and other natural disasters were not considered. Finally, the quality and spatial resolution of the weather data limit the accuracy of capacity reduction and delay estimates.

Sub-optimal Signal Timing

Sub-optimal traffic controls caused an estimated 0.3 billion vehicle-hours (0.5 billion person-hours) of delay on principal arterials in 1999—about 8 percent of delay estimated by this study.  These estimates differ from the other delay estimates in TLC2 in that they represent the potential for benefits due to improved operations.  Delays caused by fixed-time and actuated signals appear to be roughly proportional to the number of signals of each type.

These impact estimates are believed to be among the most reliable, although a number of areas for refinement have been identified.

Railroad Crossings

It is estimated that railroad crossings caused about 3 million vehicle-hours (4.7 million person-hours) of delay on principal arterials in 1999—freeways do not have railroad crossings. At-grade railroad crossings caused only about 0.1 percent of delay in 1999.

Toll Facilities

Toll facilities caused an estimated 21 million vehicle-hours (about 34 million person-hours) of delay on freeways and principal arterials in 1999. Average delay per transaction was estimated at about 12 seconds. TLC2 estimates that toll facilities were responsible for only half a percent of the delay attributable to TLC events within the scope of the study, second only to at-grade railroad crossings.

PUD Activities in Urban Areas

Large truck pickup and delivery (PUD) activities can cause delay if the truck is parked such that it blocks a lane of traffic. The study estimates that lane-blocking PUD activities in urban areas resulted in just under a million vehicle-hours of delay in 1999. The PUD-related delay share was less than three hundredths of a percent, the smallest impact from any source in the study.

ES-5. Composite Picture and Next Steps

No single empirical or modeling study provides a comprehensive estimate of all sources of delay. However, an approximate picture of total delay can be assembled by combining TTI's estimate of recurring delay for all urban areas, the TLC2 estimate of recurring delay from suboptimal signal timing and tollbooths, and TLC2 estimates of nonrecurring delay.  The resulting 5.1 billion hours of delay is 35 percent recurring and 65 percent nonrecurring.  Recurring delay is a higher percentage in the larger cities.

While the TLC2 study breaks new ground in estimating capacity loss and delay from a wide variety of sources, it is only the beginning of needed efforts to understand all forms of congestion and their consequences.  No national studies examine recurring congestion from weekend and holiday travel or recurring weekday congestion in rural areas.  The full effects of bottlenecks, the extent and intensity of most forms of temporary capacity reductions, and the consequences of dramatic increases in trucking are not adequately understood or based on robust empirical studies.  Delay on roads smaller than freeways and other major arteries; the interactions among different types of delay; relationships between temporary capacity loss and mitigation strategies; and the effects of re-routing, rescheduling, reduced mobility, and reduced reliability are additional areas requiring data collection and analysis.

Previous | Table of Contents | Next