IAn Imperative for, and Current

Transcription

1 ., IAn Imperative for, and Current Progress toward, National Traffic Monitoring Standards BY DAVID ALBRIGHT During the Middle Ages there was no single -. way of writing letters of the alphabet. Each state, and scribes within each state, had the freedom to embellish and shape letters as they felt appropriate. This created recurring problems in understanding what others had written. Emperor Charlemagne ultimately issued and enforced an edict that all letters should be of one form. The standard shape and spacing of letters was established to facilitate clear communication and to encourage exchange of ideas. Today it is almost unimaginable that an alphabet should have no commonly agreed upon shape of the letters. It is hoped that future generations will find it equally unimaginable that there was a time in the history of the United States when commonly used traffic statistics had no standard data collection, summarization, and reporting method among states, counties, cities, and private firms. That time is today. Fortunately, progress is being made toward establishing a common national traffic monitoring practice. Standards and guidelines are being established to Facilitate clear communication and to encourage exchange of information. The structure of this discussion is in two parts. The first is an imperative for national traffic monitoring standards. The second provides an overview of progress toward defining a common practice. An Imperative for Standards of Practice Just as there was opposition to a standard shape for letters and the Carolinian minuscule in 8 A. D., so there are arguments in opposition to traffic monitoring standards in 1991 A. D. Figure 1. Duration of short-term traffic volume counts. SOURCE: Cited reference Contemporary Practice Traffic Monitoring Argument I: There is no need to establish traffic monitoring standards because current practices are typically consistent. The acceptance of traffic summary statistics derives in part from the common naming of traffic statistics. Transportation professionals request and receive statistics such as average annual daily traffic (AADT). When these statistics are reported there is rarely an indication that the information underlying these statistics might vary. In 199a survey was conducted of traffic monitoring practices among state transportation agencies. The findings suggest that traffic practices are diverse. Short-term traffic volume counts to estimate AADT are collected by all state 22. ITE JOURNAL. JUNE 1991

2 transportation agencies. State agencies utilize 13 unique combinations of volume count durations. Among these combinations there arc 12 different count durations. The number of states collecting traffic volume by duration is shown in Figure 1. There is some consistency in state traffic volume practice. This was one of tbe important benefits of Tr-ajjic Monitoring Guide, published in The Guide emphasizes 48-hour counts for statewide, system-level traffic programs. Figure 1 indicates that although half of the states conduct 48-hour volume counts, there is a surprising incidence of counts less than 24-hour duration. State traffic monitoring of vehicle classification is characterized by 12 unique combinations of count duration. Within those combinations there are 9 different durations of collecting data. Vehicle classification duration by state is shown in Figure 2. There are 13 combinations of hourly counts fbr vehicle weighing and 1 count durations. State durations for monitoring vehicle weight are shown in Figure 3. Eighteen state agencies said they conduct turning movement counts at intersections, using 12 different count periods. Thirteen of these states estimate AADT from the turning movement counts, using various procedures. There is also diversity in how state agencies respond to equipment malfunction. Portable and permanent traffic monitoring equipment fdils. When portable devices fail, 13 states acknowledged some procedure to estimate the missing values and complete the data set. When permanent devices fail, 23 states indicated some procedure to estimate the missing values. A variety of methods are used to estimate missing values. Seasonal correction factors adjust a short-term count taken in one period of tbe year for traffic conditions over the entire year. Forty-four states stated that they apply seasonal correction factors to all short-term traffic counts. Axle correction factors adjust mechanical measurements that record only axle impulses. Forty states apply axle correction fdctors to all traffic counts. The foregoing reflects the diversity of traffic monitoring practices. There has been limited communication of traffic practice among and within states. Only six states have surveyed traffic practices within their state. Without traffic monitoring standards, the practice among other agencies and private firms may be expected to bc at least as diverse as that of state agencies. In the absence of standards of practice, the tendency is toward more complexity rather than more consistency. The tendency toward more complexity can occur when information users are uninformed of the data collection activ- 5 ities that have or have not taken place and the adjustment factors that have or have not been applied. Contemporary traffic practices arc inconsistent and cannot be expected to become more consistent in the future. Impact of Diverse Practice on Traffic Statistics Argument 2: There is no need to establish I 3 I Figure 2. Duration of short-term vehicle classification counts. SOURCE: Cited reference : Figure 3. Duration of short-term vehicle weighing. SOURCE: Cited reference 1. I ITE JOURNAL ojune

3 standards because there would not be a substantial difference in the resulting traffic summary,stutistic.s. Traffic statistics are generally reported as a specific number rather than a range of probable values. The user of traffic information may assume there is an acceptable precision and bias of reported traffic statistics. The precision and bias of traffic volume summary statistics were documented in a recent Ncw Mexico analysis. Data from 35 permanent counter sites were treated as though the continuous records were short-term counts of different duration. Average annual weekday traffic (AAWDT) was estimated from each short-term record. From each short-term AAWDT estimate, the AAWDT from the complete ckita set at the permanent site was subtracted. The difference was divided by the complete data set AAWDTand multiplied by 1. This provided a distribution throughout the year of short-term count estimates in relation to an optimal statistic based on a full year of edit-accepted data. The moments of the distribution were calculated. The 5th and 95th percentiles provided the actual 9 percent distribution range of AAWDT errors from shortterm traffic counts. The distribution ranges were grouped by urban, rural, and all roads. The mean distribution range by group was plotted against count duration, The 9 percent distribution range of AADT by hourly duration of traffic count is presented in Figure 4. This figure identifies typical precision of short-term counts, not the bias associated with the distribution. Alternative practices in traffic count duration were found to impact the precision and bias of annual traffic estimates. Figure 4 indicates, for example, that an expanded count of 8 hours duration on a rural road may be expected to provide an estimate of AAWDT 9 percent of the time within a range of 86 percent of the summary statistic. Counts less than 24 hours durations are characterized by significant bias, so the distribution range should not be expressed as f 43 percent. Rural road counts of 12- hour duration 9 percent of the time have a distribution range of 7 percent. Counts of 24-hour duration have a range of 46 percent ( *23 percent), and 48- hour counts have a range of 42 percent (*21 percent). c.+- s * *,. All Roads. ~ -f* **.. E&Ea \ ,...=O ** % , I I 1 I Short Term Count Durotion in Hours Figure 4. Short-term count precision. SOURCE: Cited reference 4. On urban roads, the reduction of the 9 percent distribution range is not as substantial. Urban counts of 3, 8, and 12 hours were characterized by greater bias than the 24- and 48-hour counts. For 24- and 48-hour counts at 29 of the 35 permanent counter sites the bias was less than 3 percent, at 6 sites 3 9 percent, and at no sites did it exceed 1 percent of the AAWDT. For 12-hour counts, most of the sites had a bias of between 3 and 9 percent, and at 2 of the sites the bias exceeded 1 percent. One-fourth of the sites with 8-hour duration had a bias exceeding 1 percent. As the count durations decreased, the bias increased. Of the 3-hour counts, 7 of the 35 sites had a median of less than 3 percent, 16 sites between 3 and 9 percent, and 12 sites were 1 percent or greater. Some state, regional, and urban agencies count the urban peak hour during a weekday and multiply the traffic volume by 1 to estimate AAWDT. This practice results in a 9 percent distribution for urban road AAWDT of 23 percent. The distribution is constrained by the roadway capacity, so the distribution range is reduced. However, the bias is substantially higher. The median value at 1 of the 11 urban sites was less than the AAWDT. The entire 9 percent distribution range falls below the AAWDT at 8 of the 11 sites. The bias is variable, indicating that it is not solely the result of the selected constant. The varying hourly duration of traffic monitoring counts is not the only source of variability in the traffic summary statistics. Computed monthly adjustment factors are commonly between ().8 and If one agency applies seasonal factors and a neighboring agency does not, the resulting summary statistics may be named the same, but they will not be equivalent. Similarly, axle correction factors are not uniformly applied among state agencies. The variability in traffic summary statistics is not limited to traffic volume. Wiley Cunagin, in a presentation before the 199 Annual Meeting of the American Association of State Highway and Transportation Officials (AASHTO), detailed the impact of current diverse vehicle weighing practice on the reliability factor in AASHTO S Pavement Design Guide. ( Vehicle weigh-in-motion (WIM) data from seven sites in one state were examined. The Pavement Design Guide anticipates variances in traffic data and attempts to account for traffic and nontraffic data variability through a reliability factor. The reliability factor is calculated based on the variance of the mean statistics used in the analysis and is then applied as a multiplier of the design equivalent single axle loads (ES- ALs) to help ensure the road is not underbuilt. A range for expected traffic vuiance is included in the AASHTO Guide. The expected range for the combined vari- 24. ITE JOURNAL. JUNE 1991

4 Table 1. Diverse Statistics under Nonstandard Practice (A Hypothetical Truck Convoy of 1, Vehicles) State Estimated AADT New York New Jersey Pennsylvania Ohio Indiana Illinois lowa Nebraska Wyoming Utah Nevada California 1, 6 5 2,5 1, ,2 2,5 1 ante of the traffic variables is from.225 to.429, and the range of total standard deviations was.37 to.39. The WIM data variance is substantially higher than anticipated. The observed range of variance was from.194 to.5228, and the range of total standard deviations was.55 to.8. Current estimates of axle load variability are low. The errors of the estimate for ESALS, as for AADT, are higher than commonly considered. Impact of Diverse Practice on Data Applications Argument 3: There is no need to establish standards because the differences in resulting summary statistics would not have a significant impact on the applications of the statistics. Diverse traffic monitoring practices result in differences in the resulting summary statistic precision and bias. An example may help illustrate the impact of diverse practices. Consider the monitoring of a hypothetical truck convoy. Each day, for a period of one year, one thousand five-axle trucks were off-loaded at New York harbor. The trucks were driven daily in convoy across the nation from New York to California. The questionable legality of the convoy resulted in trucks using both Interstate 8 and other functional classifications of roadway in each state. Assume that the convoy passed one point in each state at which traffic volume was monitored. Each of the states counted or attempted to count, in accordance with their current procedures, the convoy of trucks along with other traffic. During the year, project development engineers in each state heard rumors about a convoy and the likely route through their state. After the year had passed they discovered that traffic volume had been monitored along the rumored convoy route. An engineer in each state requested a traffic report for the appropriate section of roadway. The AADT would be expected to vary from state-to-state because of the varying additional traffic. But a portion of the reported AADT in each state should represent the 1 trucks. An ideal traffic volume monitoring program would provide a portion of AADT representing the daily presence of the 1 trucks as, indeed, 1 vehicles per day. Because the states do not have the same procedure for collecting, summarizing, and reporting vehicles, the state traffic reports differed. The number of vehicles seemed to change as the convoy passed from state to state. How the states summarized the 1 trucks is shown in Table 1. The summary statistics reflect potential calculations under current practices. New Jersey conducted an 8-hour turning movement count at an intersection the trucks passed, and from which AADT was estimated. The count was adjusted from automatic traffic recorder (ATR) sites to a 24-hour period volume. The resulting 8-hour turning movement count may be expected to have a distribution range of approximately 86 percent, with a negative bias. Pennsylvania and Iowd conducted similar counts, but the ATR adjustment data for the same day were missing. The missing data were estimated from historical traffic trends, which introduced additional error. Ohio conducted a short-term axle impulse count and did not apply an axle correction factor. As a result, each truck created 5 axle impulses. The 5 impulses were divided by two to provide a vehicle count, so the convoy appeared to be two-and-a-half times the number of vehicles. The result in Nevada was the same. Although Nevada applies an axle correction factor to counts on higherclassified roads, the point at which the trucks were measured was a lower functionally classified road the convoy used to avoid enforcement. Indiana had a permanent counter installed on the convoy route. Unfortunately, the counter ceased functioning on the same day the convoy began and could not be repaired during the year. This was not known to the engineer because the missing data, which were printed and made available, were estimated from the previous year s counter data when there was no convoy. Illinois monitored the vehicles for a 48-hour period and adjusted the count for seasonality. The unbiased distribution range wds 42 percent. Nebraska and Wyoming monitored and adjusted the trucks just as Illinois did, and so their summary statistics fell within the same range. Utah attempted a short-term traffic count one day as the trucks passed by. The counter failed. The failed count was estimated from other traffic counts taken in the same corridor. Unfortunately, the convoy diverted before reaching the other count locations. California tried to take a short-term count as the convoy passed. The count fdiled, and this was one of those instances in which there wasn t enough time and staff available to retake the count. The AADT was estimated using the trend from the previous counts at this site, combined with the percentage of change along the same road. The convoy crossed only one of the sites used to calculate the percentage of change. The traffic increase appeared slight. In the absence of a standard or guide specifying disclosure of practice, neither the basis for, nor the precision and bias of, the statistics were reported to data users. Each user wds confident that an accurate AADT had been achieved. This example is hypothetical and represents a worst-case scenario. Under current practices the truck convoy estimates could vary from O to 2,5 vehicles. The results would have been different if the same agencies had employed a minimum 48-hour count period without estimating missing measurements, recounted when counts failed, and appropriately calculated and applied seasonal and axle correction factors. Using such a guide and a 9 percent confidence level, the AADT estimates would be expected to range from approximately 8 to 12 vehicles. The differences in traffic statistic precision and bias occur each and every day. The problem is neither the route nor the states along the route. Any route across the nation, through any combination of states, would produce the same potential for wide-ranging estimates of traffic volume. ITE JOURNAL. JUNE

5 Errors in volume estimates have a way of progressing beyond an AADT report. In the example, the person requesting the report was a project development engineer. If a project were being scoped, the trend among prior and current year AADT could result in design-year volumes, indicating one too many or one too few lanes of travel. If the person requesting the reports had been a pavement engineer, the data could have resulted in under- or overbuilding a facility. If the person requesting the AADT report had been a highway safety analyst, the volume estimates would have been multiplied by road segment length to calculate vehicle miles traveled. Accident experience would have been assessed in relation to exposure, and safety funds would have been allocated. Traffic statistic variability impacts many transportation applications. The current variability of traffic summary statistics, when the variability can be reduced under a standard practice, would be considered unacceptable by many traffic data users. Progress toward Standards National traffic monitoring standards and guidelines are being developed. The development efforts were initiated in response to a growing concern about the equivalence of traffic statistics. The need to develop common traffic monitoring practice was emphasized by the Strategic Highway Research Program (SHRP). Part of SHRP is to improve the understanding of long-term pavement performance. Assessing the role of vehicle loading on pavement performance requires site-specific traffic data. A SHRP national traffic database was developed to receive state traffic measurements. As awareness of different state practices increased so did the complexity of the SHRP database. One of the critical issues addressed by SHRP was how to maintain the integrity of the research activity by preventing mixed traffic data from being inadvertently misused by pavement researchers. Tb address this problem, the principle of truth-in-data was developed. Truth-in-data is the reporting of summary statistics with disclosure of information from which the statistics were calculated. Traffic summary statistics in the national traffic database may be accessed only after the researcher has identified the minimum acceptable quantity and quality of data underlying the statistic. As SHRP addressed differences among state traffic programs, interest was stimulated in a common national practice. There are now two important activities in recommending a common traffic monitoring practice. The process has been initiated by both the American Society for Testing and Materials (ASTM) and AASHTO. ASTM first responded to the issues raised by SHRF? In July 199, ASTM E , Task Group on Traffic Monitoring Standards, began defining standards. ASTM standards are currently being developed with particular application to cities, counties, and the private sector. ASTM standards will provide a common technical specification. Although the statistical interests of all agencies are addressed in the ASTM standards, they do not consider traffic reporting requirements unique to state agencies. Recommended state agency traffic monitoring practice is being developed by AASHTO. In January 1991, the AASHTO Traffic Monitoring Standards Task Force began preparing guidelines for state traffic programs. The AASHTO guidance is being developed in communication with ASTM standards to ensure that the resulting summary statistics between the states and other agencies are directly comparable. When these two efforts are complete, there will be two technical references refining current traffic monitoring practice. The foundational principles, procedures, and traffic reports will be compatible. The summary statistics will be equivalent and comparable. Summary An imperative for national traffic monitoring standards has been presented based on current diverse practices and the consequence of those practices on the variability and applicability of traffic summary statistics. The response to this need has been indicated through the work of ASTM and AASHTO. The implementation of standards will require a willingness to collect, summarize, and transmit traffic data in a manner that serves not only one s immediate concerns, but also the shared interests of the profession and the nation. Just as the traffic monitoring profession has identified the need to define common practice, it will also need the resolve to make common practice a reality. National standards will be developed, adopted, and refined across time. A common basis for communication of traffic monitoring data will help achieve the same type of consistency promoted by Charlemagne s edict. References 1. New Mexico State Highway and Transportation Department, /99.~urvey of Tr;ffic Monitoring Pructices among Stale Transportation Agencies of the United States. Report No. FHWA-HPR-NM-9-5, Santa Fe, N. M.: New Mexico State Highway and Transportation Department, De , 5. (i. cember 199. U.S. Department of Transportation. Traffic Monitoring Guide. Washington, D. C.: U.S. Department of Transportation, Federal Highway Administration, Toth, Robert B, The Economics of Standardization. Minneapolis, Minn, : Standards Engineering Society, Albright, David, ccthe Development of ASTM Highway Traffic Monitoring Standards. Standardization News 19 (February 1991). Cunagin, Wiley. The Need for Standard Traffic Monitoring Practice. Presented at the annual meeting of the American Association of State Highway and Transportation Officials, Phoenix, Ariz,, December 11, 199. American Association of State Highway and Transportation Officials. AASHTO Guide for Design of Pavement Structure. Washington, D. C.: AASHTO, ~ David Albright is the research bureau chief, New Mexico State High way and Transportation Department. He currently serves as chairperson of the SHRP Traffic Data Collection and Summarization Expert Task Group, chairperson of the ASTM Traffic Monitoring Standards Task Force, and chairperson of the AASHTO Joint Task Force on Trafic Monitoring Standards. A lbright is a Member of ITE. 26. ITE JOURNAL. JUNE 1991