Traffic Monitoring Guide (fhwa)

An Overview of

Traffic Monitoring Guide Department of Transportation Engineering & Management University of Engineering & Technology, Lahore

Contents  In

this Part we will have knowledge about

 Data Collection Design  Factoring traffic counts  Freeway-expressway ramp

counting procedures

What is Traffic Monitoring Guide? A

manual to monitor traffic for various uses given by FHWA

Why do we need to monitor traffic?  Traffic

varies over a number of different time scales, including: o time of day o day of week o season (month) of the year. o Directional variation o Geographic variation  Research has shown that truck volumes vary over time and space differently than car volumes (Hallenbeck et al 1997)

Data Collection Design

ACCOUNTING FOR VARIABILITY

Integration of data collection efforts  The

ability to simultaneously collect all three types of traditional traffic monitoring data is called “nesting” traffic counts.  Traffic data collected by other agencies within the State.  tracking of HOV lane usage  traffic surveillance centers  The Intelligent Transportation Systems

Advantages of Integration  Reduce

the # of continuous data collection sites

 Increase  Less

the # of data available

Cost

 Caution!

Care must be taken while using this data

Continuous Data Collection  Not  As

being used in proper way

input for traffic management

 Used  If

in real time and then discarded

used properly, quality of TMI can improve.

Continuous traffic monitoring data collection programs

Automatic Traffic Recorders

 Recorded

on site as hourly volumes

by lane  Downloaded periodically to a central location  Summary volume statistics : AADT, AAWDT, seasonal adjustment factors, dayof-week adjustment factors, 30th and 100th highest annual hourly volume as a fraction of AADT, lane distribution factors, growth trends

 Data

from different ATRs is averaged for getting representative factors and

Locations for ATRs  Locations

trends:

selected to measure specific

o Monitoring movement accurately on a road of particular importance o Traffic activity on a larger group of roads by monitoring on a single location.  Historic locations  Semi-random selection

of locations within specific categories of roads (e.g. rural interstates E-W)  Availability of power and/or telecommunications access to locate the

Continuous Vehicle Classification  Truck volume and  Other information

load information obtained: the size of

seasonal commodity movements, the seasonal fluctuations in truck travel on roads, trends in annual truck volumes on specific roadways, day-of-week traffic patterns for trucks as opposed to cars, the lane distribution patterns of trucks.

 Axle

and length classification for 13 classes  Aggregate vehicle categories

Locations for AVCs 

Length & axle classifiers can be placed at different locations e.g. on freeway & rural area respectively and then combined with care.

 For

specific pavement sections (LTPP sites)  For creating truck factors

Weigh-In-Motion Sites  WIM takes all three types of data  Located upstream of enforcement

scales  Semi random selection within the area because equipment only works accurately on level ground, with good pavement, and with little or no roadway curvature and are expensive.  These sites point to the most

Steps for determining Continuous Count Locations

Short Duration Counts  Largely revised each year  Frequent and Occasional counts  “Project Counts” for site specific

studies  Statewide counts and project counts combined give accurate and costeffective data  This data is not the “design data”, it requires day-of-week, seasonal and other factors for adjustment,

Short Count Program Design

 Defining

and overlaying the short duration counting programs.  Making the separate counts in an area as “general coverage “ counts  Collecting the data for most precise needs only  In general, these are taken under consideration: o counts taken to provide system coverage o counts taken to meet the HPMS needs o counts for special needs studies. § Statistical sampling is done before

Coverage Count Programs  These

are data collection efforts that are undertaken to ensure that “at least some” data exist for all roads maintained by the agency.



The TMG recommends, as a general rule, that each roadway segment be counted at least once every six years

The HPMS Sample  Highway

Performance Monitoring System is a combination of complete coverage for the NHS and other principal arterials, and a structured sample of roadway sections for the remaining functional systems excluding the rural minor collectors and local.  A primary goal of the HPMS traffic data collection effort is to provide a statistically valid estimate of total annual vehicle distance traveled (VDT).  Other statistical samples data are also collected

Special Needs Counts  Includes

the data that are not part of the HPMS or any other existing Statespecific sampling study  Project counts are also done which normally include sections with poor pavement that require repair or rehabilitation, locations with high accident rates, sections that experience heavy congestion, and roadways with other significant

FACTORING TRAFFIC COUNTS

Common Necessary Adjustments



Time-of-day adjustments for counts that consist of less than 24 consecutive hours (the TMG recommends 48hour counting periods)



Day-of-week adjustments for counts that do not measure traffic conditions for all days of the week



Seasonal adjustments for counts that do not cover periods long enough to account for variation from month to month or season to season



Axle correction adjustments for axle counts (such as counts taken with a single road tube sensor) that do not directly convert the number of axle pulses into vehicle counts by vehicle classification.

Recommendations for Factoring  There is not a single best method.  Depends upon number of continuous

counters a State can afford to operate and the extent of the roadway system for which factors must be developed and applied.  Factors must be applied to short counts  Factors should be developed to best utilize available data collection resources  Factors should be developed separately for total volume and for estimates of volume for individual truck

Creation Of Factor Groups Assumption: temporal characteristics affect all roads  Note: Analyses need to be performed separately for total volume factors and for factors that are applied to volumes by vehicle classification  Factor groups are used to create temporal variation factors to statistically convert short counts to annual averages.  Set of roads as a “group”, all group members are similar in characteristics  Data from sample of locations on roads are collected and mean is taken  The procedure assumes something so it has 

The Factoring Process (Assumptions & Errors)

Defining the Groups  It

is difficult to define groups of roads that “are similar with respect to traffic variation,” and the more “mathematically alike” the factoring groups created from the data, the more difficult it is to define the attributes that determine which roads belong to a given group.

 Example:

Group is Rural interstate Highway but the travel pattern is not

Measured Characteristic  The

grouping process is made more difficult and error prone because the appropriate definition of a “group” changes depending on the characteristic being measured.

 Example:

Volume factor groups of trucks VS computation of axle correction factors

Selecting a representative sample  It

is very difficult to select a representative sample of roads from which to collect data for calculating the mean values used as factors.

 The

primary reason for this is the location of continuous data collection sites.

Computation Of Factors  The

last source of error discussed in this section occurs in the computation of factors because the datasets used to compute those factors are not complete.

 This

is mainly due to FAILURE of collection devices

HOW TO CREATE FACTOR GROUPS  The

three techniques are: o cluster analysis o geographic/functional assignment of roads to groups o same road factor application. § Each of these techniques starts from existing permanent counter data.  The first step is to compute the adjustment factors that will be used in the group selection process

Cluster Analysis Using least squares method most similar sets of factors are determined.  Most similar stations according to factors are grouped and process is repeated  Where to stop clustering process depends on analyst e.g. not more than 5 factor groups  Next step is to assign each cluster the best continuous counter data for which the group fits. 

Geographic/Functional Classification of Roads Factor Groups Allocation of roads into alternative factor groups on the basis of available knowledge about traffic patterns.  Available knowledge is usually obtained from a combination of existing data summaries and professional experience with traffic patterns.  The characterization of roadways using functional class makes it easy to assign individual road sections to factor groups and also allows the creation of factor groups that are intuitively logical.  For each initial factor group, continuous 

Geographic/Functional Classification of Roads Factor Groups  Example  urban Interstates and expressways  other urban roads  rural Interstates  other rural roads in the eastern

portion of the state  other rural roads in the western portion of the state  recreation routes.

Same Road Application of Factors

 This

process assigns the factor from a single continuous counter to all road segments within the influence of that counter site  The boundary of that influence zone is defined as a road junction that causes the nature of the traffic volume to change significantly.  The short count in question must be taken on the same road as the continuous counter.  Limitations: More counters or less roads are required

ALTERNATIVES TO FACTORING

 Appropriate

where factor groups are not readily known and the annual traffic estimate must be very accurate.  Taking week-long counts removes the day-of-week variation. Counting at the same location four times at equally spaced intervals removes the majority of seasonal bias.

TYPES OF FACTORS For seasonal adjustments, some techniques use monthly factors, whereas others use weekly factors. Both of these techniques can be

Computation of Factors (monthly factors)  There

are two basic steps in computing the factors to be used: computing the numerator and the denominator.  The numerator is assumed to be AADT. The denominator is dependent on the factoring approach taken.

Computing AADT A simple average of all days: simple average of all 365 days in a given year. Missing data can cause biases  An average of averages (the AASHTO method): The AASHTO approach first computes average monthly days of the week. These 84 values (12 months by 7 days) are then averaged to yield the seven average annual days of the week. These seven values are then averaged to yield the AADT. This method explicitly accounts for missing data by weighting each day of the week the same, and each month the same, 

Continued

where: VOL = daily traffic for day k, of day-of-week i, and month j i = day of the week j = month of the year k = 1 when the day is the first occurrence of that day of the week in a month, 4 when it is the fourth day of the week. n = the number of days of that day of the week during that month (usually between 1 and 5, depending on the number of missing data).

Computing the Denominator for Monthly

 An

adjustment factor that converts any weekday ADT for a given month into AADT. This would convert monthly average weekday traffic to annual average daily traffic.  Definition of weekday  Example :(Monday to Friday), then the denominator is the sum of all weekdays (Monday to Friday) divided by the number of days of data present.

Continued  If

the State chooses to compute an average monthly day-of-week factor (i.e., combining the monthly variation and the individual day-of-week variation), then the denominator is the simple average of available daily volumes for that day of the week for that month.  If the State decides to use a weekly factor, the denominator is simply the

Adjustments to Short Duration Volume Counts In general, a 24-hour, axle count, is converted to AADT with the following formula:

where

AADThi = VOLhi * Mh * Dh * Ai * Gh



AADThi = the annual average daily travel at location i of factor group h



VOlhi = the 24-hour axle volume at location i of factor group h



Mh = the applicable seasonal (monthly) factor for factor group h



Dh = the applicable day-of-week factor for factor group h (if needed)



Ai = the applicable axle-correction factor for location i (if

Determining the Appropriate Number of Continuous ATR Locations The basic assumption made in the procedure is that the existing locations are equivalent to a simple random sample selection. Once this assumption is made, the normal distribution theory provides the appropriate methodology. The standard equation for estimating the confidence intervals for a simple random sample is: where B = upper and lower boundaries of the confidence interval X = mean factor T = value of Student's T distribution with 1-d/2 level of confidence and n-1 degrees of freedom n = number of locations d = significance level s = standard deviation of the factors.

FREEWAY-EXPRESSWAY RAMP COUNTING PROCEDURES

Problems Involved  Portable

counters are impossible to install because of very less safety  Two methods for counts can be applied: o Permanent Counters o Counters on ramps

Procedure  Mainline

volumes are known at two points and all input/outputs are measured between those two points. The two boundary points are normally ATRs or other instrumented mainline locations that provide a highly accurate measurement of annual traffic volumes. These points are used to control the counting and adjustment process and are referred

Establishing Anchor Points and count duration  Each

State will have to make its own determination regarding the appropriate number of anchor points. As a general rule-of-thumb, the recommended number of interchanges between anchor points is five.  The minimum period recommended for collecting ramp volume data is 24 hours. Ideally, all ramps between two anchor points should be counted for

In the next Episode…  Vehicle  Truck

Classification Monitoring

Weight Monitoring

 Format

and supplement

 Coming

Soon!!

End of Session one

A variety of traffic monitoring activities, including vehicle speed monitoring, traffic management activities, toll collection devices, and incident detection sensors, can provide traffic volume information.