Traffic Data

Computation Method

POCKET GUIDE

Publication No.

FHWA-PL-18-027

August 2018
NOTICE

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The Federal Highway Administration (FHWA) provides 

high-quality information to serve Government, 

industry, and the public in a manner that promotes 

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to ensure and maximize the quality, objectivity, utility, 

and integrity of its information. FHWA periodically 

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processes to ensure continuous quality improvement. 
Traffic Data Computation Method POCKET GUIDE | i

Table of Contents

Table of Contents ..............................................................i

List of Figures .................................................................. iv

List of Tables ....................................................................v

Abbreviations.................................................................. vii

Preface .............................................................................1

85th Percentile Speed........................................................4

Annual Average Daily Traffic (AADT)................................6

Annual Average Daily Truck Traffic (AADTT)....................8

Average Daily Traffic (ADT)............................................10

Axle Correction Factor (ACF) ..........................................12

Capacity..........................................................................14

Change Rate...................................................................16

Combination Unit Truck AADT ........................................18

Critical Speed..................................................................20

Design Hour....................................................................22

Design Vehicles...............................................................24

Design Volume................................................................26

Directional Design-Hour Volume (DDHV)........................28

Directional Factor ...........................................................30

Directional Flow Rate and Ratio......................................32

Equivalent Single Axle Load (ESAL)................................34

Free Flow........................................................................36

Future AADT...................................................................38

Gross Vehicle Weight (GVW) ..........................................40
ii | Traffic Data Computation Method POCKET GUIDE

Headway.........................................................................42

K-Factor..........................................................................44

Lane Groups ...................................................................46

Load Spectra...................................................................48

Month of Year and Day of Week Factors ........................50

Monthly Average Daily Traffic (MADT)............................52

Number of Intersections of Other Type ..........................54

Number of Signalized Intersections ................................56

Number of Stop Sign Intersections .................................58

Passenger Car Equivalent (PCE) .....................................60

Peak Hour Factor (PHF)..................................................62

Percent Green Time........................................................64

Percent Peak Combination Unit Trucks ...........................66

Percent Peak Single Unit Trucks & Buses .......................68

Roadway Occupancy.......................................................70

Saturation Flow...............................................................72

Saturation Headway........................................................74

Short Term Count ADT by Vehicle Class to AADT by 

Vehicle Class Conversion ................................................76

Short Term Count ADT to AADT Conversion...................78

Signal Type.....................................................................80

Single Unit Truck & Bus AADT ........................................82

Time of Day (TOD) Factor ..............................................84

Traffic Demand ...............................................................86

Turning Radius................................................................88

Vehicle Class Factoring ...................................................90
Traffic Data Computation Method POCKET GUIDE | iii

Vehicle Density ...............................................................92

Vehicle Miles Traveled (VMT) .........................................94

Vehicle Speed .................................................................96

Volume and Volume-to-Capacity Ratio ...........................98

Index ............................................................................100
iv | Traffic Data Computation Method POCKET GUIDE

List of Figures

Figure 1. Speed distribution curve ................................. 4

Figure 2. Determination of critical lane group and 

phasing from flow ratio .............................. 33

Figure 3. Vehicle axle loads and spacing (Source: 

FHWA)...................................................... 41

Figure 4. K-30, K-50, and K-100 factors. ..........................

Figure 5. Example of different lane group types ............ 47

Figure 6. Monthly factors ............................................ 51

Figure 7. Example for no. of “at-grade/other” 

intersections count .................................... 55

Figure 8. Signalized intersection count example ............ 57

Figure 9. Example for no. of stop sign intersections 

count........................................................ 59

Figure 10. Vehicle length and detector length ............... 71

Figure 11. Saturation headway and start-up lost time 

at a signalized intersection ......................... 75
Traffic Data Computation Method POCKET GUIDE | v

List of Tables

Table 1. Computation of VCD and THDF (source: 

TMG). .........................................................9

Table 2. Daily volume and ADT....................................11

Table 3. ACF and axles per vehicle computation from 

vehicle and axle counts...............................13

Table 4. Vehicle count adjustment using ACF ................13

Table 5. Change Rate Development .............................17

Table 6. Design Hour Selection ....................................23

Table 7. Free-flow Speed and Flow Rate for Basic 

Freeway Segment ......................................37

Table 8. Future AADT Computation ..............................39

Table 9. Average Headway calculations from Flow Rate .43

Table 10. Average Headway calculations from Average 

Spacing and Average Speed........................43

Table 11. Standard load ranges for load spectra 

sample......................................................48

Table 12. Axle type loads and load ranges for vehicles 

of class 6...................................................49

Table 13. Load Spectra example for Vehicle Class 6.......49

Table 14. PCEs of general terrain segments ..................60

Table 15. PCE computation example ............................61

Table 16. Percent green time computation for pretimed control signals ..................................64

Table 17. Percent green time computation for actuated 

control signals ...........................................65
vi | Traffic Data Computation Method POCKET GUIDE

Table 18. Computation of % peak CU trucks from 

AADT and peak hour SU truck volume ......... 67

Table 19. Computation of % peak SU trucks from 

AADT and peak hour SU truck volume ......... 69

Table 20. Saturation flow computation example............ 73

Table 21. Monthly factors for generalized vehicle 

classes...................................................... 91

Table 22. Vehicle density calculation example............... 93

Table 23. Computation of DVMT and VMT from AADT 

and section length ..................................... 95

Table 24. Volume-to-capacity ratio calculation example . 99
Traffic Data Computation Method POCKET GUIDE | vii

Abbreviations

AACR Annual Average Change Rate

AADT Annual Average Daily Traffic

AADTT Annual Average Daily Truck Traffic

AASHTO American Association of State Highway and 

Transportation Officials 

ACF Axle Correction Factor

ADT Average Daily Traffic

BS Bus 

CBD Central Business District

CU Combination Unit Truck

CTR Centerline Turning Radius

CCS Continuous Count Station

DDHV Directional Design Hourly Volume

DH Design Hour

DHV Design Hour Volume

DOW Day of Week

DSFL Design Service Flow Rate

DVDT Daily Vehicle Distance Traveled

DVMT Daily Vehicle Miles Traveled

ESAL Equivalent Single Axle Load

FFS Free Flow Speed

FHWA Federal Highway Administration

GVW Gross Vehicle Weight

HCM Highway Capacity Manual

HPMS Highway Performance Monitoring System 

HV Heavy Vehicle

LOS Level of Service

LR Load Range

LT Light Truck Over 102” 

MADT Monthly Average Daily Traffic

MADTT Monthly Average Daily Truck Traffic

MADW Monthly Average Day of the Week

MAWDT Monthly Average Weekday Daily Traffic 
viii | Traffic Data Computation Method POCKET GUIDE

MAWDTT Monthly Average Weekday Daily Truck 

Traffic 

MC Motorcycle 

MPH Miles Per Hour

MPO Metropolitan Planning Organization

NHS National Highway System

PHF Peak Hour Factor

PC Passenger Car

PCE Passenger Car Equivalent

PTR Portable Traffic Recorder

PV Passenger Vehicle Under 102” 

SU Single-Unit Vehicle 

TAZ Transportation Analysis Zone

THDF Truck Hourly Distribution Factor

TMC Transportation Management Center

TMG Traffic Monitoring Guide

TOD Time of Day

TT Tractor Trailer

TVT Traffic Volume Trend

VCD Vehicle Class Distribution 

V/C Volume-to-Capacity

VMT Vehicle Miles Traveled

WIM Weigh-In-Motion 
Traffic Data Computation Method POCKET GUIDE | 1

Preface

The Federal Highway Administration (FHWA), Office of 

Highway Policy Information, has developed this "Traffic 

Data Computation Method Pocket Guide." Traffic data 

items are performance indicators that are computed from 

raw and processed traffic information. They are used for 

operational assessment of transportation facilities, in

designing and planning, investment prioritization, and 

policy decisions. 

The objective of the pocket guide is to succinctly provide 

computational methods for selected traffic data items. The 

audience is anyone involved in the collection, processing, 

analysis, utilization, and reporting of traffic data.

Forty-eight traffic data items are covered in this guide. 

Individual data items are discussed in a two-page format. 

For each item, a brief description is first provided. This is 

followed by discussion and illustration of the data item. In 

the majority of the discussions, supplemental notes and 

tips useful for the data item computation are provided. 

Related data items and cross references are provided for 

further reading. For traffic data items that require large 

datasets, Excel-based worked examples are available for 

download from the FHWA website. Data items with a 

worked example are identified with this symbol. The 

data items are alphabetically ordered and the index 

provides easy access to any data item and key words. 
2 | Traffic Data Computation Method POCKET GUIDE

In developing the guide, the following references were 

used. 

1. FHWA Traffic Monitoring Guide (TMG) – 2016 edition.

2. American Association of State Highway and 

Transportation Officials (AASHTO) Guidelines for 

Traffic Data Programs – 2009 edition.

3. FHWA Highway Performance Monitoring System 

(HPMS) Field Manual – 2016 edition.

4. Transportation Research Board (TRB) Highway 

Capacity Manual (HCM) – 2016, 6th, edition.

5. American Association of State Highway and 

Transportation Officials (AASHTO) A Policy on 

Geometric Design of Highways and Streets – 2011, 

6th, edition. 
Traffic Data Computation Method POCKET GUIDE | 3
4 | Traffic Data Computation Method POCKET GUIDE

85th Percentile Speed

The speed below which 85 percent of vehicles in the 

traffic stream travel.

Discussion and Illustration. 85th percentile speed is 

used as a guide in setting or adjusting the posted speed 

limit of a facility. Also, it is used in the computation of 

base travel times for urban streets and signal timing plans. 

This speed is likely to be influenced by the traffic 

conditions, so it reflects the conditions during the analysis 

period.

In general, 85th percentile speed is computed from fieldcollected speed data. For freeways, speed detection 

sensors are usually placed 0.25 miles apart to ensure 

accurate depiction of speed patterns. For interrupted flow, 

the speed sensors are placed in the midway location of 

the intersection or 0.2 miles away from the intersection.

Figure 1. Speed distribution curve

As presented in Figure 1, the cumulative speed 

distribution curves break at 15 percent and 85 percent of 

the total travelers. The travelers observed in the 15th 

percentile or below are driving unreasonably slowly. 
Traffic Data Computation Method POCKET GUIDE | 5

Similarly, travelers observed in upper 15% (≥85 percent)

are driving in excess of safe and reasonable speed. 

A steep slope of speed distribution can be observed 

between 85 percent and 15 percent drivers, which 

indicates that posting a speed limit less than the critical 

85th percentile value would unfairly impact the driving 

behavior of those who would otherwise be driving at a 

safe and reasonable speed. 

Example: A reduction of the maximum speed limit by 5 

mph from the 85th percentile speed would unfairly impact 

23% of the drivers in the traffic stream, as shown in 

Figure 1. Thus, the 85th percentile speed value is 

considered to be the traffic speed that most closely 

conforms with the safe and reasonable speed. 

To compute 85th percentile speed, arrange all the 

collected speeds in an ascending order. Take the total 

sample size and multiply it by 0.85 to get the 85th

percentile vehicle. Determine the speed of the one vehicle 

representing the 85th percentile vehicle, which will be the 

85th percentile speed. 

Figure 1 shows that out of the 100-vehicle sample, the 

speed of the 85th vehicle is 65 mph. 

Additional Notes and Tips. 

• Speed checks are necessary at regular intervals to 

ensure that the posted maximum speed limit matches 

the current traffic’s 85th percentile speed.

Related Traffic Data Items. Capacity.

Cross Reference. HCM Sec. 3-14 and 4-13. 
6 | Traffic Data Computation Method POCKET GUIDE

Annual Average Daily Traffic (AADT)

AADT estimates, with as little bias as possible, the mean 

traffic volume across all days for a year for a given location 

along a roadway. AADT is different from ADT because it 

represents data for the entire year. 

Various AADT estimation methods are in use. They include: 1) 

Simple average method, 2) AASHTO method (average of 

averages method), and 3) FHWA AADT method. FHWA AADT 

method is being adopted by State DOTs. Under the simple 

average method, AADT is estimated as the total traffic volume 

passing a point (or segment) of a road in both directions for a 

year divided by the number of days in the year. It requires 

volume for every day of the year. The AASHTO method 

incorporates 84 averages, i.e., 7 averages for the days of 

week for each of the 12 months. It requires daily volumes on 

at least one of each day of the week within each month. 

While the AASHTO method is currently the adopted practice 

by FHWA, FHWA recommends use of the FHWA AADT 

method due to the reduced bias. 

Simple 

average 

method

AAAAAAAA = 1

nn �VVVVVVkk

nn

kk=1

AASHTO 

method AAAAAAAA = 1

12 � �

1

7�� 1nnjjjj

�VVVVVVii

nnjjjj

ii=1

�

7

jj=1

�

12

=1

Where:

VOLk = daily traffic on kth day of the year

n = number of days in a year (365 or 366)

VOLijm = daily volume for ith occurrence of the jth day 

of week within the mth month

i = occurrences of day j in month m for which traffic 

data are available

j = day of week (1 to 7)
Traffic Data Computation Method POCKET GUIDE | 7

m = month of year (1 to 12)

njm = number of occurrences of day j in month m for 

which traffic data are available. 

Discussion and Illustration. AADT is a basic measurement 

that indicates vehicle traffic load on a road segment. It 

measures how busy a road is and is a critical input parameter 

in many transportation planning applications as well as for 

fund allocation to transportation agencies. 

For the simple average to be accurate, it requires a complete 

traffic volume measured on every day of the year, which can 

be operationally challenging. The AASHTO method requires 

less total data but results can be low or high compared to 

true mean due to equally weighting each day of the week and 

month even though days of the week occur either 4 or 5 

times in a particular month and days within a month vary 

from 28 to 31. The 2016 TMG contains the FHWA AADT 

method that eliminates these biases and allows for partial 

days data or data from other than daily volumes to be 

utilized. 

Additional Notes and Tips.

• Multiplying AADT by the length of the road segment yields 

estimated daily vehicle miles traveled (DVMT). 

• AADT can be converted to design volume and directional 

volumes by multiplying it with appropriate factors. 

• The same AADT estimation methods can be applied to 

estimate average class-based daily volumes.

• AADT trends are used in traffic planning and forecasting.

Related Traffic Data Items. AADT, ADT, MADT, AADTT, 

DDHV, K-factor, D-factor, and DHV. 

Cross Reference. TMG Sec. 1.2.7, 3.2.1., and 3.3.1; HPMS 

Field Manual Item 21; HCM Sec. 3.2.
8 | Traffic Data Computation Method POCKET GUIDE

Annual Average Daily Truck Traffic (AADTT)

The average daily volume of truck traffic on a road

segment for a year. Trucks are defined as vehicles of 

classes 4 through 13 in the FHWA’s 13-category vehicle 

classification system. The computation of AADTT follows

that of AADT, except that only truck volumes are used. 

AADTT can be computed in two ways, i.e., the simple 

average and AASHTO methods.

Simple 

average 

method

AAAAAAAAAA = 1

nn �( TTTTTTTT VVVVVV)kk

nn

kk=1

AASHTO 

method AAAAAAAAAA = 1

12 � �

1

7�� 1nnjjjj

�( TTTTTTTT VVVVVV)ii

nnjjjj

ii=1

�

7

jj=1

�

12

=1

Where:

Truck VOLk = daily truck volume on kth day of year

n = number of days in a year (365 or 366)

Truck VOLijm = daily truck volume for ith occurrence 

of the jth day of week within the mth month

i = occurrences of day j in month m for which 

truck traffic data are available

j = day of week (1 to 7)

m = month of year (1 to 12)

njm = number of occurrences of day j in month m 

for which truck traffic data are available 

Discussion and Illustration. AADTT provides 

information on truck movements which is critical for 

design and analysis of pavements, freight, air quality, 

crash data, highway planning, and performance 

assessment. Vehicle Class Distribution (VCD) refers to the 

percentage distribution of AADTT in the population of 

FHWA vehicles of classes 4 through 13. Similarly, Truck 

Hourly Distribution Factors refer to distribution of AADTT 
Traffic Data Computation Method POCKET GUIDE | 9

over the 24 hours. Table 1 shows computations of VCD 

and Truck Hourly Distribution Factor (THDF).

Table 1. Computation of VCD and THDF (source: 

TMG).

Class AADTT VCD

4 235 2.0

5 654 5.5

6 961 8.1

7 1,620 13.6

8 1,240 10.4

9 654 5.5

10 598 5.0

11 103 0.9

12 1,245 10.4

13 4,621 38.7

Total 11,931 100

Hour Hourly-AADTT THDF

00-01 8 0.6

01-02 9 0.7

02-03 12 0.9

03-04 16 1.3

04-05 25 2.0

05-06 36 2.8

06-07 45 3.5

07-08 68 5.3

08-09 78 6.1

09-10 76 5.9

10-11 78 6.1

11-12 82 6.4

12-13 98 7.7

13-14 98 7.7

14-15 86 6.7

15-16 88 6.9

16-17 74 5.8

17-18 78 6.1

18-19 64 5.0

19-20 52 4.1

20-21 54 4.2

21-22 26 2.0

22-23 18 1.4

23-24 10 0.8

TOTAL 1,279 100

Related Traffic Data Items. AADT, Vehicle Weight, 

Vehicle Class, Class Factor, ACF, and Load Distribution 

Factor.

Cross Reference. TMG Sec. 3.2.3, 3.4, G6, G7, G8, and 

H4.
10 | Traffic Data Computation Method POCKET GUIDE

Average Daily Traffic (ADT) 

The ADT, also referred to as mean daily traffic, is the 

average number of vehicles that travel through a specific 

point of a road over a short duration time period (often 7 

days or less). It is estimated by dividing the total daily 

volumes during a specified time period by the number of 

days in the period. 

AAAAAA = 1

nn�VVVVVVii

nn

ii=1

Where: 

VOLi = daily volume in the ith day and 

n = the number of whole days.

Discussion and Illustration. ADT is the most basic unit 

used for traffic monitoring and forecasting. It provides an 

aggregated measure of traffic volume. In combination 

with other traffic data items, it is used for determining the 

dimensions or function of proposed roadways, particularly 

roads of low and moderate traffic volumes. ADT requires 

TOD, DOW, and MOY factors to properly annualize the 

values to AADT.

Example: A temporary traffic count station measures

traffic volume for seven days as shown in Table 2. The 

ADT can be calculated as follows.
Traffic Data Computation Method POCKET GUIDE | 11

Table 2. Daily volume and ADT

Day

Daily 

Volume 

(veh/day)

Daily 

Factors Weighted Volume

Day 1 4,410 0.11 4,410 x 0.11 = 490

Day 2 5,135 0.12 5,135 x 0.12 = 614

Day 3 5,270 0.13 5,270 x 0.13 = 676

Day 4 5,114 0.15 5,114 x 0.15 = 743

Day 5 5,980 0.16 5,980 x 0.16 = 971

Day 6 4,295 0.16 4,295 x 0.16 = 697

Day 7 2,890 0.17 2,890 x 0.17 = 494

ADT (sum) 4,686 (veh/day)

Additional Notes and Tips

• ADT is representative only of the time period and type 

of traffic volumes included. Related measures like AADT 

and MADT are also averages but are normalized and 

more contextually relevant. 

• Two-way design hour volume (DHV) is usually 8-12% 

of the ADT.

• The difference between ADT and AADT is the temporal 

coverage of the data used to compute them.

• ADT can be converted to AADT by applying monthly, 

day of week, axle factor, and change rate.

Related Traffic Data Items. AADT, MADT, AADTT, Dfactor, K-factor, Monthly and Day of Week Factors. 

Cross Reference. AASHTO Sec. 2.3.2.; HCM Sec. 3.2; 

TMG Sec. 1.2.7.
12 | Traffic Data Computation Method POCKET GUIDE

Axle Correction Factor (ACF)

ACFs are the ratio of number of vehicles divided by number of 

axles in each type of vehicle class. ACF are also known as 

axle factor.

AAAAAA = Total number of vehicles of Class X

TTTTTTTT NN NNNNNN oo aa aa oo XX vv hii

Discussion and Illustration. Data collection equipment, 

like pneumatic tubes, collect axle counts when only one road 

tube is used. In such cases, ACFs are used to adjust axle 

counts into vehicle counts. ACFs can be measured at an 

individual point on a roadway for each vehicle classification or 

at the system level. To measure the ACF at a system level, a 

combination of vehicle classification counts will be averaged 

to represent vehicles traveling on an entire system of roads. 

By dividing these vehicle classification counts by their 

respective number of axles, axle factor groups are formed. 

However, this method involves a certain percentage of error 

in axle factor. In general, truck percentages on roadways vary 

(even on roads within the same functional class). Therefore, 

the TMG recommends calculation of ACFs that are specific to 

vehicle classification and roadway location. ACFs must be 

determined from per vehicle axle class or weight data.

Example: Table 3 shows daily vehicle and axle counts for 

vehicle of class A at two roadway locations. Axle factor for 

this vehicle class and road is computed by dividing the daily 

vehicle volume count (X) by daily axle count (Y). 

Another method of adjusting axle counts to vehicle counts is 

by measuring average number of axles per vehicle, which is 

the inverse of axle factor. 
Traffic Data Computation Method POCKET GUIDE | 13

Table 3. ACF and axles per vehicle computation from 

vehicle and axle counts

Roadway 

ID

Daily vehicle 

Volume 

count for 

vehicle Class 

A (X)

Daily 

Axle 

Count for 

vehicle 

Class A 

(Y) 

ACF = 

X/Y

Average 

Number of 

Axles per 

vehicle = 

(Y/X)

R120A 50,000 125,000 0.40 2.50

R280K 48,000 123,000 0.39 2.56

Now that the ACFs for a roadway at a location of interest are 

available, vehicle counts for another location in the vicinity 

can be computed from axle counts or vice versa. In general, 

the day of week in both the cases should be the same. In 

Table 4, axle counts are multiplied by ACFs to compute the 

daily vehicle counts. 

Table 4. Vehicle count adjustment using ACF

Roadway 

ID

Daily Axle 

Count for 

vehicle 

Class A 

(Y)

ACF Average 

Number of 

Axles per 

vehicle

Daily 

vehicle 

Volume 

count for 

vehicle 

class A

R120A 143,000 0.40 2.50 57,200

R280A 129,000 0.39 2.56 50,390

Additional Notes and Tips.

• Because ACF is inversely proportional to the number of 

axles on a vehicle, for example, a lower ACF indicates a 

higher number of axles per vehicle, which reflects a higher 

percentage of multi-axle vehicular traffic. A typical range of 

ACF is 0.50 to 0.30. 

Related Traffic Data Items. AADT and Daily Axle Counts.

Cross Reference. TMG Sec. 1.2.6. 
14 | Traffic Data Computation Method POCKET GUIDE

Capacity 

The maximum sustainable hourly flow rate at which 

vehicles reasonably can be expected to traverse a point or 

a lane on a roadway during a given time period, typically 

15-minute intervals under prevailing roadway, traffic, and 

control conditions. It is expressed in vehicles per hour per 

lane.

Discussion and Illustration. Capacity is a key 

parameter that determines the ability of a transportation 

facility to meet the travel demand. It is used for level of 

service (LOS) analysis of a transportation facility,

designing of signal timing plans, and identification of 

bottlenecks. 

Capacity of a transportation facility is affected by many 

factors including number of lanes, width of lanes, traffic 

speed, proportion of heavy vehicles (HVs), conflicting or 

opposing traffic, roadway alignment, presence of merge 

and weave segments, weather, and both width and type 

of shoulder. Also, for signalized intersections, factors such

as signal green time portion and pedestrian counts affect 

the capacity.

In addition to the factors mentioned above, daily, 

monthly, and seasonal variations in traffic demand must 

be taken into consideration before estimating the capacity. 

This ensures year-round accommodation of traffic 

demand. 

In some instances, the capacity of a transportation facility 

may be greater than its hourly demand. However, when 

the traffic flow within a portion of the hour exceeds 

capacity, the impact created by excess demand takes a 

long time to dissipate. To mitigate this issue, both 
Traffic Data Computation Method POCKET GUIDE | 15

directions of a transportation facility must have adequate 

capacity to accommodate peak directional flow.

The Highway Capacity Manual (HCM) provides default 

values for capacities on basic freeway and multilane 

highway segments based on the factors mentioned above. 

The default capacity values, presented in HCM, assume 

ideal roadway geometry, good weather, and a traffic 

stream consisting entirely of passenger vehicles. 

The capacity of basic freeway segment depends on the 

facility’s free flow speed (FFS). The default capacity 

recommended by HCM for facilities with FFS of 75 to 70 

mph is 2,400 veh/h/ln. For facilities with FFSs of 65 mph, 

60 mph, and 55 mph, the default capacities are 2,350 

veh/h/ln, 2300 veh/h/ln, and 2,250 veh/h/ln. 

Additional Notes and Tips. 

• When the traffic demand meets the capacity, critical 

density and critical speed occur. 

• Capacity of segments with no weaving, near zero truck

volumes, and constant speed near the FFSs can be up 

to 2,850 veh/h/ln.

Related Traffic Data Items. Volume, Saturation Flow, 

LOS, Signal Green Time, and Free Flow.

Cross Reference. HCM Sec. 1 through 8.
16 | Traffic Data Computation Method POCKET GUIDE

Change Rate

Change rate (also called growth factor) is an adjustment 

factor to reflect traffic change on a facility or in an area 

over a given time period. In general, change rate is 

calculated as a ratio of AADT of the most recent year to 

the AADT of a preceding year. It can be expressed as a 

ratio or percent. 

h aa RR = (AADTCCCCCCCCCC CC YY YY ⁄AADTPP PP PP PP YY YY )

Discussion and Illustration. Change rate is a key 

parameter in designing the capacity of a transportation 

facility to meet the growing demand in the region. Change 

rate factors are applied for AADT values counted in prior 

years that need to be adjusted up to current year.

Typically, the segments in the subject area are grouped 

based on their change rates or assigned volume factor 

groups. In this way, multiple change rate groups with 

varying change rates will be identified. These groups 

include urbanized areas – central cities and older suburbs; 

urbanized areas – newer suburbs; rural and small urban 

interstate system; other rural; and other small urban.

In developing change rates, AADT values of the most 

current year along with its preceding year are required. 

Example: Table 5 presents the AADT values of segments 1 

through 5. For segment 1, the AADT value is 1,768 

vehicles/day for the most current year; and 1,723 

vehicles/day for the preceding year. The computed 

change rate for the first segment will be 1.026. A similar 

method can be used for calculating change rates for all 

segments. 

After converting the ratio to a percent, an average change 

rate of 4.4% is observed in the region between the two 
Traffic Data Computation Method POCKET GUIDE | 17

years. The segments can be categorized into three groups 

based on their change rates. Segments 1 and 5 can be 

grouped with an average change rate of 2.5% (ratios = 

1.024 and 1.026). Similarly, segments 2 and 4 can be 

grouped with an average change rate of 4.5% (ratios = 

1.044 and 1.046). Segment 3 can be treated as the third 

group with change rates of 7.8% (1.078).

Table 5. Change Rate Development

Segment Current 

AADT

Preceding 

AADT

Change 

Rate

Change 

Rate Group

1 1,768 1,723 1.026 1

2 1,985 1,901 1.044 2

3 3,120 2,895 1.078 3

4 2,285 2,184 1.046 2

5 2,349 2,294 1.024 1

Average Change Rate 1.044

Additional Notes and Tips. 

• Rural and urban areas have different change rates.

• The outlying parts of an urbanized area are likely to 

experience faster traffic change than the city center or 

older, established parts of suburbs.

• Traffic on the interstate system in the rural and small 

urban areas consists primarily of through traffic, so 

change rate on these roads are less affected by the local 

economy than on other roads.

• Large states experience regionally varying change rates in 

different rural and urbanized areas. 

Related Traffic Data Items. AADT, Design Volume, Future 

AADT, and Short Term ADT to AADT Conversion.

Cross Reference. AASHTO Guidelines for Traffic Data 

Programs Sec. 5.2.3, Table 5-3. 
18 | Traffic Data Computation Method POCKET GUIDE

Combination Unit Truck AADT

A Combination Unit (CU) Truck is any truck that meets the 

requirements established for the FHWA Truck 

Classification Method for Categories 8 through 13. AADT is 

expressed in vehicles per day units.

CC TTTTTTTT AAAAAAAA = 1

7��

1

12 ��

1

nn �AAAAAAii 8−13

nn

kk=1

�

12

jj=1

�

7

ii=1

Where:

CU Truck AADT = AADT for CU Trucks

ADTijk8-13 = daily combination traffic for day k, of 

DOW i, and month j

i = DOW

j = month of the year

k = count of occurrence of that day of week in a 

month; 4 when it occurs four times in a month 

n = the number of days of that day of the week 

during that month (for which you have data)

Discussion and Illustration. CU Truck AADT is used to 

estimate pavement deterioration, calculate operating 

speeds, and perform freight analysis for a transportation 

facility or segment. Depending on its functional class, each 

roadway segment experiences different volumes of truck 

traffic. To get accurate estimates, HPMS recommends 

actual traffic counts of the road segment. If the actual 

measured values are not available, the most credible 

method would be to use other site-specific measured 

values from sites located on the same route. Other 

methods may include: assigning site-specific measured 

values to other samples that are located on similar 

facilities with similar traffic characteristics in the same 

geographic area and in the same volume group; or 
Traffic Data Computation Method POCKET GUIDE | 19

assigning measured values from samples in the same 

functional system and in the same area type. 

HPMS contains data for this traffic parameter for NHS and 

sample segments. Outside HPMS, states have access to 

traffic classification counts and data on the percentage of 

vehicles passing through a counting point. 

Once the traffic classification counts are collected, average 

traffic counts on each day of week for each month are 

computed, e.g., the average CU Truck volume for Monday 

of every month and annual average can be computed. In 

this way, monthly and annual averages of counts for each 

day of week can be computed for all seven days. 

Averaging the annual averages of the seven day of week 

counts will yield the AADT of CU Trucks. Since this data is 

taking averages for all days, it is less prone to any bias 

issues created by any missing data due to malfunctions 

and maintenance of PTRs/CCSs. FHWA recommends at 

least the 6 vehicle types listed in the HPMS Vehicle 

summary table be collected, stored and factored to 

annualized data whenever a portable class count is taken. 

This then allows for the VMT weighting required in the 

HPMS vehicle summary table. 

Additional Notes and Tips. CU truck AADTs are 

computed bidirectionally for two-way facilities and 

directionally for one-way roadways and ramps.

Related Traffic Data Items. AADT and SU Truck AADT.

Cross Reference. HPMS Sec. 4.3 - Item24; TMG Sec. 

3.2.3 and Table 3-3.
20 | Traffic Data Computation Method POCKET GUIDE

Critical Speed

The traffic speed at which volume meets capacity of a 

segment is called critical speed or speed at capacity. It is 

expressed in Miles Per Hour (mph).

SSSS SS ( h) = �

MM FF �

VV h

HH �

DD �VV h

MM �

�

Where:

Maximum flow rate = The capacity of a 

transportation facility

Critical Density = Traffic density when flow rate 

is maximum 

Discussion and Illustration. Critical speed is a key 

parameter in understanding the prevailing traffic 

conditions for a transportation facility or corridor. 

Flow-Speed-Density relationships are presented in Exhibit 

4-2 in HCM 2016. In practical terms, the speed achieved 

by a single vehicle when there are no other vehicles in the 

corridor is called Free Flow Speed (FFS). As more traffic 

starts flowing through the transportation facility, the 

speed of the traffic stream drops and reaches the critical 

point known as the facility’s capacity. 

Operation at critical speed is very unstable as there are no 

usable gaps in the traffic stream. As a result, any small 

disturbance created by an entering or lane changing 

vehicle might cause traffic flow breakdown. For this 

reason, traffic and transportation management center 

(TMC) engineers observe the dynamic shifts in the travel 

speeds and take necessary actions before the traffic speed

reaches the critical speed. 
Traffic Data Computation Method POCKET GUIDE | 21

The critical speed varies depending on the prevailing 

traffic, roadway conditions, and segment length. For 

uninterrupted flow, the default value for density at 

capacity is 45 pc/mi/ln. Using this default value along with 

the flow rate value, critical speed could be computed. 

Example. If the maximum flow rate is 2,350 veh/h and 

critical density is 45 pc/mi/ln, then the critical speed will 

be 50 mph. 

Additional Notes and Tips. The objective of an effective 

traffic management strategy is to keep traffic speed at or 

above the critical speed. This avoids traffic flow 

breakdown and capacity drop. 

Related Traffic Data Items. Flow Rate, Critical Density, 

and Free Flow.

Cross Reference. HCM Sec. 4-8.
22 | Traffic Data Computation Method POCKET GUIDE

Design Hour

Design hour (DH) is an hour with a traffic volume that 

represents a location specific peak hour value for 

designing the geometric and control elements of a facility.

This peak hour selected will allow the designed facility to 

accommodate traffic during most of the peak hours. In 

general, the design hour for a transportation facility 

depends upon its location (e.g. rural or urban) and type 

(e.g., freeway, arterial, collector, or ramp). 

Discussion and Illustration. The design hour is a key 

characteristic in estimating the expected demand for a 

proposed transportation facility. 

Typically, the hour corresponding to the 30th highest 

hourly volume of the year is considered as the design hour 

as stated by the Highway Performance Monitoring System 

(HPMS). However, local jurisdictions and metropolitan 

planning organizations (MPOs) may adjust the design hour 

based on their local facility-specific traffic conditions. 

In rural settings, customary practice in the United States is 

to base highway design on the 30th highest hour of the 

year. The 30th hour is used because it falls in the range of 

subsequent highest hours that have similar volumes. Even 

though a considerable variance is observed between the 

peak (highest) and 30th highest hourly volumes of a year, 

designing for the peak hour would not be deemed 

economical and feasible in many regions. 

In urban settings, the difference in demand between peak 

hour and 30th hour of the year is minimal. Similarly, the 

50th and 100th highest hourly volumes of the year are 

closer together in urban than rural settings. This is true for 

urban areas because of the recurring morning and evening 

traffic patterns. 
Traffic Data Computation Method POCKET GUIDE | 23

Example: Two highways, one each in an urban and rural 

setting, require estimation of design hour based on their 

annual hourly traffic volumes. The urban highway 

observed 3,992; 3,850; 3,735; and 3,355 vph as their 1st, 

30th, 50th, and 100th peak hour volumes. Similarly, the 

rural highway observed 4,389; 3,782; 3,591; and 3,234 

vph as their 1st, 30th, 50th, and 100th peak hour volumes.

For the urban highway, a minimal traffic variance of 4% 

was observed between the 1st and 30th peak hour. 

Similarly, 3% variance was observed between 30th and 

50th hours. However, higher variances were observed 

when compared with the 100th hour. Therefore, a design 

hour within the range of the 30th and 50th hour can be 

selected for this highway.

Table 6. Design Hour Selection

Urban Settings Rural Settings

Peak Hours % Variance Peak Hour % Variance

1st and 30th 4 1st and 30th 14

30th and 50th 3 30th and 50th 5

30th and 100th 13 30th and 100th 15

50th and 100th 10 50th and 100th 10

Additional Notes and Tips. The variance of traffic for 

the 1st and 30th hours on a rural highway was considerably 

higher at 14%. However, Traffic variation is stable around 

the 30th hour. Therefore, the 30th hour can be selected as 

the design hour. 

Related Traffic Data Items. Design Volume, K-factor, 

D-factor, and DHV.

Cross Reference. HCM Sec. 3-11 and 7-3. 
24 | Traffic Data Computation Method POCKET GUIDE

Design Vehicles

Given the large variety of vehicle types using roadway 

facilities, adapting standard physical and operating vehicle 

features is essential. Design vehicles are vehicles of 

representative size, weight, and operating performance 

specifications used to establish highway design controls by 

setting criteria to accommodate designated vehicle classes to 

optimize roadway functionality. 

Discussion and Illustration. AASHTO has defined 20 

design vehicles categorized under four general vehicle 

classes, i.e., passenger cars, buses, trucks, and recreational 

vehicles. 

1. Passenger Car 11. Interstate Semitrailer 62 ft

2. Single-Unit Truck 12. Interstate Semitrailer 67 ft

3. Single-Unit Truck (3-

axle)

13. Double-Bottom

Semitrailer/Trailer

4. Intercity Bus 14. Double-Semitrailer/Trailer

5. Intercity Bus 45 ft 15. Triple-Semitrailer/Trailer

6. City Transit Bus 16. Turnpike DoubleSemitrailer/Trailer

7. School Bus 65 pass. 17. Motor Home

8. Large School Bus 84

pass.

18. Car and Camper Trailer

9. Articulated Bus 19. Car and Boat Trailer

10. Intermediate

Semitrailer 40 ft

20. Motor Home and Boat

Trailer

The proportion of vehicle types and sizes that a facility is 

expected to accommodate determines the design vehicle to 

be used, i.e., roadway features are designed to allow safe 

and easy operations of the vehicles that are likely to use the 

facility. 

Note that the design vehicle does not represent the average 

vehicle in its class; rather it has slightly larger physical 
Traffic Data Computation Method POCKET GUIDE | 25

dimensions and a larger minimum turning radius than most 

vehicles in its class. Therefore, a facility that is designed to 

accommodate the design vehicle will also accommodate most 

of the vehicles in its class and nearly all of the vehicles in the 

classes composed of smaller vehicles. 

The choice of design vehicle is also influenced by the 

functional classification of a roadway. For example, a pickup 

truck and a large school bus can be considered as the design 

vehicles when designing parkway streets and low-volume 

county highway intersections, respectively. The entire facility, 

including specific sites like intersections, curves, ramps, 

turnouts, clearances, and grades, should accommodate the 

design vehicle considered. 

Additional Notes and Tips.

• Critical features of design vehicles are length, wheelbase, 

acceleration, minimum turning radius, and rear tire path.

• Generally, the design of freeways should accommodate the 

largest of all the several design vehicles.

• A vehicle with larger wheelbase has restricted turning 

capability and calls for larger geometric design values.

• If a roadway permits bicycle use, the design procedure 

should consider a bicycle as an additional design vehicle. 

• Because combination truck sizes and turning features vary 

widely, there are several combination truck design 

vehicles. 

• In addition to accommodating the design vehicles, access 

to emergency vehicles is an essential roadway design 

criterion. 

Related Traffic Data Items. Turning Radius, Vehicle Class, 

and Vehicle Weight.

Cross Reference. AASHTO Sec. 2.1.
26 | Traffic Data Computation Method POCKET GUIDE

Design Volume

This is the projected traffic volume for the design life of a 

transportation facility, usually 10 to 20 years in the future, 

during a specified design hour. Design volumes are 

estimated in the roadway planning process and are often 

expressed as the expected traffic volume during a 

specified design hour, i.e., the peak period. 

Discussion and Illustration. Design volume is used in 

the roadway planning process for making design decisions 

during the design year. Such decisions include roadway 

structures, capacity, geometric layouts, and auxiliary 

services like size of parking areas. In addition, design 

volume is used for designing traffic control devices. To 

avoid inadequate or uneconomical roadway design, the 

30th highest hourly volume is usually used as the design 

volume. 

Design volume and Design Service Flow Rate (DSFL) are 

two separate concepts. DSFL is the maximum hourly flow 

rate of traffic that a new facility can serve without the 

degree of congestion falling below a pre-selected level. A 

roadway should be designed using a design volume that is 

less than or equal to the DSFL.

A facility should be designed to accommodate the traffic 

volume that is likely to occur within its design life. This 

can be achieved in two ways. The first method is to use a 

well-validated travel demand forecasting model and 

estimate the design volume. The second method is to use 

the average daily traffic (ADT) or annual average daily 

traffic (AADT) values from a similar facility located in 

similar transportation settings and estimate design 

volume. 
Traffic Data Computation Method POCKET GUIDE | 27

Among the two methods, use of design volume method is 

proven to be critical, as the traffic volume on the 

transportation facility can be significantly higher than the ADT 

or AADT on certain days. In design volume method, travel 

demand forecasting models consider the traffic settings, land 

uses, and change rate in the selected corridor to forecast the 

best estimate of design volume for the design life of the 

facility.

Based on the projected design volumes, AASHTO provides 

requirements for design speed, traveled way and shoulder 

width, clear roadway width, design loading, and roadway 

structural capacity.

Design volume considers directional split to understand the 

shifts in traffic volume and accommodate the peak direction. 

For example, a rural arterial has a forecasted design volume 

of 6,000 veh/h. If the directional split is 50-50, then two lanes 

in each direction will be sufficient to accommodate traffic in 

both directions, assuming a capacity of 1,500 veh/h/ln. 

However, if the split is 70-30 for the peak and off-peak 

directions, which means 70 percent of the design volume 

travels in one direction (e.g., northbound) and 30 percent in 

the opposite direction (e.g., southbound), then three lanes in 

each direction are required to accommodate the peak period 

traffic (4,200 veh/h).

Additional Notes and Tips. 

• According to AASHTO, the design life of a transportation 

facility is a maximum of 15 to 24 years. 

• Assuming higher design life can lead to higher operational 

and maintenance costs of the facility.

Related Traffic Data Items. Design Hour, DHV, Directional 

Design Hourly Volume (DDHV), K-factor, D-factor, AADT, and 

LOS.

Cross Reference. AASHTO Sec. 2.4.3, 5, 6 and 7; Table 5-1, 

5-5, 5-7, 6-1, 6-5, 6-6, 6-7 and 7-3; HCM Sec. 3-11 and 7-3.
28 | Traffic Data Computation Method POCKET GUIDE

Directional Design-Hour Volume (DDHV)

Directional Design-Hour Volume (DDHV) is the proportion 

of AADT in the peak-hour (design hour) in the 

predominant direction of traffic flow. DDHV is determined 

from field measurements on the facility under 

consideration or on parallel and similar facilities. It is given 

by multiplying AADT by K-factor and D-factor.

= AAAAAAAA × KK ×

Where: 

K = the proportion of AADT occurring in the peak 

hour, i.e., the K-factor.

D = peak-hour volume proportion in the major 

direction, i.e., the D-factor.

Discussion and Illustration. DDHV is an important 

parameter used for transportation planning and design of 

new highway construction or the reconstruction of an 

existing facility. The concept of DDHV is to guide planners 

in effectively estimating the needed capacity of a given 

facility operating under given LOS. Designing a facility 

with too much capacity will be uneconomical, and a facility 

with too little capacity will be inadequate. 

Neither the AADT nor the ADT indicates the variations in 

traffic volumes that occur during the day, specifically high 

traffic volumes that occur during the peak hour of 

travel. Therefore, AADT or ADT should be adjusted for 

peak-hour volume and directionality, which is the purpose 

of calculating DDHV. DDHV is a critical design volume with 

a directional component.

Example 1: The projected AADT of a proposed facility is 

33,000 veh/day. If the proportion of AADT in the design 

hour is 16 percent and the peak-hour directional 

distribution is 65:35, then DDHV will be:
Traffic Data Computation Method POCKET GUIDE | 29

DDHV = 33,000 × 0.16 × 0.65 = 3,430 vph.

Note that the higher directional distribution should be 

used, i.e., 65 should be used instead of 35. 

A practical application of DDHV is in computing the 

number of lanes needed for a given LOS, as shown below. 

NN = PP × MM ii × HHHH × pp

Where: 

PHF = Peak hour factor

MSFi = Maximum service flow rate for LOS i

fHV = heavy vehicles adjustment factor

fp = road user familiarity adjustment factor

 

Example 2: Assume that the PHF, fHV, and fp of the 

proposed facility are 0.91, 0.925, and 1.0, respectively, 

the maximum service flow rate for the facility to provide a 

LOS of C is 1,500 pc/h/ln. The number of lanes required 

will be:

NN = 33, 44 44

. 9999 × , 55 × . 9999 × = . 33 (uuuuuu 33 llllll )

Additional Notes and Tips.

• DDHV is one-direction volume and its unit is vph. 

• The number of lanes affects the facility’s free flow 

speed, which in turn affects the number of lanes. This 

makes the design analysis of freeways an iterative 

process.

Related Traffic Data Items. DHV, AADT, K-factor, Dfactor, PHF, and LOS.

Cross Reference. AASHTO Sec. 2.3.3., 8.2.2.; HCM Sec. 

3.2., 15.III. 
30 | Traffic Data Computation Method POCKET GUIDE

Directional Factor

Directional factor (D-factor) is the traffic volume proportion 

moving in the higher volume direction during the peak hour 

to the combined volume in both directions. It is usually 

expressed as a percentage. It represents the directional 

distribution of hourly traffic volumes. D-30 represents the 

traffic volume proportion in the 30th highest hourly volume of 

the year traveling in the peak direction. Similarly, D-100 

represent the traffic proportion in the 100th highest hourly 

volumes of the year. Guidance in the HPMS Field manual 

suggests using the 30th highest hourly volume for a given 

year (typically used) for the purposes of calculating D-factor. 

D-30 is used in design capacity analysis, while D-100 is 

typically used in calculating the LOS of a facility.

− = KK h hii h vv ii

VV ii h × 100%

Discussion and Illustration. D-factor is based on the fact 

that traffic volumes may not be evenly split in both travel 

directions during the design hour. A road located in an urban 

center often has equal traffic volumes in both directions, i.e., 

a D-factor of nearly 50%. On the other hand, a road in 

suburban areas carry imbalanced directional flows due to 

larger traffic traveling toward an urban area in the morning 

and away from an urban area in the evening, i.e., D-factor is 

always greater than 50%. Directional distribution of traffic 

may significantly affect the LOS of a facility. Therefore, Dfactor plays an important role in highway design by 

considering the directional split of traffic, especially for twolane rural highways. D factors for one way roadways is always 

100%.

Example: Suppose the hourly volume in east- and west-bound 

directions of a highway are 1,300 and 2,200 vph during the 

peak hour, respectively. Note that the west-bound direction 

has the higher volume, therefore, it is the peak direction. The 
Traffic Data Computation Method POCKET GUIDE | 31

combined volume in both directions is 3,500 vph (i.e., 1,300 

+ 2,200). Hence, the D-factor will be:

DD − ff ffffffff = ,

33, 55 × % = 66 %

AADT can be multiplied by both the K-factor and the D-factor 

to convert it to an equivalent directional design hourly volume 

(DDHV) in the major direction.

DDHV = AADT * K * D 

Example: The forecasted AADT of a highway is 48,000 vph. 

The expected K- and D-factors are 12% and 58%, 

respectively. The DDHV will be given by:

DDHV = 48,000 * 0.12 * 0.58 = 3,340 vph. 

Additional Notes and Tips

• In general, the value of D-factor for non-one way 

roadways range from 50% to 75%. 

• D-factor is best measured at continuous monitoring sites. 

• Land use pattern near a facility affects its D-factor value.

• The directional distributions of traffic change based on 

hour of the day, day of the week, and season. 

• The same peak hour volume used to calculate K-factor 

should be used to calculate D-factor. 

• In some urban areas, imbalanced directional distributions 

can be mitigated by using reversible lanes.

• The peak directional volume of two multilane highways 

with similar AADT may differ by as much as 60%.

Related Traffic Data Items. AADT, DDHV, and K-factor.

Cross Reference. HPMS Field Manual Item 27; TMG Sec. I 

Q-24 to Q-29; HCM Sec. 3.2.; AASHTO Sec. 2.3.3.
32 | Traffic Data Computation Method POCKET GUIDE

Directional Flow Rate and Ratio

Directional flow rate is the traffic flow rate in one direction

of a transportation facility, where flow rate is the 

equivalent hourly rate at which vehicles pass over a given

point or section of a lane or roadway during a given time 

interval of less than 1 h, usually 15 min. It is expressed in 

veh/h/ln.

FF RR (VVii) = � VV

PP ∗ NN�

Directional flow ratio is the ratio of directional flow rate 

and saturation flow rate in a given direction.

FF RR = �

VVii

SSii

�

Where:

PHF = Peak Hour Factor

V= Hourly Directional volume

N= Number of directional lanes

Vi= Directional flow rate in direction i

Si= Saturation flow rate in direction i

Discussion and Illustration. Directional flow rate and 

ratio are key parameters in critical lane group and critical 

signal phasing identification at an intersection as 

discussed below. 

Directional flow rate for a transportation facility is 

computed by dividing the hourly directional volume with 

the peak hour factor and number of lanes in that 

direction.

Directional flow ratio at an intersection is computed by 

dividing the demand flow rate with the saturation flow 

rate for a given direction and lane group. 
Traffic Data Computation Method POCKET GUIDE | 33

Example: Consider a two-lane (one lane per direction) 

arterial roadway. If the hourly directional volume is 750 

veh/h and peak hour factor (PHF) is 0.83, then the 

directional flow rate is 900 veh/h/ln. For the same facility, 

if the saturation flow is 1,200 veh/h of green/ln, then the 

flow ratio of this facility is 0.75.

a= critical flow ratio

Figure 2. Determination of critical lane group and 

phasing from flow ratio

At signalized intersections, the lane group with highest 

flow ratio is considered as the critical lane group and the 

phase associated with that lane group is called critical 

phase. For example, Figure 2 presents the flow ratios of 

lane groups at a minor and major arterial street 

intersection. Phases 4 and 8 refer to minor street lane 

movements. Phases 1, 2, 5, and 6 refer to major street 

lane movements. Right turns are permitted on the minor 

streets during the major street’s protected left turn 

phases. On the minor street, phase 4 is considered as the 

critical phase as it has a higher flow ratio when compared 

to phase 8. Similarly, the combination of phases 5 and 6 is 

considered as the critical phase on the major street 

(0.30+0.35=0.65), compared to the combination of 

phases 1 and 2 (0.40+0.20 =0.60).

Related Traffic Data Items. Saturation Flow, PHF, and 

Traffic Demand, D-factor, and K-factor.

Cross Reference. HCM Sec. 15-36, 19-4, 24-18. 
34 | Traffic Data Computation Method POCKET GUIDE

Equivalent Single Axle Load (ESAL)

The equivalent number of 18,000 lbf (pound force) singleaxle loads that are imposed by a mix of traffic having

different axle loads, axle grouping configurations, and 

pavement characteristics that are predicted over the

design life or analysis period of a pavement. 

18 = DD ∗ LL ∗ WW18

Where:

w18 = the ESAL for the design lane. 

DD = the directional factor for a two-way 

roadway.

DL = a lane traffic splitting factor for a roadway 

having more than one lane in each direction.

W18 = cumulative two-way ESAL projected for a 

roadway segment.

Discussion and Illustration. ESAL is a key parameter in 

determining the expected loads experienced by a 

pavement in its lifetime. Traffic modeling and design 

traffic professionals project cumulative ESAL data from all 

vehicles for the entire pavement life expectancy.

The axle distribution by axle weight range can be easily 

converted into ESAL, the most common pavement design 

loading value currently used in the United States. Even 

though load spectra is more accurate than ESAL, it is less 

commonly used due to its complex calculation 

methodology. ESAL at a local level might be different from 

the ESAL of the entire state. ESAL distribution in an area is 

affected by land uses and connectivity to other regions. 

Cumulative ESAL: AASHTO provides ESAL values for the 

gross weight of the axles. For calculating cumulative ESAL, 

consider four single axles on a roadway. The gross 
Traffic Data Computation Method POCKET GUIDE | 35

weights of the first two and last two axles are 20,000 lbs 

and 30,000 lbs each, respectively. AASHTO ESALs/axle 

values for these single axles are 1.371 (20,000 lbs) and 

6.546 (30,000 lbs). Now, as all axles are of the same type 

(single axle), multiplying the number of axles with ESALs 

will give total ESALs (1.371*2 = 2.742; 6.546*2=13.092). 

Summation of these ESALs (15.834) will produce ESAL by 

axle type (single). Summation of this single axle ESAL with 

ESAL of other axle types (tandem, tridem, etc.,) will 

produce cumulative ESAL experienced by roadway.

Load Equivalency Factor tables from AASHTO guide are 

used to determine the ESALs applied by different axles 

based on their type and gross weight. The Load 

Equivalency table is developed by using high powered 

ESAL equation, which considers various factors including 

applied axle loads, curve slope factor, pavement structural 

number, serviceability index and useful life.

If the cumulative ESAL on a two-way road is 454 ESALs 

for all vehicle types, the NB directional split is 56% (0.56) 

and the traffic split on lane 1 and 2 is 0.6 and 0.4, then 

the expected ESAL on design life of lane 1 and 2 is 152.5 

and 101.7 ESALs respectively. 

Additional Notes and Tips. 

• While ESAL has limitations as a measure of traffic 

loading for pavement design, it is still a very useful way 

of comparing the relative pavement damaging potential 

of different load spectra. 

• ESAL can only be used for axle groups up to tridems.

Related Traffic Data Items. Load Spectra and Vehicle 

Class.

Cross Reference. TMG Sec. 3.2.4 and Appendix H.3. 
36 | Traffic Data Computation Method POCKET GUIDE

Free Flow

A situation where traffic flow is unaffected by upstream or 

downstream conditions. It reflects vehicles’ desired flow, 

unconstrained by other vehicles or traffic control. It is 

typically expressed in pc/h/ln.

Discussion and Illustration. Free flow is considered as 

a base parameter to calculate various other traffic 

parameters including but not limited to speed, capacity, 

travel time index, and planning time index. It also plays a 

key role in determining the capacity and LOS of a freeway 

section.

The speed at which free-flow traffic prevails is called freeflow speed. Free-flow speed is the speed at which 

vehicles’ speeds are unaffected by surrounding conditions 

and other vehicles. Free-flow speed is the key parameter 

in estimation of freeway and multilane highway capacity. 

It is usually calculated from pre-defined algorithms or field 

observations. However, since it is used mostly in the 

design phase of freeways, pre-defined algorithms are used 

in most cases. These free-flow speeds are estimated from 

base free-flow speeds, which are considered to be the 

speeds that are only affected by the horizontal and 

vertical alignments of a transportation facility.

Once the base free-flow speed is determined, it is 

adjusted for different factors. In addition to vertical and 

horizontal alignment, free-flow speed is also affected by 

various factors of the roadway including lane widths, 

lateral clearances, weaving, median type, and access 

points.

Example: Consider a passenger car traveling at 70 mph on 

a basic freeway segment. Assuming good weather and 

unconstrained driving conditions, the car’s speed is 
Traffic Data Computation Method POCKET GUIDE | 37

expected to be unaffected until the flow on the lane 

exceeds 1300 pc/h/ln (approximately 1 pc per every 3 

seconds or 22 cars per minute). Similarly, a car traveling 

at 65 mph would face a reduction in travel speed if the 

flow rate on the freeway exceeds 1,450 pc/h/ln. These 

observations can be found in Table 7 (see HCM 2016 

Exhibit 12-7).

Table 7. Free-flow Speed and Flow Rate for Basic 

Freeway Segment

Free-Flow Speed 

(mph)

Flow Rate (pc/h/ln)

75 1,150

70 1,300

65 1,450

60 1,600

55 1,750

Source: HCM

Additional Notes and Tips.

• Free-flow traffic conditions are usually observed 

during off-peak hours. Early hours of the morning 

offer most favorable conditions for traffic free flow. 

Related Traffic Data Items. Capacity, LOS, Critical 

Speed, Critical Density, and Saturation Flow. 

Cross Reference. HCM Sec. 12-9 to 12-11.
38 | Traffic Data Computation Method POCKET GUIDE

Future AADT

AADT forecasted for a specific succeeding year of interest.

AADTFF = AADTCCCCCCCCCC CC ∗ (1 + AAAAAA )nn

Where:

AADTFuture = Annual Average Daily Traffic for the 

forecasted year (veh/day)

AADTCurrent = Annual Average Daily Traffic for the 

current year (veh/day)

AACR = Annual Average Change Rate

n = number of forecasted years

Discussion and Illustration. Future AADT is used to 

estimate capabilities and deficiencies of a selected 

transportation facility and its future improvement needs. 

Based on these estimates, cost allocation for 

transportation facility pavement modeling is determined. 

Future AADT also helps traffic engineers evaluate the 

safety of the facility.

Data parameters required for calculation of future AADT 

include current year AADT, AACR, and number of years to 

be forecasted. Typically, a 20-year forecast AADT should 

be used, which covers 18 to 25 years from the time of 

data forecast. 

AACR for a selected transportation facility is estimated 

based on the statewide modeling programs and inputs 

provided by metropolitan planning organizations (MPOs). 

In the absence of modeling programs, states can use the 

local population growth or gasoline tax growth rate to 

estimate the AACR of regional traffic conditions.
Traffic Data Computation Method POCKET GUIDE | 39

Example. Table 8 depicts information about AADT, AACR, 

and forecast years for three transportation facilities. If the 

current AADTs of transportation facilities are 24,000, 

19,000, and 34,500 veh/day and the respective AACR are 

3%, 2.8%, and 4.2%, respectively, then the forecasted 

AADT for 20 years will be 43,350, 33,009, and 78,555 

veh/day, respectively. These values can be inputted in the 

HPMS submittal software for quality checks.

Table 8. Future AADT Computation

Route AADTCurrent 

(veh/day)

1+AACR n 

(years)

AADTFuture 

(veh/day)

I-20 24,000 1.030 20 43,350

I-10 19,000 1.028 20 33,009

I-30 34,500 1.042 20 78,555

Additional Notes and Tips.

• The HPMS submittal software will perform a quality 

check and reports flag/concern if:

o Future AADT is missing;

o Future AADT less than current AADT; and

o Future AADT greater than 300% of current 

AADT. 

Related Traffic Data Items. AADT and Change Rate.

Cross Reference. HPMS Sec. 4.3 – Item 28.
40 | Traffic Data Computation Method POCKET GUIDE

Gross Vehicle Weight (GVW)

GVW refers to the sum of the axle loads exerted by 

the vehicle's chassis, body, engine, fluids, fuel, accessories, 

driver, passengers, and cargo due to gravity. Vehicle weight is 

transmitted to pavement surface through the tires in the 

wheel assembly, axle, and axle groups. 

GG = �(vv hii aa )

Discussion and Illustration. GVW and load spectra (axle 

load distribution by axle type) is a useful data item in the 

design, maintenance, and evaluation of pavements and 

bridges. Also, it is important for roads geometry, e.g., grade 

and curves. State DOTs are required to submit weigh in 

motion (WIM) data to FHWA. Such data are useful in weight 

law enforcement, economic analysis of freight movements, 

and others. 

FHWA recommends that states submit weight data using the 

Traffic Monitoring Guide (2016 TMG) weight or per vehicle 

format, i.e., one record corresponds to one vehicle and 

describes its axle weights and spacings in English units. 

Example: A three axle truck has axle weights of 12,000 lbs, 

34,000 lbs, and 34,000 lbs. The front axle is single, while the 

middle and rear are tandem (see Figure 3). The total GVW is 

80,000 lbs (12,000 + 34,000 + 34,000). 

Vehicle axle loads and spacings can be measured by using 

weigh-in-motion (WIM) scales. WIM scales record vehicles’ 

axle loads and spacing at roadway speeds without requiring 

them to come to a stop. The sum of the axle loads provides 

the measured GVW. WIM scales are calibrated to correctly 

identify the vehicle class, inter-axle spacing and its axle 

group(s), e.g., single, tandem, tridems, and larger axle 

groups. The WIM devices utilize sensor technologies such as 

piezo-electric and bending plate systems, load cells, strain 

gauges, and others. 
Traffic Data Computation Method POCKET GUIDE | 41

Figure 3. Vehicle axle loads and spacing (Source: 

FHWA).

Additional Notes and Tips.

• GVW data are collected as per vehicle records and are 

collected along with class of the vehicle. 

• For pavement and bridge design procedures, vehicle 

axle loads have historically been converted to ESAL. 

• Roads can be heavily, moderately, or lightly loaded 

depending on the allowable GVW they serve.

• Both road directions may not carry the same traffic 

load. Also, traffic load may not be equally shared by all 

lanes. 

• Vehicle axle loads can shift due to vehicle drive axle 

and suspension arrangements, wind, grade, and cross 

slope.

• Average vehicle weights may differ according to 

day/night or weekdays/weekends. 

• WIM measures the dynamic axle weights, which may 

be different from static weights due to vehicle 

dynamics. 

• A vehicle’s weight-to-power ratio, that determines the 

vehicle’s acceleration rate, is calculated by dividing the 

GVW by the net engine power.

Related Traffic Data Items. ESAL, ACF, and Axle 

Spacing. 

Cross Reference. TMG Sec. 3.2.4., and 7.6.
42 | Traffic Data Computation Method POCKET GUIDE

Headway

The time difference between successive vehicles as they 

pass a point on a roadway, measured from the same point 

on each vehicle (front bumper to front bumper or axle to 

axle). 

For uninterrupted flow, headway in a lane is the reciprocal 

of the flow rate. Headway is expressed in seconds per 

vehicle. 

HH �

vv h

� = �

SSSS �

vv h�

SSSS SS � �

�

Discussion and Illustration. Headway can be used to 

describe traffic operations on any transportation facility at 

a microscopic level. It is a key parameter in determining 

macroscopic traffic parameters like flow rate and average 

speed of the traffic stream. 

Typically, headways could be collected for 5-minute or 15-

minute intervals and can be categorized based on vehicle 

classification. Alternatively, headways could be calculated 

in two methods by using: i) flow rate for uninterrupted 

flow or ii) average spacing and average speed for 

interrupted flow.

In the first method, headway is the inverse of the flow 

rate expressed in vehicles per second, as shown in the 

formula below. 

HH �

vv h

� = � 3600 ( h )

FF �

VV h

h )�

�

Example 1. Table 9 depicts information about traffic 

counts of two transportation facilities. If the flow rate 
Traffic Data Computation Method POCKET GUIDE | 43

values of the facilities are 1100 and 1400 veh/h, then the 

headway values will be 3.27 and 2.57 sec/veh, 

respectively.

Table 9. Average Headway calculations from Flow 

Rate

Flow Rate (veh/h) Headway (sec/veh) 

1100 3.27

1400 2.57

Example 2. Table 10 depicts information about average 

spacing and average speed of two transportation facilities. 

If the average spacings between vehicles in the facilities 

are 151 and 220 feet per vehicle, then the average 

headway values will be 2.96 and 3.01 seconds/vehicle 

respectively. 

Table 10. Average Headway calculations from 

Average Spacing and Average Speed

Average 

Spacing 

(ft./veh)

Average Speed 

(ft./sec)

Average 

Headway 

(sec/veh)

151 51 2.96

220 73 3.01

Additional Notes and Tips. 

• A higher percentage of heavy vehicles in the traffic 

stream increases the average headway.

• A higher traffic demand on roadways results in 

increased density and decreased headways.

Related Traffic Data Items. Flow rate, Speed, and 

Average Spacing. 

Cross Reference. HCM Sec. 3-2, 4-2, 4-6, 4-12 to 4-16, 

4-18,4-19, 4-23, 4-37, 4-45, 6-8, 6-24, 8-2, 8-3, and 8-8.
44 | Traffic Data Computation Method POCKET GUIDE

K-Factor

K-factor is the proportion of AADT occurring in the peak 

hour, i.e., the design hour volume (DHV). Depending on 

which hourly volume is used out of the 8,760 possible 

hours in a typical calendar year, K-factor can be expressed 

in different ways. K-30 is the 30th highest hourly volume of 

the year expressed as a percentage of the AADT. K-50 

and K-100 use the 50th and 100th highest hourly volumes 

of the year, respectively. K-30 represents the reasonable 

design hour volume, but not always. Jurisdictions and 

local DOTs may use K-50 and K-100 for other purposes. Kfactor is calculated as follows.

KK − = KK h hii h vv

AAAAAAAA × 100%

Discussion and Illustration. K-factor is used in 

pavement design, geometric design (e.g., number of lanes 

needed), capacity analysis, estimation of volume-tocapacity ratios and levels of service, functional 

classification of roads, and analysis of traffic operations 

(e.g., effect of lane closures). 

Example: If the AADT of a facility is 5,292 vehicles per day 

and the 30th, 50th, and 100th highest hourly volumes are 

670, 652, and 621 vph, respectively, then:

K-30 factor = 670 / 5,292 * 100% = 12.7%

K-50 factor = 652 / 5,292 * 100% = 12.3%

K-100 factor = 621 / 5,292 * 100% = 11.7%

To calculate K-factor, one-year hourly volume data is 

required. Arrange the hourly volumes in decreasing order. 

The 30th entry in the ordered hourly volumes is the hourly

volume to be used for K-30 calculation while the 50th and 

100th entries correspond to K-50 and K-100, respectively. 
Traffic Data Computation Method POCKET GUIDE | 45

Based on the hourly volume data, calculate the AADT. The 

ratio of the 30th, 50th, and 100th highest hourly volume 

and the AADT expressed as a percentage will be the K-30, 

K-50, and K-100 factors. Graphically, this can be 

represented as shown in Figure 4. Note that the values in 

Figure 4 does not serve as a reference for any site.

Additional Notes and Tips.

• It ranges from 7% to 12% depending on whether a 

facility is in an urban, suburban, or rural area. 

• Either one direction or combined directions can be used 

to determine the peak volumes and the AADT.

• It is best estimated from continuous count stations. 

• As AADT increases, k-factor generally decreases.

• It is highest for recreational facilities, followed by rural 

facilities, and lowest for urban facilities. 

• It depends on the analysis hour, traffic flow patterns,

road geometry, and location.

Related Traffic Data Items. AADT, Design Hour 

Volume, and D-factor.

Cross Reference. HPMS Field Manual Item 26; TMG Sec. 

I Q-24 to Q-29; and HCM Sec. 3.2.

Figure 4. K-30, K-50, and K-100 factors.
46 | Traffic Data Computation Method POCKET GUIDE

Lane Groups

A lane or set of lanes designated for separate analysis. On 

a given approach, separate lane groups can be assigned 

for each movement.

Discussion and Illustration. For signalized 

intersections, the design and layout of the intersection 

depends on the demand on lane group. The lane groups 

with highest flow rate commands higher green time than 

the other lane groups on the same approach. As the traffic 

demand does not reach its peak on all approaches, HCM 

model produces the capacity for each lane group rather 

than for the entire intersection. The capacity of an arterial 

lane group could be calculated using the formula below.

= (SS FF) ∗ �

Effective Green Time

h �

In general, for arterials, a lane group is established for 

i. Each lane or combination of lanes that exclusively 

serves one movement. Examples of exclusive left, 

through, and right lane groups are depicted in 

Figure 5 (approach #3).

ii. Each lane shared by two or more movements. 

Fully shared lane group on first approach and 

through and right shared group on second 

approach are examples of shared lane groups. 

For freeways, 

o The lane groups concept was introduced to allow 

analysts to assign separate attributes to managed 

and general-purpose lanes, by retaining the ability 

to model interaction between two facilities.

o Adjacent lane groups are required to have the 

same segment length, but may have unique traffic 
Traffic Data Computation Method POCKET GUIDE | 47

demand parameters and different geometric 

characteristics.

Figure 5. Example of different lane group types

Additional Notes and Tips. 

• The lane groups that have the highest flow rates for a 

given signal phase are called critical lane groups. 

• Varying signal timings throughout the day alters the 

capacities of lane groups.

Related Traffic Data Items. Capacity.

Cross Reference. HCM Sec. 3-14 and 4-13.
48 | Traffic Data Computation Method POCKET GUIDE

Load Spectra

The load spectra for a given vehicle class and axle group type 

consists of a set of counts of axles belonging to each of 

several load ranges. Load Spectra is utilized in mechanistic 

and empirical pavement designs.

Discussion and Illustration. Load spectra is the key 

parameter in determining the expected loads experienced by 

a pavement in its lifetime. Among the AASHTO recommended 

methods, the load spectra method provides a more accurate 

value of expected load than the Equivalent Single Axle Load 

(ESAL) method. First, the input axle weights for a candidate 

segment are usually calculated using weigh-in-motion (WIM) 

sensors. Later, the standard load range (LR) values, as shown 

in Table 11, are used to categorize the counts of axle groups 

based on their respective LR. For a complete table of 

standardized load ranges, refer to Table 5-1 in AASHTO 

Guidelines For Traffic Data Programs. 

Table 11. Standard load ranges for load spectra 

sample 

Upper Limit of Load Range (kips) 

by Type of Axle Group

LR Single Tandem Tridem Quad

1 3 6 12 12

2 4 8 15 15

3 5 10 18 18

Example: Table 12 presents the loads of different axle types 

of vehicles of Class 7 including single, tandem and tridem 

axles. Each of these axle weights are categorized into a LR 

group and thereby the counts of each axle group with respect 

to its LR is determined. If vehicle 1 has a single axle with 2.5 

kips of load and two tandem axles of 8 and 10 kips of loads, 

then the single axle falls in LR 1 and two tandem axles fall in 

load ranges 2 and 3 respectively. Load ranges for all the 

vehicles on the segment could be determined using the same 
Traffic Data Computation Method POCKET GUIDE | 49

method. Following the LR calculations, counts of each axle 

group with respect to their LRs are categorized, as shown in 

Table 13. 

Table 12. Axle type loads and load ranges for vehicles 

of class 7

Vehicle 

No.

Axle Type Loads (kips) and LR

Single LR Tandem LR Tridem LR

1 2.5 1 8,10 2,3 - -

2 5 3 - - 15.5 3

3 4.75 3 9.5 3 17.5 3

Table 13. Load Spectra example for Vehicle Class 7

LR Single Axle Tandem Axle Tridem Axle

1 1 - -

2 - 1 -

3 2 2 2

Additional Notes and Tips. 

• TrafLoad software, available from NCHRP, can be 

used to estimate the load spectra. 

• Although not covered in the AASHTO table listed, FHWA is 

finding penta and larger axle groups that contain 

significantly large load spectra loading patterns that should 

be considered when designing some roadways.”

• The legal axle weight limits on roadways are 20,000 lbs. 

and 34,000 lbs. for single and tandem axles, respectively. 

Related Traffic Data Items. ESAL and Vehicle Class.

Cross Reference. AASHTO Guidelines for Traffic Data 

Programs Sec. 3-14 and 4-13.
50 | Traffic Data Computation Method POCKET GUIDE

Month of Year and Day of Week Factors

Factors are used to properly annualize short duration traffic 

counts (usually 24-48 hours). Such factors are computed by 

using continuous count data, but they are applied to short 

duration counts. Seasonal or monthly, day of week, and time 

of day factors adjust counts to correct for the corresponding 

biases. Monthly and day of week factors are commonly used, 

and are computed as follows.

MMjj = AAAA AA

MM jj

and DDii = AAAA AA

AA AAii

A daily volume can be converted to an AADT as follows. 

AADT = VOL * M * D * A * G

Where:

Mj is the monthly factor for month j of the year.

Di is the day of week factor for day i of the week.

VOL is the daily volume.

M, D, A, and G are the monthly, day of week, axle

correction (where needed), and yearly change rates, 

respectively. 

Discussion and Illustration. Factors provide an insight into 

how travel changes over time. Day of week factors can be 

developed for each day (seven factors) or for weekdays and 

weekends (two factors). The ratio of AADT to MAWDT 

captures monthly and daily variations in a single factor. 

Figure 6 shows an example of monthly factors. 

Example: If volume on Tuesday May 24, 2016 is 9,200 

veh/day, and the monthly and day of week factors for May 

and Tuesday are 0.93 and 1.01, respectively, the AADT 

(assuming that axle and change rates are both 1.0) will be:

AADT = 9,200 * 0.93 * 1.01 * 1 * 1 = 8,642 veh/day.
Traffic Data Computation Method POCKET GUIDE | 51

Figure 6. Monthly factors

Additional Notes and Tips.

• As the distance between the temporary and permanent 

traffic counter increases, factors’ accuracy usually 

decreases. 

• For day of week factors, a minimum of weekday and 

weekend factors should be developed. 

• Factors for low volume roads can be unstable, as small 

changes may result in high-percentage changes. 

• The sum of the seven daily and twelve monthly factors do 

not add up to 7 and 12, respectively. 

• Months with higher MADT have lower monthly factors. 

• Monthly and day of week factors cannot be derived from 

hourly volume data, but from daily volume data. 

• Factors are not applicable for atypical traffic patterns. 

• Change rates should be applied when estimating current 

AADT from short duration counts in previous years.

Related Traffic Data Items. AADT, ADT, MADT, ACF, and 

Class Factors. 

Cross Reference. TMG Sec. 3.2.1. and 3.3.1.
52 | Traffic Data Computation Method POCKET GUIDE

Monthly Average Daily Traffic (MADT)

MADT estimates the average daily traffic volume over one 

month. It can be computed by the simple average method 

or the AASHTO method. In the simple average method, 

MADT is computed by summing the daily volumes during a 

given month and then dividing the sum by the number of 

days in the month. It requires volume for every day of the 

month. In the AASHTO method, MADT is computed by 

calculating the average daily volume for each of day of 

week in a month and then averaging the seven average 

daily volumes. It requires at least one day of volume data 

for each day of the week within a month.

Simple 

average 

method

MM jj = 1

nn�VVVVVVii

nn

ii=1

AASHTO 

method 

MM kk = 1

7�� 1nnjj

�VVVVVVii

nnjj

ii=1

�

7

jj=1

Where:

VOLij = daily traffic on i

th day of the jth month

n = number of days in j

th month

VOLijk = daily volume for i

th occurrence of jth day 

of week within the kth month

i = occurrence of a particular day of week in a 

month

j = day of week (1 to 7)

k = month of year

nj = number of times day j occurs in a month for 

which traffic data is available

Discussion and Illustration. By using monthly traffic 

volume correction factors, MADT can be converted to 

AADT. MADT statistics for a particular roadway often 
Traffic Data Computation Method POCKET GUIDE | 53

exhibit a monthly pattern of higher and lower values 

compared to overall roadway AADT that is called 

seasonality. The seasonality pattern often repeats similarly 

from year to year for a roadway. 

The AASHTO method may provide a biased MADT 

estimate compared to the simple average method due to 

equal weighting of each day of the week within a month

even though all months (except February in non-leap 

years) have a mixture of four and five occurrences of each 

day of week. FHWA has developed a new MADT method 

for computing MADT that removes this bias and allows for 

smaller than daily volumes to be utilized – see the 2016 

TMG for more details. FHWA recommends use of this 

method.

Additional Notes and Tips.

• Monthly correction factors are estimated by dividing 

AADT by the MADT of each month. 

• MADT, in combination with other volume measures, is 

used in monitoring of traffic volume trends and 

grouping traffic patterns. 

Related Traffic Data Items. ADT, AADT, MADTT, and 

Monthly Factors. 

Cross Reference. TMG Sec. 1.2.7, 3.2.1.1, and 3.3.1.
54 | Traffic Data Computation Method POCKET GUIDE

Number of Intersections of Other Type

Number of at-grade intersections, where full sequence traffic 

signal or stop sign traffic control devices are not present, in 

the inventory direction. Continuously operating flashing yellow 

and roundabouts are considered as other at-grade type of 

traffic control devices.

Discussion and Illustration. The numbers of intersections 

that are not controlled by either signal or stop sign are used 

to calculate capacity and estimate delay for a roadway 

segment or geographic boundary. To estimate the number of 

other type intersections, the first rule is to choose a statewide 

direction for inventory purposes. The second rule is to either 

count the beginning at-grade intersection or the ending atgrade intersection, but never both. For divided roadways, 

continuous cross streets are to be counted as a single 

intersection the separation between cross streets is at least 

50 feet. In the latter case, it shall be counted as two 

intersections. If the sample section does not begin or end 

with another type of intersection, then all the other type 

intersections in between would be reported, when using 

either the beginning rule or ending rule. 

Access points to large traffic generators (e.g., shopping 

centers, malls, large work sites, office parks, apartment 

complexes, etc.) shall be included in the intersection counts. 

Example: In the upper portion of Figure 7, 2 other type 

intersections would be reported, when using either the 

beginning rule or ending rule. In the bottom portion of 

Figure 7, 2 other type intersections would be reported, when 

using the beginning only rule; 1 other type intersection would 

be reported, when using ending only rule. Note that the 

inventory direction in Figure 7 goes from bottom to top.
Traffic Data Computation Method POCKET GUIDE | 55

Figure 7. Example for no. of “at-grade/other” 

intersections count

Additional Notes and Tips. 

• The number of intersections of other type in a sample 

section depends on the geographic boundaries and 

road functional class. This data item does not have a 

specific range. 

• The summation of signalized, stop sign-controlled and 

at-grade/other intersections results in the total number 

of intersections in a given geographic boundary. 

Related Traffic Data Items. Number of Signalized 

Intersections, Number of Stop Sign Intersections.

Cross Reference. HPMS Sec. 4.3–Item 33 Figure 4.45-

4.46. 
56 | Traffic Data Computation Method POCKET GUIDE

Number of Signalized Intersections 

A count of at-grade intersections where traffic signals are 

present. Only signals that cycle through a complete 

sequence of signalization (i.e., red, yellow (amber), and 

green) for all or a portion of the day are counted.

Discussion and Illustration. Numbers of signalized 

intersections are used to calculate capacity and estimate 

delay for a roadway segment or the area within a 

geographic boundary. To estimate the number of 

signalized intersections, the first rule is to choose a 

statewide starting direction (e.g., south to north, west to 

east) for inventory purposes. The second rule is to either 

count the beginning at-grade intersection or the ending 

at-grade intersection of the defined segment, but never 

both. For divided roadways, continuous cross streets are 

to be counted as a single intersection unless the 

separation between cross streets is at least 50 feet. In the 

latter case, it is counted as two intersections. If the 

sample section does not begin or end with a signalized 

intersection, then all the signalized intersections in 

between would be reported, when using either the 

beginning rule or ending rule. Access points to large traffic 

generators (e.g., shopping centers, malls, large work sites, 

office parks, apartment complexes, etc.) should be 

counted as intersections if the access point is controlled by 

a traffic signal.

The number of signalized intersections and the distance 

between them plays a key role in determining the offset 

time between consecutive intersections while designing 

the signal cycle timings. In a coordinated signal system, a 

timely offset would ensure orderly progression of traffic.

Example: In the upper portion of Figure 8, 2 signalized 

intersections would be reported when using either the 
Traffic Data Computation Method POCKET GUIDE | 57

beginning rule or ending rule. In the bottom portion of 

Figure 8, 2 signalized intersections would be reported 

when using the beginning only rule; 1 signalized 

intersection would be reported when using the ending 

only rule. Note that the inventory direction in Figure 8

goes from bottom to top.

Figure 8. Signalized intersection count example

Additional Notes and Tips. 

• The number of signalized intersections in a sample 

section depends on the geographic boundaries and 

road functional classification. 

• A geographic area with high traffic demand requires 

more signalized intersections. This data item does not 

have a specific range. 

Related Traffic Data Items. Number of Stop Sign 

Intersections and Number of Intersections.

Cross Reference. HPMS Sec. 4.3 – Item 31 and Figure 

4.42.
58 | Traffic Data Computation Method POCKET GUIDE

Number of Stop Sign Intersections

The number of at-grade intersections where one or more 

stop signs are present.

Discussion and Illustration. Numbers of stop sign

intersections are used to calculate capacity and estimate 

delay for a roadway segment or an area within a 

geographic boundary. To estimate the number of stop 

sign intersections, the first rule is to choose a statewide 

starting direction (e.g., north, east) for inventory 

purposes. The second rule is to either count the beginning 

at-grade intersection or the ending at-grade intersection, 

but never both. For divided roadways, continuous cross 

streets are to be counted as a single intersection the 

separation between cross streets is at least 50 feet. In the 

latter case, it is counted as two intersections. If the 

sample section does not begin or end with a signalized 

intersection, then all the stop sign intersections in 

between would be reported, when using either the 

beginning rule or ending rule. 

Access points to large traffic generators (e.g., shopping 

centers, malls, large work sites, office parks, apartment 

complexes, etc.) should be counted as intersections if the 

access point is controlled by a stop sign. The stop signs on 

grade separated intersecting roadways should not be 

included in the total count.

Example: In the upper portion of Figure 9, 2 stop sign

intersections would be reported, when using either the 

beginning rule or ending rule. In the bottom portion of

Figure 9, 2 stop sign intersections would be reported 

when using the beginning only rule; 1 stop sign

intersection would be reported when using the ending 

only rule. Note that the inventory direction in Figure 9

goes from bottom to top.
Traffic Data Computation Method POCKET GUIDE | 59

Figure 9. Example for no. of stop sign intersections 

count

Additional Notes and Tips. 

• Number of stop sign intersections in a sample section 

depends on the geographic boundaries and road 

functional classification. This data item does not have a 

specific range. 

Related Traffic Data Items. Number of Signalized 

Intersections and Number of Intersections.

Cross Reference. HPMS Sec. 4.3 – Item 32, Figure 4.43,

and Figure 4.44. 
60 | Traffic Data Computation Method POCKET GUIDE

Passenger Car Equivalent (PCE)

PCE is the number of passenger cars that will result in the 

same operational conditions as a single heavy vehicle of a 

particular type under identical roadway, traffic, and control 

conditions.

Discussion and Illustration. PCE factors are used to 

convert counts of heavy vehicles into counts of passenger 

cars such that a mixed flow of heavy and light vehicles is 

converted to an equivalent traffic stream consisting entirely of 

passenger cars. 

Depending on the type and location of a facility, length and 

type of grade, and other factors, one heavy vehicle might 

occupy as much roadway space as two to twelve passenger 

cars. This variance in key operational characteristics of heavy 

vehicles and passenger cars is accounted in the design of 

roads by providing additional capacity as needed. 

In general, heavy vehicles are classified into SU Trucks and 

Tractor Trailers (TTs). Recreational vehicles and buses are 

treated as SU Trucks. PCE values (ET) for heavy vehicles on 

level and rolling terrain are provided in Table 14. However, at 

higher grades PCE values could range up to 11.81 (~12).

Table 14. PCEs of general terrain segments

Factor Type of Terrain

Level Rolling

ET 2.0 3.0

Example: Table 15 depicts the information about traffic 

volume on a two-lane freeway, percentage share of heavy 

vehicles, and their PCE. The freeway has a 4.5% grade for a 

1-mile segment length. The total volume on a transportation 

facility is 1500 veh/h, of which 14% are TTs and 6% are SU 

trucks. From the HCM PCE values, each TT and SU Truck 
Traffic Data Computation Method POCKET GUIDE | 61

displaces 2.85 passenger cars (ETT & ESUT). Here ETT & ESUT

represent PCE values for TTs and SU Trucks. 

The number of PCEs in the traffic stream is computed by 

multiplying the number of vehicles in each class by its 

respective PCE, noting that PCE for passenger cars is 1.0 by 

default. Computed values of TT and SU Truck passenger-car 

equivalent volumes are 599 and 257 pc/h, respectively. 

Thereby, the total equivalent passenger cars in the traffic 

stream summed up to 2056. Thus, the prevailing traffic 

stream of 1500 veh/h operates as if it contains 2056 pc/h. 

Note that this is a 30% SU Truck and 70% TT mix. Exhibit 

12-26 in HCM was used to compute the PCE values.

Table 15. PCE computation example

Vehicle 

Type

Total 

Volume 

(veh/h)

% 

Truck

Heavy 

Vehicle 

volume

ET Passenger 

Car 

Equivalent 

Volume 

(pc/h)

Truck

1,500

14 210 2.85 599

RV and 

Bus

6 90 2.85 257

VV PP PP = (1500 − 210 − 90) + (599 + 257)

= 2056 pp

h

Additional Notes and Tips. 

• PCE values for various percentage mixes of SUTs and TTs 

with respect to the grade and length of roadway segment 

are provided in HCM. 

Related Traffic Data Items. Volume, SU Truck & Bus 

AADT, Percent Peak SU Trucks & Buses, Combination Truck 

AADT, and Percent Peak Combination Trucks.

Cross Reference. HCM Sec. 7-31; Exhibits 12-26, 12-27, 

12-28.
62 | Traffic Data Computation Method POCKET GUIDE

Peak Hour Factor (PHF)

Traffic demand varies; it changes even within the analysis 

hour. PHF is a measure of traffic demand variation within the 

analysis hour and describes the relationship between full 

hourly volume and the peak 15-min flow rate within the hour. 

It is given by dividing the hourly volume by the peak 15-min 

flow rate within the analysis hour. In estimating PHF, 15-min 

is used because it is considered as the minimum time period 

over which traffic flow is statistically stable.

PP = VV

MM = VV

4 × VV 15

Where: V = hourly volume in veh/h

Vm15 = maximum volume during the peak 15-min of 

the analysis period (veh/15-min)

Conversely, the maximum flow rate within an hour can be 

estimated by dividing the hourly volume by the PHF. 

Discussion and Illustration. PHF is an important variable 

for facility design and capacity analysis because the design 

should always seek to accommodate the traffic demand in 

most peak-hour periods of the year. The PHF is primarily used 

for planning purposes when DHV is known but peak 15-min 

analysis is sought. It is best estimated from local peak traffic 

trends.

Example 1: If the PHF during the design hour is predicted to 

be 0.85 and the design hourly volume is 6,000 veh/h, then 

the maximum flow rate will be:

VVmm mm = 66,

. 8855 = 77, vv vv/

Example 2: Given the data shown below, estimate the PHF. 
Traffic Data Computation Method POCKET GUIDE | 63

Time 

interval

Vehicles 

count Hourly volume = 

1,150+1,275+1,210+1,130 = 4,765 

veh/h

Maximum volume during the analysis 

hour, i.e., Vm15, is 1,275 veh/15-min

PPPPPP = 44, 77

44 × , = . 99

4:00 pm 1,150

4:15 pm 1,275

4:30 pm 1,210

4:45 pm 1,130

The maximum and minimum PHF values are 1.00 and 0.25, 

respectively. A PHF of 1.00 indicates no variation in traffic 

flow within the analysis hour and a PHF of 0.25 indicates that 

the entire hourly volume occurred during the peak 15-min 

interval. 

Additional Notes and Tips.

• The notion of PHF is that the flow rate observed in the 

peak 15-min is not sustained throughout the entire hour.

• PHF is unitless. It is applicable to directional volume only. 

• High PHF indicates steady traffic.

• The use of hourly volume divided by a PHF is preferred 

when volumes are projected.

• On freeway segments, PHF ranges from 0.85 to 0.98 while 

on multilane highways it ranges from 0.75 to 0.95. 

• A single average PHF is used for intersection analysis. 

• Given the LOS, multiplying the service flow rate with the 

PHF gives the service volume over a full peak hour. 

• PHF is applicable to any analysis hour, i.e., peak or offpeak.

• PHF affects the number of lanes needed for traffic to 

operate at desired LOS. 

Related Traffic Data Items. DHV, DDHV, DH, capacity, and 

LOS.

Cross Reference. HCM Sec. 4.2.; AASHTO Sec. 2.4.4.
64 | Traffic Data Computation Method POCKET GUIDE

Percent Green Time

The percentage of green time allocated for through traffic 

at intersections is called percent green time. It is 

calculated using the green time for through movements 

and the total control signal cycle length.

PP GG

= � Green Cycle Time for Through Movement

TTTTTTTT SS h AA h

�

Discussion and Illustration. Percent green time is 

critical for the assessment of performance of traffic signals 

at any intersection. This provides an insight on the time 

allocated for the through traffic movement. It is used to 

calculate the capacity of a traffic signal and to perform 

congestion analysis.

Table 16. Percent green time computation for pretimed control signals

Through 

Green 

Time 

(sec)

Amber 

or 

Yellow 

Time 

(sec)

Red 

Clear.

Interval 

(sec)

Green 

for 

Turning 

Move.

(sec)

Total 

Cycle 

Time 

(sec)

Percent 

Green 

Time 

(%)

40 3.5 1.5 12 57 70.2

24 3 1 8 36 66.7

11 3 1 - 15 73.3

Example. Table 16 depicts the information about signal 

timings on three different approaches. If the total green, 

amber and red times of the first approach are 40, 3.5, 1.5 

seconds, then the percent green time for the approach is 

70.2%. Note that green time for the turning movements 

are not included in the green time. In the case of actuated 

signals, several checks of peak period light cycles are 
Traffic Data Computation Method POCKET GUIDE | 65

performed to determine the typical or average green time. 

This typical green time is used to calculate the percent 

green time.

Example. Table 17 depicts the percent green time 

computation methodology for actuated control signals.

Table 17. Percent green time computation for 

actuated control signals

Green 

Time 

(sec)

Amber 

or 

Yellow 

Time 

(sec)

Red 

Clearance 

Interval 

(sec)

Total 

Cycle 

Time 

(sec)

Percent 

Green 

Time 

(%)

Cycle 1 35 3.5 1.5 40

33.5 / 38 

= 88.2

Cycle 2 28 3 1 32

Cycle 3 40 3.5 1.5 45

Cycle 4 31 3 1 35

Average 33.5 3.25 1.25 38

Additional Notes and Tips. 

• An approach with high percentage green time indicates 

it serves higher through traffic. 

Related Traffic Data Items. Traffic flow.

Cross Reference. HPMS Item 30. 
66 | Traffic Data Computation Method POCKET GUIDE

Percent Peak Combination Unit Trucks 

A Combination Unit (CU) truck is any truck in FHWA 

Categories 8 through 13. The Percent Peak CU Trucks 

value is the number of combination trucks during the hour 

with the highest total volume (i.e., the peak hour) divided 

by the AADT (i.e., the total daily traffic). 

PP PP CC TTTTTTTT = �

Peak Hour CU Trucks

AAAAAAAA ∗ 100�

Where:

AADT = Annual Average Daily Traffic (veh/day)

Peak Hour CU Trucks = Volume of CU trucks 

during the peak traffic hour

Note: This data item is based on the truck traffic during 

the peak traffic hour (for all vehicle classes) and not the 

hour with the most truck traffic.

Discussion and Illustration. Percent Peak CU Trucks is 

used to calculate capacity, peak volumes, ACFs, and 

pavement deterioration for a roadway segment or 

functional class or facility. Depending on its functional 

class, each roadway segment experiences different 

percentage of truck traffic. To get accurate estimates, 

HPMS recommends actual traffic counts of the road 

segment. If the actual measured values are not available, 

the most credible method would be to use other sitespecific measured values from sites located on the same 

route. Other methods may include: assigning site-specific 

measured values to other samples that are located on 

similar facilities with similar traffic characteristics in the 

same geographic area and in the same volume group; or 

assigning measured values from samples in the same 

functional system and in the same area type. 
Traffic Data Computation Method POCKET GUIDE | 67

States have access to traffic classification counts and data 

on the percentage of vehicles passing through a counting 

point. This information is used to estimate Percent Peak 

CU trucks for an entire roadway or city or county or state 

using weighted average method.

Example: Data parameters required to estimate % Peak 

CU Trucks are traffic counts of a road or facility or 

functional class and truck volume for the peak traffic hour.

Table 18 depicts information about traffic counts and peak 

hour CU truck traffic counts of two roadways. If the AADT 

values of a facility are 165,000 and 7,000 per day and the 

respective peak hour truck traffic counts are 1,825 and 60,

then the % Peak CU Trucks will be 1.106% and 0.857% 

respectively. HPMS rounds % peak CU trucks to the 

nearest thousandth. 

Table 18. Computation of % peak CU trucks from 

AADT and peak hour CU truck volume

Roadway 

ID

AADT 

(veh/day)

Peak Hour CU 

Truck Volume 

(veh/h)

Percent 

Peak CU 

Trucks 

R100A 165,000 1,825 1.106

R200B 7,000 60 0.857

Additional Notes and Tips. The HPMS submittal 

software will perform a quality check and reports error if:

Percent Peak CU Truck

100

× AAAAAAAA > 30% oo CC TTTTTTTT AAAAAAAA

Related Traffic Data Items. AADT and Truck Volume

Cross Reference. HPMS Sec. 4.3 – Item 25; TMG Sec. 

3.2.3 and Table 3-3.
68 | Traffic Data Computation Method POCKET GUIDE

Percent Peak Single Unit Trucks & Buses 

A Single Unit (SU) truck is any truck in FHWA Categories 4 

through 7, as defined above. Single Unit trucks include 

buses. The Percent Peak Single-Unit Trucks and Buses is 

the number of SU trucks and buses during the hour with 

the highest total volume (i.e., the peak hour) divided by 

the AADT (i.e. the total daily traffic). 

PP PP SS TTTTTTTT = �

(Peak Hour SU Trucks)

AAAAAAAA ∗ 100�

Where:

AADT = Annual Average Daily Traffic (veh/day)

Peak Hour SU Trucks = Volume of SU trucks 

during the peak hour

Note: This data item is based on the truck traffic during 

the peak hour (for all vehicle classes) and not the hour 

with the most truck traffic.

Discussion and Illustration. Percent Peak SU Trucks is 

used to calculate capacity, peak volumes, ACFs, and 

pavement deterioration for a roadway segment or facility. 

Depending on its functional class, each roadway segment 

experiences a different percentage of truck traffic. To get 

accurate estimates, HPMS recommends actual

classification traffic counts on the road segment. If the 

actual measured values are not available, the most 

credible method would be to use other site-specific 

measured values from sites located on the same route. 

Other methods may include: assigning site-specific 

measured values to other samples that are located on 

similar facilities with similar traffic characteristics in the 

same geographic area and in the same volume group; or 

assigning measured values from samples in the same 

functional system and in the same area type. 
Traffic Data Computation Method POCKET GUIDE | 69

States have access to traffic classification counts and data 

on the percentage of vehicles passing through a counting 

point. This information is used to estimate Percent Peak 

SU Trucks for an entire roadway or region using weighted 

average method.

Example: Data parameters required to estimate Percent

Peak SU Trucks are traffic counts of a road or facility or 

functional class and truck volume for the peak traffic hour. 

Table 19 depicts information about traffic counts and peak 

hour SU Truck traffic counts of two roadways. If the AADT 

values of facilities are 140,000 and 2,000 veh/day and the 

respective peak hour truck traffic counts are 1510 and 15

veh/h, then the Percent Peak SU Trucks will be 1.078% 

and 0.750%, respectively. HPMS rounds Percent Peak SU 

trucks to the nearest thousandth. 

Table 19. Computation of % peak SU trucks from 

AADT and peak hour SU truck volume

Roadway 

ID

AADT 

(veh/day)

Peak Hour SU 

Truck Volume 

(veh/h)

Percent 

Peak SU 

Trucks

R100A 140,000 1,510 1.078

R200B 2,000 15 0.750

Additional Notes and Tips. The HPMS submittal 

software will perform a quality check and reports error if:

Percent Peak SU Truck

100

× AAAAAAAA > 30% of SS TTTTTTTT AAAAAAAA

Related Traffic Data Items. AADT and Truck Volume.

Cross Reference. HPMS Sec. 4.3 – Item 23; TMG Sec. 

3.2.3 and Table 3-3.
70 | Traffic Data Computation Method POCKET GUIDE

Roadway Occupancy

It can be defined in two ways, i.e., occupancy in space 

and in time. Occupancy in space refers to the proportion 

of the actual length of the roadway occupied by each 

vehicle to the total roadway length, while occupancy in 

time refers to the proportion of time a detector was 

occupied by vehicles in a defined period of time.

= h oo vv hii

TTTTTTTT h oo

= TTTTTTTT h ii

Where:

Total length of roadway = length of roadway 

defined for occupancy detection. Any length of 

roadway can be considered. 

Defined time period = Total time period defined for 

occupancy detection. Typically, 5- or 15-minute time.

Discussion and Illustration. Roadway occupancies are 

often used in the calculations of traffic speed and density. 

Estimation of roadway occupancy is easy to collect in the 

field through the use of detectors. In traffic data 

collection, roadway occupancy is often considered as a 

surrogate for vehicle density. Similar to density, roadway 

occupancy is a key parameter in characterizing the quality 

of traffic operations on a transportation facility. Even 

though both traffic parameters indicate the amount of 

congestion on a roadway, occupancy is a percent of 

congestion in space or time, where density is a count of 

vehicles.
Traffic Data Computation Method POCKET GUIDE | 71

Example. In a 1-mile roadway segment, if 1,400 feet is

occupied by vehicles, the occupancy is 0.265

= 1,400

1 ∗ 5280 = 0.265

Figure 10. Vehicle length and detector length

Similarly, in a 15-minute time period, if the detector is 

occupied for 180 seconds, the occupancy for that time 

period is 0.200.

= 180

15 ∗ 60 = 0.200

Given the length of the average vehicle and field detector, 

density can be calculated from the occupancy values. For 

a detailed example, please refer to Density data item.

Additional Notes and Tips. 

• Average vehicle length and sensitivity of sensors act as 

limiting factors for the accuracy of roadway occupancy 

estimation. 

• A lane occupancy value of 0.350 or higher indicates 

congestion in traffic stream. 

Related Traffic Data Items. Critical Speed, Critical 

Density, Capacity, and Density.

Cross Reference. HCM Sec. 4-6.
72 | Traffic Data Computation Method POCKET GUIDE

Saturation Flow

Saturation flow is used in the context of signalized 

intersections. It is the number of vehicles per hour per 

lane that could pass through a signalized intersection if a 

green signal was displayed for the full hour, the flow of 

vehicles never stopped, and there were no large 

headways. This parameter is expressed in vehicles per 

hour of green per lane.

SS = 3,600

h

Where:

SS = Saturation Flow (veh/hg/ln)

h = Saturation Headway (sec/veh)

Discussion and Illustration. Saturation flow is a key 

parameter in analyzing intersection capacity. It represents 

the capacity of an intersection lane or lane group 

assuming that the light is always green. 

Once the saturation flow rate is computed, the capacity of 

that lane can also be computed based on the effective 

green time. On-field video detectors are often used for 

estimation of saturation flow. 

In reality, the saturation flow rate varies widely with a 

variety of prevailing conditions, including lane widths, 

heavy-vehicle presence, approach grades, parking 

conditions near the intersection, transit bus presence, 

vehicular and pedestrian flow rates, and other conditions.

To calculate saturation flow, the total number of seconds 

in an hour (3,600) should be divided by the saturation 

headway of the vehicles in the traffic stream. Total 

seconds in the hour are used, as it is assumed that the 

traffic signal at the intersection remains green throughout 

the hour. Because signal lights are not always green, 
Traffic Data Computation Method POCKET GUIDE | 73

mechanisms for dealing with starting and stopping 

movements in signal cycles must be developed. 

Example: Table 20 depicts the saturation headways for 

three roadway sections. If their saturation headways are 

3.8, 2.9, and 4.1 seconds, respectively, then their 

respective saturation flows will be 947, 1241, and 878 

veh/hg/ln.

Table 20. Saturation flow computation example

Roadway 

ID

Saturation 

Headway 

(sec/veh)

Saturation 

Flow 

(veh/hg/ln)

R210A 3.8 947

R320H 2.9 1,241

R570K 4.1 878

These saturation flow values should be multiplied by the 

number of lanes in the respective roadway sections to get 

saturation flow for all lanes.

Additional Notes and Tips.

• A typical range of saturation flow is between 600 and 

1,800 veh/hg/ln. Higher values of saturation flows 

indicate normal to good operating conditions. 

• Similarly, lower values of saturation flows indicate 

congested operating conditions. Saturation flows in 

central business districts (CBDs) are significantly lower 

than those in less constrained and visually less intense 

areas.

Related Traffic Data Items. Saturation Headway and 

Capacity.

Cross Reference. HCM Sec. 4-14, 4-16, 4-17, 4-20, 4-21, 

4-23, 4-25, 4-38, 6-20 and 7-4.
74 | Traffic Data Computation Method POCKET GUIDE

Saturation Headway

Saturation headway at intersections can have slightly 

different meanings depending on the intersection types

being considered. 

In signalized intersections, saturation headway is the 

average headway between vehicles occurring after the 

fourth vehicle in the queue and continuing until the last 

vehicle of the initial queue at the beginning of the traffic 

signal green time clears. It is expressed in seconds per 

vehicle, and it represents the average headway that can 

be achieved by a queue of vehicles, e.g., vehicles queuing 

at saturated intersection approaches.

SS HH = h4 + h5 + h6 + h6 + ⋯ + hnn

nn − 4

Where:

h4 to hnn = headways of vehicles 4 to vehicle n 

In an all-way-stop-intersection, saturation headway is 

the time between departures of successive vehicles on a 

given approach, assuming continuous queue.

Discussion and Illustration. Saturation headway is a 

key parameter in analyzing intersection capacity. On-field 

video detectors are often used for estimation of saturation 

headway. As depicted in Figure 11, the first four vehicles 

are subject to a start-up lost time taken by the drivers to 

perceive the change in signal indicator from red to green 

and react to the signal change by accelerating. This 

phenomenon of start-up lost time is expected to dissipate 

with the fourth vehicle in the initial queue. For all-waystop intersections, saturation headway on a subject 

approach increases with the degree of conflict between 

vehicles on other approaches. For example, vehicles on 

opposing approaches (oncoming traffic) will have less 
Traffic Data Computation Method POCKET GUIDE | 75

impact on increases in saturation headway compared to 

vehicles on conflicting approaches (perpendicular traffic).

Figure 11. Saturation headway and start-up lost 

time at a signalized intersection

Additional Notes and Tips.

• Saturation headway ranges from 2 to 6 seconds. 

• Lower saturation headways indicate normal to good 

operating conditions and vice versa. 

• Saturation headways on Central Business Districts 

(CBDs) are significantly longer than those in less 

constrained and visually less intense areas.

Related Traffic Data Items. Saturation Flow, Headway, 

and Capacity.

Cross Reference. HCM Sec. 4-15 to 4-18. 
76 | Traffic Data Computation Method POCKET GUIDE

Short Term Count ADT by Vehicle Class to 

AADT by Vehicle Class Conversion

A weighted average of short duration ADT count of a 

select vehicle class by considering its occurrence in day of 

the week and month of the year will result in AADT by 

vehicle class. Both ADT and AADT are expressed in 

vehicles per day units.

AAAAAAAA = 1

7��

1

12 ��

1

nn �VVVVVVii

nn

kk=1

�

12

jj=1

�

7

ii=1

Where:

i = day of the week

j = month of the year

AADTc = annual average daily traffic for vehicle 

class c 

VOLijkc = daily truck traffic for class c, day k, of 

DOW i, and month j

k = 1 when the day is the first occurrence of that 

day of the week in a month, 4 when it is the 

fourth occurrence of that day of the week

n = the number of days of that day of the week 

during that month 

Discussion and Illustration. The conversion of ADT 

into AADT by vehicle class is useful in areas without long 

term traffic counters. AADT by vehicle class for a 

transportation facility is a key parameter in the analysis of 

pavement conditions and design of transportation facilities 

with similar roadway and traffic settings. Portable Traffic 

Recorder (PTR) stations are deployed in these sites and 

traffic data is collected for a set of consecutive days. 
Traffic Data Computation Method POCKET GUIDE | 77

The AAAAAAAA formula stated above computes an average 

DOW value for each month, and then computes an annual 

average value from those monthly averages, before finally 

computing a single annual average daily value. This 

process effectively removes most biases that result from 

missing days of data, especially when those missing days 

are unequally distributed across months or days of the 

week.

In this worked example, daily volume counts of passenger 

cars (pc) for 205 days out of 365 days were available. The 

AAAAAAAA formula was applied to this data to estimate the 

AADT for each day of week, month averages, and annual 

average. These AADTs are useful to understand and plan 

for the changing travel patterns throughout the year.

Additional Notes and Tips. 

• TMG recommends collection of daily counts for 25 to 

30% of the days in each year to calculate a reliable 

estimate of AADT.

Related Traffic Data Items. AADT, ADT, and Vehicle 

Class.

Cross Reference. TMG Sec. 3.4.5.
78 | Traffic Data Computation Method POCKET GUIDE

Short Term Count ADT to AADT Conversion

A short duration count of Average Daily Traffic (ADT) at a 

location can be converted to Annual Average Daily Traffic 

(AADT) by applying adjustment factors to month (MOY),

DOW, HOD, ACF, and change rate computed from a 

continuous count station on a similar location. Both ADT 

and AADT are expressed in vehicles per day units.

AAAAAAAA = (SSh TT AAAAAA ∗ MMii ∗ ii ∗ AAii ∗ GGii)

Where:

Short Term ADT= Daily traffic volume computed 

for a short duration (veh/day)

Mi = the monthly factor for factor group i

DOWi = the DOW factor for factor group i

Ai = the axle-correction factor for location i

Gi = the change rate for factor group i

Discussion and Illustration. The conversion of ADT to 

AADT is useful in areas without continuous count stations. 

Portable Traffic Recorder (PTR) stations are deployed in 

these sites and traffic data is typically collected for a set of 

consecutive days. In parallel, data on AADT, monthly 

average daily traffic (MADT), and daily traffic counts are 

exported from a similar transportation facility that is 

nearby or adjacent to the location. Based on the adjacent 

facility’s counts, HOD, DOW and MOY factors are 

developed. 

If the traffic counts at the select location are collected for 

a partial day, then each hours’ count for that day needs to 

have a HOD factor to account for hourly variations in 

traffic. If the traffic counts at the select location are 

collected for less than seven consecutive days, then each 

day’s count is multiplied with the DOW factor to account 
Traffic Data Computation Method POCKET GUIDE | 79

for daily variations in traffic. Similarly, a monthly factor is 

used to adjust the short-term ADT for the monthly 

variations. If the short-term ADT counts and the adjacent 

facility counts are for different years, then a change rate 

must be applied to account for the traffic change between 

years. If the detector counts the number of axles instead 

of the number of vehicles, then an appropriate ACF is 

applied.

Example: Consider that 9,100 vehicles were observed 

during a seven consecutive days count. Assuming the 

monthly factor is 1.14 and change rate is 1, then AADT 

can be estimated in the following way. 

SSh TT AAAAAA = TTTTTTTT VV ⁄ = 9100/7

= 1300 vv h/

Since the seven-day count includes all the days in a week, 

DOW factor is not necessary. 

Now, the short-term ADT is multiplied with the monthly 

factor to obtain the AADT, which is 1,482 veh/day. 

AAAAAAAA = SSh TT AAAAAA ∗ MMii = 1300 ∗ 1.14

= 1482 vv h/

Additional Notes and Tips. 

• Misusing the monthly factor can lead to an 

overestimation of AADT by as much as 15%. For 

example, summer season experiences higher traffic 

than winter. A misuse of a summer month in place of 

a winter month could cause overestimation.

Related Traffic Data Items. AADT, ADT, Seasonal 

Factor, and Change Rate. 

Cross Reference. TMG Sec. 3.3.1. 
80 | Traffic Data Computation Method POCKET GUIDE

Signal Type

The predominant type of traffic signal system used on a 

sample section.

Discussion and Illustration. The type of signal installed 

at an intersection determines the mobility functioning of 

the corridor. Signal type is used in the calculations of 

intersection capacity and delay.

In general, four signal types are available based on the 

location of the intersection, expected traffic demand, and 

type of traffic operation.

I. Uncoordinated Fixed Time Signals: These traffic 

signals are observed in rural areas that do not have 

traffic signals in close proximity and thereby do not 

require coordination. The signal timings in these 

signals are pre-timed to suit the existing traffic 

demand. Timely revisions to signal timing plans will 

be performed when congestion is identified.

II. Uncoordinated Traffic Actuated Signals: These 

traffic signals are usually observed at a major and 

minor street intersections. In-pavement loop 

detectors are installed to detect the minor street 

traffic and thereby activate the minor street signal 

timing. These signals are beneficial in intersections 

with minimum minor street traffic, as major street 

traffic is not disturbed unless a minor street 

demand is observed. 

III. Coordinated Progressive Signals: These traffic 

signals can be observed in urban areas with busy 

traffic conditions. Several intersection signals will be 

operated through coordination and hence a 

constant flow of traffic is maintained. 
Traffic Data Computation Method POCKET GUIDE | 81

IV. Coordinated Traffic Adaptive Signals: These traffic 

signals are observed in streets with unpredictable 

traffic demand shifts. Actuated signals will identify 

the approaches with peak demand and adjust the 

signal timings accordingly. In addition, coordination 

of signals will help in active and dynamic 

management of traffic demand. A corridor level 

benefit in mobility performance could be achieved.

However, on field identification of these signals is 

difficult and requires communication with local 

traffic personnel. 

Criteria for selecting signal type: In general, pre-timed 

control signals are less expensive to install and maintain. 

Uncoordinated traffic signals are most suitable for 

intersections with simple and predictable traffic 

movements on all approaches. However, in areas with 

complex and unpredictable traffic conditions, actuated 

signals are proven to be more beneficial. Further, actuated 

signals have the flexibility of operating fully actuated, semi 

actuated, or pre-timed. The operation type may also 

change by time of day. 

Coordinated and pre-timed signal types have proven to be 

more efficient than actuated signals. The pre-timed signals 

ensure consistent starting time and duration of intervals. 

This capability permits progressive traffic movements and 

maximizes efficiency.

Additional Notes and Tips. 

• Actuated signals are interchangeable between 

manufacturers due to National Electrical Manufacturers 

Association or Type 170 standards.

Related Traffic Data Items. Flow and Capacity.

Cross Reference. HPMS Item 29; 4-61. 
82 | Traffic Data Computation Method POCKET GUIDE

Single Unit Truck & Bus AADT

A Single Unit (SU) truck is any truck that meets the 

requirements established for the FHWA Truck 

Classification Scheme for Categories 4 through 7. Single 

Unit trucks include buses. SU truck AADT is expressed in 

vehicles per day units.

SS TTTTTTTT AAAAAAAA = 1

7��

1

12 ��

1

nn �AAAAAAii 4−7

nn

kk=1

�

12

jj=1

�

7

ii=1

Where:

SU Truck AADT = AADT for SU Trucks

ADTijk4-7 = daily SU traffic for day k, of DOW i, 

and month j

i = day of the week (DOW)

j = month of the year

k = count of occurrence of that day of week in a 

month; 4 when it is fourth occurrence of that day 

of week 

n = the number of days of that day of the week 

during that month (for which you have data)

Discussion and Illustration. SU Truck AADT is used to 

estimate pavement deterioration, calculate operating 

speeds, and perform freight analysis for a transportation 

facility. Depending on its functional class, each roadway 

segment experiences different volumes of truck traffic. To 

get accurate estimates, HPMS recommends actual traffic 

counts on the road segment. If measured values are not 

available, the most credible method would be to use 

values measured from other sites located on the same 

route. Alternatively, measured values from other sites that 
Traffic Data Computation Method POCKET GUIDE | 83

are located on similar facilities with similar traffic 

characteristics in the same geographic area and volume 

group can be assigned. The last option would be assigning 

measured values from sites in the same functional class 

with similar geographic area.

Once the traffic classification counts are collected, average 

SU truck counts on each day of week for each month are 

computed. For example, the average SU truck volume for 

Monday of every month and annual average can be 

computed. In this way, monthly and annual averages of 

counts for each day of week can be computed for all 

seven days of the week. Averaging the annual averages of 

the seven day of week counts will yield the AADT of SU 

trucks and buses. Since this data is taking averages for all 

days, it is less prone to any bias issues created by any 

missing data due to malfunctions and maintenance of 

continuous count stations (CCS). 

Additional Notes and Tips. 

• SU truck and bus AADTs are computed bidirectionally 

for two-way facilities and directionally for one-way 

roadways and ramps.

Related Traffic Data Items. AADT.

Cross Reference. HPMS Sec. 4.3 – Item 22; TMG Sec. 

3.2.3 & Table 3-3.
84 | Traffic Data Computation Method POCKET GUIDE

Time of Day (TOD) Factor

Time of Day factor is the ratio of an hourly traffic average 

divided by sum of hourly averages for that day on a given 

roadway segment.

oo DD = HH TTTT TT AA

SS oo HH AA

Discussion and Illustration. The Time-of-Day factors 

or ratios are only required if partial day classification 

counts are collected. Typically, these ratios are developed 

from automated classification counts collected during 

weekdays in urban areas for periods of at least 24 hours 

(and, preferably, at least 48 hours). 

Separate TODs are collected for different groups of vehicle 

classes. AASHTO recommends grouping of vehicles classes 

1 through 3 into personal-use vehicles and classes 4 

through 13 into buses and trucks. 

In addition, transportation agencies should establish factor 

groups to distinguish between roads based on amount of 

overnight truck activity relative to the amount during the 

mid-day period when manual classification counts are 

performed during the quarterly and annual mobility 

studies.

TOD factors can be developed based on the data from 

continuous classification counts or, more frequently, from 

short-duration weekday classification counts.

For continuous count locations, the 24 annual average 

weekday hour-of-the-day volumes are computed by using 

the Monthly Average Day of the Week (MADW) procedure.

For short-duration classification counts, the weekday 

counts obtained for each hour of the day are averaged to 

produce 24 weekday averages.
Traffic Data Computation Method POCKET GUIDE | 85

Once the average hourly counts are available for 24 hours, 

each of the hourly averages is divided by the sum of 

hourly averages to obtain the TOD factors for each of the 

24 hours.

Finally, the 24 hourly traffic ratios for the vehicle class and 

factor group are obtained by averaging the corresponding 

traffic ratios produced for each of the sites in the factor 

group.

In the worked example, hourly truck counts from shortterm classification counts collected for a 65-hour period 

were used. This data was collected in an urban area on 

four consecutive weekdays. 

Additional Notes and Tips. 

• Typically, at the sites that experience most of their 

truck volume during the business or daylight hours and 

minimal traffic during overnight hours, estimates of 

truck traffic developed from manual classification might 

lead to an overestimation of truck volumes. 

• At such sites, application of TOD factors to manual 

truck counts avoids this overestimation. 

Related Traffic Data Items. Month of Year and Day of 

Week Factors, ADT, and AADT.

Cross Reference. TMG Sec. 3.4.5.
86 | Traffic Data Computation Method POCKET GUIDE

Traffic Demand

The number of vehicles or other road users desiring to use 

a given roadway segment during a specific time period,

typically, 1 h or 15 min. Also known as Travel Demand.

Discussion and Illustration. Traffic demand is the key 

parameter used by traffic engineers and transportation

planners to assess the capacity requirements of a 

transportation facility. Typically, travel demand models are 

used to model and forecast the demand for a 

transportation facility. These models employ a four-step 

process:

1. Trip generation. This step determines the number 

of trips to be made by the travelers. The entire 

analysis area is categorized into Transportation 

Analysis Zones (TAZ). Several factors including 

number and size of households, vehicle ownership 

rate, land use, and density of development in each 

TAZ are considered to determine the trips generated 

by each TAZ. Most of the information used in trip 

generation will be gathered through surveys and 

travel diaries.

2. Trip distribution. This step determines the 

destinations of the trips generated in each TAZ. Each 

TAZ is weighted with an attractiveness factor based 

on the number of attractions present in that TAZ and 

its distance from other TAZs. 

3. Mode Choice. This step determines the modes used 

by the travelers to travel from their origin to 

destination. Modes can include personal car, transit, 

carpooling, and others. Factors including the existing 

or forecasted transit and carpooling capacities are 

considered for this step.
Traffic Data Computation Method POCKET GUIDE | 87

4. Route Assignment. This step determines the route(s) 

taken by the travelers from their origin to destination. 

Generally, the model assumes that the traveler will 

choose the route with the least travel time to their 

destination. 

Based on the location of the analysis area or transportation 

facility, various travel demand models including multi-county, 

countywide, sub-regional, regional, cross-state, and statewide 

models are available. In general, the travel demand is 

predicted for the model lifecycle span of the transportation 

facility (for example, 20 years). Typically, demands during the 

peak periods are used as the baseline for designing the 

capacity of transportation facilities. 

To check the compliance of transportation facilities with 

dynamic changes in travel demand, travel demand analysis 

will usually be performed every 10 years. This analysis will 

compare the existing travel demand to the capacity of the 

transportation facility. If the travel demand exceeds the 

capacity, long queues at bottlenecks will be observed. Several 

alternatives can be modeled to improve the bottlenecks and 

thereby ensure smooth traffic operations. 

Additional Notes and Tips. 

• In most regions, the forecasted demand was observed by 

transportation facilities in shorter time ranges than 

originally forecast, due to dynamic shifts in development 

densities in the respective region. 

• In cases where the demand exceeded the capacity, instead 

of expanding the transportation facility, alternative demand 

management strategies, like congestion pricing, can be 

employed.

Related Traffic Data Items. AADT and Future AADT.

Cross Reference. HCM Sec. 3-5, 4-2, 5-4, 6-1, 6-3, 6-7, 6-

10, 6-27, 7-8, 7-11 to 7-14, 7-15, 7-26, 7-30, 7-31, and 7-32. 
88 | Traffic Data Computation Method POCKET GUIDE

Turning Radius

Turning radius is the radius of the circular arc formed by 

the smallest turning path of the front outside tire of a 

vehicle. This is also referred to as curb-to-curb radius. It is 

expressed in feet throughout the U.S.

Discussion and Illustration. Turning radius is a 

principal characteristic dimension that affects horizontal 

roadway design. The principal dimensions affecting the 

design of turning radius are the minimum Centerline 

Turning Radius (CTR), the out-to-out track width, the 

wheelbase, and the path of the inner rear tire. Here, the 

minimum CTR refers to the turning radius of the centerline 

of the front axle of a vehicle with its steering wheels at 

the steering lock position. Based on the principal 

dimensions, each design vehicle requires roadways to 

have a specific minimum turning radius to make a turning 

maneuver. The effect of driver characteristics and slip 

angles on the design are minimized by assuming the 

maximum turning speed of the design vehicle to be less 

than 10 mph. AASHTO Green Book 2011 includes 20 

design vehicles. For any design vehicle,

• Wheelbase is the distance between the centerline of 

the front axle and the centerline of the rear axle. 

• Steering Angle is the average of the angles made by 

the left and right steering wheels with the longitudinal 

axis of the vehicle when the wheels are turned to their 

maximum angle. 

• CTR is the turning radius of the centerline of the front 

axle of a vehicle with its steering wheel at the steering 

lock position.
Traffic Data Computation Method POCKET GUIDE | 89

Higher values of CTR, wheel base, and maximum steering 

angle lead to greater minimum turning radius. In addition 

to steering angle, all the combination units with an 

attached articulating part will have an articulating angle. 

This angle is measured between the longitudinal axes of 

the tractor and trailer (articulating part) as the vehicle 

turns. All the above mentioned turning characteristics are 

depicted in AASHTO 2011 Figure 2-10.

Additional Notes and Tips. 

• Larger trucks with more articulating units require wider 

turning radius.

Related Traffic Data Items. Design Vehicles, Centerline 

Turning Radius.

Cross Reference. AASHTO Sec. 2.1.2 Table 2-2a and 

2-2b; Figures 2-1 through 2-9 and 2-13 through 2-23.
90 | Traffic Data Computation Method POCKET GUIDE

Vehicle Class Factoring

Factors used for volume counts do not differentiate among 

different vehicle classes and therefore are not applicable 

for adjusting vehicle classification data. Vehicle class 

factors are specifically used to adjust short duration 

vehicle counts to estimate AADT by vehicle class. The 

monthly class factors, e.g., for trucks, are computed as a 

ratio of AADTT to MADTTs. Generally, monthly class 

factors are given by:

MM h CCCC CCCC,jj = AAAAAAAACCCC CCCC

MM CCCC CCCC,jj

Where:

Class represents a specific vehicle class. 

j represents a specific month of the year. 

Discussion and Illustration. FHWA adopts a 13-

category vehicle classification system. However, for 

vehicle class factoring purposes, a minimum of six 

generalized vehicle classes are recommended, i.e., 

motorcycles (MC), passenger vehicles under 102” (PV), 

light trucks over 102” (LT), buses (BS), single-unit trucks 

(SU), and combination unit trucks (CU). MC, PV, LT, and 

BS represent the FHWA 13-category classes of 1, 2, 3, and 

4, respectively. SU represents classes 5, 6, and 7, while 

CU represents classes 8, 9, 10, 11, 12, and 13 in the 

FHWA’s 13-category system. Monthly factors for the 

generalized classes can be computed as shown in Table 

21. Fewer generalized categories may be sufficient if class 

counts are low or have similar patterns, e.g., combine PV 

and LT. 

Removing the temporal bias in the estimates of classspecific volumes is the vehicle class factor’s objective. This 

is critical for many purposes, e.g., predicting pavement life 
Traffic Data Computation Method POCKET GUIDE | 91

requires seasonal and daily truck volume variations as the 

structural response of most pavements changes with 

environmental conditions. 

Table 21. Monthly factors for generalized vehicle 

classes

MC PV LT BS SU CU Total 

MADT 35 3,111 811 61 191 1,111 5,320

AADT 33 4,311 911 51 211 1,211 6,728

Class factors 0.94 1.39 1.12 0.84 1.10 1.09 1.26

Additional Notes and Tips.

• Given the seasonal and daily variations of class counts and 

total volumes are not the same, class factors are computed 

and applied independently of volume factors.

• Class factors are computed from permanent classification 

counts and are used to adjust short duration class counts. 

• Class-specific AADT, e.g., AADTT, is computed in the same 

manner as that of the general traffic AADT.

• Volumes of through and local delivery trucks, industrial 

activities, and population size affect class factors. 

• Low daily volumes within some vehicle class categories 

result in unstable and unreliable class factors.

• There is a trade-off between accuracy and complexity of 

computing class factors when too many classes are used.

• Two alternative methods are used to estimate and apply 

class factors, i.e., roadway-specific and traditional. In the 

roadway-specific method, factor application is done one 

road at a time. The traditional procedure develops average 

factors for roadway groups of similar characteristics. The 

roadway-specific and traditional procedures can be 

combined, as a good compromise.

Related Traffic Data Items. AADTT, MADTT, Volume 

Factors, and ACFs.

Cross Reference. TMG Sec. 3.2.3., 3.3.1., and 3.3.3.
92 | Traffic Data Computation Method POCKET GUIDE

Vehicle Density

The number of vehicles occupying a given length of a 

transportation facility at a particular instant. Approximate 

density is calculated by dividing flow rate by average 

travel speed, and is expressed as vehicles per mile per 

lane. 

DD = FF

AA

Where:

Flow rate = traffic volume on a facility (vph) 

Average travel speed = Average traffic speed of 

a vehicle platoon passing through a facility (mph) 

Discussion and Illustration. Density is the key 

parameter in characterizing the quality of traffic 

operations on a transportation facility. Density is used as 

the measure of effectiveness to define the LOS of 

transportation facilities with uninterrupted flow. 

Density computation from photographs and video 

recordings is usually difficult and subject to human error. 

Computing density from flow rate and average travel 

speed is relatively easy. The flow rate and travel speed 

data can be captured through loop detectors and speed 

sensors respectively.

Example. Table 22 depicts information about traffic counts 

and average travel speeds of two transportation facilities. 

If the flow rate values of the facilities are 1,600 and 2,250 

veh/h and the respective average travel speeds are 45

and 65 mph, then the vehicle density values will be 35.5

and 34.6 veh/mi, respectively. 
Traffic Data Computation Method POCKET GUIDE | 93

Table 22. Vehicle density calculation example

Transportati

on Facility

Flow Rate 

(veh/h)

Average 

Travel 

Speed 

(mph)

Density 

(veh/mi)

Arterial 1,600 45 35.5

Freeway 2,250 65 34.6

Given the length of an average vehicle and field detector, 

density can also be calculated from the occupancy values. 

For example, if the length of a detector is 3.5 feet and the 

length of a vehicle is 26 feet, and the occupancy recorded 

by the detector is 0.240, then the traffic density on the 

facility is:

DD = 5,280 ∗

h oo vv hii + h oo

= 5,280 ∗ 0.240

26 + 3.5 = 43 vv h

/

Additional Notes and Tips. 

• For basic freeway segments, 45 pc/mi/ln is considered 

as the traffic density when flow is at capacity. 

• Density is a critical parameter for uninterrupted-flow 

facilities as it characterizes the quality of traffic 

operations. It is not applicable to interrupted flow 

facilities, as the flow is affected by traffic control 

devices (TCDs).

Related Traffic Data Items. AADT, Capacity, 

Occupancy, Critical Density, and Critical Speed.

Cross Reference. HCM Sec. 2-10, 2-18, 4-39 to 4-41, 5-

9, 7-10, and 8-5.
94 | Traffic Data Computation Method POCKET GUIDE

Vehicle Miles Traveled (VMT)

VMT is the total miles traveled by vehicles in a specific 

area (e.g., a route, a functional road classification, or 

geographic area) over a period of one year. VMT is used 

for monitoring travel demand as well as analysis of a 

facility’s performance on emissions, safety, and mobility. 

Daily vehicle miles traveled (DVMT) refers to total miles 

travelled by vehicles in one day.

VV hii MM TTTT TTTT = AADTx ∗

Section lengthx (miles) ∗ nn oo ii

Where: 

AADTx = AADT for section x

Section lengthx = Length of section x in miles

Discussion and Illustration. VMT plays a pivotal role in 

analyzing the infrastructure performance at section and 

network levels. VMT can be estimated using a geometric 

boundary approach or a trip generation approach. The 

geometric boundary approach uses traffic counts to 

estimate VMT within a geographic boundary of interest. 

The trip generation method uses travel survey data to 

estimate VMT from travel demand modeling. The Federal 

Highway Administration (FHWA) estimates VMT based on 

the geometric boundary approach. Traffic Volume Trends 

(TVTs) monthly reports published by FHWA estimate VMT 

by state and functional classes of roads. VMT is not the 

same as Daily Vehicle Distance Traveled (DVDT), which 

measures the distance traveled by vehicles in a day, not 

how many vehicles traveled over a given distance in a 

day.

HPMS contains link level AADT for all roadway segments. 

Outside HPMS, given traffic classification counts at a point 

and the HPMS vehicle summary data table, the VMT share 
Traffic Data Computation Method POCKET GUIDE | 95

for each vehicle class on a given roadway class can be 

estimated. 

VMT is calculated by multiplying the AADT value for each 

section of road by 365 and by the section length (in miles) 

and summing all sections to obtain VMT for a complete 

route.

Example: Table 23 depicts information about traffic counts 

and section length of two roadways. If the AADT values of 

a facility sections are 50,000 and 48,000 veh/day and the 

respective section lengths are 3 and 5.5 miles, then the 

VMT on the facility sections will be 150,000 and 264,000 

vehicle miles, respectively.

Table 23. Computation of DVMT and VMT from 

AADT and section length

Roadway 

ID

Section 

AADT 

(veh/day)

Section 

Length 

(miles)

DVMT

(vehmiles)

VMT

(vehmiles) = 

DVMT*365

R120A 50,000 3 150,000 54,750,000

R280K 48,000 5.5 264,000 96,360,000

Additional Notes and Tips. 

• The range of VMT for a specific roadway section 

depends on its functional class, section length, and 

traffic demand. 

• In general, higher VMT values indicate good traffic 

throughput with less congestion. Similarly, lower VMT 

values indicates poor traffic performance with higher 

delays and safety issues. In this way, high crash 

locations could be identified using VMT data.

Related Traffic Data Items. AADT.

Cross Reference. HPMS Sec. 1.2.7; 3.3; 6.2; and 

Appendix A; TMG Sec. A-3.
96 | Traffic Data Computation Method POCKET GUIDE

Vehicle Speed

How fast vehicles are traveling, computed as distance 

traveled per unit time. It is an important variable for 

monitoring safety and mobility performance. Some 

applications of vehicle speed data include: design of 

highways, monitoring speed trends, establishing traffic 

operation and control parameters, analyzing accident 

data, estimating treatments effect, establishing speed 

zones and speed limits, and designing traffic signals 

timing. 

Measurement of individual vehicles’ speeds passing a 

roadway point is referred to as spot speeds. Such speed 

can be aggregated in two ways, i.e., time mean speed and 

space mean speed. Time mean speed measures traffic 

speed over a period of time, while space mean speed 

measures speed over a roadway segment.

vv = 1

nn ∑ vvii nn

ii=1 and vv = nn

∑ 1

vvii

nn

ii=1

Where:

vt is the time mean speed

vs is the space mean speed

vi is the spot speed of i

th vehicle

n is the number of vehicles observed

Discussion and Illustration. Speed, travel time, and 

delay are all related measures that describe the level of 

traffic mobility, though travel time and delays are based 

on space mean speed. Some important speed parameters 

include: median, mean, pace (10 mph speed increment 

containing the highest proportion of observed speeds), 

85th and 15th percentiles, and standard deviation. 

Example: In a 5-minute interval, 10 vehicles with spot 

speeds of 63, 52, 49, 65, 69, 57, 51, 62, 72, and 58 mph 
Traffic Data Computation Method POCKET GUIDE | 97

were observed. Then the time and space mean speeds will 

be:

vv = 63 + 52 + 49 + 65 + 69 + 57 + 51 + 62 + 72 + 58

10 = 60 h

vv = 10

1

63 + 1

52 + 149 + 165 + 159 + 157 + 151 + 162 + 172 + 158

= 59 h

Speed data are collected either per vehicle or aggregated at 

some level. Safety studies require per vehicle speed data, 

while aggregated measures are good for traffic operations. 

States are encouraged to submit spot speed data to FHWA in 

5 mph speed bins (minimum 12 and maximum 25 bins) in 

aggregation intervals of either 5 minutes, 15 minutes, or 1 

hour for specified time periods. 

Additional Notes and Tips.

• Traffic speed depends on traffic conditions, environmental 

factors, geometric features, and roadside interference 

level. 

• Variations in the speed of vehicles within and across lanes 

are important traffic safety indicators.

• Vehicle speed can be aggregated by vehicle class. 

• Generally, speed data should be collected from locations 

that adequately reflect traffic characteristics, e.g., avoid 

locations of excessive acceleration or deceleration.

• Vehicle speed varies by day of week and time of day; 

therefore, collected data should capture the variation. 

• Though density is the primary measure of freeway traffic, 

speed is also an important performance measure. 

• Speed data are often collected using inductive loops, radar, 

infrared, video, and GPS instrumented probe vehicles.

Related Traffic Data Items. Vehicle Weight, 85th 

Percentile Speed, and Free Flow.

Cross Reference. TMG Sec. 3.2.2.
98 | Traffic Data Computation Method POCKET GUIDE

Volume and Volume-to-Capacity Ratio

Volume is the number of vehicles passing through a point 

on either a lane, a direction, or a highway. It is expressed 

as vehicles per time. Volume-to-Capacity (v/c) ratio, also 

known as demand-to-capacity ratio, is the ratio of current 

or projected demand flow rate to capacity of a segment. 

VV =

Where:

Demand flow rate = volume of vehicles on a 

transportation facility (vehicles per hour per lane, 

or veh/h/ln) for a given segment length

Capacity = the maximum number of vehicles a 

transportation facility can handle (veh/h/ln)) for a 

given segment length

Discussion and Illustration. V/C ratio is an indicator of 

quality of traffic operations on a segment. It also indicates 

how close a roadway is operating to its capacity. V/C 

ratios are used to perform capacity and LOS, intersection,

or geographic boundary analyses. Capacity analysis 

determines the sufficiency of estimated capacity to match 

to the forecasted demand flow rate requirements. For LOS 

analysis, HCM uses the v/c ratio as a main indicator. As 

the v/c ratio get close to 1, traffic operations in and 

upstream of the segment are negatively affected.

Traffic volumes captured at count locations are 

customarily used in v/c ratio estimation. However, these 

volumes might not reflect the actual demand, as the 

capacity constraints upstream of the count location may 

limit the number of vehicles that can reach the count 

facility. The HCM provides base values of capacities for 
Traffic Data Computation Method POCKET GUIDE | 99

different facility types and settings. However, these values 

assume base conditions including ideal roadway 

conditions, a traffic stream of only passenger cars, and 

favorable weather. Any variations from the base 

conditions can be balanced by using adjustment factors 

for adverse roadway, weather, and traffic conditions. 

These adjustment factors are available in exhibits 11-20 

and 11-23 in HCM Chapter 11. 

Example. Table 24 depicts information about traffic 

volume and capacity for two transportation facilities. If the 

volumes of the facilities are 1,600 and 2,250 veh/h/ln and 

the respective capacity values are 1,700 and 2,300 

veh/h/ln, then the volume-to-capacity ratios will be 0.94

and 0.98 respectively. 

Table 24. Volume-to-capacity ratio calculation 

example

Roadway 

Type

Volume 

(veh/h/ln)

Capacity 

(veh/h/ln)

v/c 

ratio

Arterial 1,600 1,700 0.94

Freeway 2,250 2,300 0.98

Additional Notes and Tips. 

• A v/c ratio less than 1 indicates under saturated traffic flow 

conditions. 

• Similarly, a value of greater than 1 indicates over saturated 

flow conditions. 

• The v/c ratio is directly proportional to density and travel 

time and inversely proportional to speed. 

• Increase in v/c ratio indicates longer vehicle delays and 

queuing.

Related Traffic Data Items. AADT, Capacity, and LOS.

Cross Reference. HCM Sec. 2-10, 2-18, 4-39 to 4-41, 5-9, 

7-10, and 8-5. 
100 | Traffic Data Computation Method POCKET GUIDE

Index

0-9

85th–percentile speed, 4, 5, 96, 97

A

Access points, 36, 54, 56, 58

Amber or Yellow Time, 56, 64, 65

Annual Average Change Rate, 38

Annual Average Daily Traffic (AADT), 6, 7, 8, 11, 16, 17....

Annual Average Daily Truck Traffic (AADTT), 8, 9, 90, 91

Articulating Angle, 89

Average Daily Traffic (ADT), 17, 18, 26, 27, 28, 76, 78....

Average spacing, 42, 43

Axle correction factors (ACF), 11, 12, 13, 66, 68, 79

Axle counts, 12, 13

Axle spacing, 40, 41

B

Beginning rule, 54, 56, 58

Bottleneck, 14, 87

C

Capacity, 14, 15, 16, 20, 26, 28, 30....

Centerline Turning Radius (CTR), 88, 89

Centerline, 88, 89

Change rate, 11, 16, 17, 50, 51, 78, 79

Combination unit (CU) truck, 18, 66, 89, 90

Combination unit truck AADT, 18, 19

Coordinated progressive signals, 80

Coordinated traffic adaptive signals, 81

Critical density, 15, 20, 21, 37, 71, 93

Critical lane groups, 47

Critical phase, 33

Critical speed, 15, 20, 21, 37, 71, 93
Traffic Data Computation Method POCKET GUIDE | 101

Cumulative ESAL, 34, 35

Curb-to-curb radius, 88

D

Daily Vehicle Distance Traveled (DVDT), 10

Day of the Week (DOW), 18, 76, 77, 78, 79, 82

Demand flow rate, 32, 98

Density, 15, 20, 43, 70, 86, 92, 93....

Design hour factor (k-factor), 28, 29, 31, 44, 45

Design hour, 22, 23, 26, 27, 28, 30, 31, 44, 45, 62

Design Hourly Volume (DHV), 11, 28, 29, 31, 44, 62

Design Service Flow Rate (DSFR), 26

Design vehicle, 24, 25, 88

Design volume, 7, 17, 26, 27, 28

Design year, 26

D-factor, 28, 30, 31, 45

Directional Design Hourly Volume (DDHV), 27, 28, 31, 63

Directional flow rate and ratio, 32, 33

Directional Split, 27, 30, 35

E

Effective green time, 46, 72

Ending rule, 54, 56, 58

Equivalent single axle load (ESAL), 34, 35, 41, 48, 49

F

Flow rate, 14, 20, 21, 26, 32, 42, 46....

Free Flow Speed (FFS), 15, 20, 29

Future AADT, 38, 39

G

Green time, 14, 15, 46, 64, 65, 72, 74

Growth factor, 16
102 | Traffic Data Computation Method POCKET GUIDE

H

Headway, 42, 43, 72, 73, 74, 75

I

Interrupted flow, 4, 21, 42, 92, 93

K

K-factor, 28, 31, 44, 45

L

Lane groups, 32, 33, 46, 47, 72

Lane traffic splitting factor, 34

Level of Service (LOS), 14, 15, 28, 29, 30, 37, 63....

Load range (LR), 48, 49

Load spectra, 34, 35, 40, 48, 49

M

Major street, 33, 80

Maximum service flow rate, 29

Minor street, 33, 80

Mode choice, 86

Monthly Average Daily Traffic (MADT), 11, 51, 52, 53, 78

Monthly Average Day of the Week (MADW), 84

Monthly factor, 50, 51, 79, 90, 91

N

National Highway System (NHS), 19

Number of at-grade intersections, 54, 58

Numbers of signalized intersections, 55, 56, 57, 59

Numbers of stop sign intersections, 57, 58, 59

P

Passenger Car Equivalent (PCE), 60, 61

Peak hour factor, 29, 32, 62

Peak hour, 22, 23, 28, 29, 30, 31, 62....
Traffic Data Computation Method POCKET GUIDE | 103

Percent green time, 64, 65

Percent Peak CU trucks, 66, 67

Percent Peak SU trucks, 68, 69

Q

Quad axle, 48

R

Red clearance interval, 64, 65

Roadway occupancy, 70, 71

Route assignment, 86

S

Saturation flow, 32, 33, 72, 73, 75

Saturation headway, 32, 33, 74, 75

Seasonal factor, 79

Short term ADT to AADT conversion, 17, 78

Short term ADT, 17, 78

Short term count ADT by vehicle class to AADT by class 

conversion, 76

Signal Type, 80, 81

Single axle, 33, 34, 48, 49

Single Unit (SU) truck, 19, 60, 61, 67, 68, 69, 82....

Single Unit truck and Bus AADT, 82, 83

Space mean speed, 96

Start-up lost time, 74, 75

Steering angle, 88, 89

T

Tandem axle, 35, 40, 48, 49

Time mean speed, 96

Time of day (TOD) factor, 84, 85

Traffic directional factor, 30, 34

Tridem axle, 35, 41, 48, 49

Trip distribution, 86
104 | Traffic Data Computation Method POCKET GUIDE

Trip generation, 86

Truck hourly distribution factor, 8

Turning movements, 64

Turning radius, 25, 88, 89

U

Uncoordinated fixed time signals, 80

Uncoordinated traffic actuated signals, 80

Uninterrupted flow, 21, 42, 92

V

Vehicle axle loads, 40, 41

Vehicle Class Distribution (VCD), 8

Vehicle class factoring, 90

Vehicle class, 8, 12, 13, 40, 48, 49, 66....

Vehicle density, 70, 92, 93

Vehicle Miles Traveled (VMT), 7, 94, 95

Vehicle speed, 96, 97

Vehicle weight, 40, 41

Volume, 6, 7, 8, 10, 11, 12, 14....

Volume-to-Capacity (v/c) ratio 44, 98, 99 

W

Weigh-in-motion (WIM), 40, 41, 48

Wheelbase, 25, 88
Traffic Data Computation Method POCKET GUIDE | 105
U.S. Department of Transportation 

Federal Highway Administration 

Office of Highway Policy Information 

1200 New Jersey Ave., SE 

Washington, D.C. 20590 

https://www.fhwa.dot.gov/policyinformation

August 2018

FHWA-PL-18-027