Examining the Speed-Flow-Delay 

Paradox in the Washington, DC 

Region: 

Potential Impacts of Reduced Traffic on 

Congestion Delay and Potential for Reductions in 

Discretionary Travel during Peak Periods 

 Final Report 

December 2008 

Prepared for the Federal Highway Administration 

Prepared by: 

The Louis Berger Group, Inc. 

2445 M Street, NW 

Washington, DC 20037
Foreword 

Highway studies have determined that once traffic volumes exceed the capacity of the roadway, the system can 

rapidly “break down” to the point where all traffic slows markedly, and the capacity and throughput of the roadway 

drops precipitously. This study examines traffic volumes, speeds and delays at various freeway traffic counters in 

the Washington, DC region to specifically evaluate congested versus uncongested travel. The observations identify 

the specific “tipping point(s)” at which free-flow traffic “breaks down”, and conversely estimate the volume of 

traffic that would have to be reduced in peak periods to keep traffic free-flowing. The study also examined survey 

data to estimate the number and percent of trips that people take in peak hours on our freeways that are discretionary 

trips. Finally, the study briefly reviewed empirical findings on experiences with congestion pricing in the US and 

abroad. 

This report will be of interest to policymakers who are concerned about highway congestion and approaches that can 

be used to reduce congestion. It will also be of interest to transportation planners and engineers who are interested 

in empirical verification of traffic engineering principles. 

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. This report does not constitute a standard, specification, or regulation.

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 objective 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. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region: 

Potential Impacts of Reduced Traffic on Congestion Delay and Potential for 

Reductions in Discretionary Travel during Peak Periods 

 Final Report 

December, 2008 

Prepared by the Louis Berger Group, Inc. 

Dr. Robert M. Winick, MotionMaps 

Deborah Matherly, AICP, The Louis Berger Group 

Dane Ismart, The Louis Berger Group 

Lead Authors 

Prepared for the Federal Highway Administration 

Acknowledgements:

Mr. Patrick deCorla Souza, FHWA Office of Operations, provided expert guidance and support. 

The Metropolitan Washington Council of Governments, in particular Ron Kirby, Bob Griffiths, 

Daivamani Sivasailam (Siva) and Mike Clifford provided technical assistance, advice and access 

to the MWCOG Personal Travel Survey. 

Dr. Robert Winick, Principal, Motion Maps developed the approach and led the traffic evaluation 

procedure, with technical assistance from Nikola Ivanov and Michael Pack of the University of 

Maryland CATT Laboratory. Ms. Hikari Yukiko Nakamoto performed the SAS trip chaining 

analysis of the survey and developed the analysis tables. 
Technical Report Documentation Page 

1. Report No. 

FHWA-HOP-09-017 

2. Government 

Accession No. 

3. Recipient's Catalog No. 

5. Report Date 

December, 2008 

4. Title and Subtitle 

Examining the Speed-Flow-Delay Paradox in the Washington, DC 

Region: Potential Impacts of Reduced Traffic on Congestion Delay and 

Potential for Reductions in Discretionary Travel during Peak Periods 6. Performing Organization Code: 

7. Author(s) 

Dr. Robert M. Winick (MotionMaps), Deborah Matherly and Dane Ismart 

(both with The Louis Berger Group, Inc.) 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 10. Work Unit No. 

The Louis Berger Group 

2445 M Street NW 

Washington, DC 20037 

11. Contract or Grant No. 

DTFH61-01-C-00180 

13. Type of Report and Period Covered 

Research Report 

12. Sponsoring Agency Name and Address 

Office of Operations 

Federal Highway Administration 

U.S. Department of Transportation 

1200 New Jersey Avenue, SE 

Washington, DC 20590 

14. Sponsoring Agency Code 

15. Supplementary Notes Mr. Patrick De Corla-Souza, Contracting Officer’s Task Manager 

16. Abstract 

Traffic congestion in the Washington, DC area, especially congestion on our freeways, costs our residents every day 

in terms of wasted time, fuel, and increased air pollution. Highway studies have determined that once traffic volumes 

exceed the capacity of the roadway, the system can rapidly “break down” to the point where all traffic slows 

markedly, and the capacity and throughput of the roadway drops precipitously. The Federal Highway Administration 

commissioned this study to specifically evaluate speed, volume and delay for congested versus uncongested travel on 

some of the major roadways in the metropolitan Washington region, in order to identify the specific “tipping 

point(s)” at which free-flow traffic “breaks down”, and conversely, the volume of traffic that would have to be 

reduced in peak periods to keep traffic free-flowing. The study also analyzed survey data to estimate the number and 

percent of trips that people take in peak hours on our freeways that are discretionary trips. Finally, the study briefly 

reviewed empirical findings on experiences with congestion pricing in the US and abroad. 

17. Key Words 

Areawide pricing, congestion pricing, electronic road pricing, 

traffic congestion, congestion delay, travel demand management, 

HOT lanes, HOV lanes, discretionary trips 

18. Distribution Statement 

No restrictions. 

19. Security Classification (of this report) 

Unclassified 

20. Security 

Classif. (of this 

page) 

Unclassified 

21. No. of 

Pages 

80 

22. Price 

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

o

F Fahrenheit 5 (F-32)/9 

or (F-32)/1.8 

Celsius oC 

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) 

o

C Celsius 1.8C+32 Fahrenheit o

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

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply 

with Section 4 of ASTM E380. 

(Revised March 2003) 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 i

Table of Contents 

Executive Summary ........................................................................................................................ 1

1. Introduction: Work Order Objectives......................................................................................... 4

2. Traffic Delay Comparisons ........................................................................................................ 6

I-95 Northbound for PM Peak Period......................................................................................7 

I-270 Corridor ........................................................................................................................ 19 

I-495 Corridor ........................................................................................................................ 37 

AM Peak Volume Analysis ................................................................................................... 47 

What Does This All Mean?.................................................................................................... 49 

Summary Traffic Analysis.....................................................................................................49

I-95 Section Delay Analysis – I-95 Northbound from Brooklyn Bridge Road to Montgomery 

Road.............................................................................................................................. 51 

I-270 Delay Analysis: I-270 Northbound – Section from I-370 to MD 109 .........................52 

3. Washington, DC Household Survey Key Findings ................................................................... 55

Methodology Used to “Mine” the MWCOG 1994 Survey....................................................55 

MWCOG Survey Findings .................................................................................................... 56 

Morning and Afternoon Peak Longer Discretionary Trips.................................................... 59 

What Does This All Mean?.................................................................................................... 61 

4. Effectiveness of Congestion Pricing Strategies: Literature Review Key Findings.................... 63

What Does This All Mean?.................................................................................................... 66 

Appendix A: Detailed Traffic Analysis for I-270 Corridor ............................................................... 68
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 1

Volume-Speed Relationship at Selected CHART Detectors:

 I-95 Northbound on Wed 8-1-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Welcome Ctr

ØØØ Executive Summary 

Traffic congestion and delay have become an endemic component of commuting life in the 

Washington DC region. To many, the unpredictability of travel time is almost more annoying 

than delay- one day a 10 mile trip may require 20 minutes, and the next day 45 minutes. Because 

the system is so near capacity, and exceeding capacity in some areas, a minor incident or a 

rainstorm, or simply too much traffic, causes major breakdowns and systemic delays. In this 

research study we demonstrate that there is a way to restore reliability and predictability to our 

highway system, without spending billions on new lanes of traffic. 

Traffic congestion in the Washington, DC area, especially congestion on our freeways, costs our 

residents every day in terms of wasted time, fuel, and increased air pollution, including green 

house gases that are a primary cause of climate change. Highway studies have determined that 

once traffic volumes exceed the capacity of the roadway, the system can rapidly “break down” to 

the point where all traffic slows markedly, and the capacity and throughput of the roadway drops 

precipitously. The Federal Highway Administration commissioned this study to specifically 

evaluate congested versus uncongested travel on some of the major roadways in the metropolitan 

Washington region, to identify the specific “tipping point(s)” at which free-flow traffic “breaks 

down”, and conversely, the volume of traffic that would have to be reduced in peak periods to 

keep traffic free-flowing. The study also examined travel behavior based on the Metropolitan 

Washington Council of Governments Household Travel Survey, to estimate the number and 

percent of trips that people take in peak hours on our freeways that are discretionary trips. With 

appropriate incentives or disincentives, many of these discretionary trips could be shifted to offpeak hours or otherwise deferred. Finally, the study reviewed empirical findings on experiences 

with congestion pricing in the US and abroad, to provide ranges of estimates of the amount or 

percentage of traffic that could be shifted out of the peak period or encouraged to use ridesharing through a comprehensive pricing and transportation demand management program. 

The traffic analysis (Section 2) focused on a 12.9 mile segment of I-270, a 10.5 mile segment of 

I-95, and two independent count locations on the Capital Beltway for the evening rush hours 

(PM Peak).1

 The study compared speeds and volumes in the summer and on holidays to dates 

and times in September and October, when the system can be observed to “break down”. This 

pair of figures is one of many examples – the left-hand figure shows volumes (horizontal axis) 

and speeds (vertical axis) on August 1, the right-hand shows October 11, when a short “spike” in 

demand generates an extensive period of lower volumes and lower speeds. 

 

1 The analysis also examined volume and speed for two segments in the morning rush hour (AM Peak).

Volume-Speed Relationship at Selected CHART Detectors:

 I-95 Northbound on Thu 10-11-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Welcome Ctr
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 2

Total vs. Discretionary AM Peak Trips 

(All PV trips > 20 Minutes, 0-1 Stops for Discretionary)

Non-w ork 

discretionary, 

77,350, 7.7%

Restaurant, 

4,526 , 0.5%

Fam/Pers, 35,945, 

3.6%

Shopping, 

9,423 , 0.9%

Work trips, 

803,535 , 79.7%

Non-w ork nondiscretionary, 

127,254 , 12.6%

Soc/Rec, 27,456 , 

2.7%

Total vs. Discretionary PM Peak Trips 

(All PV trips > 20 Minutes, 0-1 Stops for Discretionary)

Non-w ork 

discretionary, 

118,549, 10.5%

Restaurant, 

19,012, 1.7%

Soc/Rec, 

59,646 , 5.3%

Non-w ork nondiscretionary, 

294,206 , 26.0%

Work trips, 

719,824 , 63.5%

Shopping, 

21,791 , 1.9%

Fam/Pers, 

 18,100 , 

1.6%

A main finding of this study has been that if relatively small changes can be made in peak 

demand (volume) through various programs and strategies, such as congestion pricing, then two 

beneficial things can happen: (1) there can be relatively large decreases in congestion and delay, 

especially at key choke points, and (2) there can be increased through-put along those roads 

during peak times of travel – thus by effectively managing the demand more travelers can be 

served per time period with the available fixed-supply of roadway capacity. 

The analysis has shown that in many instances the amount of needed demand reduction can be 

on the order of five to ten percent of the peak period flow. However, there still may be a 

particularly difficult bottleneck in a corridor that would need reductions on the order of 15 to 20 

percent. While demand reduction and operational strategies may be able to go a long way in 

improving the flow at those locations so that capacity is exceeded less often and/or recovery is 

quicker, there still may need to be localized geometric or lane use changes at those locations to 

routinely have freer flowing traffic at those troublesome locations. 

We established that in general a 10 to 14 percent decrease in traffic on congested freeways will 

reduce delay by approximately 75 to 80 percent. For example, on I-95, an average traveler during 

the peak period would save 110 seconds each day, or about 5.3 cents per vehicle mile for that 

10.5 mile stretch of road. During the most congested peak hour, the average savings in delay 

would be 310 seconds per person per day, or about 14.6 cents per vehicle mile. On I-270, the 

average traveler would save 220 seconds during the full peak period, or about 8.5 cents per 

vehicle mile. During the peak hour on I-270, a traveler would save about 340 seconds for that 

almost 13 mile stretch, or about 13.2 cents per vehicle mile. Those savings per traveler, 

multiplied by all drivers, multiplied by 250 work days, yields millions of dollars in annual time 

savings from reduced delay for these two roadways alone. 

In Section 3 we establish that from 7.7 percent (AM Peak) to 10.5 percent (PM Peak) of the 

longer trips in personal vehicles (PV) are typically discretionary. The definition of discretionary 

trips includes restaurant visits, family/personal trips, shopping trips and social/recreational trips. 

Discretionary trips, by definition, should be fairly easy to divert to non-peak times or to routes 

other than freeways. We do not expect to divert all discretionary trips, but the 7 to 10 percent 

gives us a substantial base to start from, as we aim for a 10 to 14 percent reduction. 

In Section 4 we demonstrate that modest pricing signals for private vehicles can reduce traffic 

enough to significantly reduce congestion and save time for all drivers, while at the same time 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 3

increasing the “people-carrying capacity” of the roadway. Experiences across the country and 

around the world have found that charging a modest toll for single-occupant vehicles while 

improving the availability of carpools, vanpools and transit can create significant shifts in travel 

behavior, in the ranges necessary for the DC area. 

It therefore appears feasible to restore and maintain free-flow on the freeways in the 

Metropolitan Washington area, without adding capacity (except to alleviate selected 

bottlenecks), by applying congestion pricing to the major facilities, and at the same time 

increasing transit, carpool and vanpool programs. The combination of diverting most 

discretionary trips to other times and diverting an additional five to ten percent of personal 

vehicle work trips to HOV modes should achieve the needed 10 to 14 percent overall decrease in 

traffic needed to achieve major reductions in delay. 

Such a system would require phased implementation and experimentation to identify workable 

technologies and appropriate rates to achieve the desired result.2

 (Implementation strategies are 

beyond the scope of this study, but many different proposals have been evaluated for the 

Washington region.) Rates could be adjusted up or down, to ensure the roadway is used to nearmaximum capacity, without exceeding capacity to the point of breaking down and failing. 

Revenues collected could be used to improve HOV alternatives as well as maintain the 

roadways, address choke points and bottlenecks, and improve alternate routes. Finally, travelers 

in the region would truly benefit and travel in confidence, knowing that they can reliably predict 

their travel time on a daily basis. 

 

 

2

 It is significant to note that the MWCOG Climate Change Report, approved by the COG Board November 12, 

2008, mentions both evaluation of financial incentives such as congestion pricing and incentives for expanded 

transit use to reduce Vehicle Miles Traveled (VMT) and greenhouse gases (page 61). 

www.mwcog.org/publications 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 4

ØØØ 1. Introduction: Work Order Objectives 

The Federal Highway Administration (FHWA) supports operational improvements to reduce 

delay and congestion throughout the country. In many cases it has been determined that 

relatively minor reductions in peak traffic can result in major reductions in delay for all travelers. 

The FHWA wanted to test this premise for the Washington, DC region, and engaged SAIC 

(supported by the Louis Berger Group) to carry out the research study. The study approach is 

designed to answer specific research questions, as summarized in the Tables below. 

The first objective of this research is to produce empirical evidence of the changes in rush hour 

traffic demand (volumes) and delays for weekdays in early August, relative to other months of 

the year, in the Washington, DC metropolitan area. The three broad questions to be answered for 

this objective and the approaches to answering the questions are: 

Research Question Approach 

What is the amount and percentage of reductions 

in weekday peak period traffic on freeways in 

(early) August in the Washington, DC area, when 

traffic congestion is reduced significantly relative 

to other (non-summer) months? (Section 2) 

Evaluate traffic count and speed data at 

selected locations at different times and 

dates, ideally where (and when) GPS 

probe data are available to demonstrate 

free flow and congested conditions. 

What is the relationship between peak period and 

peak-hour traffic reduction and delay reduction 

on freeways, i.e., how much delay might be 

reduced by various levels of reductions in traffic? 

(Section 2) 

Graph and analyze speed – volume 

relationships to determine “tip points” for 

congestion and delay versus free flow 

conditions 

Given the above relationships, how much traffic 

needs to be taken off freeways operating at 

various levels of congestion in the metropolitan 

area, to restore free flow during rush hours? 

(Section 2) 

Calculate values and percentages for 

selected locations from analysis, above, 

extrapolate to larger regional relationship 

The second objective is to identify the approximate numbers and proportions of persons traveling 

on the congested freeways who may be able to shift their time of travel to off peak periods, or 

shift their mode of travel to high-occupancy vehicles. The three questions to be answered and the 

approaches are: 

Research Question Approach 

What is the share of travel that is made for 

various purposes other than commuting on 

congested freeways during peak periods in the 

Washington, DC area? (Section 3) 

Perform in-depth analysis on Metropolitan 

Washington Council of Governments 

(MWCOG) 1994 Household Travel 

Survey 3– identify trip chains by time of 

day, by trip purpose, and travel time. 

 

3

 MWCOG conducted a detailed household travel survey in 2007 but the data will not be available until mid-2009, 

and thus was not available for this study 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 5

Research Question Approach 

What share of these travelers may have some 

flexibility to shift their time of travel to off-peak 

periods? (Section 3) 

Identify subsets of survey with greater 

likelihood to be able to shift travel times, 

review literature for appropriate 

definitions of “discretionary trips” 

What is the potential for other modes (e.g., 

transit, vanpools, carpools, etc.) to attract 

additional mode share, if free-flow service on all 

freeways were guaranteed to these modes through 

aggressive and active traffic management 

strategies such as congestion pricing? (Section 4) 

Conduct literature review on travel 

demand management and mode shifts in 

analogous situations, summarize results 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 6

ØØØ 2. Traffic Delay Comparisons

The first research question is: What is the amount and percentage of reductions in weekday peak 

period traffic on freeways in (early) August in the Washington, DC area, when traffic congestion 

is reduced significantly relative to other (non-summer) months? Correspondingly, the general 

question was to determine the relationship between volume, speed, and delay on the freeway 

system. As part of the analysis not only did the Study Team look at traffic during August versus 

non-summer months but also other time periods such as holidays and non-school days to develop 

the speed –volume relationships. 

The Study Team evaluated PM Peak traffic volume and speed data at selected locations along I95, I-270 and I-495, derived from operational data from the CHART advanced transportation 

management system of the Maryland DOT, and archived by the University of Maryland. The 

first two of these corridors are radial with respect to regional travel, while the third one is part of 

the main circumferential route within the region. A generally similar analysis approach was used 

for each corridor. First, graphical summaries of the variation of average speed by time-of-day at 

each detector, and cumulative delay along the analysis segment were developed to identify the 

best potential days for analysis: (a) “August lite” volumes and delay, (b) September and October 

typical volumes and delay, and (c) special days with unusual traffic demand and/or use patterns. 

Second, the consistency in average speed and cumulative volumes at sequential detectors were 

studied to identify peaking patterns along the corridor as they relate to roadway features, such as 

bottlenecks or lane drops. Third, the analysis examined the paired-relationship between 

concurrent observations of volume and average speed to better understand how the relationship 

varies under various circumstances. The analysis demonstrates the following: 

• Peak period volumes are generally lower and delays less intense in August compared to peak 

period volumes and delays during typical conditions found in September and October. 

However the lower volumes do not always lead to reduced delay due to non-recurring events , 

for example, in the case of I-95, one of the sample dates in August included an incident, 

which led to widespread delays. 

• Graphs and analysis of speed – volume relationships identified “tip points” where a small 

amount of extra volume precipitates large amounts of delay from what moments before was a 

free-flow condition. The analysis found different “tip points” for each segment, which may 

vary due to differences in roadway layout and/or the mix of types of traffic. Unique aspects 

of this analysis have been to trace this phenomenon along a series of detectors, as well as 

to tie the results back to specific times of generally known variations in travel demand.

• Commonalities were identified to generally extrapolate to the broader region answers to the 

third research question of, given the above relationships, how much traffic needs to be taken 

off freeways operating at various levels of congestion, to restore free flow during rush hours? 

• The key finding is that if relatively small changes can be made in peak demand (volume) 

through various programs and strategies, such as congestion pricing, then two beneficial 

things can happen: (1) there can be relatively large decreases in congestion and delay at 

key choke points, and (2) there can be increased through-put along those roads during 

peak times of travel – thus by effectively managing the demand more travelers can be 

served per time period at higher speeds with the available fixed-supply of roadway 

capacity. 

The summary analysis for each of the three corridors is presented separately below. Additional 

detail on I-270 is provided in Appendix A. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 7

A separate analysis of AM Peak traffic volumes and speeds was also performed. AM Peak 

volumes are typically lower than PM Peak volumes, and the locations of the traffic detectors are 

not as conducive to a delay analysis. This analysis is located subsequent to the I-495 analysis. 

I-95 Northbound for PM Peak Period 

I-95 Corridor: General Locations of Detectors 

The CHART system of the Maryland DOT operates a set of traffic flow detectors along the I-95 

Corridor between the Baltimore and Washington, D.C. beltways. The five-minute summaries of 

the volume and average speed are archived by the Center for Advanced Transportation 

Technology of the University of Maryland (UMD-CATT). This analysis focuses on data from 

the six detectors of Brooklyn Bridge Road to Montgomery Road on I-95. Two other working 

detectors that were not included in the detailed analysis are located on I-95 near I-495, the 

Capital Beltway. The length of the I-95 analysis section is approximately 10.5 miles with a 

consistent directional cross-section of four lanes in each direction. The evaluation only included 

the six sites identified on the display below and does not include the two sites shown on MD 32. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 8

Traffic Characteristics 

As part of our analysis, the Study Team examined various traffic characteristics obtained from 

measured variations in five-minute volumes and five-minute average speeds at each of the six 

detectors of the analysis section. The data is nominally collected “24 x 7 x 365”, or twenty-four 

hours a day, 7 days per week, for 365 days per year. The operational requirements of CHART, 

the program for which the data is collected does not need to have data from each and every 

detector for each and every time increment. Some detectors and/or parts of the communication 

system are more reliable in the collection of the traffic flow data than others. That typically 

results in small gaps of one or a few observation time periods and sometimes for hours and even 

days on end in the archived data. Nevertheless, generally speaking there is an adequate amount 

of reliable data to identify various traffic characteristics including: (a) variation in volume by 

time of day that is used to define the “peak traffic volume periods”, (b) comparative speed range 

that can be used to define different degrees of congestion, such as free-flow/uncongested, 

slowing, slow, or jammed or stop-and-go conditions, and (c) section delay, the difference 

between the time it would take to travel the section at the observation time compared to the time 

it would take to travel that same distance at the speed-limit (Speed-Limit-Travel-Time, SLTT) or 

when traveling at free-flow. This characteristic can have its own peaking patterns that may be 

similar to or differ from volume-peaks. 

The traffic characteristic of cumulative “section delay” for a typical Thursday and Friday in 

October are plotted and are shown in Figure 2-1 below. At the speed limit of 65 mph in this 

section it takes about 9 minutes and 30 seconds (570 seconds) to travel the approximate 10.5 

miles. However, for a traveler beginning this section about 10 minutes before 5 PM on Friday 

10-12-07, the amount of calculated section delay was about 540 seconds, or about 9 minutes 

more than the expected SLTT of 9 minutes, 30 seconds. 

Figure 2-1

Section Delay for Sample Days by Time of Day 

I-95 Northbound: Section from Brooklyn Bridge Rd to Montgomery Rd

0

60

120

180

240

300

360

420

480

540

600

15 16 17 18 19

Time of Day

Section Delay -- Travel Time minus SLTT (seconds)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

Notes: . 

1. The section is about 10.5 mile long. 

2. The speed limit in this section of I-95 is 65 mph.

3. The travel time for travel at the speed limit is 9 

min 30 sec (570 seconds).

10-12-07 Fri

10-11-07 Thu

3 PM 5 PM 6 PM 7 4 PM

PM
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 9

A good understanding of the patterns of variation in delay may assist in identifying and 

analyzing the effectiveness of strategies aimed at managing the delay. For example, as shown in 

Figure 2-1, congestion on Thursday, 10-11-07 in this section was more prolonged and occurred 

later in the afternoon relative to congestion shown for Friday 10-12-07, that was more peaked 

and started earlier in the afternoon. Examination of archived data for other pairs of Thursdays 

and Fridays would show a similar set of relative peaking patterns. Thus, strategies that address 

differences between days of the week will help address the effectiveness of strategies trying to 

restore freer flowing traffic. 

The second characteristic considered was the length or duration of the peak period. During the 

light traffic days of early August, the duration of the peak within the peak period is about 90 

minutes (about 4:30 to 6:00 PM) as shown in Figure 2-2 below. For Wednesday 8-1-07, even 

though section delay was very light, for most of the 5 to 6 PM period, it was somewhat slower 

than the other times of day. However, for a typical October day of 10-11-07, the traffic data 

shows that the section delay was much slower and longer in duration extending to about 7:00 

PM. During the summer it seems that relatively more people leave work earlier and do not stay 

as late as they do in October. Thus, strategies that account for seasonal as well as time-of-day 

patterns of travel behavior will be more effective than those that do not do so. 

Figure 2-2

Section Delay for Sample Days by Time of Day 

I-95 Northbound: Section from Brooklyn Bridge Rd to Montgomery Rd

0

60

120

180

240

300

360

420

480

540

600

15 16 17 18 19

Time of Day

Section Delay -- Travel Time minus SLTT (seconds)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

Notes: . 

1. The section is about 10.5 mile long. 

2. The speed limit in this section of I-95 is 65 mph.

3. The travel time for travel at the speed limit is 9 

min 30 sec (570 seconds).

10-11-07 Thu

3 PM 5 PM 6 PM 7 4 PM

PM

8-02-07 Thu

8-16-07 Thu

8-01-07 Wed
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 10

A third traffic characteristic that affects traffic volume, speed, and delay is that of trip purpose 

and the effect trip purpose has on the temporal and spatial distribution of travel demand. An 

interesting illustration of this can be seen by examining the section delay characteristics for 

another day that same week in October, that of the Columbus Day Holiday of Monday 10-8-07. 

Even though Federal offices and some schools in the region and vicinity of this section were 

closed, section delay is seen in Figure 2-3 in the 3:30 to 5 PM time period. It is inferred that 

enough people did not have their usual trip purpose of a work trip heading home that afternoon. 

Instead, many people attended mostly to personal business and shopping, that is typical for that 

holiday. However, during the usual commute peak time of 5 to 6 PM, the section delays were as 

light as they are during the light demand days of August. Strategies for restoring freer flowing 

traffic need to address trip purpose too. 

Figure 2-3 

Volume-Speed-Delay Relationships 

The overall data set for this section of I-95 was also analyzed in more depth to address the first 

research question of, how much traffic needs to be taken off freeways operating at various levels 

of congestion to restore free flow during rush hours? 

To address that question the Study Team has taken the approach of first needing to “boil-down” 

the time-of-day variation in section delay to one indicator of delay. The measure of average 

delay per 5-minute increment over the 4-hour peak period was selected as the representative 

indicator of delay. Thus, the patterns of section delay from the prior three figures were reduced 

to one four-hour average. For example, the day with the most peak section delay of those shown 

Section Delay for Sample Days by Time of Day 

I-95 Northbound: Section from Brooklyn Bridge Rd to Montgomery Rd

0

60

120

180

240

300

360

420

480

540

600

15 16 17 18 19

Time of Day

Section Delay -- Travel Time minus SLTT (seconds)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

Notes: . 

1. The section is about 10.5 mile long. 

2. The speed limit in this section of I-95 is 65 mph.

3. The travel time for travel at the speed limit is 9 

min 30 sec (570 seconds).

3 PM 5 PM 6 PM 7 4 PM

PM

10-11-07 Thu

10-08-07 Mon 

Columbus Day

8-02-07 Thu

8-01-07 Wed
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 11

in the prior figures was that of Thursday,10-11-07, and averaging over the entire four hours 

results in a value of about 156 seconds of average delay over the four-hour peak period in excess 

of the SLTT. That is an indicator measure for the entire analysis section of 10.5 miles. In 

addition, we have a measure of the variation in demand along the section for that four-hour 

period by adding up the cumulative volumes observed at each detector. For example, nearly 

30,000 vehicles passed the detector at Brooklyn Bridge Road as the traffic the entered the section 

from the south heading northbound in the four-hour peak period on Thursday 10-11-07, while 

about 24,000 vehicles left the section at the north end at Montgomery Road during the four-hour 

peak period. 

In Figure 2-4a the Study Team graphed the average section delay versus the total four-hour 

volume for each detector for the several example days being considered. The analysis results 

shown here have the three “August-lite” samples with less peak-period volume and less peakperiod section delay than the two selected “Mid-October” sample days. Figure 2-4a also shows 

that there is a generally consistent relationship among the different detectors across the sample 

days – generally speaking as the volumes decline or increase there appears to be a linear 

relationship to the values of average delay. 

While Figure 2-4a tends to show the “traditional relationship” of volume versus delay – more 

volume – more delay, however, examination of other examples of “special days” tends to 

indicate that the nature of the relationship between volume and average delay is more complex. 

Figure 2-4a: Examination of Volume – Delay Relationships 

Volume at each Detector versus Average Delay for whole Section 

I-95 Northbound: Section from Brooklyn Bridge Rd to Montgomery Rd

10,000

15,000

20,000

25,000

30,000

0 60 120 180 240 300 360

Average Delay over the Peak Period in excess of the Speed Limit Travel Time (seconds)

PM Peak (4-Hr) Volumes @ each Detector (vehicles)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

Notes: 

1. Reliable data from some detectors were not available on some sample days. 

2. The section is about 10.5 mile long. 

3. The speed limit in this section of I-95 is 65 mph.

4. The travel time for travel at the speed limit is 9 min 30 sec (570 seconds).

@ Montgomery Rd

@ MD100 to 

Montgomery Rd

@ South of MD175

@ MD 32

@ Welcome Center

@ Brooklyn Br. Rd

Detector Located at:

8-01-07 Wed

8-02-07 Thu

8-16-07 Thu

"August" Samples

10-12-07 Fri

10-11-07 Thu

Samples for Other 

Times of the Year

• The analysis results shown here have 

the “August-lite” samples with less 

peak-period volume and less peakperiod delay than selected sample 

days for other times of the year

• However, all days do not follow the 

typical pattern – as shown in the next 

exhibit with two special sample days

Volume at each Detector versus Average Delay for whole Section 

I-95 Northbound: Section from Brooklyn Bridge Rd to Montgomery Rd

10,000

15,000

20,000

25,000

30,000

0 60 120 180 240 300 360

Average Delay over the Peak Period in excess of the Speed Limit Travel Time (seconds)

PM Peak (4-Hr) Volumes @ each Detector (vehicles)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

Notes: 

1. Reliable data from some detectors were not available on some sample days. 

2. The section is about 10.5 mile long. 

3. The speed limit in this section of I-95 is 65 mph.

4. The travel time for travel at the speed limit is 9 min 30 sec (570 seconds).

@ Montgomery Rd

@ MD100 to 

Montgomery Rd

@ South of MD175

@ MD 32

@ Welcome Center

@ Brooklyn Br. Rd

Detector Located at:

8-01-07 Wed

8-02-07 Thu

8-16-07 Thu

"August" Samples

10-12-07 Fri

10-11-07 Thu

Samples for Other 

Times of the Year

• The analysis results shown here have 

the “August-lite” samples with less 

peak-period volume and less peakperiod delay than selected sample 

days for other times of the year

• However, all days do not follow the 

typical pattern – as shown in the next 

exhibit with two special sample days
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 12

Figure 2-4a, for 3 of the 5 detectors for the two typical October days the volumes and average 

delay show an “inverse relationship” - when section delay increases, the total volume throughput 

declines somewhat. On examining the similar results for two special days – that of incident 

conditions on Thursday 8-9-07 and of Columbus Day on Monday 10-8-07, in Figure 2-4b we see 

that for the former, significantly different patterns of regional demand can result in volumes and 

section delay that are different than usual, while for the latter, the section delays increase 

significantly with little change in volume – in fact the volumes appear to be somewhat lower 

relative to the typical days of October, but significantly greater than the August-lite days. While 

the “connecting-of-the-dots” as illustrated in Figure 2-4b does nominally provide for what seems 

to be a larger continuum of conditions, it is not clear whether a more extensive analysis of many 

more samples would result in the relationship that is depicted. 

Figure 2-4b: Examination of Volume – Delay Relationships with More Data 

Returning to comparing the October to August samples, a reduction of about 10% of the volume 

(about 3,000 vehicles in the four hours) at the Brooklyn Bridge Rd detector may be sufficient to 

return traffic there to freer flowing conditions, while at the next detector of the Welcome Center, 

a reduction of traffic of about 4%, or about 1,150 vehicles may be sufficient. At the northernmost detector at Montgomery Road about an 8% volume reduction or about 2,000 vehicles 

appears to be needed. Being able to achieve significant reductions in section delay based upon 

volume reductions at MD 32 may require a reduction of about 20% or 4,500 vehicles. Perhaps 

the significant amounts of ramp-to-ramp turning movements and weaving of traffic are creating 

more conflicting movements at the MD 32 interchange area. 

Volume at each Detector versus Average Delay for whole Section 

I-95 Northbound: Section from Brooklyn Bridge Rd to Montgomery Rd

10,000

15,000

20,000

25,000

30,000

0 60 120 180 240 300 360

Average Delay over the Peak Period in excess of the Speed Limit Travel Time (seconds)

PM Peak (4-Hr) Volumes @ each Detector (vehicles)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

Notes: 

1. Reliable data from some detectors were not available on some sample days. 

2. The section is about 10.5 mile long. 

3. The speed limit in this section of I-95 is 65 mph.

4. The travel time for travel at the speed limit is 9 min 30 sec (570 seconds).

10-12-07 Fri

10-11-07 Thu

8-09-07 Thu 

Incident

Samples for Other 

Times of the Year

Samples for Unique 

Conditions

8-01-07 Wed

8-02-07 Thu

8-16-07 Thu

"August" Samples

10-08-07 Mon 

Columbus Day

@ Montgomery Rd

@ MD100 to 

Montgomery Rd

@ South of MD175

@ MD 32

@ Welcome Center

@ Brooklyn Br. Rd

Detector Located at:
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 13

Comparison of Paired-Values: The next part of the analysis examined the paired-relationships 

between concurrent observations of volume and average speed. It has been found that the data 

analyzed both for the system of detectors along the analysis section as well as for each detector 

generally replicate the types of patterns associated with the volume-speed-density curves of the 

Highway Capacity and Quality of Service Manual (HCQSM). However, the Study Team thinks 

that this analysis has a few aspects that distinguish it from the work that underlies the HCQSM. 

In particular, unique aspects of this analysis have included: (a) tracing this phenomenon along a 

series of detectors, along this 10.5 mile section, as well as (b) tying the results back to specific 

dates and times of generally known variations in travel demand. 

In Figure 2-5 and Figure 2-6 for each detector the paired-values of volumes (x-axis) and average 

speed (y-axis) per each 5-minute summary interval are plotted, respectively first for a typical day 

in August (8-1-07) and then for a typically congested day at another time of the year, shown here 

as October (10-11-07). Four hours of such data for each day are shown in this case for each of 

the six detectors. The following observations are noted based upon Figure 2-5 and Figure 2-6: 

• For each detector, 5-minute volumes and average speeds are plotted for a typical day in 

August and October. 

• The 8-1-07 sample day (Figure 2-5) shows several relatively tight clusters of points – one 

cluster for each detector. 

• In Figure 2-5 there is a wide range of 5-minute volumes varying from about 300 to about 

650 vehicles. 

• In Figure 2-5 there is a more narrow range of average speeds, compared to Figure 2-6, 

varying from about 40 to about 70 mph. 

• In Figure 2-5 the vast majority of the 5-minute paired data sets have average speeds of 55 

mph or faster (free-flowing) and only a handful of points show “slowing” average speeds 

in the 40 to 55 mph range. There was no 5-minute time period that had “slow” or “very 

congested” average speeds. 

• However, now examining the 10-11-07 sample day (Figure 2-6) 5 of the 6 clusters of 

points are relatively widely dispersed with the tightest cluster being data from the detector 

between Maryland 100 and Montgomery Rd. 

• In Figure 2-6, the range of volumes is still just as wide compared to that of Figure 2-5 

varying from about 350 vehicles to about 700 vehicles per 5-minute interval, but the range 

has shifted to the right by about 50 vehicles more for the set of paired-values. 

• In Figure 2-6 there is a much wider range of average speeds per 5-minutes compared to 

those shown in Figure 2-5, with about half of the paired-values being faster than the freeflowing speeds of 55 mph or more, with the other half or so of the observations having 

slower average speeds, which are widely and somewhat evenly dispersed between about 55 

mph to ones as low as 15 mph for 5-minutes. 

In Figures 2-5 and 2-6 there are 576 independent sets of paired-values of 5-minute volumes and 

average speed (4 hours, times 12 observations per hour, times 6 detectors, times 2 days). If all of 

those points were put on one graph and the different marker shapes and color-codes for the 

detectors were not shown, then the reader would find it just about impossible to discern the 

relationships among the volumes and average speed that are there. In the remainder of the 

analysis we do the opposite of that and highlight similarities and differences for paired-graphs 

for the pair-values of volume and average speed for 3 of the 6 detectors. Those paired-graphs 

demonstrate a general consistency with HCQSM findings and they are used to estimate the “tip 

capacity” that breaks down the flow of traffic and creates significant delays. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 14

Paired Values of Volume – Average Speed by Sample Day 

Volume-Speed Relationship at Selected CHART Detectors: 

I-95 Northbound on Wed 8-1-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Montg. Rd

@ MD100 to 

Montg. Rd

@ S. of MD175

@ MD 32

@ Welcome Ctr

@ Brooklyn Br. 

Volume-Speed Relationship at Selected CHART Detectors: 

I-95 Northbound on Thu 10-11-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Montg. Rd

@ MD100 to 

Montg. Rd

@ S. of MD175

@ MD 32

@ Welcome Ctr

@ Brooklyn Br. 

Figure 2-5: Wednesday, Aug 1, 2007

Figure 2-6: Thursday, Oct 11, 2007
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 15

The questions addressed in the remaining three sets of paired-graphs are: (1) does temporally 

increasing travel demands, reflected by marginally higher and higher 5-minute volumes, result in 

traffic congestion?, and (2) if it does, what are the specific values of demand (high volumes) that 

we should manage to avoid through application of various strategies, so as to maintain the freerflowing traffic conditions? 

On the following page, just the 5-minute paired-values of volume and average speed of I-95 at 

the Welcome Center are plotted paying attention to the temporal sequence of the observations, 

which is graphically illustrated by the line that is “connecting-the-dots”. In Figures 2-7 and 2-8 it 

is demonstrated graphically that a small degree of marginally increasing demand (volume) is a 

main ingredient that results in the precipitation of congested and slow average speeds for one or 

more of the subsequent 5-minute time intervals. A key conceptual consideration is to examine 

the relationship among the different paired-values of the quantity of the traffic flow (volume) 

relative to the quality of the traffic flow (average speed) sequentially through time. 

It has long been observed and reported in the HCQSM and elsewhere that when the quantity of 

flow is very high approaching the capacity of the roadway that further small increases in the 

quantity of flow can result in a quick and sustained reduction in the quality of the traffic flow, as 

measured by the average speed. That in turn has a negative feedback on subsequent time 

intervals when the same quantity of flow can no longer be served and queues form back 

upstream from the point of constriction. A series of relatively closely spaced detectors 

summarizing flow characteristics at relatively short time intervals can generally discern such 

traffic flow behavior, as illustrated in Figure 2-7 and 2-8 although the fixed location of the 

detector may not be exactly where the traffic flow initially broke down. 

Another factor in precipitating a sudden decrease in traffic speed is usually some random event 

such as very short-term driver inattentiveness. Drivers taking their eyes off the road ahead for a 

very small interval may result in drivers braking extra hard when their eyes are back on the road 

and perceive that the vehicles ahead are now too close. That hard braking can cause the driver 

behind to react in a similar fashion, and the driver behind that one to do the same, and so on. A 

“shock-wave” of brake lights ahead can quickly propagate upstream at speeds that approach that 

of the forward movement of the traffic flow. This phenomenon of a “reverse compression shock 

wave” moving back upstream in the flow, at speeds of 100 mph or more relative to the forward 

speed of the vehicles can result in minor and even severe crashes taking place. 

In Figure 2-7 for 8-1-07 it shows that at the “Welcome Center” detector on I-95, the maximum 5 

minute volume approaches but does not exceed about 650 vehicles per 5-minute interval. 

However, in Figure 2-8 the 5-minute volumes initially start to exceed the capacity, which is 

somewhat elastic. However, for a short period of time the flow is not a sustained enough flow to 

cause an immediate break down. Shortly afterward the volume increases again and there is 

enough sustained volume and within a twenty minute period to cause the average speed 

decreases to about 25 mph and then 10 minutes later it decreases again to about 15 mph. It then 

took over an hour and a half more before the average speeds returned to free flow levels at about 

7 PM. The maximum observed flow at this location was about 690 vehicles, enabling the 

maximum flow per lane to be calculated. In the I-95 section there are 4 through lanes. Expanding 

the 5-minute volume of 690 vehicles by 12 (12 – 5 minute time periods per hour), the hourly 

maximum volume is about 8,300 vehicles per hour. Dividing by the 4 lanes gives a maximum 

volume of about 2,075 vehicles per lane per hour. This corresponds well with the HCQSM’s 

maximum volume of 2,400 passenger cars per lane under ideal condition. If the I-95 data is 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 16

adjusted for trucks and the peak hour factor, the recorded maximum traffic volume is close to the 

theoretical HCQSM maximum flow. 

In Figure 2-8 it should also be noted that during congested flow the traffic flow is much lower at 

the slower speeds. As traffic becomes congested there is a drop in the maximum flow rate of 10 

to 20% versus the flow rate just prior to a traffic breakdown. By avoiding traffic breakdown not 

only does the traffic travel at higher speeds, but under the non-traffic breakdown conditions a 

higher throughput volume may be maintained, serving more travelers in less time. 

Marginally Higher Volumes Cause Congestion? 

Volume-Speed Relationship at Selected CHART Detectors:

 I-95 Northbound on Wed 8-1-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Welcome Ctr

Volume-Speed Relationship at Selected CHART Detectors:

 I-95 Northbound on Thu 10-11-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Welcome Ctr

• Figure 2-7 shows that traffic at the “Welcome Center” flows freely when the volume 

(demand) is less than 650 vehicles per 5-minutes. 

• The connect-the-points-graph (Figure 2-8) shows that marginally higher volumes of 

between 650 and 700 vehicles seems to result in a break-down of flow to speeds to under 

20 mph and lasts for two hours. 

Figure 2-7: Wednesday, Aug 1, 2007 

Figure 2-8: Thursday, Oct 11, 2007 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 17

• However, as shown in the next set of paired-graphs on the following page, for the 

downstream detector at MD 32, the breakdown there might have instead been the cause. 

Each Freeway Segment has its Own Capacity 

Volume-Speed Relationship at Selected CHART Detectors:

 I-95 Northbound on Thu 10-11-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 32

Volume-Speed Relationship at Selected CHART Detectors:

 I-95 Northbound on Wed 8-1-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 32

Figure 2-9: Wednesday, Aug 1, 2007 

Figure 2-10: Thursday, Oct 11, 2007 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 18

• Figure 2-9 shows that at the MD 32 detector when volumes stay under 400 vehicles per 5-

minutes there is no slowdown; at 450 vehicles there is a local slowdown without enough 

spill-back to affect the traffic flow at the “Welcome Center” segment. 

• However, in Figure 2-10, at a demand of 500 or more vehicles per 5-minute period at MD 

32, the queue may be long enough to back-up upstream and precipitate a slowdown 

upstream in the Welcome Center segment of the roadway section. 

In Figures 2-11 and 2-12 we again are able to show and estimate the maximum flow for the 

roadway section just prior to congestion breakdown. Figure 2-12 also demonstrates the 10 to 

20% reduction in traffic flow due to being in a congested flow versus a non-congested flow. The 

relationships shown in this series of figures are important to understanding and estimating the 

reductions in delay with very modest reductions in traffic volumes. 

Next Freeway Segment also has its Own Capacity 

Volume-Speed Relationship at Selected CHART Detectors:

I-95 Northbound on Thu 8-9-07

0

10

20

30

40

50

60

70

80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ S. of MD175

Volume-Speed Relationship at Selected CHART Detectors:

I-95 Northbound on Wed 8-1-07

0

10

20

30

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50

60

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80

0

100200300400500600700800

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ S. of MD175

Figure 2-11: Wednesday, Aug 1, 2007 

Figure 2-12: Thursday, Aug 9, 2008 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 19

• Graphs show that at the detector south of MD 175 the breakdown capacity seems to be 

about 575 vehicles per 5-minutes. 

• While the consistency of such a capacity value on a day-to-day basis is beyond the scope 

of this research, the sample day of Thursday Aug 9, 2007 (Figure 2-12) indicates a higher 

value of about 650 vehicles per 5-minutes. That higher capacity value corresponds to an 

hourly maximum observed flow of about 1,950 vehicles. 

I-270 Corridor 

I-270 Corridor: General Locations of Detectors 

Parts of the I-270 Corridor were also analyzed to evaluate and confirm many of the traffic 

characteristics and speed-volume relationships discussed in the previous section for parts of I-95. 

In a similar fashion this analysis relied on the CHART system of the Maryland DOT that 

operates a set of traffic flow detectors along the I-270 Corridor in Frederick and Montgomery 

Counties. The five-minute summaries of the volume and average speed are also archived by the 

Center for Advanced Transportation Technology of the University of Maryland (UMD-CATT). 

This analysis focuses on data from the six detectors on I-270 from I-370 north to MD 109, in 

Montgomery County, a distance of about 12.9 miles. The general location and names for those 

detectors are shown in Figure 2-13 below. Seven of the I-270 detectors located in Frederick 

County are shown here for informational purposes. It should be noted that the number and use of 

lanes for this section of I-270 changes a few times and differs for southbound and northbound 

travel. One of those lane changes highlighted in the analysis is the lane-drop from 3 to 2 lanes in 

the Northbound direction between MD 121 and Comus Road, where the concurrent-flow High 

Occupancy Vehicle lane also ends, which operates weekdays between 3:30 and 6:30 PM. 

Congested conditions are often found at and near this lane drop. 

Figure 2-13 

1 to 20 20 to 35 35 to 50> 50 mph

Note: the speed ranges shown reflect a 

PM Peak Period northbound sample of 

GPS Probe Data for the corridor, not 

any overall assessment
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 20

Traffic Characteristics: 

As part of our analysis, the Study Team examined various traffic characteristics obtained from 

measured variations in five-minute volumes and five-minute average speeds at each of the six 

detectors of the analysis section of I-270. Similar to I-95 discussed previously, this operational 

data is nominally collected “24 x 7 x 365” and the other features previously discussed also apply. 

Generally speaking there is an adequate amount of reliable data to identify various traffic 

characteristics including: (a) variation in volume by time of day used to define the “peak traffic 

volume periods”, (b) comparative speed range that can be used to define different degrees of 

congestion, such as free-flow/uncongested, slowing, slow, or jammed or stop-and-go conditions, 

and (c) section delay, the difference between the time it would take to travel the section at the 

observation time compared to the time it would take to travel that same distance at the speedlimit (Speed-Limit-Travel-Time, SLTT) or when traveling at free-flow. This last characteristic 

can have its own peaking patterns that may be similar to or differ from volume-peaks. This latter 

traffic characteristic of cumulative “section delay” for a typical Thursday and Friday in October, 

as well as a Tuesday in April are plotted and are shown in Figure 2-14 below. For this corridor 

the analysis uses a 5-hour peak period of 2 to 7 PM as congested section delays seems to be more 

prolonged in this corridor than in the I-95 Corridor. As noted above, this analysis section of I270 is about 12.9 miles long. The speed limit there is mostly 65 mph and the travel time to travel 

at that speed is just under 12 minutes (11 minutes, 56 seconds, or 716 seconds). For a traveler 

beginning this section about before 4 PM on Friday 10-12-07, the amount of calculated section 

delay was about 720 seconds, or about 12 minutes more than the expected SLTT of coincidently 

also about 12 minutes. 

Figure 2-14: Typical Peaking of Section Delay for the Analysis Section of I-270 

Section Delay for Sample Days by Time of Day 

I-270 Northbound: Section from I-370 to MD 109

0

60

120

180

240

300

360

420

480

540

600

660

720

780

840

900

14 15 16 17 18 19

Time of Day

Section Delay -- Travel Time minus SLTT (seconds)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

1. The section is about 12.9 mile long and the speed 

limit in this section of I-270 is mostly 65 mph.

2. The travel time for travel at the speed limit is 11 

min 56 sec (716 seconds).

3 PM 5 PM 6 PM 7 4 PM

PM

2 PM

10-12-07 Fri

4-29-08 Tue

10-11-07 

Thu

HOV Lane in Operation
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 21

A good understanding of the patterns of variation in delay can better inform strategies aimed at 

managing that delay. For example, as shown in Figure 2-14 above, congestion on Thursdays is 

usually more prolonged and later in the afternoon relative to congestion on Fridays, which is 

usually more peaked and starts earlier. This pattern was also found in the I-95 Corridor analysis. 

For the Tuesday example of 4-29-08, the peak is even later in the PM. Strategies that address 

differences between days of the week will help the effectiveness of other strategies trying to 

restore freer flowing traffic. 

Another characteristic considered was the length or duration of the peak period. Even during the 

lighter traffic days of August, the duration of the peak within the peak period is about 3 hours 

shown in Figure 2-15 below. Even one of the least congested summer days examined of 7-30-07 

shows a peak of about 30 minutes duration at about 5:30 PM. For the typical October day of 10-

11-07 Figure 2-15 shows that the duration of the peak section delay is even longer and starting 

before 3 PM and extending to nearly 7 PM. Thus strategies that account for seasonal as well as 

time-of-day patterns of travel behavior will be more effective than those that do not do so. 

Figure 2-15: Peking of Section Delay During August-Lite Traffic Conditions 

A third traffic characteristic that affects traffic volume, speed, and delay is that of trip purpose 

and the effects that systemic changes in trip purpose have on the temporal and spatial distribution 

of travel demand. An interesting illustration of this can be seen by examining the I-270 section 

delay characteristics for two “special days”. The first of these special days is the Columbus Day 

Holiday of Monday 10-8-07, which was also examined in the I-95 analysis. For the I-270 

analysis even though Federal offices are closed as are some schools in the region, the schools in 

Montgomery County remain open for parent visitations. The section delay for Columbus Day is 

Section Delay for Sample Days by Time of Day 

I-270 Northbound: Section from I-370 to MD 109

0

60

120

180

240

300

360

420

480

540

600

14 15 16 17 18 19

Time of Day

Section Delay -- Travel Time minus SLTT (seconds)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

1. The section is about 12.9 mile long and the speed 

limit in this section of I-270 is mostly 65 mph.

2. The travel time for travel at the speed limit is 11 

min 56 sec (716 seconds).

8-06-07 Mon

8-13-07 Mon

7-30-07 Mon

3 PM 5 PM 6 PM 7 4 PM

PM

2 PM

10-11-07 Thu

HOV Lane in Operation
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 22

seen in Figure 2-16 shows that I-270 was still congested for about 2 hours between about 4:30 

and about 6:30 PM. This suggests that many people still traveled based on their usual trip 

purpose of a work trip heading home that afternoon, even though a significant proportion of 

workers (Federal employees) had the day off. Perhaps that is why during the first half of the PM 

peak period from about 2 to 4:30 PM the section delay was much lighter than usual for October 

and even lighter than an August-lite day of Monday 8-6-07. In addition, many people who were 

off work probably attended to personal business and shopping, that is typical for that holiday. 

Strategies for restoring freer flowing traffic need to anticipate and address trip purpose as well. 

Figure 2-16: Relative Changes in Section Delay for the Columbus Day Holiday

The second of these “special days” systemically affecting travel demand is somewhat unique to 

Montgomery County and the analysis for this section of I-270 appears to be sensitive to those 

systemic changes. A relatively significant portion of residents (perhaps 10 to 15%) observe the 

religious holiday of Rosh Hashanah and do not go to work on that day, which was Thursday, 9-

13-07 in 2007. In addition, for the past few decades if that Holiday falls on a weekday then the 

schools are closed county-wide. About 25 % of the households in the County have school age 

children and a significant proportion of the adults from those households chose not to work and 

remain home with their children. In addition, for many of the observers of the religious Holiday, 

there is a custom of visiting relatives and friends throughout the afternoon after the religious 

services are over resulting in higher than normal social trips and travel than normally occurs on a 

weekday. Figure 2-17 below shows the effects of the combination of these relatively large and 

systemic changes in travel demand on the section delay along I-270. The peak delay occurred 

between 2:30 and 4:30 PM, which probably corresponded to the extra amount of social travel 

Section Delay for Sample Days by Time of Day 

I-270 Northbound: Section from I-370 to MD 109

0

60

120

180

240

300

360

420

480

540

600

14 15 16 17 18 19

Time of Day

Section Delay -- Travel Time minus SLTT (seconds)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

1. The section is about 12.9 mile long and the speed 

limit in this section of I-270 is mostly 65 mph.

2. The travel time for travel at the speed limit is 11 

min 56 sec (716 seconds).

10-11-07 Thu

10-08-07 Mon 

Columbus Day

8-06-07 Mon

3 PM 5 PM 6 PM 7 4 PM

PM

2 PM

HOV Lane in Operation
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 23

and trip making. On the other hand, during the usual commuter peak-times, there in essence was 

no section delay and the traffic conditions were more like those associated with weekend travel. 

Because of the relatively uniformly light traffic congestion along I-270 on that religious Holiday, 

and the temporal and spatial changes in travel due to the shifts in trip purpose and destination, 

the throughput of traffic was high, which will be reviewed later in the analysis summary. Due to 

those high volumes of throughput and the low congestion, and because data was available at all 

detector locations, September 13, 2007 was selected as a “benchmark” for non-congested time 

periods for I-270 instead of one of the August-lite days previously examined. Appendix A in this 

document provides the detailed speed/volume plots for each detector site for the “August-lite” 

days that were considered plus discussion as to the relative merits of each day and the rationale 

for the September 13 selection. 

Figure 2-17: Relative Changes in Section Delay for the Religious Holiday Example 

Volume-Speed-Delay Relationships 

The overall data set for this section of I-270 was also analyzed in more depth to address the first 

research question of, how much traffic needs to be taken off freeways operating at various levels 

of congestion to restore free flow during rush hours? 

To address that question the Study Team has taken the approach of first needing to “boil-down” 

the time-of-day variation in section delay to one indicator of delay. The measure of average 

delay per 5-minute increment over the 5-hour peak period was selected as the representative 

indicator of delay. Thus, the patterns of section delay from the prior four figures were reduced to 

Section Delay for Sample Days by Time of Day 

I-270 Northbound: Section from I-370 to MD 109

0

60

120

180

240

300

360

420

480

540

600

14 15 16 17 18 19

Time of Day

Section Delay -- Travel Time minus SLTT (seconds)

HOV Lane in Operation

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

1. The section is about 12.9 mile long and the speed 

limit in this section of I-270 is mostly 65 mph.

2. The travel time for travel at the speed limit is 11 

min 56 sec (716 seconds).

3 PM 5 PM 6 PM 7 4 PM

PM

2 PM

10-11-07 Thu

9-13-07 Thu 

Relig.Holiday

8-06-07 Mon
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 24

one five-hour average value. For example, the day with the most peak section delay of those 

shown in the prior figures was that of Friday,10-12-07, and averaging over the entire five hours 

results in a value of about 280 seconds of average delay over the five-hour peak period in excess 

of the SLTT. That is an indicator measure for the entire analysis section of 12.9 miles. 

In addition, we can measure the variation in demand along this analysis section of I-270 for that 

five-hour period by adding up the cumulative volumes observed at each of the six detectors. For 

example, about 27,500 vehicles passed the detector at MD 118 (Germantown Road) and that 

appears to be the location of the maximum quantity of flow along this analysis section of I-270 

based upon the data from the detectors. However, since there is a long spatial gap of about 5.25 

miles between the prior detector at the Express Lanes at I-370 and the one at MD 118, there may 

actually be a higher volume location based upon other data sources. 

In Figure 2-18a the Study Team graphed the average section delay versus the total five-hour 

volume for each detector for the several example days being considered. The analysis results 

shown there have the three “August-lite” samples (7-30-07, 8-6-07, and 8-13-07) with less peakperiod volume and less peak-period section delay than the three selected sample days for other 

times of the year (10-11-07, 10-12-07, and 4-29-08). Figure 2-18a also shows that there is a 

generally consistent relationship among the different detectors across the sample days – 

generally speaking as the volumes decline or increase there appears to be a linear relationship to 

the values of average delay. However, for two of the detector locations the relationship seems 

less strong, as discussed more next. 

Figure 2-18a: Relationship between Total Volume and Average Section Delay for I-270 

Volume at each Detector versus Average Delay for whole Section 

I-270 Northbound: Section from I-370 to MD 109

10,000

15,000

20,000

25,000

30,000

0 60 120 180 240 300 360 420 480 540 600

Average Delay over the Peak Period in excess of the Speed Limit Travel Time (seconds)

PM Peak (5-Hr) Volumes @ each Detector (vehicles)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

@ MD 109

@ Comus Rd

@ MD 121

@ Truck Area

@ MD 118

@ I-370 - Express 

Lanes only

Detector 

Located at:

Notes: 

1. Data from some detectors were not available on some sample days. 

2. The section is about 12.9 mile long. 

3. The speed limit in this section of I-270 is mostly 65 mph.

4. The travel time for travel at the speed limit is 11 min 56 sec (716 seconds).

10-11-07 Thu 10-12-07 Fri

Samples for Other 

Times of the Year

4-29-08 Tue

7-30-07 

Mon

8-13-07 Mon

8-06-07 

Mon

"August-Lite" 

Samples
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 25

In Figure 2-18a above, for the detectors at “I-370 – Express Lanes Only”, and at the “Truck 

Weigh Station” there appears to be a less firm relationship between total volume average section 

delay in that the apparent linearity for the other four seems more independent at these two 

locations. For the detector at I-370, the archived data from UMD-CATT also had a set of values 

of 5-minute volumes and average speed for the “Local Lanes” at I-370. The study team chose to 

ignore that data set as we reasoned that we were interested in the variation in “mainline” 

volumes, speed, and delay and thus traffic flow of the local lanes represented a different set of 

traffic flows for our study purposes. At the truck weigh station the detector is positioned after the 

off ramp and before the on ramp from the weigh station. Thus traffic that turns into that area 

does not get counted in either the total volumes or the average speed. 

For purposes of this part of the analysis, we feel comfortable with excluding the information for 

those two detectors in a revised version of the graph, which is termed Figure 18b given below. In 

addition, while Figure 2-18a tends to show the “traditional relationship” of volume versus delay 

– more volume – more delay, however, examination of other examples of three distinct “special 

days” in Figure 18b tends to indicate that when we “connect-the-dots” that the nature of the 

relationship between volume and average delay is perhaps richer in information and more 

complex. 

Figure 2-18b: Relationship between Total Volume and Average Section Delay for I-270 for Selected 

Detectors and a Range of “Special Days” 

Before returning to comparing the October to August samples, we need to put the three “special 

days” in context. We have already identified and partially discussed two of them: Monday 10-

08-07, Columbus Day, and Thursday, 9-13-07, which was a religious holiday. Both of those days 

had systemic changes in significant components of the overall regional travel demand so it is not 

Volume at each Detector versus Average Delay for whole Section 

I-270 Northbound: Section from I-370 to MD 109

10,000

15,000

20,000

25,000

30,000

0 60 120 180 240 300 360 420 480 540 600

Average Delay over the Peak Period in excess of the Speed Limit Travel Time (seconds)

PM Peak (5-Hr) Volumes @ each Detector (vehicles)

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

@ MD 109

@ Comus Rd

@ MD 121

@ MD 118

Detector 

Located at:

10-11-07 Thu 10-12-07 Fri

Samples for Other 

Times of the Year

4-29-08 Tue

7-30-07 

Mon

8-13-07 Mon

8-06-07 

Mon

"August-Lite" 

Samples

10-08-07 

Mon 

Columbus 

Day

9-13-07 

Thu; Rosh 

Hashanah 

School 

Holiday

9-14-07 Fri 

Weekend 

Get-away
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 26

surprising that the values for total volume and average section delay do not “fall in line” with the 

more normal traffic days with consistent demand patterns. The third “special day” that is shown 

in Figure 2-18b is the day after the last one mentioned – that of Friday 9-14-07. For that day, this 

analysis section of I-270 experienced the most congested conditions of the samples examined for 

this analysis – and as shown in the Figure it had an average section delay of about 480 seconds 

(about 8 minutes) for over the whole 5-hour time period studied. That day is the one most to the 

right in Figure 2-18b. That sample tends to illustrate that more congestion can have a negative 

feedback and result in less throughput within a fixed time period than other less congested 

sample days. That pattern appears to be the case for each of the four detector locations that are 

shown in Figure 2-18b. 

Yet, for the left side of that Figure, the relationships seen indicate that by better managing and 

reducing demand, there can be significant reductions in the amount of congestion and delay 

associated with that corridor. The particular amounts of demand reductions needed for the four 

locations associated with these detectors is the following going northbound in the flow direction 

in the PM: 

• A reduction of about 4% of the volume (about 1,000 vehicles in the five-hours) is needed 

in the vicinity of the MD 118 Detector and may be sufficient to return traffic there to freer 

flowing conditions, 

• At the next detector at MD 121, a reduction of traffic of about 15%, or about 2,900 

vehicles over the five-hour period may be sufficient. 

• In the vicinity of the next detector at Comus Road about a 5% reduction in the five-hour 

volume is needed, or about 800 vehicles to return traffic there to freer flowing conditions 

• At the northern-most detector at MD 109 about a 7% volume reduction or about 1,700 

vehicles over the five-hour period appears to be needed. 

Perhaps the larger and more difficult reduction would be needed in the vicinity of the MD 121 

detector because: (a) that is where the number of lanes drops from 3 to 2 which results in more 

traffic friction, and (b) during the 3:30 to 6:30 PM period the HOV lane ends there as well. The 

users of the HOV lane, who are in the left lane, do not need to merge to the right. Rather the right 

most traffic lane is the one that is dropped and the users of that lane need to merge with the 

traffic in the center lane. 

Comparison of Paired-Values: The next part of the analysis examined the relationships 

between concurrent observations of paired-values of five-minute volume and average speed. It 

has been found that the data analyzed both for the system of detectors along this analysis section 

of I-270, as well as for each detector, generally replicate the types of patterns associated with the 

volume-speed-density curves of the HCQSM. However, the Study Team thinks that this analysis 

has a few aspects that distinguish it from the work that underlies the HCQSM. In particular, 

unique aspects of this analysis have included: (a) tracing this phenomenon along a series of 

detectors, along this 12.9 mile section, as well as (b) tying the results back to specific dates and 

times of generally known variations in travel demand. 

In Figure 2-19 and Figure 2-20 for each detector the paired-values of volumes (x-axis) and 

average speed (y-axis) per each five-minute summary interval are plotted, respectively first for 

an identified day of little congestion of Thursday 9-13-07, given in Figure 2-19. As noted and 

discussed above, that day was a religious Holiday for many residents in the corridor, and many 

others had changed travel patterns due to the schools being closed for the day. Figure 2-20 shows 

the similar paired-values for a typical congested day of Thursday 10-11-07. Five hours of such 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 27

data for each day are shown in this case for each of the six detectors. The following observations 

are noted based upon Figure 2-19 and Figure 2-20: 

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area

@ MD 109

Thursday Sept 13, 2007 Thursday Sept 13, 2007

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area

@ MD 109

Thursday Sept 13, 2007 Thursday Sept 13, 2007

Figure 2-19: Paired-Values of Volume and Average Speed for Thursday 9-13-07

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area @ MD 109

Thursday Oct 11, 2007 Thursday Oct 11, 2007

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area @ MD 109

Thursday Oct 11, 2007 Thursday Oct 11, 2007

 

Figure 2-20: Paired-Values of Volume and Average Speed for Thursday 10-11-07
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 28

• For each detector 5-minute volumes and average speeds are plotted for a typical day in 

October of Thursday 10-11-07, and a lightly congested day of Thursday 9-13-07. 

• The 9-13-07 sample day (Figure 2-19) shows several relatively tight clusters of points – 

one cluster for each detector. 

• In Figure 2-19 there is a wide range of 5-minute volumes varying from about 200 to about 

575 vehicles. (MD 118 detector had a peak volume of 583 vehicles per 5-minutes.) 

• In Figure 2-19 there is a more narrow range of average speeds, compared to Figure 2-20, 

varying from about 50 to over 70 mph. 

• In Figure 2-19 the vast majority of the 5-minute paired data sets have average speeds of 55 

mph or faster (free-flowing) while about 15 to 20 points show “slowing” average speeds in 

the 50 to 55 mph range. There was no 5-minute time period that had “slow” or “very 

congested” average speeds. 

• However, the 10-11-07 sample day (Figure 2-20) 5 of the 6 clusters of points are relatively 

widely dispersed with the tightest cluster being data from the detector located near MD 

109, at the northern end of this analysis section. 

• In Figure 2-20, the range of volumes is still just as wide compared to that of Figure 2-19 

also varying from about 225 vehicles to about 575 vehicles per 5-minute interval. 

• In Figure 2-20 there is a much wider range of average speeds per 5-minutes compared to 

those shown in Figure 2-19, with most of the paired-values being faster than the freeflowing speeds of 55 mph or more, with the remainder of the observations having slower 

average speeds, which are widely and somewhat evenly dispersed between about 55 mph 

to ones as low as 17 mph for 5-minutes. 

In Figures 2-19 and 2-20 taken together there are 720 independent sets of paired-values of 5-

minute volumes and average speed (5 hours, times 12 observations per hour, times 6 detectors, 

times 2 days). If all of those points were put on one graph and the different marker shapes and 

color-codes for the detectors were not shown, then the reader would find it very difficult to 

discern the relationships among the volumes and average speed that are there when such 

distinctions are accounted for. In the remainder of the analysis we do the opposite of that and 

highlight the similarities and differences for “paired-graphs” for the pair-values of volume and 

average speed for each of the six of the detectors taken one at a time and in sequence. For those 

paired-graphs we also “connect-the-points” sequentially in time so as to focus on the temporal as 

well as the spatial variations in how the paired-values of volume and average speed change over 

the five-hour PM peak period. Those paired-graphs demonstrate a general consistency with 

HCQSM findings and they are used to estimate the “tip capacity” that breaks down the flow of 

traffic and creates significant delays. 

The following series of figures (Figures 21a and b through Figures 26a and b) compare the 

paired-values for each detector in sequence heading northbound from I-370. Text annotations are 

added to the figures in order to have them appear as large graphically as feasible for clarity of 

viewing their content. However, the pairs are kept on the same page to better enable the readers 

to more easily do their own comparisons between the paired-graphs and among the different 

paired sets, which may also help readers who may have difficulty seeing different colors. After 

this series of figures, Figure 27 shows six similar graphs of paired-values of five-minute volumes 

and average speed for Friday, 9-14-07. On that day in the afternoon and evening there was 

intensive “get-away congestion” along most of I-270 for many hours and each of the 6 detectors 

in this analysis section of I-270 operated at or near the capacity of the roadway at those locations. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 29

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07, for Detector @ I-370

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ I-370

Figure 21a: Thursday Sept 13, 2007 Figure 21a: Thursday Sept 13, 2007

Shows that traffic at the I-370 

detector flows freely 

Volume (demand) is no more than 

420 vehicles per 5-minutes that day

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07, for Detector @ I-370

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ I-370

Figure 21a: Thursday Sept 13, 2007 Figure 21a: Thursday Sept 13, 2007

Shows that traffic at the I-370 

detector flows freely 

Volume (demand) is no more than 

420 vehicles per 5-minutes that day

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ I-370

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ I-370

Figure 21b: Thursday Oct 11, 2007 Figure 21b: Thursday Oct 11, 2007

Higher volumes of about 540 veh per 5-

min are associated with a slowing of flow 

to speeds near 40 mph – happened twice

Overall, on 10-11-07 the volumes were 

greater and the speeds slower than those 

for 9-13-07

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ I-370

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ I-370

Figure 21b: Thursday Oct 11, 2007 Figure 21b: Thursday Oct 11, 2007

Higher volumes of about 540 veh per 5-

min are associated with a slowing of flow 

to speeds near 40 mph – happened twice

Overall, on 10-11-07 the volumes were 

greater and the speeds slower than those 

for 9-13-07
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 30

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07, for Detector @ MD118

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 118

Figure 22a: Thursday Sept 13, 2007 Figure 22a: Thursday Sept 13, 2007

Shows that at the MD 118 detector 

5-min veh volumes of just over 570 

veh can be obtained and still keep 

speed fairly free flowing at 50-55 

mph or more

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07, for Detector @ MD118

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 118

Figure 22a: Thursday Sept 13, 2007 Figure 22a: Thursday Sept 13, 2007

Shows that at the MD 118 detector 

5-min veh volumes of just over 570 

veh can be obtained and still keep 

speed fairly free flowing at 50-55 

mph or more

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ MD118

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 118

Figure 22b: Thursday Oct 11, 2007 Figure 22b: Thursday Oct 11, 2007

Shows that at a demand just above of 575 

to 580 veh per 5-min at MD 118, seems to 

precipitate slow speeds to 30 mph 

The resulting queue does backup 

upstream in the direction of the I-370 

segment of the roadway section

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ MD118

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 118

Figure 22b: Thursday Oct 11, 2007 Figure 22b: Thursday Oct 11, 2007

Shows that at a demand just above of 575 

to 580 veh per 5-min at MD 118, seems to 

precipitate slow speeds to 30 mph 

The resulting queue does backup 

upstream in the direction of the I-370 

segment of the roadway section
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 31

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07, for Detector @ MD121

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

Figure 23a: Thursday Sept 13, 2007 Figure 23a: Thursday Sept 13, 2007

While on 9-13-07, I-270 was lightly 

congested overall, the graph on the 

left shows that traffic flow did begin 

to reach capacity at the MD121 

detector, where the highest volume 

was 427 vehicles per 5-min

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07, for Detector @ MD121

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

Figure 23a: Thursday Sept 13, 2007 Figure 23a: Thursday Sept 13, 2007

While on 9-13-07, I-270 was lightly 

congested overall, the graph on the 

left shows that traffic flow did begin 

to reach capacity at the MD121 

detector, where the highest volume 

was 427 vehicles per 5-min

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ MD121

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

Figure 23b: Thursday Oct 11, 2007 Figure 23b: Thursday Oct 11, 2007

For a more typical day 

volumes reached 432 veh

per 5-min and speed slowed 

to 20 to 50 mph for much of 

the 5-hour period

The congested conditions 

were enough to back up 

traffic towards and reach the 

upstream detector at MD118, 

which precipitated the 

congestion there, but it 

lasted only for about 30 

minutes at MD 118

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ MD121

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

Figure 23b: Thursday Oct 11, 2007 Figure 23b: Thursday Oct 11, 2007

For a more typical day 

volumes reached 432 veh

per 5-min and speed slowed 

to 20 to 50 mph for much of 

the 5-hour period

The congested conditions 

were enough to back up 

traffic towards and reach the 

upstream detector at MD118, 

which precipitated the 

congestion there, but it 

lasted only for about 30 

minutes at MD 118
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 32

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Mon 8-06-07, for Detector @ Comus Rd

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Comus Rd

Figure 24a: Thursday Sept 13, 2007 Figure 24a: Thursday Sept 13, 2007

At Comus Rd when volume spiked at 

411 veh per 5-min, average speed 

slowed very slightly to under 60 mph; 

Volume then went back to the 360 per 

5-min range and average speeds were 

60 mph or more 

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Mon 8-06-07, for Detector @ Comus Rd

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Comus Rd

Figure 24a: Thursday Sept 13, 2007 Figure 24a: Thursday Sept 13, 2007

At Comus Rd when volume spiked at 

411 veh per 5-min, average speed 

slowed very slightly to under 60 mph; 

Volume then went back to the 360 per 

5-min range and average speeds were 

60 mph or more 

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ Comus Rd

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Comus Rd

Figure 24b: Thursday Oct 11, 2007 Figure 24b: Thursday Oct 11, 2007

Slow-downs from MD 121 were such that slow flows of less 

than 50 mph persisted 0.9 miles north here at Comus Rd; 

and as seen for the next detector continued there as well

There were a few periods of volume of 360 to 364 veh per 5-

min, and whether the slower speeds were the result is not 

clear; other summaries for 9-14-07 get-away traffic seem to 

show a capacity of about 367 veh per 5-min there

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ Comus Rd

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Comus Rd

Figure 24b: Thursday Oct 11, 2007 Figure 24b: Thursday Oct 11, 2007

Slow-downs from MD 121 were such that slow flows of less 

than 50 mph persisted 0.9 miles north here at Comus Rd; 

and as seen for the next detector continued there as well

There were a few periods of volume of 360 to 364 veh per 5-

min, and whether the slower speeds were the result is not 

clear; other summaries for 9-14-07 get-away traffic seem to 

show a capacity of about 367 veh per 5-min there
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 33

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ Truck Area

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Truck Area

Figure 25b: Thursday Oct 11, 2007 Figure 25b: Thursday Oct 11, 2007

Upstream slow-downs and “turbulence”

continued to result in some slower traffic 

here, but it had mostly recovered; and did 

almost fully recover by MD 109, the next 

detector

Graph for 9-14-07 (see next page) shows a 

capacity value of 410 veh per 5-min here

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07, for Detector @ Truck Area

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Truck Area

Figure 25b: Thursday Oct 11, 2007 Figure 25b: Thursday Oct 11, 2007

Upstream slow-downs and “turbulence”

continued to result in some slower traffic 

here, but it had mostly recovered; and did 

almost fully recover by MD 109, the next 

detector

Graph for 9-14-07 (see next page) shows a 

capacity value of 410 veh per 5-min here

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Mon 8-06-07, for Detector @ Truck Area

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Truck Area

Figure 25a: Thursday Sept 13, 2007 Figure 25a: Thursday Sept 13, 2007

At the Truck Weigh Station detector 

the breakdown capacity seems to 

be about 400 vehicles per 5-minutes

highest volume of 388 per 5-min, 

smooth and fast free flowing 

speeds were obtained at this 

detector, and continued to MD 109

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Mon 8-06-07, for Detector @ Truck Area

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Truck Area

Figure 25a: Thursday Sept 13, 2007 Figure 25a: Thursday Sept 13, 2007

At the Truck Weigh Station detector 

the breakdown capacity seems to 

be about 400 vehicles per 5-minutes

highest volume of 388 per 5-min, 

smooth and fast free flowing 

speeds were obtained at this 

detector, and continued to MD 109
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 34

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 109

Figure 26a: Thursday Sept 13, 2007 Figure 26a: Thursday Sept 13, 2007

Graphs show that at the detector at 

MD 109 the breakdown capacity 

seems to be about 455 vehicles per 

5-minutes

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 109

Figure 26a: Thursday Sept 13, 2007 Figure 26a: Thursday Sept 13, 2007

Graphs show that at the detector at 

MD 109 the breakdown capacity 

seems to be about 455 vehicles per 

5-minutes

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 109

Figure 26b: Thursday Oct 11, 2007 Figure 26b: Thursday Oct 11, 2007

This analysis in the proceeding graphs 

showed that it can take a few miles for 

the effects of traffic congestion to return 

to free flow 

I-270 operates as a system and 

management strategies need to also take 

that into account

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 109

Figure 26b: Thursday Oct 11, 2007 Figure 26b: Thursday Oct 11, 2007

This analysis in the proceeding graphs 

showed that it can take a few miles for 

the effects of traffic congestion to return 

to free flow 

I-270 operates as a system and 

management strategies need to also take 

that into account
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 35

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Fri 9-14-07, for Detector @ MD121

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

Figures 2-27 a thru f: Six Detector Segments at or Near Capacity on 9-14-07

.

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Fri 9-14-07, for Detector @ MD118

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 118

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Fri 9-14-07, for Detector @ I-370

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ I-370

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Fri 9-14-07, for Detector @ Comus Rd

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Comus Rd

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Fri 9-14-07, for Detector @ Truck Area

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Truck Area

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Fri 9-14-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 109
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 36

• While these data sets for volume and speed pairs of six detectors of I-270 show similar 

patterns, from the perspective of the overall section the capacity values differ (see the set 

of magenta lines for specific values). 

• For a demand reduction strategy, use the “weakest link” approach – which in this case is 

the segment near MD 121, where I-270 narrows from 3 lanes (one is a HOV) to 2 lanes. 

• In summary, while it appears that the segment near MD 121 is the main capacity 

constraint, upstream and downstream segments also operated at or near capacity. 

• Further, strategies that keep the 5-minute volumes at about 400 vehicles or less would 

seem to be desirable from the perspective of restoring and retaining freer flow conditions 

in this section of I-270. 

We conclude this analysis of this section of the I-270 Corridor with a check of some of the 

derived capacities shown in Figure 27 (a thru f) compared to information from the HCQSM. The 

maximum observed flow at the MD 118 detector was about 580 vehicles, enabling the maximum 

flow per lane to be calculated. In that part of I-270 section there are 3 through lanes. Expanding 

the 5-minute volume of 580 vehicles by 12 (12 – 5 minute time periods per hour), the hourly 

maximum volume is about 6,960 vehicles per hour. Dividing by the 3 lanes gives a maximum 

volume of about 2,320 vehicles per lane per hour. This corresponds well with the HCQSM’s 

maximum volume of 2,400 passenger cars per lane under ideal condition. Compared to the I-95 

Corridor analyzed above, the part of the I-270 data has many fewer trucks particularly during the 

peak period. Thus, it would be expected that the recorded maximum traffic volume is close to the 

theoretical HCQSM maximum flow. 

The maximum observed flow at the three more northern detectors from MD 121 to MD 109 had 

derived capacities of about 420 to 430 vehicles per 5-minutes, enabling the maximum flow per 

lane to be calculated. In that part of I-270 section there are 2 through lanes. Expanding the 5-

minute volume of 420 vehicles by 12 (12 – 5 minute time periods per hour), the hourly 

maximum volume is about 5,040 vehicles per hour. Dividing by the 2 lanes gives a maximum 

volume of about 2,520 vehicles per lane per hour. This is somewhat higher in value that the 

HCQSM’s maximum volume of 2,400 passenger cars per lane under ideal condition. However, 

given the relatively low percent of trucks compared to the I-95 Corridor analyzed above, 

particularly during the peak period and the experienced set of commuters using the roadway it 

may be possible that a capacity value that is about 5% higher than the theoretical HCQSM 

maximum flow could be found. In fact the HCQSM has reported capacities exceeding 2700 

passenger cars per hour for individual lanes for short periods of time. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 37

 

I-495 Corridor 

I-495 Corridor: General Locations of Detectors 

In addition to the I-95 and I-270 Corridors, a small part of I-495, the Capital Beltway, was also 

evaluated to confirm many of the traffic characteristics and speed-volume relationships discussed 

in the previous sections. The two previous corridors are “radials” with respect to their traffic 

orientation and function within the region, while I-495 serves “circumferential” traffic patterns. 

One question for this research is whether the radial versus circumferential orientation would 

result in findings and conclusion that differ from those found for the two radial corridors. 

Our analysis shows that the same basic results have been found. However, the traffic demand 

patterns around the Beltway frequently are less continuous than those of the radial corridors, 

with “crisscross-flows” quickly adding or subtracting volumes. Sequential continuity of flows 

between adjacent detectors is not always found and as a result the detectors may not be 

measuring the same traffic demand composition. We think that was the case with the three 

detectors used here. Further, data quality issues with the volume summation for the detector at 

Persimmon Tree Road, resulted in it not being used as part of the analysis. Thus the analysis 

summarized here does differ somewhat in form and content from that presented for the prior two 

corridors. 

The general location of the detectors used in the analysis is shown in Figure 2-28. CHART 

operates a limited set of traffic flow detectors along I-495 in Montgomery and Prince George’s 

Counties. Traffic flow detectors along the Virginia parts of the Capital Beltway have been out of 

service for a while and data was not available. The analysis used 5-minute summaries for total 

volume and average speed archived by UMD-CATT as derived from the CHART system, but 

only data for the flows on the “Outerloop” of I-495 are available. The southbound HOV lane of 

I-270 ends just prior to the merge of the I-270 Spur with the I495 Outerloop. 

Figure 2-28: General Location of Three of the Detectors on I-495, the Capital Beltway 

Persimmon 

Tree Rd

MD 190 

River Rd

Greentree Rd

I-495

I-495-X

Potomac River

I-495

I-270 

Spur

I-270

I-270

Persimmon 

Tree Rd

MD 190 

River Rd

Greentree Rd

I-495

I-495-X

Potomac River

I-495

I-270 

Spur

I-270

I-270

Persimmon 

Tree Rd

MD 190 

River Rd

Greentree Rd

I-495

I-495-X

Potomac River

I-495

I-270 

Spur

I-270

I-270
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 38

Traffic Characteristics 

As part of the analysis, the Study Team looked at potential differences in the peaking of volume 

and average speed traffic flow characteristics of two detectors on the Outerloop near Greentree 

Rd and MD 190, River Road. Estimates of delay in the vicinity of those detectors were plotted 

and are shown in Figure 2-29 and 2-30, at Greentree Rd and MD 190, respectively. It should be 

noted that these delays were estimated in a different but similar manner to those given for the 

two prior corridors. Rather than estimating and using the cumulative delay for the section of the 

corridor as was done for the other two cases, delay for each of the two detectors was separately 

estimated relative to the observed free-flow average speed at each detector and assumed that 

speed was representative of the speed about one-half mile on either side of the detector. Thus the 

inverse of the average speed applied over one mile yielded an estimate of the travel time per 5-

minute interval, and subtracting the free-flow travel time resulted in the delay for each 5-minute 

period. 

One notable traffic characteristic that is different at the I-270 and I-95 locations is that of the 

length of the peak period. During the heavier and more normal traffic days of September and 

October, the duration of the peak within the peak period is about 90 minutes between about 5:30 

and 7 PM, as shown in Figure 2-29. The duration of the peak within the peak period shown in 

Figure 2-30 is substantially longer starting about 3:30 PM and continuing until 6:30 to 7 PM. 

Thus strategies that account for time-of-day patterns of travel behavior will need to be prepared 

to function over prolonged time periods and not just be peak-hour oriented. 

Figure 2-29: Delay by Time of Day at Greentree Rd on the Outerloop of I-495 

Delay near Detector Station for Sample Days by Time of Day 

I-495 Outerloop: at Greentree Rd

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

15 16 17 18 19

Time of Day (5-min increments) 

Delay near Detector, Travel Time - FFTT (sec)

3 PM 5 PM 6 PM 7 4 PM

PM

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

1. This part of I-495 from Greentree Rd to Persimmon Tree Rd is about 3.1 

miles long with a speed limit of 55 mph, but free-flow speeds are faster.

2. While there are three detectors in this part, they measure somewhat 

different travel demand patterns and traffic flows from one to the next tends 

to be more independent than dependent

3. Thus delay for each detector was separately analyzed relative to the freeflow speeds and an influence area of about one mile, rather than as a a 

sequential system among the three detectors using cumulative travel times

4. For some detectors and particular days data quality is an issue.

9-14-07 Fri 

9-13-07 Thu 

Holiday

9-11-07 Tue 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 39

An important aspect of these two Figures is to show that delay during the lighter traffic volume 

days of summer can result in the near absence of congestion and delay. The example the delay 

shown for Wednesday 8-8-07 given in Figure 2-30 shows that to be the case. However, for the 

Greentree Road detector shown in Figure 2-29, the traffic volume data at that location for the 

week of August 6, 2007 were questionably low and were not used. Instead one of the traffic days 

of mid-September, that of Thursday 9-13-07 was used as that was a day that the entire 

Montgomery County school system was closed due to a religious holiday resulting in a 

significant and systemic shift from normal traffic demands. 

Indeed the two other days shown in Figure 2.29 that straddle that low-demand day, those of 

Tuesday 9-11-07 and Friday 9-14-07 had more normal traffic characteristics typical for that time 

of year. Of the days sampled for this detector in the analysis, those two other days are the ones 

that had the most delay at this detector. Returning to Figure 2-30, it can be seen that the delay by 

time-of-day for Thursday 9-13-07 was very light, being very similar to that found for Wednesday 

8-8-07. Another distinguishing traffic characteristic shown in Figure 2-30 for the detector at MD 

190 is that the high traffic delays for Friday 9-14-07 were not only long in duration, they were 

also very erratic and volatile within this time period, probably indicating that surges of different 

traffic flow demands were passing this location. The shape of the highest delay patterns shown in 

Figure 2-29 are more similar to that usually associated with a “normal distribution”, indicating 

that more consistent travel demands were being observed. 

Figure 2-30: Delay by Time of Day at MD 190 (River Rd) on the Outerloop of I-495 

Delay near Detector Station for Sample Days by Time of Day 

I-495 Outerloop: at MD 190 River Road

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

15 16 17 18 19

Time of Day (5-min increments) 

Delay near Detector, Travel Time - FFTT (sec)

3 PM 5 PM 6 PM 7 4 PM

PM

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

1. This part of I-495 from Greentree Rd to Persimmon Tree Rd is about 3.1 

miles long with a speed limit of 55 mph, but free-flow speeds are faster.

2. While there are three detectors in this part, they measure somewhat 

different travel demand patterns and traffic flows from one to the next tends 

to be more independent than dependent

3. Thus delay for each detector was separately analyzed relative to the freeflow speeds and an influence area of about one mile, rather than as a a 

sequential system among the three detectors using cumulative travel times

4. For some detectors and particular days data quality is an issue.

10-12-07 Fri

9-14-07 Fri 

8-08-07 Wed

9-13-07 Thu 

Holiday
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 40

Volume-Speed-Delay Relationships 

The data set was also analyzed in detail to address the research question of: 

• How much traffic needs to be taken off freeways operating at various levels of 

congestion to restore free flow during rush hours? 

To address that question in this case the particular and similar data for other days shown in the 

prior two figures needed first to be “boiled-down” to one indicator of delay. The measure of 

average delay per 5-minute increment over the 4-hour peak period was selected as the 

representative indicator of delay. Thus all of the erratic and volatile ups and downs of delay for 

Friday 9-14-07 from Figure 2-30 above were reduced to a value of about 35 seconds of average 

delay per 5 minutes over the 4-hour period, as shown below in Figure 2-31. Similarly, the more 

normal variation of delay shown in Figure 2-29 for Friday 9-14-07 was reduced to a value of 

about 13 seconds of average delay over the four-hour peak period, as shown below in Figure 2-

31. The other dimension of Figure 2-31 is the cumulative volume for the corresponding 4-hour 

peak period at each detector on the day shown. 

Figure 2-31: Examination of Volume – Delay Relationships for the Two I-495 Detectors 

The derived information shown in Figure 2-31 indicates a similar pattern for the two detectors 

analyzed. Maximum flows seem to occur when the average delay is minimal – on the order of 5 

to 10 seconds per 5-minute period. As the average delay increases above that range, there 

appears to be a reduction in total volume that is served by that part of the roadway, with 

reductions of about ten percent being found. However, going to the left in Figure 2-31 away from 

Volume versus Average Delay at each Detector Analyzed on I-495 

I-495 Outerloop Detectors near: Greetree Rd and MD 190, River Rd

10,000

11,000

12,000

13,000

14,000

15,000

16,000

17,000

18,000

19,000

20,000

21,000

22,000

23,000

24,000

0 5 10 15 20 25 30 35 40 45 50 55 60

Delay per Detector over the Peak Period in excess of Free-Flow Travel Time (seconds)

PM Peak (4-Hr) Volume @ each Detector (veh)

9-14-07 Fri

10-12-07 Fri @ MD190 River Rd

@ Greentree Rd

Based upon Sample Days of CHART Detector Data, obtained from UMD_CATT Archives, and Analyzed by Motion Maps, LLC 

10-08-07 Mon Columbus Day

9-13-07 Thu Relg. Holiday

10-12-07 Fri

9-13-07 Thu Relg. Holiday

8-8-07 Wed 10-10-07 Wed

8-6-07 Mon

9-11-07 Tue9-14-07 Fri
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 41

the maximum flow, as the average delay approaches zero, there also appears to be a relatively 

sharp drop in total volume. 

An important observation to note goes to the main premise of the research question, which 

assumes that the traffic volume or demand is the independent variable, while the average speed 

or delay is the dependent variable. That may not necessarily be the case. The findings in this and 

the prior two cases show that strategies that reduce demand by small, marginal amounts can 

result in significantly less amounts of delay or congestion. However, trying to have a goal of 

restoring free-flow at all times and completely reduce congestion may not be the optimum 

situation. Trying to manage demand so that the traffic flows are located and timed everywhere at 

the same time to have free-flow conditions will be extremely difficult if not near impossible from 

a practical sense. That is because there seems to be a feedback in the relationship between 

volume and delay – perhaps interdependencies. 

To keep flows at or near maximum during the peak of the peak requires the flow to be just 

upstream and ready to move onto the next part of the roadway, but without queuing or delay. 

However, if the managers try to avoid any delay, then being able to sustain a maximum traffic 

flow to pass through the next roadway segment becomes nearly impossible too – so avoidance of 

some minor delay will likely result in less than maximum flow or throughput of traffic. There 

appears to be a trade-off between minimizing delay and maximizing volume. Achieving both at 

the same time everywhere throughout a roadway system is truly a significant challenge. This is 

how we interpret the results shown in Figure 2-31. 

On sample days in early August when demand is low there still tends to be some minor amount 

of congestion or delay, as shown in Figure 2-31 for 8-6-08 and 8-8-08 for the detector near MD 

190. Even weekdays with large systemic demand reductions, the holidays of 9-13-07 and 10-08-

07, still have some minor amounts of average delay. Trying to manage the demand so that the 

flows have no delay will likely result in under-utilized capacity – there would still be some minor 

amounts of delay but significant drops in throughput would likely result. 

To better understand this apparent trade-off in management objectives, the next part of the 

analysis examined the paired-relationship between concurrent observations of volume and 

average speed. 

In Figure 2-32 and Figure 2-33 for each Outerloop detector the paired-values of volumes (x-axis) 

and average speed (y-axis) per each 5-minute summary interval are plotted, respectively first for 

a typical day in August (8-8-07) and then for a typically congested day at another time of the 

year, such as in September (9-14-07). Eight hours of such data are shown in this case with the 

clusters of points on the left-side being for the 4-hour AM peak period of 6 to 10 AM, and the 

clusters in the mid and right-sides being for the 4-hour PM peak period of 3 to 7 PM. The 

following observations are noted based upon Figures 2-32 and 2-33: 

• For the 8-8-07 sample day (Figure 2-32) the smaller volumes concurrently have typically 

high speeds and almost no 5-minute periods of congestion, although two observations in 

the AM have average speeds between 45 and 50 mph. 

• For the 9-14-07 sample day (Figure 2-33) there are numerous 5-minute periods when the 

speeds are in the 20 to 50 mile per hour range representative of moderately congested 

conditions. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 42

Paired Values of Volume – Speed by Sample Day 

Volume-Speed Relationship at Selected CHART Detectors: I-495 Outerloop:Wed 8-8-07

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Total Volume by 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD190 River Rd

@ Persimmon Tree Rd (volume data not valid in the AM and PM)

@ Greentree Rd

Wednesday August 8, 2007 Figure 2-32: Wednesday August 8, 2007 

Volume-Speed Relationship at Selected CHART Detectors: I-495 Outerloop on Fri 9-14-07

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Total Volume by 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD190 River Rd

@ Persimmon Tree Rd (volume data not valid in the AM and PM)

@ Greentree Rd

Friday September 14, 2007

Figure 2-33: Friday, September 14, 2007 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 43

• For the 9-14-07 sample day (Figure 2-33) the larger values of the volume seems to have 

exceeded the capacities of the roadway in the vicinity of both of those detectors. Once the 

demand has exceeded the capacity the traffic flow breaks down; with average speeds 

dropping from the 55 to 60 mile per hour range to the 20-50 mile per hour range; and 

volume or throughput declining as well to about 350 to 500 vehicles per 5-minutes. 

• The 8-8-07 Figure 2-32 does not show the breakdown in speed because the flow rates 

either do not exceed the capacity or they were still approaching capacity. In the subsequent 

steps, our analysis estimates what is the “tip capacity” that will break down the flow of 

traffic, create significant delays, and result in constricted flows. 

So the question we need to address is: Do marginally higher volumes cause congestion? And if 

they do, what are the values of the higher volumes when that starts to happen? 

As noted in the previous Figures, higher volumes are associated with speeds beginning to slow 

down; and if the flow rate volumes become high enough, and begin to exceed the roadway 

capacity, then: (a) significantly slower speeds, congestion and delay rapidly increase at the point 

of observation-detection; (b) the flow rate declines significantly; and (c) queues begin to form 

upstream from the point of observation-detection. 

In Figure 2-34 the same sets of the 5-minute volumes of I-495 for the detector near Greentree 

Road are plotted versus average speed with lines connecting sequential points. The low-demand 

weekday of 9-13-07 was used here instead of the early August sample because the PM peak 

period volumes for 8-8-07 were exceedingly low even for a lower volume day. (There may be 

some data quality issues with part of the sample for that day.) Even with using the higher 

demand day of 9-13-07, as shown in Figure 2-38 almost all of the paired-values had average 

speeds over 60 mph, five had speeds between 55 and 60 mph, and just one had a speed of about 

53 mph. In essence almost the whole peak period was free-flowing. 

Examining the volumes in Figure 2-34, the 5-minute volumes barely exceeded a value of 400 

vehicles, which is a good deal away from the estimated 5-minute flow rate capacity of about 525 

vehicles. The volumes for 9-14-07 given in Figure 2-35 have perhaps two-thirds to three-quarters 

of the paired-observations over the value of 400 vehicles per 5-minute period – and perhaps half 

of the observations having moderately congested speeds of less than 50 mph, but over 35 mph. 

It is noted that in both Figures the onset of the flow breakdown seems to have begun at volumes 

that are closer to the value of 400 vehicles per 5-minutes. Perhaps an explanation for this 

apparent discrepancy stems from the roadway geometrics just downstream from the location of 

this detector. The roadway in the vicinity before and after the detector is on a long down-grade 

that about 2,000 feet past the detector enters a relatively sharp turn (for Interstate standards) after 

which I-495 merges with the southbound I-270 Spur. There are warning signs indicating a sharp 

turn ahead that can be seen from the general vicinity of the detector near Greentree Rd. 

Unfamiliar or overly cautious drivers may slow down even if the volumes are lighter than 

capacity flow rates. That could explain why at lower volumes slower speeds are seen. 

Another different finding seen in Figures 2-34 and 2-35 is in both AM clusters of points where 

lighter volumes have slower speeds – the opposite of one of the premises of the analysis. Perhaps 

here too the roadway geometrics can help explain the observation if the drivers widely separated 

from others may actually be more observant of speed limits and warning signs. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 44

Volume-Speed Relationship at Selected CHART Detectors: I-495 Outerloop: Thu 9-13-07

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Total Volume by 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Greentree Rd in 

the PM

@ Greentree Rd in 

the AM

Thursday September 13, 2007

Figure 2-34: Thursday, September 13, 2007 

Volume-Speed Relationship at Selected CHART Detectors: I-495 Outerloop on Fri 9-14-07

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Total Volume by 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ Greentree Rd in 

the PM

@ Greentree Rd in 

the AM

Friday September 14, 2007

Figure 2-35: Friday, September 14, 2007 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 45

If the maximum 5-minute flow rate in the vicinity of this detector is about 525 vehicles then the 

maximum hourly flow per lane may be calculated. At this detector near Greentree Rd the I-495 

Outerloop has 3 through lanes. Expanding the 5 minute volume of 525 vehicles by 12 (12 – 5 

minute time periods per hour), the hourly maximum volume is 6,300 vehicles per hour. Dividing 

by 3 since there are three lanes, yields a per-lane maximum volume of 2,100 vehicles per hour. 

This corresponds well with the HCQSM Manual’s maximum volume of 2,400 passenger cars per 

lane under ideal condition. 

Turning the remainder of the analysis towards the other detector near MD 190, River Road, it is 

located just before the interchange with MD 190. Figure 2-36 shows the paired 5-minute volume 

and speed data for the early –August light demand conditions of the sample day of 8-8-07. 

Figure 2-37 shows the heavier demand sample day from other times of the year of 9-14-07. The 

following general observations about the volume-speed relationships at that general location are 

made: 

• For the 8-8-07 sample day (Figure 2-36) the smaller volumes concurrently have typically 

high speeds with no 5-minute periods of congestion. 

• For the 8-8-07 sample day (Figure 2-36) one 5-minute period has a maximum volume of 

about 570 vehicles, and since that demand did not persist there was not a resultant sharp 

drop in speeds. 

• For the 9-14-07 sample day (Figure 2-37) almost the entire 4-hour PM peak period has 

slowing, moderately congested, and congested speeds with speeds ranging from about 20 

to 55 mile per hour. Further, the wide dispersion of the points is another indicator of the 

erratic and volatile nature of the traffic flow that day, as was observed earlier by examining 

the pattern of delay by time-of-day. 

• For the 9-14-07 sample day (Figure 2-37) the observed volumes fall short of the estimated 

capacity of about 575 vehicles per 5-minutes as the unevenness of the flow rate and speed 

is probably unable to sustain a ready-high-volume-flow upstream of the detector. 

Regarding the capacity of the I-495 Outerloop near the MD 190 Detector, if the maximum 5-

minute flow rate in the vicinity of this detector is about 575 vehicles then the maximum hourly 

flow per lane may be calculated. At this detector near the I-495 Outerloop effectively has three 

through lanes and two auxiliary lanes for traffic exiting at MD 190 and immediately after for I495X, Clara Barton Parkway. Expanding the 5 minute volume of 575 vehicles by 12 (12 – 5 

minute time periods per hour), the hourly maximum volume is 6,900 vehicles per hour. Dividing 

by 3 since there are effectively three lanes, yields a per-lane maximum volume of 2,300 vehicles 

per hour. This corresponds very well with the HCQSM Manual’s maximum volume of 2,400 

passenger cars per lane under ideal condition. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 46

Volume-Speed Relationship at Selected CHART Detectors: I-495 Outerloop: Wed 8-8-07

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Total Volume by 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

Wednesday August 8, 2007

@ MD190 River Rd 

in the PM

@ MD190 River Rd 

in the AM

Figure 2-36: Wednesday, August 8, 2007

Volume-Speed Relationship at Selected CHART Detectors: I-495 Outerloop on Fri 9-14-07

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Total Volume by 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD190 River Rd 

in the PM

@ MD190 River Rd 

in the AM

Friday September 14, 2007

Figure 2-37: Friday, September 14, 2007 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 47

AM Peak Volume Analysis 

Comparison of AM and PM Peak Period Volumes 

The analyses presented above in the Task 2 discussion focused on evening PM peak period 

traffic conditions. During the review of the analysis a question was raised as to whether the 

seasonal reductions in volumes observed in comparing PM peak period summer-lite traffic to 

PM peak period traffic at other times of the year would also be the case for the AM peak period. 

That question was based upon the premise that is a long-standing generalization in transportation 

planning that, “the PM peak period tends to be much longer and more uniform in shape, while 

the AM peak is shorter and much more sharply peaked in its shape”. 

To address this question some additional analysis, using archived detector data from RITIS at the 

UMD CATT Lab, was carried out that examined the volume data for one detector on I-95 at 

Cherry Hill Rd and one detector on I-270 at MD 118, Germantown Road. Peak direction

volumes for a four-hour AM peak period of 6 AM to 10 AM and a four-hour PM peak period of 

3 PM to 7 PM in the opposing direction were summarized for selected days that were used in the 

prior analyses. The tabulation of that information for the Cherry Hill Road detector and the MD 

118, Germantown Road detector is given below in Figures 2-38a and 2-38b, respectively. The 

tables identify the particular days that were selected for this analysis including: their date, dayof-the-week, and whether there were special conditions. 

Date Day-of- Special

(YYMMDD) the-Week Conditions AM Peak PM Peak

070801 Wednesday 25,567 29,047

070802 Thursday 26,456 27,966

071008 Monday Columbus Day 21,396 25,402

071011 Thursday 24,750 27,883

I-095 Detector at

4-Hour Volume in the 

Cherry Hill Rd

Figure 2-38a: AM and PM Peak Period, Peak Direction Volumes: I-95 and Cherry Hill Rd.

Date Day-of- Special

(YYMMDD) the-Week Conditions AM Peak PM Peak

070806 Monday 17,882 21,374

070813 Monday 18,321 21,781

070913 Thursday Relig. Holiday 17,293 22,188

070914 Friday 17,962 20,500

071008 Monday Columbus Day 15,547 19,954

071011 Thursday 18,605 22,281

071012 Friday Incident in AM 15,252 21,180

I-270 Detector at MD 118 Germantown Rd

4-Hour Volume in the 

Figure 2-38b: AM and PM Peak Period, Peak Direction Volumes at I-270 and MD 118 

Figure 2-39 is a graph that plots and compares the AM versus the PM data from the two previous 

Figures. The AM peak volume scale is on the vertical axis, with the PM peak volume scale on 

the horizontal axis. The blue diagonal indicates the center point where data points would lie if 

there were equal volumes in the AM and the PM peak. This shows that in all cases the AM peak 

period peak direction volumes are less than the corresponding PM peak period peak opposing 

direction volumes for that same day. In the graph the days that were summer-lite days are 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 48

marked with a circle around their data points, and the days that had special conditions are 

marked with a square around their data points. A generalization from the pattern shown in the 

graph for the limited number of days analyzed for these two locations is that there seems to be a 

relatively consistent relationship between the AM peak period peak direction volumes and the 

PM peak period opposing direction volumes for the same day that do not appear to differ much 

for: (a) the summer-lite days compared to the more normal traffic days at other times of the year, 

nor (b) the special days with light traffic compared to the more normal traffic days at other times 

of the year. 

We thus conclude this additional analysis with the finding that the AM peak period peak 

direction volumes are about 80 to 85 percent of the PM peak period opposing direction volumes 

as a first approximation, although on any given day that range could be a somewhat smaller or 

larger percentage. That implies that the AM-oriented strategies of travel demand reductions may 

need shorter durations than during the afternoon peak period in order to have reduced delays. It 

is important to note that during the morning peak period significant congestion is experienced.4

The congestion however does not have as long a duration as the afternoon peak. But just as it is 

the case in the afternoon, a 10 to 14% drop in the morning peak volume will reduce delay. 

However, the reduction needed will only apply to that portion of the morning peak period that is 

congested. 

Figure 2-39: AM and PM Peak Period Peak Direction Volumes on I-95 and I-270

 

4

 Although AM Peak 4-hour volumes are 15 to 20 percent lower than PM Peak volumes, drivers do experience 

congestion and delay in the AM Peak, although it may be more peaked or of shorter duration than congestion in 

the PM Peak. A full analysis of bottlenecks and series of data, comparable to that performed for the PM Peak, 

would be required to establish “why” and “where” there is AM Peak delay, and “how much” is there, in order to 

define comparable volume reduction targets for the AM Peak as for the PM Peak. 

Peak Direction Volumes for AM Peak vs. PM Peak

10,000

12,500

15,000

17,500

20,000

22,500

25,000

27,500

30,000

10,000 15,000 20,000 25,000 30,000

Peak Direction Volume: 4-Hour PM Peak Period

Peak Direction Volume: 4-Hr AM Peak Period

= a summer-lite day 

= a special condition day 

I-270 at MD 118, 

Germantown Rd

I-095 at 

Cherry Hill Rd

Finding: AM peak period, peak 

direction volumes are about 80 to 

85 percent of the PM peak period 

volumes of the opposing direction
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 49

What Does This All Mean? 

The analysis for these sections of I-95, I-270 and I-495 has demonstrated that there are clearly 

many places where the capacity of those parts of the roadways can be reached and exceeded. 

When that starts to happen there are relatively quick, sharp and prolonged drops in speed and a 

decrease in throughput of the traffic flow. It can sometimes take a considerable amount of time 

until the upstream volumes and demand decline enough to enable the roadway operating at 

reduced performance to once again have flows that can get back to freer flowing speed. 

We also have seen examples in the analysis where the reduced roadway performance propagates 

or flows downstream from the constriction locations. We have seen that the turbulence in flow 

that occurs at the bottlenecks does not always clear up just after vehicles pass that point. Many 

drivers may still be driving cautiously while others are impatient so that in the mix of traffic 

there can be speed-ups and slow-downs taking place somewhat simultaneously until the vehicle 

density spreads out enough to absorb the fluctuations in flow. Then the cautious drivers begin to 

drive at freer flowing speeds and the whole flow itself tends to operate without congested 

conditions. 

In conclusion, a main finding of this Study has been that if relatively small changes can be made 

in peak demand (volume) through various programs and strategies, such as congestion pricing, 

then two beneficial things can happen: (1) there can be relatively large decreases in congestion 

and delay at key choke points, and (2) there can be increased through-put along those roads 

during peak times of travel – thus by effectively managing the demand more travelers can be 

served per time period with the available fixed-supply of roadway capacity. 

The analysis has shown that in many instances the amount of needed demand reduction can be 

on the order of five to ten percent of the peak period flow. However, there still may be a 

particularly difficult bottleneck in a corridor that would need reductions on the order of 15 to 20 

percent. While demand reduction and operational strategies may be able go a long way in 

improving the flow at those locations so that capacity is exceeded less often and/or recovery is 

quicker, there still may need to be localized geometric or lane use changes at those locations to 

routinely have freer flowing traffic at those troublesome locations. 

Based on our analysis we now have the information and data available to estimate the delay 

reduction associated with traffic reduction. 

ØØØ Summary Traffic Analysis 

The review and evaluation of traffic data on the Washington DC Interstate System has provided 

a clear picture of the relationship between traffic flow, speed, and delay. All three highway 

sections on I-95, I-270, and I-495 showed similar traffic flow characteristics. In fact, there were 

no differences in the relationship between flow, speed, and delay whether the facility was a 

radial or circumferential freeway. And the traffic flow relationships from the field data validated 

both the maximum per lane capacity and the operational flow during congestion. 

The recorded maximum capacities in all three sections ranged approximately between 2100 and 

2400 vehicles per hour. These values compare quite well with the Highway Capacity and Quality 

of Service Manual (HCQSM) speed – flow curves as shown in Figure 2-40. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 50

 

In the Speed Flow Density Curve, the maximum flow rates are in passenger cars per lane per 

hour. The Congestion Delay Study results are in vehicles per lane per hour. If the Study’s 

capacity results were adjusted for heavy vehicles, and geometric conditions such as interchange 

spacing and traffic friction at the ramps and weaving sections, the maximum capacities would be 

even closer to the HCQSM Speed Flow Density Curves. 

It should also be noted that Figure 2-40 (HCQSM Exhibit 23-3 – Speed Flow Density Curve) is a 

discontinuous curve. In other words, if the demand on the freeway system exceeds the maximum 

capacity (in this case 2400 passenger per hour), then the flow breaks down, speeds drop to the 20 

to 40 mile per hour range and the capacities are reduced to approximately 2000 passenger cars 

per lane per hour. Based on the field data this is exactly what occurred in all three sections that 

were analyzed in the study. 

Having verified the speed-flow-delay relationship, the reduction in delay due to a reduction in 

traffic flow or the shifting of traffic from the peak period may be estimated. As part of the study, 

Figure 2-40: Speed Flow Density Curve from the Highway Capacity and Quality of Service Manual 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 51

traffic volume, speed, and delays in August and during holidays was evaluated and compared to 

the volume, speed, and delays in October when the traffic was heavier. Therefore the reduction 

in delay if modest reductions in volume occur may be estimated. What follows is an estimate of 

the potential delay reduction that may result if volumes are reduced. 

I-95 Section Delay Analysis – I-95 Northbound from Brooklyn Bridge Road to 

Montgomery Road 

Based on Figure 2-2 and Figure 2-3 for the 4 hour afternoon peak period that was analyzed the 

delay for the comparatively light travel days in August was estimated to be an average of 40

seconds per vehicle. During October, with heavier traffic flows the delay was estimated to be 

about 150 seconds per vehicle. As noted earlier in the report delay is calculated by taking the 

time difference between driving the 10.5 mile section of roadway at 65 miles an hour versus the 

time it would take to drive the section at the congested speed. Therefore the travel time would 

be: 

Free flow travel time for 10.5 miles at 65 miles per hour = 570 SECONDS 

October Congested travel time for 10.5 miles at congested speed = 720 SECONDS 

August Congested travel time for 10.5 miles at congested speed = 610 SECONDS 

The estimated congested travel times are the average over the 4 hour afternoon peak period. 

Individual afternoon peak hours will have significantly higher and lower delays. 

The next step in the analysis is to estimate the vehicle hours of delay for the typical days selected 

in August and October. 

On a typical day approximately 25,000 vehicles traverse the I-95 study section. The difference 

between average delay in August versus October is 110 seconds (150 – 40). 

Therefore: 

Vehicle Hours of Delay = 25,000 peak period vehicles X 110 seconds of delay 

 3,600 seconds per hour 

 = 764 Vehicle Hours of Delay 

If the average vehicle occupancy rate for all trip purposes in the Washington, DC area is 1.2 then 

the person hours of delay is 920 hours. A typical driver values their time at 1/3 to 1⁄2 of their 

hourly wages. If each person values their time at $15 per hour then the delay on this one 10.5 

mile section of I-95 is costing $13,800 per afternoon peak period (920 X $15/hour). If converted 

to a yearly cost, the delay cost would be $3,450,000 ($13,800 X 250 working days). 

This can also be expressed as a delay cost / vehicle mile which would be: 

Delay Cost / Vehicle Mile = $13,800 = 5.27 cents/vehicle mile 

 25,000 vehicles X 10.5 miles 

Percent Reductions in Traffic and Delay 

In October it takes an extra 150 seconds to traverse the I-95 10.5 mile study section. In August 

during light traffic it takes an extra 40 seconds. Therefore if we reduce our traffic below the 

tipping point of congestion then the percentage reduction of delay would be: 

Percent Delay Reduction = (150-40) = 73 % 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 52

 150 

In order to get this reduction in the I-95 corridor we would need a drop in the traffic from 25,000 

vehicles during the afternoon peak to 22,000. This is a traffic reduction of 12% and corresponds 

to the average traffic difference between August and October. A 12% traffic reduction would 

result in a 73% reduction in delay. 

Analysis of the Peak Hour of the Afternoon Peak Period 

During the peak hour in the afternoon peak period, the traffic flow is approximately 8,000 

vehicles versus 25,000 vehicles during the entire afternoon peak period. From Figure 2.1 the 

average delay on the I-95 study section during the peak hour is 350 seconds. With no congestion 

the sections travel time is 570 seconds and with peak hour congestion it is 920 seconds. By 

reducing the demand by 12% (typical traffic reduction for August versus October) to 7400 

vehicles during the peak hour there would be a significant percent delay reduction as the 

following calculations will show: 

 Percent Delay Reduction = 350 Seconds(October Delay) – 40 Seconds(August Delay) 

 350 Seconds 

 = 89 % Delay Reduction 

By reducing demand 12% we can achieve a delay reduction of 89%. 

The cost savings to the individual vehicle on a per mile basis would be: 

Peak Hour Delay Savings = 8000 Vehicles Peak Hour X 310 seconds Delay Reduction

 3600 Seconds 

 = 690 Vehicle Hours of Delay Reduction 

Delay Savings = 690 Vehicle Hours X 1.2 Vehicle Occupancy X $15 VOT = $12,400 

Note: VOT = Hourly Value of Time 

Delay Cost / Vehicle Mile = $12,400 = 14.76 cents/vehicle mile 

 8000 vehicles X 10.5 miles 

The savings in delay during the peak hour if traffic is reduced during the peak hour by 12% 

would be 14.76 cents/ vehicle mile. 

I-270 Delay Analysis: I-270 Northbound – Section from I-370 to MD 109 

Based on Figure 2-14 and Figure 2-15 for the 5 hour afternoon peak period that was analyzed the 

delay for the comparatively light travel days in August was estimated to be an average of 80

seconds per vehicle. During October, with heavier traffic flows the delay was estimated to about 

300 seconds per vehicle. As noted earlier in the report delay is calculated by taking the time 

difference between driving the 12.9 mile section of roadway at 65 miles an hour versus the time 

it would take to drive the section at the congested speed. Therefore the travel time would be:

Free flow travel time for 12.9 miles at 65 miles per hour = 716 SECONDS 

October Congested travel time for 12.9 miles at congested speed = 1016 SECONDS 

August Congested travel time for 12.9 miles at congested speed = 796 SECONDS 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 53

The estimated congested travel times are the average over the 5 hour afternoon peak period. 

Individual afternoon peak hours will have significantly higher and lower delays. 

The next step in the analysis is to estimate the vehicle hours of delay for the typical days selected 

in August and October. 

On a typical day approximately 22,000 vehicles traverse the I-270 study section. The difference 

between average delay in August versus October is 220 seconds (300 – 80). 

Therefore: 

Vehicle Hours of Delay = 22,000 peak period vehicles X 220 seconds of delay 

 3,600 seconds per hour 

 = 1344 Vehicle Hours of Delay 

If the average vehicle occupancy rate for all trip purposes in the Washington, DC area is 1.2 then 

the person hours of delay is 1613 hours. A typical driver values their time at 1/3 to 1⁄2 of their 

hourly wages. If each person values their time at $15 per hour then the delay on this one 10.5 

mile section of I-270 is costing $24,195 per afternoon peak period (1613 X $15/hour). If 

converted to a yearly cost, the delay cost would be $6,048,750 ($24,195 X 250 working days). 

This can also be expressed as a delay cost / vehicle mile which would be: 

Delay Cost / Vehicle Mile = $24,195 = 8.53 cents/vehicle mile 

 22,000 vehicles X 12.9 miles 

Percent Reductions in Traffic and Delay 

In October it takes an extra 300 seconds to traverse the I-270 12.9 mile study section. In August 

during light traffic it takes an extra 80 seconds. Therefore if we reduce our traffic below the 

tipping point of congestion then the percentage reduction of delay would be: 

Percent Delay Reduction = (300-80) = 73 % 

 300 

In order to get this reduction in the I-270 corridor we would need a drop in the traffic from 

22,000 vehicles during the afternoon peak to 19,000. This is a traffic reduction of 14% and 

corresponds to the average traffic difference between August and October. A 14% traffic 

reduction would result in a 73% reduction in delay. 

 

Analysis of the Peak Hour of the Afternoon Peak Period 

During the peak hour in the afternoon peak period, the traffic flow is approximately 6,800 

vehicles versus 22,000 vehicles during the entire afternoon peak period. From Figure 2.14 the 

average delay on the I-270 study section during the peak hour is 420 seconds. With no 

congestion the sections travel time is 716 seconds and with peak hour congestion it is 1136 

seconds. By reducing the demand by 14% (typical traffic reduction for August versus October) 

to 6000 vehicles during the peak hour there would be a significant percent delay reduction as the 

following calculations will show: 

 Percent Delay Reduction = 420 Seconds(October Delay) – 80 Seconds(August Delay) 

 420 Seconds 

 = 81 % Delay Reduction 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 54

By reducing demand 14% we can achieve a delay reduction of 81%. This demand to delay 

percentage ratio is similar in scale to the delay reduction found on I-95. 

The cost savings to the individual vehicle on a per mile basis would be: 

Peak Hour Delay Savings = 6800 Vehicles Peak Hour X 340 seconds Delay Reduction

 3600 Seconds 

 = 642 Vehicle Hours of Delay Reduction 

Delay Savings = 642 Vehicle Hours X 1.2 Vehicle Occupancy X $15 VOT = $11,560 

Note: VOT = Hourly Value of Time 

Delay Cost / Vehicle Mile = $11,560 = 13.18 cents/vehicle mile 

 6800 vehicles X 12.9 miles 

The savings in delay during the peak hour if traffic is reduced during the peak hour by 14%

would be 13.18 cents/ vehicle mile. 

In Sections 3 and 4 that follow, we address how many peak period trips can be truly deemed 

discretionary, and therefore may be able to shift times to reduce congestion (Section 3), and how 

effective congestion pricing and similar strategies have been in encouraging travel mode and 

travel time pattern changes (Section 4). 

 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 55

Ø 3. Washington, DC Household Survey Key Findings 

The purpose of this section is to address the first two questions on the second objective, namely: 

 What is the share of travel that is made for various purposes other than commuting on 

congested freeways in the Washington, DC area? and 

 What share of these travelers may have some flexibility to shift their time of travel to offpeak periods? 

Methodology Used to “Mine” the MWCOG 1994 Survey 

The MWCOG 1994 household travel survey is a statistically valid survey for the Washington 

region, with a sample of 4,863 households. One drawback is that the survey is more than twelve 

years old. (MWCOG is in the process of conducting a new household travel survey for the 

region. However, data will not be available until mid-2009, well outside the timeframe for this 

project.) The Study Team obtained the documentation and an electronic version of the MWCOG 

1994 survey in ASCII comma-delimited format from MWCOG. The structure and definitions are 

similar to National Household Travel Survey (NHTS). Some “data cleaning” was required. 

The following data sorts, combinations and analyses were performed on the survey data, as 

defined below: 

1. Separated trip records by peak and non-peak and created trip chains for work and non-work 

travel. Work trips were defined as all trips with an ultimate origin or destination of work as 

long as interim stops are 30 minutes or less (to overcome the problem of a work trip with a 

stop to pick up coffee being coded or interpreted as a shopping trip). Work trips include 

home to work trips (HW), work to home trips (WH), work to work trips (WW), work to other 

trips (WO), and other to work trips (OW). Non-work trips include home to home trips (HH), 

home to other (HO), other to home (OH), and other to other. The morning peak period is 

defined as trips beginning from 6 am to 9 am, while the evening peak period is defined as 

trips beginning from 3 pm to 6 pm.5

2. The MWCOG survey did not include questions on distance, but did include travel time 

records. Trip origin and destination addresses were not available, however, information on 

the city or county of origin was provided. (The study scope and time frame precluded a 

detailed analysis by Transportation Analysis Zone or TAZ). Travel times greater than twenty 

minutes are used as surrogates for trips that are likely to use freeways. These are referred to 

as longer trips; the majority of the analysis focuses on these longer trips. 

3. Identified mode of travel (personal vehicle versus all other; all other meaning rail and bus 

transit, bike, walk, and other) to focus on longer trips using a personal vehicle as the “target 

universe” 

4. Evaluated longer AM Peak trips with stops on the way to work, including travel time from 

home and purpose (e.g., stopping for daycare or coffee) and longer PM Peak trips on the way 

home from work. 

5. Compared AM Peak and PM Peak trip purposes and patterns for longer work and non-work 

trips using a personal vehicle, narrowing the focus further to those trips with 0 or 1 stops. 

 

5

 MWCOG typically defines the PM Peak as 4 PM to 7 PM for regional modeling purposes, but it is not anticipated 

that the composition of travel by purpose is substantially different for this time period. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 56

Trips by Time of Day

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

Morning 211,734 80,787 292,521 162,556 36,832 199,388

AM Peak 1,445,096 927,187 2,372,283 1,029,131 394,067 1,423,198

Midday 1,370,921 1,653,034 3,023,955 482,949 675,864 1,158,813

PM Peak 1,453,814 1,475,508 2,929,322 918,607 538,079 1,456,686

Evening 588,232 1,829,404 2,417,636 299,123 612,744 911,867

Work Trips Non-Work Trips Total Trips Work Trips > 20 min Non-Work Trips > 20 

min Total Trips > 20 Min

6. Analyzed longer non-work personal vehicle 0-stop or 1-stop trips to identify those 

discretionary trips that appear most likely to be able to shift time. The study defines 

discretionary trips based on trip purposes. The discretionary category includes family and 

personal business, social and recreational activities, shopping, and restaurant trip purpose 

destinations as discretionary. 

7. Developed graphic representations of the data to summarize essential findings. 

MWCOG Survey Findings 

Note: All Findings are based on trip chains, not individual unlinked trips, so results are likely to 

vary from other studies. We begin with broad overviews of travel purposes and patterns, then 

evaluate subsets of the data to address the research questions as fully as possible. 

1. Who travels in peak and off-peak periods? How many trips are for work versus non-work? 

How many of these trips are longer trips (greater than 20 minutes)? 

Figure 3-1: Trips by Time of Day and Overall Trip Purpose 

Figure 3-1 demonstrates that there are very significant volumes of travel throughout the day and 

during peak periods, including large numbers of non-work trips. The three columns to the left 

summarize total trips during the day. Approximately 61 percent of the 2.37 million total AM 

Peak Trips are work trips, while slightly less than half of the PM Peak Trips are work trips. 6

Trips that take more than 20 minutes travel time (longer trips) are depicted in the three righthand columns, and show somewhat different patterns from the total trips. For example, in the 

 

6

 This analysis groups “Home – Work”, “Work – Home”, “Work – Work”, “Work – Other” and “Other –Work” to 

define Work Trips; and combines “Home – Other”, “Home – Home”, “Other – Home” and “Other – Other” to 

define non-work trips. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 57

AM Peak, the 1.4 million longer trips comprise 60 percent of the 2.4 million total trips, while in 

the PM Peak the 1.5 million longer trips comprise 50 percent of the 2.9 million total trips. 

Figures 3.2 and 3.3 illustrate the major differences between the AM Peak and PM Peak in terms 

of proportions of longer trips, and in proportions of work and non-work trips for these longer 

trips. The AM Peak and PM Peak total volumes of longer trips are very close to the same, at 1.42 

and 1.46 million trips, but the proportions of work and non-work trips are quite different. In the 

AM Peak, over 1 million (72 percent of the 

1.42 million longer trips, or 43 percent of all 

AM Peak trips) are work trips, while less 

than 400,000 (28 percent of the 1.42 

million, or17 percent of all AM Peak trips) 

are non-work trips. In the PM Peak, over 

900,000 (63 percent of the 1.46 million 

longer trips, or 31 percent of all trips) are 

work trips, with almost 540,000 (37 percent 

of the longer trips, 18 percent of all PM 

Peak trips) non-work trips. 2

The remainder of the analysis will focus on 

these longer trips in the AM and PM Peak 

Periods, steadily narrowing the focus to 

identify the appropriate “universe” and our 

“target population” of longer peak period 

discretionary trips. The first step is 

discerning mode: private vehicle use versus 

other modes. 

AM Peak Trips

Non-w ork trips > 

20 min, 394,067, 

17%

Total trips < 20 

min, 949,085, 40%

Work trips > 20 

min, 1,029,131, 

Total trips > 20 43%

min, 1,423,198, 

60%

Figure 3.2 Key AM Peak Trip Characteristics 

Figure 3.3 Key PM Peak Trip Characteristics 

PM Peak Trips

Non-w ork trips > 

20 min

538,079

18.4%

Work trips > 20 min

918,607

31.4%

Other

1,456,686

49.7%

Total trips < 20 min

1,472,636

50.3%
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 58

Among the universe of longer trips in the AM and PM peaks, the personal vehicle is the mode of 

choice for work trips and non-work trips, as shown in Figures 3.4 and 3.5. 

Figure 3.4 demonstrates that 

personal vehicles account for 1.0 

million trips or 71 percent of all 

longer trips in the AM Peak. 

Because the other 29 percent of the 

trips include walking, biking, and 

transit, as well as “other”, we will 

consider only the personal vehicles 

as our “universe” for traffic for the 

remainder of the analysis. Once we 

eliminate the “other mode” trips, 

work trips represent 80 percent of 

the AM Peak longer trips. 

 Figure 3.4 AM Peak Longer Trip Distribution by Type 

Figure 3.5 shows a very different 

pattern for the PM Peak trips, 

compared with the AM Peak Trips. 

Personal vehicles account for 1.13 

million of the longer PM Peak 

period trips, or 77 percent of the 

total. Of these 1.13 million trips, 

only 64 percent are work trips, with 

36 percent non-work trips. It is also 

significant that the volumes of nonwork personal vehicle trips in the 

PM Peak are more than double the 

volumes in the AM Peak. 

AM Peak Trips > 20 Minutes, Work/Non-Work, Personal 

Vehicle (PV)or Other Mode

PV non-w ork trips , 

203,594 , 14% PV w ork trips , 

803,535 , 57%

Other mode w ork 

trips, 225,596 , 16%

Other mode nonw ork trips, 190,473 

, 13%

PM Peak Trips > 20 Minutes, Work/Non-Work, Personal 

Vehicle (PV)or Other Mode

PV non-w ork trips , 

409,064 , 28%

PV w ork trips , 

719,824 , 49%

Other mode w ork 

trips, 198,783 , 14%

Other mode nonw ork trips, 129,015 

, 9%

Figure 3.5 PM Peak Longer Trip Distribution by Type 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 59

Morning and Afternoon Peak Longer Discretionary Trips 

One of the challenges for the study is to estimate the non-work trips that are using freeways 

during peak periods. Without a complete origin-destination matrix of trips, and without distance 

being part of the survey, we must make some broad assumptions about travel patterns in order to 

address this question. As noted in the introduction to the survey, since we do not have direct 

distance information, we are using time as a surrogate and assuming that many of the longer trips 

– more than 20 minutes each - are using freeways. For this “drilling-down” into the data, we ask 

the following questions: 

• What are the key trip purposes for the longer non-work trips? 

• Of these longer, non-work trips, which categories are commonly deemed to be 

discretionary and therefore capable of moving the travel to other time periods, other days, 

or other routes? 

Many trip destination purposes are included in the survey, including Home, Work and Related, 

School, Child Care, Pick Up / Drop Off, Shopping, Family / Personal, Restaurant, Changing 

Mode, and Other. Activity Modeling analyzes work and non-work trips into three main 

categories: Maintenance, Subsistence, and Discretionary, as well as some trips that are generally 

not separately categorized in the analysis bridging trips and activities (marked as N/A – Home is 

self-defined).7 Work trips are considered in the Subsistence category. The key to the Trip 

Purposes delineated in the MWCOG survey for the following analysis and figures is as follows: 

Abbreviation Explanation Category Included as Discretionary? 

Home Non-Work-Home Trip N/A No 

PckUpDrp Pick up or Drop Off N/A No 

ChngMode Change Mode N/A No 

Other N/A No 

School School Subsistence No 

ChldCare Child Care Maintenance No 

Shop Shop (for goods or services) Maintenance Yes 

Restrant Restaurant Discretionary Yes 

Fam/Pers Family Personal Discretionary Yes 

SocRec Social / Recreational Discretionary Yes 

Although shopping is identified as a maintenance activity, we believe that in general it may be 

considered as discretionary, particularly in terms of choice of time of day or day of week for 

most types of shopping, in particular for the longer trips included for this analysis. We analyze 

AM Peak and PM Peak travel in turn. In order to err on the side of caution in our estimates of 

discretionary trips, we focus our attention only on non-work trips with 0 or 1 stops, to reduce or 

eliminate the likelihood of identifying a discretionary trip with an interim non-discretionary 

destination purpose. Figures 3.6 and 3.7 identify the trip purposes for AM Peak and PM Peak 

 

7

 “Activity Analysis Using the NHTS”; http://nhts.ornl.gov and “Using NHTS to Estimate Activity Patterns”, 

Planning Applications Conference, Powerpoint presentation courtesy of Heather Contrino, FHWA Travel Surveys 

Team Leader. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 60

PM Peak Personal Vehicle 0-1 Stop Non-Work Trip Purposes For Trips > 20 Minutes

Home, 239,367 , 62%

Shop, 21,791 , 6%

Restaurant, 19,012 , 5%

Fam/Pers, 18,100 , 5%

SocRec, 59,646 , 16%

PckUpDrp, - , 0%

ChngMode, 1,260 , 0%

School, 10,269 , 3%

ChldCare, 721 , 0%

Other, 12,862 , 3%

AM Peak Personal Vehicle 0-1 Stop Non-Work Trip Purposes for Trips > 20 Minutes

Home, 48,245 , 25%

Other, 14,044 , 7%

School, 51,082 , 26%

Restaurant, 4,526 , 2%

Fam/Pers, 35,945 , 19%

SocRec, 27,456 , 14%

Shop, 9,423 , 5%

ChldCare, 2,107 , 1%

ChngMode, 1,028 , 1%

PckUpDrp, - , 0%

non-work trips, longer than 20 minutes, taken in personal vehicles. In each figure, the four pieces 

of the pie that are extracted represent discretionary trips, as defined for this study. 

Figure 3.6 illustrates the non-work trip purpose destinations for these longer trips in the AM 

Peak period, using a personal vehicle. After eliminating home, school, child care, etc. we have 

over 77,000 longer trips that can be identified as discretionary. This represents 38 percent of the 

pertinent non-work trips, and a full 7.7 percent of the total personal vehicle longer trips (work 

and non-work). 

Figure 3.7 illustrates 

that, as expected, there 

are substantial 

differences in trip 

purpose distributions 

between the AM Peak 

and the PM Peak, as 

well as differences in 

volumes, as previously 

noted in relation to 

Figure 3.5. PM Peak 

discretionary trips total 

about 118,500, about 

53 percent more than in 

the AM Peak. Though 

these discretionary trips 

represent only 29 

percent of the PM Peak 

non-work trips, they 

account for a full 10.5 

percent of all the 

personal vehicle longer 

PM Peak trips. This is 

due to the earlier 

finding, that non-work 

trips represent a much 

greater portion and 

volume of travel in the 

PM Peak compared 

with the AM Peak. 

Figure 3.6 Non-Work AM Peak Trip Purposes 

Figure 3.7 Non-Work PM Peak Trip Purposes 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 61

Total vs. Discretionary AM Peak Trips 

(All PV trips > 20 Minutes, 0-1 Stops for Discretionary)

Non-w ork 

discretionary, 

77,350, 7.7%

Restaurant, 

4,526 , 0.5%

Fam/Pers, 35,945, 

3.6%

Shopping, 

9,423 , 0.9%

Work trips, 

803,535 , 79.7%

Non-w ork nondiscretionary, 

127,254 , 12.6%

Soc/Rec, 27,456 , 

2.7%

Total vs. Discretionary PM Peak Trips 

(All PV trips > 20 Minutes, 0-1 Stops for Discretionary)

Non-w ork 

discretionary, 

118,549, 10.5%

Restaurant, 

19,012, 1.7%

Soc/Rec, 

59,646 , 5.3%

Non-w ork nondiscretionary, 

294,206 , 26.0%

Work trips, 

719,824 , 63.5%

Shopping, 

21,791 , 1.9%

Fam/Pers, 

 18,100 , 

1.6%

What Does This All Mean? 

Longer discretionary trips form a substantial portion of the AM Peak and PM Peak trips. 

However it is not feasible to discourage or remove all discretionary trips from freeways, and 

even if it were, removing all such trips would not necessarily be sufficient to ensure free-flow 

conditions on all segments of the freeways. 

Figure 3.8 illustrates how the discretionary trips, “teased out” of the non-work trips, form a 

notable portion (7.7 percent) of the AM Peak “universe” of personal vehicle trips. These trips 

have the greatest flexibility to shift time or route away from crowded freeway facilities; this shift 

can be encouraged through pricing and other incentives. Shifting this base of trips away from 

peak congested facilities would help congestion in many corridors; as noted in the traffic analysis 

section above, the reduction in traffic needed to restore “free-flow” conditions varies by facility, 

but in general will require a range of 10 percent to 14 percent to attain free-flow. 

We have established that reducing traffic by 10 to 14 percent (in the PM Peak, generalizing in 

this case to the AM Peak) will result in a 75 to 80 percent reduction in delay, which would 

benefit millions of travelers every day. Achieving a 10 to 14 percent reduction in the AM Peak 

cannot be accomplished solely by diverting discretionary travelers. In addition to strongly 

encouraging AM Peak discretionary travelers to shift travel to other times or routes, it will likely 

be necessary to encourage other travelers 

to shift as well. Strategies to reduce work 

trips in personal vehicles, such as 

telecommuting or transit/ carpool/ 

vanpool incentives and facilitation, have 

also proven effective in the region, as 

shown by the 16 percent “other modes” 

for AM Peak work trips in Figure 3.4. 

More can be done; strategies that 

decrease travel times for carpools, 

vanpools and buses increase the appeal 

and market share of these modes, as 

discussed in Section 4. Thus, shifting 

AM Peak discretionary trips off our 

congested freeways, to other times or 

other routes, represents a promising 

strategy that must be used in concert 

with other strategies. 

Figure 3.9 summarizes the somewhat 

more promising potential for reducing 

discretionary trips in the PM Peak. 

Discretionary personal vehicle trips 

making one or no stops represent a full 

10.5% percent of longer PM Peak 

personal vehicle trips- within the target 

range of the 10 percent to 14 percent 

reduction required to achieve a 75 to 80 

percent reduction in delay. Since PM 

Peak trip volumes are substantially 

 Figure 3.9 Summary PM Peak Discretionary Trip Breakdown 

Figure 3.8 Summary AM Peak Discretionary Trip Breakdown 

Figure 3.9 Summary PM Peak Discretionary Trip Breakdown 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 62

greater than AM Peak trip volumes, and since this is largely due to the higher volumes of nonwork trips, strategies that encourage discretionary trips to seek other times or other routes show 

great promise for reducing congestion in the PM Peak. Strategies that reduce commute trips by 

using pricing as a tool to promote carpools, vanpools, transit and telecommuting for work trips 

provide another significant “pool” of trips for reducing congestion, as discussed in Section 4. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 63

ØØØ

4. Effectiveness of Congestion Pricing Strategies: Literature Review Key

Findings

Is there empirical evidence that people will shift travel times and/or travel modes in response to 

traffic management strategies such as congestion pricing? What is the potential for other modes 

(e.g., transit, vanpools, carpools, etc.) to attract additional mode share, if free-flow service on all 

freeways were guaranteed to these modes through aggressive and active traffic management 

strategies such as congestion pricing? 

Many studies have been conducted to answer the many facets and potential solutions to the 

traffic congestion question. Experience from around the world and in the United States has 

demonstrated that congestion pricing represents one of the most powerful tools to change driving 

behavior. Empirical studies of congestion pricing have been conducted in many different venues, 

and in many types of applications. It should be noted that other direct influences on traffic 

volumes and occupancy include fuel prices8

 and parking costs (which are not addressed in detail 

in this review). Comprehensive approaches that include “carrots” of faster travel times, preferred 

parking and/or lower costs for shared rides, and “sticks” of higher costs and/or greater 

inconvenience or travel times for single-occupancy vehicles, appear to have the greatest potential 

for modifying behavior and decreasing congestion on key roadways. For example, TCRP Project 

B-4, “Cost Effectiveness of TDM Programs”, found the following: “The average reduction in 

vehicle trips among “successful” programs was 15.3%. Programs that provided enhanced 

alternatives, such as vanpools or shuttle buses, realized an 8.5% reduction in trips. Programs that 

focused on financial incentives and disincentives realized a 16.4% reduction of trips, and 

programs that combined enhanced alternatives with incentives/disincentives for their use, 

realized a 24.5% reduction in vehicle trips.” (Reference 7, below, p. 46, citing COMSIS, 1994). 

Subsequent studies and practical experience have validated the power of even token pricing 

signals to influence behavior, change traffic patterns, and reduce congestion. The effectiveness is 

greatly increased when coupled with transit improvements, HOV-3 lanes (including facilitation 

of informal carpools or “slugging”, employer ride share incentives and/or parking disincentives, 

and all the other tools in the travel demand management (TDM) strategies “toolbox”. 

The key findings on empirical evidence for the effectiveness of congestion pricing are 

summarized in the tables below (reference numbers in parentheses), with brief annotated 

references describing each case and/or reference below the tables. 

 

8 The recent US surge in fuel prices resulted in significant drops in total driving. In Maryland and Virginia, for example, prices 

increased by approximately 25 percent over the prior year for the months of March through June, (average prices per 

gasbuddy.com) and miles driven on urban arterials decreased by a little less than 3 percent over the same period, (per FHWA 

reports). Washington DC miles actually increased slightly. However, an increase in fuel prices or parking will not necessarily 

induce a shift from a congested roadway, or to a less-congested time period. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 64

Table 1: Observed Changes in Traffic due to Congestion Pricing 

 Percentage Shift 

 Person Trips Vehicle Trips 

Shift from Peak to Off-Peak -14% (1: Seattle) -8% (1: Seattle- varied rates) 

-7% (3: NY- $1.50 fee) 

Change in Peak (or all) 

Traffic on Tolled Facilities 

+18% (4: Mn I-394) +54% (6: I-15 CA HOV to HOT) 

-7% (3: NY-$1.50 fee) 

-7% (6: FL- $.25/ $.50 discount 

offpk) 

-13% (1: Seattle - varied rates) 

-15% (7: NJ Tpk) 

-20% to 24% (6: Singapore -$2.50) 

-21% to 30% (2: London -£5 then 

£8)

Increase in HOV Use + 3% (4: Mn I-394) 

+10% (1: Seattle - 

work trips) 

+25% (6: I-15 CA) 

+33% to 40% (6: Singapore) 

Increase in Transit Use +3% (1: Seattle - 

work trips) 

+23% (4: Mn I-394) 

+37% (2:London - £5 

then £8) 

N/A 

The “outlier” in the above table (the 54% increase in vehicle trips on I-15 in California) 

represents the successful introduction of HOT lanes to a previously under-utilized HOV lane 

facility. When the enforcement on illegal SOVs increased with the introduction of tolls for 

SOVs, HOV use increased significantly, as shown. The introduction of tolls results in traffic 

reductions ranging from 7% to 30%, depending on the pricing and the breadth of the strategy. 

The highest values, in Singapore and London, represent comprehensive central city core area 

pricing programs, that were introduced with concurrent increases in transit and shared-ride 

alternatives. Most examples use electronic tolling. It is significant that decreases in vehicle trips 

can result in increases in person-trips, both through improved through-put based on reduced 

congestion, and on improved efficiency based on the balance between SOV and HOV. 

Some empirical studies have evaluated changes in traffic with corresponding changes in 

congestion. Results vary greatly, due to the existing conditions and the methods used to evaluate 

congestion. However, as demonstrated in the Section 2 Traffic Analysis above, modest changes 

in volumes can generate significant decreases in delay. 

Location Traffic Change Congestion Change 

London (2: £5 then £8) -20% -30% (2003-2004) 

Stockholm (5: variable fee) -15% -50% 

Singapore (6) -20% to 24% -30% (avg. speeds up) 

1) Puget Sound Regional Council, “Traffic Choices Study- Summary Report”, April, 2008. 

Report on 18-month study of 275 individual drivers who participated in a voluntary GPSmonitored tolling study. As this was a sample, drivers did not have the option to pay a toll to 

drive on uncongested major roads. Reported results are from page 12 of the study, and 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 65

Minneapolis I-394

0

2000

4000

6000

8000

10000

12000

14000

Qtr 1 2005 v. Qtr 1 2007

Pre HOT 5727 4026 229 9982

Post HOT 5123 4933 1464 218 11738

Car/van 

pools Bus riders Tolled SOV 

(violators)

People 

moved

extrapolated from Tables 2 and 3 on page 23 of the study. 

http://psrc.org/projects/trafficchoices/in-summary.pdf

2) Transport for London, “Central London Congestion Charging: Impacts Monitoring - Fifth 

Annual Report, July 2007.” London introduced congestion pricing for its central section in 

2003, along with improved bus services, and has been monitoring and adjusting the system 

since that time. In the first year congestion was reduced by 30% (p.35). In 2006, traffic with 

four or more wheels was down 21 percent from 2002, and vehicles that could potentially be 

charged was down 30% (p.19); nevertheless in 2006 congestion increased significantly from 

earlier years (but is still 8% lower than 2002), largely due to roadway construction, changes 

in signals and other factors (p. 48-53). This is being studied intensively to work out 

problems; for example, bus service times and delays increased, even though signal priority 

schemes for buses were in effect (p.72). 

(http://www.tfl.gov.uk/assets/downloads/fifth-annual-impacts-monitoring-report-2007-07-

07.pdf) 

3) Breakthrough Technologies Institute and Environmental Defense, “Changing Lanes – 

Linking Bus Rapid Transit and High Occupancy Toll Networks in Northern Virginia”, 

September, 2005. The purpose of the study is clear from its title. It included a review of 

previous experiences. Cited here is the example from the Port Authority of New York-New 

Jersey that introduced time-of-day tolls on Hudson River bridges and tunnels and Staten 

Island bridges (7% peak hour traffic reduction- see p. 13). 

4) Turnbull, Katherine, “High-Occupancy Toll (HOT) Lanes and Public Transportation”, TRB 

2008 Annual Meeting CD-ROM. Ms. Turnbull examines the introduction of Single-Occupant 

Vehicles (SOVs) paying tolls onto High-Occupancy Vehicle (HOV) lanes, examining the 

impacts on public transportation services and ridership levels. The three cases explore bus 

services operating on the HOV/HOT lanes on I-15 in San Diego, I-394 in Minneapolis, and I25 in Denver. In San Diego, HOVs account for approximately 75 to 78 percent of total 

vehicle volumes, with FasTrakTM users accounting for most of the remainder. Bus riders 

comprise about 10 to 11 percent of daily users, mostly for reverse commutes, and taking 

advantage of service that was introduced and partially funded by the new tolls. Denver’s I-25 

has from 68% to 72% of its lanes occupied by HOV, with 28%to 32% paying tolls. Denver 

instituted a performance monitoring system, using GPS to track the on-time performance of 

buses. They have been able to use this information to fine-tune signals near exit ramps and 

other potential problems on the route for buses as well as other vehicles. 

The Minneapolis HOT/HOV lane 

deliberately improved transit services 

when it opened the HOV lanes as HOT 

lanes. Although carpool and vanpool 

riders declined by about 10%, bus 

ridership increased by over 22%, for a 

3% increase in total HOV use, and an 

18% increase in people moved along the 

corridor. Figure to right derived from 

study. 

5) Replogle, Michael, “Is Congestion 

Pricing Ready for Prime Time?”, 

Planning, Vol. 74, No. 5, May 2008, pp. 

6-11. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 66

6) TCRP Report 95, Chapter 14, “Road Value Pricing- Traveler Response to Transportation 

System Changes” – This comprehensive study completed in 2003 includes detailed analysis 

of major road, city center, and other US and international experience in congestion pricing 

strategies, including Singapore’s ALS experience beginning in 1975 and the United 

Kingdom’s experience in Durham (beginning in 2000) and London (beginning in 2003). 

Florida reference page 14-16. I-15 California reference p. 14-56, Table 14-13, Oct. 1996 v. 

Oct. 1999. Singapore reference p. 14-10 and 14-53, extrapolating between mode shares from 

Table 14-2 and the traffic volumes shown on page 14-53. Pre-ALS volume is estimated at 

308,500 based on the statement on p. 14-10 that inbound traffic during restricted hours has 

been capped at below 70% of 1975 pre-ALS volumes. 

7) FHWA, “Mitigating Traffic Congestion: The Role of Demand-Side Strategies, 2004, 

www.ops.fhwa.gov. This study provides a few examples of congestion pricing, and is also an 

accessible and valuable reference with “fast facts” and case studies on a wide range of 

Transportation Demand Management (TDM) strategies that will enhance the effectiveness of 

congestion pricing efforts. NJ Reference p. 22 “Fast Facts”. 

What Does This All Mean? 

Traffic congestion and delay have become an endemic component of commuting life in the 

Washington DC region. To many, the unpredictability of travel time is even more annoying- one 

day a 10 mile trip may require 20 minutes, and the next day 45 minutes. Because the system is so 

near capacity, and exceeding capacity in some areas, a minor incident or a rainstorm causes 

major breakdowns and systemic delays. In this paper we have shown that there is a way to 

restore reliability and predictability to our highway system, without spending billions on new 

lanes of traffic. 

In Section 2 we established that a 10 to 14 percent decrease in traffic on congested freeways will 

reduce delay by approximately 75 to 80 percent. In Section 3 we established that from 7 to 9 

percent of the longer trips in personal vehicles during peak periods are discretionary. In Section 4 

we have established that modest pricing signals for private vehicles can reduce traffic enough to 

significantly reduce congestion and save time for all drivers, while at the same time increasing 

the “people-carrying capacity” of the roadway, by increasing the use of carpools, vanpools and 

transit. It therefore appears feasible to restore and maintain free-flow on the freeways in the 

Metropolitan Washington area, without adding capacity, by applying congestion pricing to the 

major facilities, and at the same time increasing transit, carpool and vanpool programs. The 

combination of diverting most discretionary trips to other times and diverting an additional five 

to ten percent of personal vehicle work trips to HOV modes should achieve the needed 10 to 14 

percent overall decrease in traffic needed to achieve major reductions in delay.9

Tolls are typically set to match the value of the reduction in delay. Some of the most effective 

programs vary the tolls by as little as 15 minute increments based on volumes and demand, in 

order to keep the facility operating smoothly. Others have a fixed rate for the “peak of the peak” 

(usually the peak hour), with a discount for the “shoulders” of the peak, and no charge in off-

 

9

 Caution must be used in developing the program and publicizing and enforcing the HOV restrictions. In many 

cases discretionary trips, such as social and recreational trips, involve multiple people traveling together, who may 

consider themselves “HOV”. Thus it must be made clear that the HOV refers to registered carpools or vanpools 

for work trips, to avoid the unintended consequence of facilitating discretionary group travel. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 67

peak hours. Most systems use transponders (similar to the E-Z Pass in use throughout much of 

the East Coast) with cameras to identify violators, designed to collect information at high speeds. 

Going forward with such a system would require political will, as well as phased implementation 

and experimentation to identify workable technologies and appropriate rates to achieve the 

desired result. However, based on the cited examples, it could be expected that a charge of 

approximately $.15 per mile (or $1.50 for the 10 mile freeway segments in the analysis) would 

be a reasonable starting point, comparable to the value of time saved. It is expected that 

registered car pools (HOV 3 or more, preferably), vanpools and transit would not be charged. 

Rates could be adjusted up or down, to ensure the roadway is used to near-maximum capacity, 

without exceeding capacity to the point of breaking down and failing. Revenues collected could 

be used to improve HOV alternatives as well as maintain the roadways, address choke points and 

bottlenecks, and improve alternate routes. Finally, travelers in the region would travel in 

confidence, knowing that they can reliably predict their travel time on a daily basis. 
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 68

ØØØ Appendix A: Detailed Traffic Analysis for I-270 Corridor

The following graphics and text summarize the rationale for the selection of September 13, 2007 

as the “benchmark” non-congested day for I-270. 

Paired Volume – Speed for “Summer-lite” Days 

Figure A-1: Volume-Speed relationship for 7-30-07 

• Shows generally tight clusters of points – one cluster for each detector 

• 5-minute volumes go from about 100 to 475 veh 

• Each cluster has mostly high speeds of 60-75 mph; some speeds drop to 40 to 60 mph 

range 

• The points are consistent with HCQSM findings 

• Although this was a lightly congested day, the absence of data for MD 118 makes this 

day less useful for comparison to congested days 

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Mon 7-30-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

Note: detector @ MD 118 did not report values this day

@ Comus Rd

@ I-370

@ Truck Area @ MD 109
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 69

Paired Volume – Speed for “Summer-lite” Days 

Figure A-2: Volume-Speed relationship for 8-6-07: 

• Also shows generally tight but more dispersed clusters of points – one cluster for each 

detector. 

• 5-minute volumes go from about 100 to 550 veh. 

• Each cluster has mostly high speeds of 50-75 mph; some speeds drop to 30 to 50 mph 

range. 

• The points are consistent with HCQSM findings. 

• Presence of data for MD 118 makes this day more useful, but minor congested conditions 

at three detectors makes this day less useful. 

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Mon 8-06-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area

@ MD 109
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 70

Volume – Speed for a Summer & a “Special” Day 

Figure A-3: Volume-Speed relationship for 8-13-07: 

• Specific clusters of points are more dispersed 

• 5-minute volumes go from about 150 to 550 veh 

• Most speeds of 50-75 mph, but speed drops at: 

o MD121: many speeds of 20 to 50 mph 

o Comus Rd: some speeds of 30 to 50 mph 

• The points are consistent with HCQSM findings 

• Presence of congested conditions makes this day less useful for comparison purposes 

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Mon 8-13-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area

@ MD109
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 71

Volume – Speed for a Summer & a “Special” Day 

Figure A-4: Volume-Speed relationship for 9-13-07: 

• Specific clusters of points are fairly tight. 

• 5-minute volumes go from about 200 to 575 veh. 

• Each cluster has mostly high speeds of 60-70 mph; some speeds 50 to 60; few over 70; 

only two 5-minutes intervals just under 50 mph 

• The points are consistent with HCQSM findings. 

• Although not in “August”, combination of high volumes, light congestion, and systemic 

demand reductions makes this day good for comparison. 

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area

@ MD 109
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region Final Report December, 2008 72

Comparison of an Uncongested to a Congested Day 

Figures A-5 and A-6 

• The 10-11-07 graph directly above, for a typical congested day shows: 

o Volumes vary from 225 to 575 vehicles (MD 118 had peak volume of 583 

veh) 

o Five of the six detectors have speeds below 50 mph 

o At MD 121 many speeds less than 30 mph (slowest average speed of 17 mph) 

Again, the points for both graphs are consistent with HCQSM findings.

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 10-11-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370

@ Truck Area @ MD 109

Volume-Speed Relationship at Selected CHART Detectors:

I-270 Northbound, Thu 9-13-07 for Section Detectors

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Volumes per 5-Minute Increments

Average Speed per 5-Minute Increment (mph)

@ MD 121

@ MD 118

@ Comus Rd

@ I-370 @ Truck Area

@ MD 109
Examining the Speed-Flow-Delay Paradox in the Washington, DC Region: 

Potential Impacts of Reduced Traffic on Congestion Delay and Potential for 

Reductions in Discretionary Travel during Peak Periods

U.S. Department of Transportation 

Federal Highway Administration 

Office of Operations 

1200 New Jersey Ave., SE 

Washington, DC 20590 

Toll-Free "Help Line" 866-367-7487 

FHWA-HOP-09-017