TRB Paper No: 07-0116_. TRB 86th Annual Meeting EVALUATING THE 2000 HIGHWAY CAPACITY MANUAL ADJUSTMENT FACTOR FOR AWDT TO AADT BY APPLYING A CONSISTENT TRAFFIC DATA METHODOLOGY Martin Lewis Planning Technologies, Inc. 10131 Coors Blvd, N.W. Suite 1-2 PMB 894 Albuquerque, New Mexico 87114-4048 Telephone (505) 898-0032 [email protected]David Albright Bernalillo County Public Works Division 2400 Broadway Avenue, S.E., Building N Albuquerque, New Mexico 87102 Telephone (505) 848-1516 Fax (505) 848-1510 [email protected]TRB 2007 Annual Meeting CD-ROM Paper revised from original submittal.
Transcript
TRB Paper No: 07-0116_.
TRB 86th Annual Meeting
EVALUATING THE 2000 HIGHWAY CAPACITY MANUAL ADJUSTMENT FACTOR FOR AWDT
TO AADT BY APPLYING A CONSISTENT TRAFFIC DATA METHODOLOGY
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EVALUATING THE 2000 HIGHWAY CAPACITY MANUALADJUSTMENT FACTOR FOR AWDT TO AADT BY APPLYING A
CONSISTENT TRAFFIC DATA METHODOLOGY
Abstract
A methodology was developed by Bernalillo County Public Works Division, New Mexico, to understand and assess traffic monitoring data. The methodology was applied to a national default value used to adjust traffic field measurements. This paper describes research to improve both the traffic monitoring process and product.
The proposed methodology identifies seven steps to understand traffic data. The steps can be implemented by an individual analyst, but are recommended for discussion by all stakeholders in the traffic monitoring process including field personnel, office personnel who summarize and report the data, and data users.
The methodology was applied to a national default value presented in the 2000 Highway Capacity Manual that has been widely implemented including by Bernalillo County. The factor adjusts traffic summary statistics to represent Annual Average Daily Traffic. The factor is used by local governments to adjust short-term traffic counts taken during the work week so the summary statistic can be used in a variety of applications including accident exposure rates. Accident exposure rates, for example, are based on traffic for all days, not the work week. The result of the application of the methodology is that the national default value was found to be inappropriate. Modification to the 2000 Highway Capacity Manual is recommended.
Research to further improve the traffic monitoring process will develop, train and exercise a team approach to understanding traffic data. Research to further improve the traffic monitoring product will be to identify local data collection sites and compare national, state and local adjustment factors.
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Evaluating the 2000 Highway Capacity Manual Adjustment Factor for AWDT to AADT by Applying a Consistent Traffic Data Methodology
A methodology was developed by Bernalillo County Public Works Division, New Mexico, to understand and assess traffic monitoring data. The methodology was applied to a national default value used to adjust summary statistics derived from traffic field measurements. This paper describes the methodology, its application and impact.
METHODOLOGY
NeedThe purpose of the methodology is to help individuals and agencies to understand traffic
monitoring data. To develop the methodology, Bernalillo County built upon prior and ongoing national efforts. There was a time when there were no traffic monitoring standards in the United States, and it was uncomfortable if not threatening for some agencies to disclose their traffic monitoring practice. Today, through the efforts of the Federal Highway Administration, the American Association of State Highway and Transportation Officials, the American Society of Testing and Materials and others, traffic monitoring standards and guidelines are well established.
The need today is not for standards. It is that practitioners may not understand how the standards apply. Traffic data professionals have also come to rely on output from technology as though the output were both compliant with standards and the measurements accurate. Practitioners are sometimes overwhelmed by the sheer volume of data recorded and output, and may not attempt to read rows after rows, columns after columns of data. As a result, practitioners may not understand what the data mean and do not mean.
As a result of standard practice, practitioners thankfully no longer make up traffic numbers based on “professional judgment”. There is a need to find a way to grasp the datacollected and wring out all that can be wrung in understanding what - if anything – the data have to tell us about what is taking place on the road network. A methodology is needed to enable us to understand base data and their summary statistics. Summary statistics include default valuesin certain applications of traffic data.
The methodology to be technology independent, like traffic monitoring standards and guidelines. Traffic monitoring depends on technology. However, the need is for a methodology that can be applied to traffic data whatever technology is used to generate the data. Technology changes, and hopefully improves. Hopefully, understanding improves whatever technology is used today or implemented tomorrow.
The imperative to understand traffic monitoring data is driven by the uses of the datacollected, summarized and reported. Traffic data affect all aspects of highway transportation, including design, operational issues such as signal warrants, environmental impact analyses andsystem-level investment strategies. It is important to understand the data on which thesedecisions are made. A seven-step methodology was developed to meet this need.
The Seven Steps
Step1. Question the data.
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Questioning traffic monitoring data is called “functional conflict” – raising difficult questions not to be difficult, but to make sure use of the data is appropriate. Functional conflict has a constructive purpose, and the purpose is improvement. Examples are,
“What does this number mean?”“Why does this number go up (or down)?”“How many missing field data are there?”“How were missing field data treated?”“Were any adjustment factors used to estimate the summary statistic?”“What is the source of the adjustment factors?”“How precise is this summary statistic?”
Step 2. Identify problems and patternsAfter questioning the data, recreate them. Recreating the data is a helpful way of
identifying problems and patterns. To understand the data, handle at least a portion of the dataset being examined. If the data are in rows and columns, one alternative is to create a spreadsheet or database to work with the data. While it may be efficient to electronically transfer Traffic Monitoring Device (TMD) data to a spreadsheet, it may not be effective in identifying potential problems with the data. To gain familiarity with the data, personally key enter some or all of the data.
If the dataset is small, all data should be entered into a spreadsheet or database. With all of the data accessible, check and confirm or reject what has been observed about data patterns and learned about potential problems with the data.
If vehicle classification and speed data are being examined and the dataset is large, a portion of the dataset may be recreated until the data are understood. Once the data are understood, the data can be downloaded from the device to a spreadsheet or database for further analysis. The key, though, is to actively “handle” the data rather than passively accept and attempt to interpret the data reports.
If all or a portion of a dataset is recreated, a practitioner can calculate what the devices do not. The simpler the check on calculations the easier it is and the better it is whether or not the TMD computer software calculations are confirmed. An example of a simple calculation: do the percentages reported for a traffic count equal 100%. If not, why not?
Recreating some or all of a dataset, will dispel any illusions that TMDs, field sensors, and the software that interprets the field measurements are without error. All field measurements involve error. The question is whether or not the errors are understandable and acceptable given their intended application. Recreate the datasets to identify patterns and problems. Use a spreadsheet, use a calculator, use a notepad - but use something to check the work of traffic monitoring machinery.
Step 3. Gather resources There are resources that can help improve understanding of traffic monitoring data. With
improved understanding, more informed questions can be asked. The resources gathered may be written – such as ASTM International traffic monitoring standards – or contact with other professionals who have addressed the same or similar problem. By identifying and gatheringneeded resources, knowledge is deepened and expanded.
Step 4. Ask better questions
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Better questions can be asked as the data are understood. There are many site-specific issues related to traffic monitoring, including physical location, weather conditions, technology, traffic characteristics, and software version. Practitioners and teams of practitioners may reach a point that they become the experts on a specific dataset. At this point, the next step may be taken, to understand why.
Step 5. Understand whyMove from identifying problems to formulating hypotheses to understand why the
problems took place. Understanding why is being “inside the data”. This is the beginning of making appropriate use of the data and of making improvements.
If the practitioner remains outside of the data, there will be an endless cycle of trying to match the numbers received with personal, professional judgment.
The hard lesson is that practitioners cannot surrender responsibility of understanding the data because a machine recorded them, computer software summed them, and a printer generated them. It doesn’t matter how well they are mapped or how attractively they are graphed. If the data are wrong, and are used, it is the responsibility of the practitioner.
Understanding how data impact decisions in transportation is addressed by each generation of transportation professionals. Hopefully, understanding is cumulative over a succession of careers. It is our turn, now, to improve the data for those who follow. To do this, first understand the data.
Step 6. Decide what data are useful.Once the traffic analyst or analysis team understands the data, the data can be matched
with the requirements for how the data might be used. Some or all of the data may be useful, and some or all of the data may need to be discarded.
Step 7. Decide how to improve.The final step is designed to improve traffic monitoring practice. Based on what has been
learned, data accepted and discarded, identify what can be done to improve the traffic monitoring program. If the earlier steps are not followed, deciding how to improve almost always involves purchasing new equipment. If the earlier steps have been followed, new equipment may or may not be needed. Consider first, though, the procedures followed at each stage of the process such as: when, how and where the equipment is set; how ground truth is determined; how the data are summarized; how the measurements are checked for errors; the results of the error checks; how the data were used and the impact of that use.
ProcessThe steps can be implemented by an individual analyst. The steps are recommended for
discussion by all stakeholders in the traffic monitoring process ranging from field personnel, office personnel who summarize and report the data, to users of the data.
APPLICATION
The methodology outlined above was applied to a national default value presented in the Highway Capacity Manual that has been widely implemented including by Bernalillo County. The factor is used to adjust traffic summary statistics representative of average weekday traffic to
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Annual Average Daily Traffic. The factor is applied by local governments such as Bernalillo County to adjust short-term traffic counts taken during the work week so the summary statisticcan be used in calculating accident exposure rates.
Accident exposure rates are based on traffic for all days of the week, not just the work week. Therefore, methodology was applied to an Annual Weekday Traffic (AWDT) to Annual Average Daily Traffic (AADT) conversion factor.
Step1. Question the data. Questions were raised during review of intersection accident exposure rates in the
Albuquerque, New Mexico, region. There was a colorful Geographic Information System map of a regional street network that illustrated intersections with high accident rates. This is an important map, because it indicates where resources should be allocated to improve intersection safety.
The question was asked how the accident rates were calculated. When asking a question of the person responsible for generating a report how the traffic summary statistics were calculated, and the answer is, “I don’t know, a computer application calculates it,” it is important to discover what the computer application does and why. This is the first step, question the data.
The answer to the question was a computer application that included a factor to adjust Average Weekday Traffic (AWDT) to Annual Average Daily Traffic (AADT). The factor was 1.07, and in keeping with the best practice in traffic monitoring, and consistent with standard Truth in Data practice, the equation included the reference for the factor. It was the 2000 Highway Capacity Manual. The 2000 Highway Capacity Manual does identify the default value to adjust AWDT to AADT.
If average annual daily traffic is not known, it can be estimated from average weekday traffic using Equation 8-2 derived from the Highway Performance Monitoring System (HPMS)
AADT = AWDT/1.07 (8-2)Where
AADT = annual average daily traffic (veh/day), andAWDT = average weekday daily traffic (veh/day) (1)
So, in questioning the data the issue arose what would the impact be of using other adjustment factors, perhaps local factors? The factors chosen for the comparison were statewide factors by functional classification of roadway. This decision was based on the notion, right or wrong, that maybe a single number to adjust AAWDT for every road in America might be less helpful than, at a minimum, roads in the same state by functional classification. It was at least something to consider at this point in applying the methodology.
Step 2. Identify problems and patternsRecreate the data.
The AAWDT to AADT conversion factors were requested from the state transportation agency. The agency installs, maintains, and gathers data from permanent vehicle classifiers grouped by functional classification of roadway. The state agency provided the data with the functional classification of roads listed in Table I.
TABLE INew Mexico State AAWDT to AADT Adjustment Factors
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by Roadway Functional Classification(2004)
Functional Classification State Adjustment Factor________________________________________________________
The order of functional classification of roadway groups in the original list was unhelpful to understanding the data. When an analyst recreates the traffic monitoring data, the data or data fields can be added and compared. The benefit from reorganizing the data and adding additional fields is shown in Table II.
TABLE IINew Mexico State AAWDT to AADT Adjustment Factors by Roadway Functional Classification Compared With Highway Capacity Manual National Default Value(2004)
Functional State Default ChangeClassification Factor Factor_______________________________________________________
Data are examined as they are recreated. Look for patterns and potential problems. The analyst gains familiarity with the data. Like recreating the dataset, looking for patterns of problems is an essential step in the process.
In the above dataset, while the problems are not apparent as they are in some traffic data sets, a pattern can be seen. Most of the differences between the state adjustment factors and the national adjustment factor are negative. All of the rural road classifications are negative.
The state functional classification factors are lower, in most cases, than the national factor. Is there a reason? With a pattern identified that cannot yet be explained, it is time to gather resources.
Step 3. Gather resources Gathering resources does not stop with the Highway Capacity Manual. In this instance,
the main text of the Highway Capacity Manual cites a reference for the national default value. The text of the Manual states that the source is the Highway Performance Monitoring System (HPMS). However, HPMS does not recommend the default value. The HPMS Field Manualdescribes the traffic monitoring procedures that should be followed and references the FHWA Traffic Monitoring Guide.
Report rural and urban vehicle activity information for interstate system and functional system groups. The Traffic Monitoring Guide (TMG) should be consulted for recommended practices regarding the development of the vehicle classification coverage count program. The procedures are flexible, allow incorporation of existing automated sites, and are sufficient to meet the area wide and standard sample section reporting needs of the HPMS. If the TMG procedures have not been fully implemented, the source and derivation of the cell values should be thoroughly documented (as discussed in Appendix F). (2)
Appendix F to the HPMS Field Manual does not include the national default value. The Traffic Monitoring Guide does not include the national default value. (3)
While the text of the Highway Capacity Manual states that the source is HPMS, the reference page in the Highway Capacity Manual appropriately enough does not note HPMS. The reference listed is a 1996 study on the development of daily traffic distributions for air quality planning. (4)
The cited study was one of three FHWA sponsored studies concerned with estimating the cumulative effects of congestion on vehicle speeds over the course of a day. In addition to the study noted in the reference page of the Highway Capacity Manual, the other two were “Speed Determination Models for HPMS,” (5) and “Roadway Usage Patterns: Urban Case Studies”. (6)
In 1998, Richard Margiotta, an author of the three documents, presented a lecture in which he used a 1.0757 default value, and cited the source of the value as, “Roadway Usage Patterns: Urban Case Studies”. (6) The Roadway Usage Patterns study is based on urban roadway data. The test sections were Urban Freeways and Urban Principal Arterials. The factor
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was derived from 713 urban automatic recording devices. Margiotta noted the following qualification for the use of this factor to estimate AADT from AWDT.
The adjustment factor report in Section 2 (i.e., the inverse of 1.0757) provides a default value in lieu of locally developed factors. (6)
The default is used in lieu of locally developed factors. Unfortunately, the 2000 Highway Capacity Manual makes no such qualification and encouragement to develop locally developed factors. The Highway Capacity Manual also apparently truncated the value of 1.0757 to two decimal points, 1.07, rather than rounding it.
The national default value seems to have taken on a life of its own independently of its initial purpose. In addition to the Highway Capacity Manual, the factor is used in a variety of other documents. The methodology for estimating incident-related impacts on congestion, for example, is expressed as:
AADT = AWDT/FawdtWhere, Fawdt is the area-wide ratio of AAWT/AADTThe default value for Fawdt is 1.0757. (7)
Through communication with an investigator of the original studies, Richard Margiotta, the author determined that the published AWDT to AAADT adjustment factor was not intended as a national default value. The factor was derived from urban sites – freeways and principal arterials – and would not apply to all roadways. The coefficient of variation was not calculated, but that it would undoubtedly have been quite high. The investigator concurred that the factor should not be used for all roads across America, a decade after the urban data were grouped and the factor was developed for a different and specific application. (8)
In Step 3, additional resources were gathered. The national default value came from data collected in urban areas. The study in which the default value was originally developed and applied stated that it should be used when local factors are unavailable. As a result, there is a solid basis to proceed with local factors. To do this, local factors should be assessed with the same rigor as the national default factor. Then, state and national adjustment factors can be compared.
Step 4. Ask better questionsFocus can now be placed on the local adjustment factors. The local factors available for
this purpose are derived from permanent traffic monitoring devices, are grouped by functional classification of roadway, and are summarized and reported by state departments of transportation. Table IIII presents the state adjustment factors with the number of sites used to calculate the factors.
Better questions because of what has been learned in the previous steps.
TABLE IIINumber of Permanent Traffic Recorder Sites Used to CalculateNew Mexico State AAWDT to AADT Adjustment Factors(2004)
Functional State Default Difference NumberClassification Adjustment Adjustment of Sites
1 a “-“ denotes the factor value was supplied by system operator, not derived from the traffic monitoring data
What are the differences and similarities in change from the national factor to the state factors? Why are two of the values 1.0? Is there some similarity between these functional classes and the source of the national default value? Why are the values the same?
Step 5. Understand whyIdentifying a problem is necessary but not sufficient. There is a need to understand why
there is a problem.The number of sites used to calculate the functional classification factors provides insight
into why two of the values were 1.0 – there were no counters at these sites, so the operator in the absence of an adjustment factor did not adjust the AWDT by providing a factor of 1.0. These classifications would not be helpful in an analysis of the change between the national and state adjustment values. On this basis, Other Urban/Express Freeway and Other Rural groups were removed from the study.
At two sites the operator assigned adjustment factors to the functional classification other than 1.0. The rationale for doing so was not stated, so it is not known if these are judgments or calculations. Since there is no documented rationale, these numbers violate one aspect of the Truth-in-Data requirement for traffic monitoring data and are not included in the study.
Step 6. Decide what data are useful.What data should be included? The 2001 Traffic Monitoring Guide “bare minimum”
value of 2 counter sites was considered useful in this example. (3, pp. 2-45 and 2-26) A minimum threshold of 2 sites was determined to be reasonable. As a result, one group, Interstate Small Urban had only one site and was removed from further assessment. The remaining state adjustment factors can now be compared with the national default value.
Rural roads have a greater difference from the functional classification to default adjustment factor. All rural roads decrease in relation to the default adjustment factor. The largest change from the default value is for two higher classification rural roads: Interstate Rural and Principal Arterial Rural.
The urban roads are also characteristically below the national default value, although less so than the rural roads. The only state group with a higher value than the national default is a group with 2 sites.
Using the statewide values, a lower adjustment factor will be divided into the AWDT to estimate the AADT. The AADT will increase at each site. Therefore, it can be concluded that for this state, the national default value in the Highway Capacity Manual underestimates AADT.
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Underestimating AADT similarly underestimates the exposure rate and overestimates the accident rate.
The national default value underestimation of AADT is not consistent among functional classifications. The AADT underestimation is greater for rural roads than urban roads. Rural intersection underestimated AADT resulted in higher accident rates. As a result, rural intersections appeared to be relatively more dangerous than urban intersections. Bernalillo County could be improving intersections for safety reasons where the safety was less a concern than on other roads. There may be reasons to improve an intersection whether or not the accident rate decreases after this analysis, but it should be based on the best information available.
Step 7. Decide how to improve.The final step is to decide how to improve, and act. In this application, one decision is
whether or not to use the national or state adjustment factors. Another decision is how to improve the traffic monitoring data.
To answer the question of which factor should be used on an ongoing basis, the national default and state functional classification factors were applied to 510 intersections in the Albuquerque metropolitan area. The mean traffic accident rate dropped slightly from 1.54 accidents to 1.52 accidents per million miles traveled. The range of accident rates dropped from 4.68 to 4.59.
The top 50 most dangerous intersections in this metropolitan area were similar using thestate and national factors. Understanding the data confirmed that the intersections with primary safety concerns were identified whether the national or statewide factor was applied. There were significant changes in Bernalillo County understanding of the safety of intersections in the top 100 most dangerous intersections.
One intersection was previously ranked 47th, and with the new methodology was ranked 73rd. At one intersection the relative rank among intersections in the metropolitan area increased by 174 positions from 394th to 220th, with an accident rate increase of 0.54. At another intersection, the accident rate dropped from 2.55 to 1.95, and the intersection dropped 66 positions from the 55th to the 121st intersection of safety concern in the metropolitan area.
Applying the methodology led to identification of limitations of the national default value. The statewide adjustment factors will be used rather than the national default value.
More can be done to improve. The question of whether or not an adequate sample of sites in this metropolitan area, rather than using statewide factors, should be examined. The increase in accuracy for this and other adjustment factors may be worth the investment in additional counters on some functional classes both regionally and statewide. Assessment will be made of whether additional improvement can be made by moving from a statewide factor to a regional factor.
Some of the variability in the analysis could be the result of misclassification of roadwayfunction. Are the roads correctly classified by their function? Seven-day counts during different seasons could help advise if the functional classification of a roadway is consistent with one or another set of permanent counters.
The data at the permanent counter sites should be assessed. There will be missing or invalid data at the permanent counter sites. What quality checks were used to accept or reject the data, and how many days of data each month were used in calculating the summary statistic?
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IMPACTThe methodology was found to be helpful and will be used by Bernalillo County on an
ongoing basis in examining traffic monitoring data. Statewide adjustment factors will be used as the potential value of local factors is assessed.
There is an obligation to our profession and to our Nation. The Highway Capacity Manual should not provide a single default value to adjust AWDT to AADT. The default value should not be published in the next version of the Highway Capacity Manual without at a minimum the notation that state or local adjustment factors should be assessed prior to the single national default value.
The default is used in lieu of locally developed factors. Unfortunately, the HCM makes no such qualification and encouragement to develop locally developed factors. The HCM also apparently truncated the value of 1.0757 to two decimal points, 1.07, rather than rounding it.
The problem with this section of HCM is not simply the question of correcting the impression that the default is from HPMS, by stating it is from HPMS urban data from the early 1990’s. The problem is not confirming the appropriate study to cite among the three related studies. The problem is the adjustment factor itself.
The adjustment factor is presented as a national default value for traffic on all roads of all functional classification. There is no indication of the coefficient of variation among the sites in the original study, much less the errors induced by applying these factors to rural roads. For the State of New Mexico, the HCM adjustment factor systematically underestimates the AADT for all rural functional classifications of roadway. While the HCM adjustment factor also underestimates the AADT for many of the urban functional classifications of roadway in New Mexico, the error is less than for the rural roads. This systematic error should not be surprising given the urban data underlying the HCM adjustment factor.
The HCM should not provide a single default value to adjust AWDT to AADT. An alternative using HPMS data would be to identify that, in the absence of local factors, statewide adjustment factors derived from the HPMS dataset, for each functional classification of roadway are available from State Departments of Transportation. This alternative should not suggest that functional classification of roadways provides the optimal grouping of traffic data. This alternative would, however, maintain the user interest and HCM intent of providing a default approach that is more consistent with the heterogeneous weekday traffic being adjusted. The suggested default approach would also be based on more current data more closely associated with the differences in traffic among roadways. A change is recommended in how the HCM advises estimation of AADT from AAWDT. The recommended change is for Chapter 8, Traffic Characteristics (8-12) text and references, with related changes to the Glossary and Symbols.
Replace the current language (1) with the following. If annual average daily traffic is not known, it can be estimated from annual average
weekday traffic. Local adjustment factors are recommended. Local adjustment factors are calculated as AADT/AAWDT, and are derived from permanently installed traffic monitoring devices. Data from these devices are grouped by functional classification of facility, or by similar traffic characteristics as described in the Traffic Monitoring Guide (9). In the absence of local factors, statewide adjustment factors by functional classification of facility may be used. These factors are annually derived from the Highway Performance Monitoring System (HPMS) dataset and are available from each State Department of Transportation. The AAWDT to AADT adjustment factor may be applied using Equation 8-2.
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AADT = AAWDT * FCF (8-2)Where,
AADT = Annual Average Daily Traffic (veh/day),AAWDT = Annual Average Weekday Traffic (veh/weekday), andFCF = Functional Classification Factor, AADT/AAWDT,
by functional classification of facility derived from local data or statewide HPMS data.
The conversion factor from AWDT to AADT is an example of the importance of getting inside traffic data adjustment factors. It is a beginning.
CONTINUING RESEARCHThere are three areas in which this research is proposed to continue. They are refining
local adjustment factors, considering additional variables when identifying groups of traffic monitoring sites, and moving from individual to multi-disciplinary team assessment of data.
Local Adjustment FactorsThe use of the state functional classification adjustment factor instead of a national
default value is a first step in improving local adjustment factors. The relatively modest impact on specific intersection safety analysis in Bernalillo County may in part result from applying a statewide factor rather than a factor in the region within which the intersections are located. There are differences in regional traffic characteristics within the State of New Mexico. Continuing research will include identification of additional permanent traffic monitoring sites within the Mid-Region of New Mexico. The hypothesis is that installing additional permanent traffic monitoring sites within the Mid-Region area of New Mexico will reduce the variability introduced by statewide adjustment factors. This will provide a more direct comparison of intersections by accident rate using the national, state, and local (Mid-Region) permanent counter datasets. The development of an adequate sample of sites within the Mid-Region will also allow alternative grouping of the data and assessment of current road classifications. Additional Variables for Grouping Traffic Monitoring Sites
Building on the principle of Truth-in-Date, there is an ongoing interest at all levels of government in improving our understanding of traffic data. In accord with this principle, repeatability is ensured by combining base data integrity with documentation of each step in data collection, summarization and reporting. In this context, a continuing research issue is the variable or variables to be considered when identifying sites for grouping traffic data.
One set of variables for grouping traffic data are associated with intended service and trip types, and is expressed as the functional classification of roadway. Use of functional classification allows data collected at a sample of similarly classified sites to be applied to other sites with the same designated function. The current functional classification is the beginning point. Other options for grouping data can be associated with each element of the transportation system: vehicle, user and infrastructure.
Some variables may be associated with vehicle types, such as heavy commercial vehicle use. It may be helpful to consider grouping traffic data sites with similar volumes of heavy commercial vehicles.
Some variables may be associated with users. Trip type, trip length and trip frequency by time of day or period of year are variables that may be considered. User-related variables would
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include recreational routes and farm-to-market routes that may exhibit specific seasonal characteristics based on user demand.
Some variables may be associated with infrastructure. Within an urban area, for example, radial roads may exhibit different traffic characteristics than circumferential or loop roads.
These variables are defined by time and space. Geographic scalability will in part be addressed by comparing national, state and local (regional) factors such as initiated by Bernalillo County. Time scalability can be assessed in part by examining annual changes within and between grouped sites.
Whatever variables are considered and eventually incorporated to better understand how to group traffic data, a related issue is how to appropriately apply summary statistics from grouped data. To make the results of grouped data extensible to similar roadways, what makes the roadways “similar” must be clear. Further definition of the functional classification system is one alternative.
Team Approach to Traffic DataAt this point, the methodology has been implemented in Bernalillo County by individual
analysts. The next process step in this research effort will be to develop, train and exercise a team approach to understanding traffic data.
The very persons who need to understand traffic monitoring data are often those with the least time to do so. A multidisciplinary team approach within an organization, however, has the potential to reduce the impact on any one person while improving understanding generally.Ongoing research is needed to test the team concept in practice.
AcknowledgementsA team is always a better choice than an individual when addressing a complex subject
such as traffic monitoring. Steve Miller is the primary person responsible for our pursuit of traffic data improvements. Members of the team that helped advise this work are Ken White; Ruinan Jiang; Joe Wilkinson; Anthony Caswell; and, Shannon Terry.
References(1) Highway Capacity Manual, Transportation Research Board of the National Academies,
Washington, D.C., 2000, second printing with corrections, July 2005, HCM, p 8-12(2) Highway Performance Monitoring System Field Manual, OMB No. 21250028, Office of
Highway Policy Information, Federal Highway Administration, December 2000(3) Traffic Monitoring Guide, Office of Highway Policy Information, Federal Highway
Administration, U.S. Department of Transportation, Washington, D.C., May 2001(4) Margiotta, Richard, Cohen, Harry, and DeCorla-Souza, Patrick, Development of Diurnal
Traffic Distribution and Daily, Peak and Off-Peak Vehicle Speed Estimation Procedures for Air Quality Planning, Final Report, Federal Highway Administration Work Order B-94-06, April, 1996
(5) Margiotta, Richard, Cohen, Harry, and DeCorla-Souza, Patrick, “Speed and Delay Prediction Models for Planning Applications,” Conference Proceedings, Transportation Planning for
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Small and Medium-Sized Communities, Transportation Research Board Committee A1D05, Spokane, Washington, September 1998.
(6) Margiotta, Richard, Cohen, Harry, Morris, R., Dixson, A., and Trombly, J., Roadway Usage Patterns: Urban Case Studies, Final Report, prepared for Volpe National Transportation System Center and the Federal Highway Administration, July 22, 1994
(7) Sketch Methods for Estimating Incident-Related Impacts, Cambridge Systematics for the Office of Environment and Planning, Federal Highway Administration, Washington, D.C., December 1998
(8) Rich Margiotta, discussion with David Albright, April 7, 2006
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