Roadway Network Productivity Assessment: 

System-Wide Analysis 

Under Variant Travel Demand 

Final Report 

November 2008 

 

prepared for 

U.S. Department of Transportation 

Federal Highway Administration 

prepared by 

Noblis, Inc. 

3150 Fairview Park Drive 

Falls Church, Virginia 22042 
Roadway Network Productivity Assessment: 

System-Wide Analysis 

Under Variant Travel Demand 

Final Report 

November 2008 

Prepared by 

Noblis, Inc. 

Soojung Jung, Ph.D 

Karl Wunderlich, Ph.D 

Prepared for 

Federal Highway Administration 

U.S. Department of Transportation 
ii

Notice 

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

object of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

Standards and policies are used to ensure and maximize the quality, objectivity, utility, and 

integrity of its information. FHWA periodically reviews quality issues and adjusts its programs 

and processes to ensure continuous quality improvement. 
iii

Technical Report Documentation Page 

1. Report No. 

 FHWA—HOP-09-019 

2. Government Accession No. 3. Recipient's Catalog No. 

5. Report Date 

 November 2008 

4. Title and Subtitle 

 Roadway Network Productivity Assessment: 

System-Wide Analysis Under Variant Travel Demand 

6. Performing Organization Code 

7. Author(s) 

 Soojung Jung, Karl Wunderlich (Noblis, Inc.) 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

 Noblis, Inc. 

 3150 Fairview Park Drive 

 Falls Church, Virginia 22042 

11. Contract or Grant No. 

 DTFH61-05-D-00002 

13. Type of Report and Period Covered 

 

12. Sponsoring Agency Name and Address 

 Federal Highway Administration 

 1200 New Jersey Avenue, SE 

 Washington, DC 20590 14. Sponsoring Agency Code 

 HOTM 

15. Supplementary Notes 

16. Abstract 

The analysis documented in this report examines the hypothesis that the system-wide productivity of a 

metropolitan freeway system in peak periods is higher in moderate travel demand conditions than in excessive 

travel demand conditions. The approach in this effort characterizes system-wide productivity by aggregating 

link-level speed and traffic volume (count) data. 

The study utilized 2007 Los Angeles and Chicago traffic data (both flow and speed) archived as part of the 

Urban Congestion Report (UCR) databases. In addition, an extensive archive of incident, work zone and weather 

data were available to identify the underlying conditions related to congestion patterns and bottleneck locations 

in the network. System-wide travel demand, delay and productivity were estimated for two Mondays in 

September-October 2007. One day was the worst congested Monday (September 10 2007) during the two-month 

period and the other Columbus Day (October 8 2007). Our a priori expectation was that travel demand on 

Columbus Day would be lower than on September 10 2007 since government and other workers receive a paid 

holiday on Columbus Day. Several measures of productivity based on a combination of flow and speed data were 

developed to capture system-level efficiencies of the freeway networks. 

The study suggests that higher system-level productivity and efficiency can be observed when travel demand is 

observed to closely match overall system capacity, resulting in less frequent onset and reduced duration of 

freeway breakdown conditions. By reducing demand and preventing congestion from taking hold, demand 

management through pricing or other mechanisms could recover the daily waste of time and drop in freeway 

system productivity that occurs on congested highways when traffic flow breaks down. 

17. Key Words 

 Performance Measurement, Throughput, 

Reliability, VMT, Los Angeles, Chicago, System 

Performance, Bottleneck Analysis 

18. Distribution Statement 

 No restriction. This document is available to the 

 public from the sponsoring agency at the website 

 http://www.ops.fhwa.dot.gov. 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

20 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 
iv

SI* (MODERN METRIC) CONVERSION FACTORS

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km

AREA

in2 square inches 645.2 square millimeters mm2 

ft2 square feet 0.093 square meters m2 

yd2 square yard 0.836 square meters m2 

ac acres 0.405 hectares ha 

mi2 square miles 2.59 square kilometers km2

VOLUME

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3

NOTE: volumes greater than 1000 L shall be shown in m3

MASS oz ounces 28.35 grams g 

lb pounds 0.454 kilograms kg 

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees)

°F Fahrenheit 5 (F-32)/9 Celsius °C 

or (F-32)/1.8

ILLUMINATION 

fc foot-candles 10.76 lux lx 

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2

m2 square meters 10.764 square feet ft2

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac 

km2 square kilometers 0.386 square miles mi2 

VOLUME

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3

m3 cubic meters 1.307 cubic yards yd3 

MASS

g grams 0.035 ounces oz 

kg kilograms 2.202 pounds lb 

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees)

°C Celsius 1.8C+32 Fahrenheit °F 

ILLUMINATION

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl 

FORCE and PRESSURE or STRESS

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2
v

Table of Contents 

ABSTRACT................................................................................................................................... 1

INTRODUCTION ........................................................................................................................ 2

HYPOTHESIS .............................................................................................................................. 3

STUDY AREAS ............................................................................................................................ 5

Freeway Networks .................................................................................................................... 5

Period of Analysis ..................................................................................................................... 5

APPROACH.................................................................................................................................. 7

System-Level Productivity Measures...................................................................................... 9

RESULTS .................................................................................................................................... 12

Columbus Day vs. High Demand Monday: Los Angeles Results....................................... 12

Columbus Day vs. High Demand Monday: Chicago Results.............................................. 13

System-Wide Uncongested VMT Delivered By Time of Day: Los Angeles....................... 13

Quality-Weighted Productivity Ratio: Los Angeles and Chicago..................................... 16

CONCLUSIONS ......................................................................................................................... 19

REFERENCES............................................................................................................................ 20
vi

List of Figures 

Figure 1 Study Hypothesis, Peak Period Productivity Under Moderate Demand versus Excessive 

Demand........................................................................................................................................... 4 

Figure 2 Los Angeles UCR Network............................................................................................. 5 

Figure 3 Chicago UCR Network.................................................................................................... 6 

Figure 4 Speed/Volume Relationships Observed on Northbound I-405 (Los Angeles), .............. 8 

Figure 5 VMT Classification by Time of Day, Northbound I-405 (Los Angeles),....................... 8 

Figure 6 System-Wide VMT and Speed Comparison (Los Angeles),........................................... 9 

Figure 7 System-Level Delay Comparison, Los Angeles,........................................................... 12 

Figure 8 System-Wide VMT Classification by Time of Day, Los Angeles,............................... 14 

Figure 9 System-Wide VMT Classification by Time of Day, Los Angeles,............................... 14 

Figure 10 System-Wide VMT Classification by Time of Day, Los Angeles,............................. 15 

Figure 11 Maximum System-Wide Uncongested VMT by Time of Day, Los Angeles ............. 16 

Figure 12 Quality-Weighted Productivity Ratio by Time of Day, Los Angeles, ........................ 17 

Figure 13 Quality-Weighted Productivity Ratio by Time of Day, Chicago,............................... 18 

List of Tables 

Table 1 Percentage of Government Workers by State, and Travel Demand Differences 

Estimated Between Columbus Day and September 10 2007........................................................ 11 
 1

ABSTRACT 

The analysis documented in this report examines the hypothesis that the system-wide 

productivity of a metropolitan freeway system in peak periods is higher in moderate travel 

demand conditions than in excessive travel demand conditions. The approach in this effort 

characterizes system-wide productivity by aggregating link-level speed and traffic volume 

(count) data. The study utilized 2007 Los Angeles and Chicago traffic data (both flow and speed) 

archived as part of the Urban Congestion Report (UCR) databases. In addition, an extensive 

archive of incident, work zone and weather data were available to identify the underlying 

conditions related to congestion patterns and bottleneck locations in the network. System-wide 

travel demand, delay and productivity were estimated for two Mondays in September-October 

2007. One day was the worst congested Monday (September 10 2007) during the two-month 

period and the other Columbus Day (October 8 2007). Our a priori expectation was that travel 

demand on Columbus Day would be lower than on September 10 2007 since government and 

other workers receive a paid holiday on Columbus Day. Examining differences in uncongested 

link-level flow rates at five-minute periods throughout the day, we estimated that over the entire 

day travel demand is slightly lower on Columbus Day when compared September 10 2007 (0.5% 

in Los Angeles, 1.3% in Chicago). A more significant reduction in estimated travel demand was 

estimated in the AM peak (6-9 AM), however (1.6% in Los Angeles, 9.9% in Chicago). 

Incidents, weather, and work zone conditions were comparable on both Mondays in both cities. 

Several measures of productivity based on a combination of flow and speed data were developed 

to capture system-level efficiencies of the freeway networks. Overall daily system delay, 

compared to the worst Monday, is reduced by more than 35% in both Los Angeles and Chicago, 

with larger delay reductions in the AM peak period. Aggregate system-wide uncongested flow 

rates are higher on Columbus Day (19% in Chicago, 33% in Los Angeles) in the AM peak period. 

The study suggests that higher system-level productivity and efficiency can be observed when 

travel demand is observed to closely match overall system capacity, resulting in less frequent 

onset and reduced duration of freeway breakdown conditions. By reducing demand and 

preventing congestion from taking hold, demand management through pricing or other 

mechanisms could recover the daily waste of time and drop in freeway system productivity that 

occurs on congested highways when traffic flow breaks down. 

KEYWORDS: Performance Measurement, Throughput, Reliability, VMT, Los Angeles, 

Chicago, System Performance, Bottleneck Analysis. 
 2

INTRODUCTION 

Since 2002, Noblis has processed travel time and traffic volume data from urban freeway 

systems to calculate three key network performance measures (hours of congestion, travel time 

index, and planning time index) to provide a national snapshot of urban mobility on a monthly 

basis. This national snapshot is reported as a part of the Urban Congestion Report (UCR) effort 

in a series of graphical dashboards illustrating congestion trends and factors that contribute to 

congestion: weather, work zones, incidents and variations in travel demand. 

In collecting, assuring quality control, and analyzing these data, Noblis has developed a large 

repository of traffic volume and speed data for more than twenty U.S. cities and metropolitan 

areas. In 2007, this data repository was used to quantitatively analyze the key components of 

traffic congestion for the Chicago metropolitan area (Hardy et al., 2007). One element of this 

analysis examined area-wide aggregations of vehicle miles traveled (VMT) at five-minute 

periods on weekdays. These VMT data were correlated with average network speed over the 

same five minute intervals. In this congested network, the relationship between system-level 

aggregated VMT and speed suggested that an overall system maximum productivity could be 

observed when average network speeds operated in a relatively narrow range. Based on the 

results of that study, FHWA tasked Noblis to conduct additional research on the characterization 

of system-level productivity under a range of estimated travel demand and network speed 

conditions. 

The analysis documented in this report examines the hypothesis that total freeway system peak 

period productivity is higher in moderate travel demand conditions than in excessive travel 

demand conditions. The Noblis analysis addressed the hypothesis using two approaches: 

1. A micro-level study examining interactions between individual bottlenecks on the urban 

roadway network that characterizes travel demand and productivity relationships on the 

“edges” of congestion evolving throughout the day; and 

2. A macro-level study using aggregated measures for the entire urban roadway network 

system to examine changes in system-wide productivity measures for various travel 

demand conditions. 

Noblis conducted these studies using archived UCR speed and traffic volume data for roadway 

networks in Los Angeles and Chicago metropolitan areas. This report summarizes the macrolevel study of aggregate system productivity in UCR networks from Los Angeles and Chicago. 
 3

HYPOTHESIS 

The focus of this study was to characterize efficient operations in a multi-link network utilizing 

time-variant aggregations of link-level speed and traffic count data. The study examined the 

capability of these particular data to support a range of quantitative productivity measures. In 

general, we sought measures that characterize the productive output of the roadway system over 

time. In addition, we sought productivity measures that would support meaningful comparison 

between different time periods, both within a day and between days. Given a particular measure 

or approach, overall network productivity was estimated to test the study hypothesis: 

System-level productivity in a roadway network with multiple bottlenecks is highest when 

travel demand is moderate and close to some optimal threshold, but declines when excessive 

demand causes disruption to network-wide operations. 

The study hypothesis is illustrated graphically for within-day variation in Figure 1 in the lower 

graph representing an excessive demand condition for the roadway system. The x-axis represents 

the time of day around a peak period where the network is initially lightly loaded, e.g., 12 AM – 

3 PM. System-level travel demand (dashed line) and productivity (solid line) are plotted on the 

y-axis. In the early, lightly-loaded period, travel demand is lower than some system maximum. 

No congestion at bottlenecks form and system productivity rises with increased travel demand. 

When the demand for travel rises above some threshold, congestion begins to form at individual 

bottlenecks, reducing the volume of vehicles passing through the bottlenecks. These individual 

bottlenecks experience lower link-level productivity in a congested state. System-level 

productivity is also reduced since the component link-level bottlenecks serve fewer vehicles in 

any five minute time period. 

At this critical point, desired demand for travel exceeds system capacity and the two plotted lines 

in the excessive demand graph begin to diverge. In fact, as demand continues to rise in the peak 

period, additional bottlenecks form and congestion between bottlenecks begin to interact, further 

reducing the capacity of the system to deliver vehicles along the network efficiently. This causes 

overall system productivity to decline throughout the peak period. After the peak period has 

ended, the system must continue to process the remaining inventory of vehicles even as new 

demand drops off. This may cause congestion at bottlenecks to dissipate and system level 

productivity to rise. Peak system-level productivity and system-level utilization will be observed 

close to the breakdown point where travel demand and system-level productivity measures 

diverge. 

In comparison, under more moderate demand conditions (illustrated in the upper graph in Figure 

1), desired demand for travel closely approaches but does not greatly exceed the roadway system 

maximum productivity. The result is little overall system productivity loss throughout the peak 

period. If one compares the total network productivity within the peak period, the total 

productivity in the moderate demand case will be higher than in the excessive demand case. 

To examine this hypothesis, multiple system-level measures were developed using archived 

speed and link-level vehicle-miles-traveled (VMT) data from the Los Angeles and Chicago 

metropolitan areas from late 2007. These system-level measures differ from traditional point 

measures of capacity or volume because they characterize a comprehensive network-wide 
 4

capability of a roadway system to serve vehicular demand, rather than capability to deliver 

higher vehicle counts at a single location on a specific facility. 

I

II

I > II

STUDY

HYPOTHESIS

Total freeway system

peak period productivity

Is higher in moderate travel

demand conditions than

excessive travel demand

conditions

[TIME OF DAY]

DEMAND

(dashed)

SYSTEM

PRODUCTIVITY

(solid)

DEMAND

(dashed)

SYSTEM

PRODUCTIVITY

(solid)

MAX PRODUCTIVITY

PEAK PERIOD

[TIME OF DAY]

MAX PRODUCTIVITY

PEAK PERIOD

MODERATE DEMAND CONDITIONS

EXCESSIVE DEMAND CONDITIONS

Breakdown

FREEWAY NETWORK

FREEWAY NETWORK

I

II

I > II

STUDY

HYPOTHESIS

Total freeway system

peak period productivity

Is higher in moderate travel

demand conditions than

excessive travel demand

conditions

[TIME OF DAY]

DEMAND

(dashed)

SYSTEM

PRODUCTIVITY

(solid)

DEMAND

(dashed)

SYSTEM

PRODUCTIVITY

(solid)

MAX PRODUCTIVITY

PEAK PERIOD

[TIME OF DAY]

MAX PRODUCTIVITY

PEAK PERIOD

MODERATE DEMAND CONDITIONS

EXCESSIVE DEMAND CONDITIONS

Breakdown

FREEWAY NETWORK

FREEWAY NETWORK

Figure 1 Study Hypothesis, Peak Period Productivity Under Moderate Demand versus 

Excessive Demand
 5

STUDY AREAS 

Given the high levels of recurrent congestion in the Los Angeles and Chicago metropolitan areas, 

these regions were selected as test-beds to consider the study hypothesis. The studies utilized 

2007 traffic data (both flow and speed) archived as part of the Urban Congestion Report (UCR) 

databases in both metropolitan areas. In addition, an extensive archive of incident, work zone 

and weather data were available to identify the underlying conditions related to congestion 

patterns and bottleneck locations in the network. 

Freeway Networks 

The Los Angeles and Chicago UCR networks are illustrated in Figures 2 and 3. These are two of 

the largest roadway networks in the UCR archive, with over 1,200 directional miles of coverage 

in Los Angeles and over 590 miles in Chicago. The Los Angeles network is divided into 153 

links of an average length of 7.9 miles. The Chicago network is similarly subdivided into 75 

links with an average length of 7.9 miles. The UCR archive contains a link-level VMT at five 

minute intervals as well as an average link speed for each five minute time interval. These 

estimates are generated from combining station-level data from the multiple sensor stations along 

the links. 

Figure 2 Los Angeles UCR Network 

Period of Analysis

Noblis considered a range of days from the UCR archive to identify low, medium and high travel 

demand days. To choose the appropriate days to be used for comparisons, Noblis analyzed the 

daily VMT level (VMT estimates by link every 5 minutes) and speeds for the entire system for 

September and October 2007. The results showed that Mondays had the most congested 

conditions; i.e., lowest aggregate daily network speeds. Based on this analysis, the following 

three days were identified for comparison: 
 6

Figure 3 Chicago UCR Network 

 

 Low Demand Day—Labor Day, a national holiday falling on a Monday where most 

businesses and government offices are closed (September 3, 2007). 

 Medium Demand Day—Columbus Day, a federal holiday falling on a Monday where 

government offices are closed but most businesses remain open (October 8, 2007). 

 High Demand Day—A non-holiday Monday from identified in the months of September 

and October 2007 with the lowest aggregate network speed of all non-holiday Mondays 

(September 10, 2007). 

For all three days in both cities, data relating to work zones, incidents and weather conditions 

were identified. Weather was similar in all cases. Although work zone and incident records did 

differ by each of these days, there were no significant differences in the aggregate number of 

these disruptions. Further, there were no major incidents (freeway closures or crashes blocking 

lanes of major freeways for more than 30 minutes in the peak period). Work zone activity 

remained nearly constant throughout the two month period, that is, no new work zones were put 

in place during peak periods between Labor Day 2007 and Columbus Day 2007. 
 7

APPROACH 

In this analysis, VMT and speed data are utilized at five minute intervals throughout the entire 

24-hour period. In both cities, the link-level VMT and speeds in the UCR archive are calculated 

using a weighted combination of multiple fixed location sensor stations along the link. These 

data are used to characterize the aggregate system level demand, total delay in the system, and 

estimate overall system productivity using a variety of measures. This section describes how 

these key measures are calculated using the UCR data in both Los Angeles and Chicago. 

First, let us examine the fundamental speed and count data obtained for the UCR project to better 

understand the nature of the underlying data. Figure 4 presents sample link level data from a 

bottleneck along northbound I-405 in the Los Angeles network. These data are taken from three 

Mondays in September-October 2007 (Labor Day, Columbus Day, and a typical congested 

Monday 9/10/2007). Each data point in the figure represents a 5-minute speed and volume linklevel estimate combining data from multiple sensor locations along the roughly 10-mile long link. 

The result is the familiar backward bending speed flow relationship with high-speed travel 

maintained up to some critical link capacity. For this link, both high-speed and high-volume 

flow can be observed at around 50 mph. The observed pattern of speed and volume is consistent 

with expectations from both the HCM and other analyses of freeway traffic flow. When the link 

becomes congested, both speed and flow decline. Many other bottleneck links with similar 

properties can be found across both the Chicago and Los Angeles networks, although the critical 

breakpoints for speed and volume vary. This relationship confirms the fundamental data in the 

analysis contains the bottleneck effect at the link-level. 

When the link carries traffic volume near a critical capacity with high speed, we characterize the 

productivity of the link as being higher than in the cases where congestion causes a drop to both 

speed and carrying volume. In addition, one can also argue that in low-volume, high-speed 

conditions, the link itself could carry additional volume without breakdown and is underutilized 

compared to the high-volume and high-speed case. Therefore, in any five minute period, one 

way of characterizing the productivity of the link would be to examine the volume of vehicles 

traversing the link. One could also characterize productivity by looking at the volume of vehicles 

traversing the link in a high-speed or uncongested state. 

Figure 5 shows the same segment of I-405 plotting VMT (average weighted volume by sensor 

along the link multiplied by link length) against time of day for a congested (typical) Monday. 

Note that peak volume in Figure 4 (or VMT in Figure 5) is reached after 6:00 AM and then 

declines sharply (27%) during the AM peak period. Link-level VMT only recovers this loss in 

the mid-day period. Note that the VMT data in Figure 5 are also color-coded by speed to 

indicate congestion levels. The green portions of the figure indicate five-minute intervals where 

speeds are 50 mph or greater. The yellow (speeds between 25 mph and 50 mph) and red (slower 

than 25 mph) segments show lower speeds associated with congestion and the low productivity 

regime associated with breakdown conditions. 
 8

10

20

30

40

50

60

70

0 1000 2000 3000 4000 5000 6000 7000 8000

Vehicle/Hour

Speed

Labor day September 10 2007 October 8 2007

Figure 4 Speed/Volume Relationships Observed on Northbound I-405 (Los Angeles), 

Three Mondays in September-October 2007 

0

1000

2000

3000

4000

5000

6000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

VMT

UnCongested VM T Congested VMT Failure VM T

Figure 5 VMT Classification by Time of Day, Northbound I-405 (Los Angeles), 

September 10 2007 
 9

System-Level Productivity Measures 

The aggregation of link-level data into system-wide measures should fairly reflect contributions 

from all links in the system. To do this, we chose to aggregate link-level VMT data rather than 

average link volumes to weight longer links higher than shorter links. This helps to reduce the 

impact of arbitrary network definition on measures. For example, simply breaking a longer link 

into three pieces would triple the amount of impact from that section of the system if a VMT 

measure had not been utilized. 

As we discussed earlier in this section, one method of characterizing productivity we examined 

was to aggregate link-level VMT across all links in the network. However, this total VMT 

aggregation showed little difference between significantly different network conditions. One 

example appears in Figures 6(a) and 6(b) comparing September 10 (High Demand) and October 

8 (Medium Demand) in Los Angeles. Both days have a similar VMT time-of-day pattern, but 

significantly different AM peak speed patterns. Our observations of the data indicate that there 

is a self-regulating component to a connected, but open system like a freeway network. In a 

closed system, a link-level bottleneck may develop and cause a reduction in volume that can be 

observed downstream. In an open system, with many access points, additional volume can fill 

downstream of the bottleneck with little apparent loss of productivity. Queued vehicles held 

upstream of a bottleneck are converted into increased volume (and productivity) downstream 

from the bottleneck. For this reason, although aggregate VMT is one way of characterizing 

system productivity, it does not reflect the quality of travel in the system. 

One analogy is the consideration of two factories, each of which produces 100 widgets per day. 

The first factory has a reject rate for its widgets of 50% while the other has a reject rate of 1%. 

By simply comparing the number of widgets produced (100 vs. 100), we do not fairly represent 

the inherent higher productivity realized by the second factory (50 vs. 99). To reflect the quality 

of travel in the system, we may choose to characterize productivity as the total amount of VMT 

operating above the critical link capacity across the network. 

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Sum of Total VMT

40.0

45.0

50.0

55.0

60.0

65.0

70.0

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Average Speed (MPH)

9/10/2007

10/8/2007

Average of Speed all

Date

a) Aggregate VMT b) Average Speed 

Figure 6 System-Wide VMT and Speed Comparison (Los Angeles), 

High and Medium Demand Days 
 10

Classification of VMT. To bring this notion of travel quality into the analysis, we utilized fiveminute link-level speed estimates to differentiate between overutilization and underutilization 

conditions. Because the identification of breakpoints for each link in both large networks was 

out of scope for this effort, a set of speed breakpoints were established for three categories of 

link-level productivity for each city: 

 Uncongested VMT: Average speeds > 50 mph (LA), 45 mph (Chicago) 

 Congested VMT: Average speeds 25-50 mph (LA), 22.5-45 mph (Chicago) 

 Failure VMT: Average speeds < 25 mph (LA), 22.5 mph (Chicago) 

Total System-Wide Uncongested VMT was used in both networks as one measure of system-wide 

productivity. The aggregation is a simple summation of link-level VMT meeting the 

uncongested criterion established above at five-minute intervals throughout the day. VMT 

associated with links operating in the congested or failure regimes are not included in the 

system-level calculation. 

Aggregate System-Level Travel Demand Estimation. Our study hypothesis is based on the a 

priori assumption that aggregate travel demand will be lowest on Labor Day and highest on the 

High Demand day, and that Columbus Day demand will be somewhat less than on the High 

Demand day. There is evidence that such an assumption has merit. For example, a Society of 

Human Resource Management research of 2007 holiday schedules (SHRM, 2007) shows that 

94% of organizations close their offices on Labor Day, while only 11% do so on Columbus Day. 

Further, both Labor Day and Columbus Day are federal holidays, and federal employees have a 

paid holiday. Most state and local government offices are also closed on Labor Day, while 

policies with regard to Columbus Day are less clear-cut. Census data from 2000 indicates that 

federal, state and local government workers represent 14.6% of the total workforce in California; 

in Illinois the figure is 12.7%. Whether these figures are clearly indicative of reduced total travel 

demand is a subject for debate. Simply having a day off does not mean that workers do not 

travel – in fact, one may argue total travel demand could possibly be increased on Labor Day 

reflecting return traffic from the long holiday weekend during the afternoon period. 

In order to quantify the change in travel demand at the system level, another key element in this 

study is the interpretation of volume and speed data to characterize travel demand. At the link 

level, the VMT data in the UCR database always reflects traffic counts and link length, but may 

not always describe the travel demand for the link. 

We cannot determine travel demand from VMT data in the case where a link is in a congested 

condition. Instead, we estimate travel demand by looking at differences between days on links 

during times of day when we can confirm the link is operating in an uncongested condition for 

both days. We assess link condition based on the speed criteria used to classify VMT (above). 

The result is that, for this study, travel demand differences between two days (e.g., Columbus 

Day and the High Demand Monday) are made as pair-wise comparisons. Such a comparison is 

shown in Table 1. The results indicate that demand over the entire day is slightly lower (0.5-

1.6%) but that demand in the AM peak period is more significantly reduced (1.6-9.9%). This 
 11

makes some intuitive sense because people are likely to defer travel to later in the day on 

holidays compared to normal work days. 

California Illinois

8.3% 6.8%

4.0% 3.8%

2.3% 2.1%

14.6% 12.7%

Los Angeles Chicago

AM -1.6% -9.9%

PM 1.4% -0.6%

Entire

Day -0.5% -1.3%

Local 

State 

Federal 

Total 

Noblis 

Demand 

Estimate from 

UCR Data

Percent of Employees 

(2000 Census Data)

Government 

Employees

Table 1 Percentage of Government Workers by State, and Travel Demand Differences 

Estimated Between Columbus Day and September 10 2007

System-level Delay Estimation. Another key productivity measure that reflects quality of travel 

is system delay. In this study, system level delay is defined as travel times on links lower than 

the posted speed limit. Because we lack speed limit data for each link in the UCR archive, we 

estimated the posted-speed as the closest 5 mph interval that falls below the observed 85th 

percentile travel speed in uncongested conditions. This approximation has been demonstrated to 

be a relatively accurate (plus/minus 5 mph) when compared against posted speed data at selected 

locations in both the Chicago and the Los Angeles networks. Aggregate system level delay in 

each five minute period is calculated as the VMT-weighted sum of all link level delay in that 

time interval. 
 12

RESULTS

This section presents results from the study in both Los Angeles and Chicago. First, a pair-wise 

comparison of system productivity on Columbus Day compared to the High Demand Monday is 

summarized for each city. Second, a more detailed examination of aggregate VMT classified by 

speed within a day for the Los Angeles network is presented. Some observations about a critical 

value for maximum system-level uncongested VMT is explored across the set of three Mondays 

in Los Angeles. Finally, this observation is converted into a time-variant maximum system 

capacity figure and a quality-weighted productivity ratio calculated. This methodology is 

extended to the Chicago network and results presented and compared. 

Columbus Day vs. High Demand Monday: Los Angeles Results 

Over the period from 5 AM – 10 PM traffic delay is reduced by almost 40 percent on Columbus 

Day. In particular, AM peak (6-9 AM) travel delay is reduced by more than 47%. PM peak 

travel delay (4-7 PM) also declines by over 37%. Figure 7 plots system-level delay by time of 

day. Over the entire day, uncongested VMT is 14% higher on Columbus Day than on the High 

Demand Monday, 33% higher in the AM peak, and over 18% higher in the PM peak. Total 

VMT (regardless of speed) across the entire day differs by less than 0.05%, with similarly small 

differences in both peak periods as indicated in Table 1. 

 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:0024:00

Hours of Delay for Entire Network 

Sep. 10 2007(High Demand Monday) Oct. 8 2007 (Columbus Monday-Medium Demand Monday)

AM Peak 47.7%

PM Peak 37.1%

Figure 7 System-Level Delay Comparison, Los Angeles, 

Columbus Day and September 10 2007 
 13

Columbus Day vs. High Demand Monday: Chicago Results 

Over the period from 5 AM – 10 PM traffic delay is reduced by over 35 percent on Columbus 

Day. In particular, AM peak (6-9 AM) travel delay is reduced by more than 45%. PM peak 

travel delay (4-7 PM) also declines by over 51%. Over the entire day, uncongested VMT is 14% 

higher on Columbus Day than on the High Demand Monday, 19% higher in the AM peak, and 

over 49% higher in the PM peak. Total VMT (regardless of speed) across the entire day differs 

by less than 0.03%, with a similarly small difference in the PM peak period, as indicated in Table 

1. 

The results obtained in Los Angeles and Chicago in the pair-wise examination of system-level 

productivity generally support the study hypothesis when travel quality is considered. When 

total system VMT is considered without consideration for travel quality, both days are similarly 

productive. System delay or speed-classified aggregate VMT show a significant difference 

between the two days, however. Delay is reduced by 35-40% while uncongested VMT rises 

roughly 14% in both networks. 

System-Wide Uncongested VMT Delivered By Time of Day: Los Angeles 

One consideration within the analysis was to seek evidence that some analog of the link-level 

critical capacity (or productivity) could be found at the system-level. To pursue this idea, we 

examined system-wide aggregated uncongested VMT patterns by time of day in Los Angeles 

over the three Mondays. 

Results of this analysis are shown in Figures 8-10. The presentation of the data is analogous to 

that of Figure 5, where link-level VMT is displayed by time-of-day and color-coded by speed 

level. In Figures 8-10, however, we are reporting VMT from many links in both networks. At 

any point in time throughout the day, the network may have component links in any one of the 

three speed-depended classification regimes (uncongested, congested and failure). Total VMT is 

plotted against time-of-day (blue line), summing over all VMT in the system regardless of 

classification. In addition, however, the VMT in the system in each of the three speed 

classifications are also plotted (green, yellow and red lines). 

One noteworthy element of Figures 8-10 can be observed by a consideration of uncongested 

VMT (green line). Figure 8 shows results for Labor Day, our lowest demand Monday. One can 

see that nearly all of the VMT throughout the day in Los Angeles is in the uncongested regime 

except for some moderate congestion between 3-10 PM. A system-level limit of 300-350K 

VMT per five minute period seems to be roughly a point where uncongested VMT begins to be 

converted to congested VMT on Labor Day. 

Similarly, if we consider Figure 9 (High Demand) we see breakpoints for uncongested VMT that 

are close to the limits on Labor Day. The first breakpoint occurs prior to 6 AM at around 300K 

VMT per five minute period. The system never fully recovers to a completely uncongested state 

until the mid-day, but system-wide uncongested VMT reaches 350K per five minute period just 

prior to the PM peak (2-3 PM). In Figure 10 (Medium Demand – Columbus Day) a similar 
 14

pattern can be observed, with a slightly higher peak in the afternoon above 350K uncongested 

VMT per five minute period. 

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:00

20:0021:0022:0023:00

VMT

Failure VMT Congested VMT Uncongested VM T Total VMT

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:00

20:0021:0022:0023:00

VMT

Failure VMT Congested VMT Uncongested VM T Total VMT

Figure 8 System-Wide VMT Classification by Time of Day, Los Angeles, 

Low Demand Monday, Labor Day 2007 

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:001:002:003:004:005 :0 06:007:008:009:00

10:0011:001 2 :0 013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

VM

T

Failure VM T C o nge s te d V M T Uncongested V MT Total V M T

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:001:002:003:004:005 :0 06:007:008:009:00

10:0011:001 2 :0 013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

VM

T

Failure VM T C o nge s te d V M T Uncongested V MT Total V M T

Figure 9 System-Wide VMT Classification by Time of Day, Los Angeles, 

High Demand Monday, September 10 2007 
 15

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

VM

T

Failure VMT Congested VMT Uncongested VMT Total VMT

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

VM

T

Failure VMT Congested VMT Uncongested VMT Total VMT

Figure 10 System-Wide VMT Classification by Time of Day, Los Angeles, 

Medium Demand Monday, Columbus Day 2007 

The maximum level of uncongested VMT the system is capable of moving in a five-minute 

period appears to have analogous properties to the link-level relationship between speed and 

flow. Up to roughly some level, the system can accept additional travel demand without any 

components undergoing breakdown and congested (low-flow, low-speed) conditions. Clearly 

this level depends on many factors: e.g., weather, incidents, and variations in travel demand 

(both in terms of total demand but also the distribution of demand across the network). 

Does higher system-wide uncongested VMT indicate higher system-level productivity? After all, 

total VMT (regardless of classification) continues to climb in the peak periods even after the 

observed breakpoints for uncongested VMT are observed. In both Figures 9 and 10, total VMT 

peaks later in the peak periods at levels roughly 25% higher than at these critical break points. 

From a vantage point that is indifferent to travel quality, these uncongested VMT breakpoints are 

less critical. However, one can argue that if the notion of system-wide productivity includes 

consideration of travel quality, then peak system-wide uncongested VMT does represent a peak 

in system-wide productivity. 

If one accepts that maximum system-level uncongested VMT is one way of quantifying a 

quality-weighted measure of network-wide productivity, then one may want to look across a 

large number of days to find conditions under which this measure is maximized. An analysis of 

the Los Angeles data looking across every day in the two-month period September-October 2007 

is presented in Figure 11. Uncongested VMT for our familiar trio of Labor Day, Columbus Day 

and September 10 2007 are plotted by time of day. In addition, the thick green line represents a 

composite day built up from finding the highest uncongested VMT found in any five-minute 
 16

time slice across the 61 days considered from September 1-October 31, 2007. It is likely that 

incident delays were minimal during these times. 

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Uncongested VMT 

Best Composite Day

Sep 10 2007 (High Demand Monday)

Oct 8 2007 (Columbus Day-Medium Demand Monday)

Sep 3 2007 (Labor Day- Low Demand Monday)

Figure 11 Maximum System-Wide Uncongested VMT by Time of Day, Los Angeles 

High, Medium and Low Demand Days 

One observation is that the uncongested VMT observed in any five-minute time slice from the 

composite day is always higher than any uncongested VMT value obtained in any of the three 

Mondays we considered in our detailed analysis. This implies that there was no five minute time 

slice in our three Mondays that represented a maximum level of quality-weighted productivity 

seen over the two-month period. At some times of day (early AM peak period), particularly for 

Columbus Day and September 10 2007, system-level uncongested VMT approaches the 

composite day. 

Quality-Weighted Productivity Ratio: Los Angeles and Chicago 

Clearly, the amount of uncongested VMT that can be loaded onto the Los Angeles network is 

highly dependent on the underlying travel demand pattern. This includes notions of geographic 

distribution in demand, as well as temporal distribution. In particular, different days of the week 

are observed to have different times most likely to reach peak uncongested VMT. This peak 

occurs later in the morning on Fridays, and in the mid-afternoon period on weekends. Because 

of this, and the possibility that incident delay may be minimal at some times randomly, the 

composite probably does not represent an achievable goal for any particular day when one 
 17

manages a freeway system. However, it can be useful as a uniform measuring stick to 

characterize quality-weighted system productivity and to provide a measure when comparing 

two different days. 

To explore this concept, we define the quality-weighted productivity ratio (QPR) as the level of 

uncongested VMT carried by the system in any five minute time slice compared against a 

composite day comprised of the maximum uncongested VMT observed in that same time slice 

across many days. Figures 12-13 show results using the ratio in Los Angeles and Chicago. 

Figure 12 Quality-Weighted Productivity Ratio by Time of Day, Los Angeles, 

High, Medium and Low Demand Mondays, 2007 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Quality-Weighted Productivity Rat

Sep 3 2007 (Labor Day-Low Demand Monday)

Sep 10 2007 (High Demand Monday)

Oct 8 2007 (Columbus Day-Medium Demand Monday)

Sep 3 2007 Sep 10 2007 Oct 8 2007

Entire Day 0.61 0.59 0.67

AM Peak 0.40 0.47 0.62

PM Peak 0.63 0.52 0.61
 18

Figure 13 Quality-Weighted Productivity Ratio by Time of Day, Chicago, 

High, Medium and Low Demand Mondays, 2007 

In Figure 12, Columbus Day is shown to have the highest QPR in the AM peak (0.62). On Labor 

Day, the system does not see sufficient demand to fully load the network in the AM peak (QPR 

0.40). During the congested High Demand Monday, the system is overloaded during the AM 

peak and the QPR is lower than on Columbus Day (0.47). In the PM peak, Labor Day posts the 

highest QPR (0.63), better than on Columbus Day (0.61) and September 10 2007 (0.52). 

Columbus Day has the highest QPR when the 5 AM – 10 PM (All Day) period is considered. 

Similar results are obtained when data from Chicago are considered (Figure 13). Columbus Day 

has the highest All Day QPR and AM peak QPR, while Labor Day has the highest PM peak QPR. 

Other observations of note regard the extreme values of QPR in both networks observed over the 

three days studied in each city. In Chicago, a minimum QPR of 0.2 can be observed at 6 AM on 

Labor Day, while the highest QPR of 0.93 occurs later that same day between 8-9 PM. In Los 

Angeles, the lowest QPR of 0.3 is observed at 8 AM on September 10 2007. This low for Los 

Angeles is only a short time after the peak QPR of 0.87 is reached just before 6 AM. 

One can argue that when the QPR is near 1.00 the system has the most productive balance of 

travel demand and overall system carrying capacity. Certainly these conditions are the most 

desirable for travelers and system managers – the highest level of vehicles operating at 

conditions above link-level breakdown conditions and minimal system-level delay.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Quality Weighted Productivity Ratio 

Sep 3 2007 (Labor Day-Low Demand Monday)

Sep 10 2007 (High Demand Monday)

Oct 8 2007 (Columbus Day-Medium Demand Monday)

Sep 3 2007 Sep 10 2007 Oct 8 2007

Entire Day 0.65 0.64 0.72

AM Peak 0.30 0.59 0.70

PM Peak 0.71 0.42 0.63
 19

CONCLUSIONS

Results from the analysis support the study hypothesis, with a single important caveat. When 

system productivity is defined using measures like delay or quality-weighted VMT, then it is 

clear that higher productivity is linked with conditions that best match overall system demand 

with system carrying capacity. The QPR measure appears to be the best single measure in this 

study to illustrate this concept. The QPR drops as low as 0.2 when travel demand is far lower 

than system carrying capacity, as in at 6 AM in Chicago on Labor Day. It also drops to as low as 

0.3 when excessive demand has caused broad network congestion as at 8 AM in Los Angeles on 

September 10 2007. High QPR values can be found during the AM peak on Columbus Day or in 

the latter portions of the Labor Day PM peak period when networks are loaded close to some 

level where a large volume of vehicles can be accommodated without significant breakdown. 

The caveat from the study is that the hypothesis is less well supported when measures of 

productivity that are indifferent to quality of travel are considered. For example, Total VMT 

(regardless of speed) does not indicate a significant difference between Columbus Day and 

September 10, 2007 in any peak period or when the entire day is considered. Our observations of 

these measures indicate that there is a self-regulating component to a connected, but open system 

like a freeway network. In a closed system, a link-level bottleneck may develop and cause a 

reduction in volume that can be observed downstream. In an open system, with many access 

points, additional volume can fill downstream of the bottleneck with little apparent loss of 

productivity. Queued vehicles held upstream of a bottleneck are converted into increased 

volume (and productivity) downstream of the bottleneck. 

One analogy that may be helpful when considering productivity measures is the consideration of 

two factories, each of which produces 100 widgets per day. The first factory has a reject rate for 

its widgets of 50% while the other has a reject rate of 1%. By simply comparing the number of 

widgets produced (100 vs. 100), we may not be fairly representing one kind of higher 

productivity realized by the second factory (50 vs. 99). 

The QPR and other measures provide a new and potentially insightful way of considering the 

productivity of a large complex system like a freeway network. Maximum uncongested VMT 

appears to be a useful measure in characterizing overall system resilience and carrying capacity. 

Either measure could be used to systematically identify productivity losses associated with 

incidents, work zone activity, weather or other impacts if these other data were combined with 

the concurrent speed and traffic count data. 
 20

REFERENCES 

Matthew H. Hardy and Brian Higginbotham, Quantifying the Contributing Factors of Traffic 

Congestion using the Urban Congestion Report Data, presented at the Transportation Research 

Board 87th Annual Meeting, Washington, DC, 2008. 

SHRM Weekly Online Survey on 2007 Holiday Schedule 

http://www.shrm.org/hrresources/surveys_published/bydate/1In%202007,%20which%20holiday

s%20will%20your%20organization%20observe%20by%20closing%20its%20offices_.ppt
Federal Highway Administration 

1200 New Jersey Avenue, SE 

Washington, DC 20590 

Publication # FHWA-HOP-09-019