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1 0 0 0 QUALITY COUNTS FOR PEDESTRIANS AND BICYCLISTS: Quality Assurance Procedures for Non Motorized Traffic Count Data by Shawn Turner, P.E. Senior Research Engineer and Division Head Texas Transportation Institute The Texas A&M University System, TAMU College Station, Texas Phone: (), Fax: () 00, E mail: shawn turner@tamu.edu Philip Lasley Assistant Transportation Researcher Texas Transportation Institute The Texas A&M University System, TAMU College Station, Texas Phone: () 0, Fax: () 00, E mail: p lasley@ttimail.tamu.edu Submitted for presentation and publication for the Transportation Research Board s nd Annual Meeting January 0 Washington, D.C. Word Total =,0 words =,0 (base words) +,000 ( tables/figures x 0 words per table/figure)

2 Turner and Lasley INTRODUCTION More public agencies are collecting pedestrian and bicyclist count data, and it is being used for various agency decisions as well as being reported in the news media. For example: Salt Lake City, Utah's second annual bike count showed a % increase in cycling. () Almost 0% more bicyclists and pedestrians were counted at Middle Tennessee intersections (as compared to 00) () The Washington (DC) area has seen an % increase in people bicycling to work from 000 to 00 () As pedestrian and bicyclist monitoring increases among public agencies, it is critically important that data quality principles be included in the data collection practices. Pedestrian and bicyclist data quality is important because of the potential funding that may be at stake, as well as the credibility of this data among other transportation professionals, elected officials, and the general public. There are several challenges associated with gathering quality pedestrian and bicyclist traffic count data: Limited data collection resources: Limited resources have constrained pedestrian and bicyclist monitoring to what is realistically affordable, rather than statistically reliable. Higher variability: Pedestrian and bicyclist traffic is more variable in several time dimensions than motorized traffic, and thus more difficult to collect statistically representative samples. Lower magnitude: Pedestrian and bicyclist traffic typically is a smaller magnitude than motorized traffic, and even modest absolute errors or changes (e.g., 0 bicyclists per hour) can result in high percentage changes in time series trends. Automatic counter equipment: Commercially available equipment for automatically counting pedestrians and bicyclists is still evolving and maturing, and error rates associated with different technologies and configurations are not well known. The main objective of this paper is to outline key quality assurance principles and their application to pedestrian and bicyclist traffic count data. It is hoped that the quality assurance principles and procedures outlined in this paper will become common practice in pedestrian and bicyclist data programs, and that better, higher quality data results. The rest of this paper is organized as follows: Background: summarizes the literature and current agency practices in regards to data quality; Key Quality Assurance Principles: outlines key data quality principles as applied to pedestrian and bicyclist data; Procedures for Evaluating Data Accuracy: describes procedures for assessing the accuracy of automated counter equipment; Procedures for Assessing Data Validity: describes procedures for reviewing data after it has been collected; and, Conclusions and Recommendations: summarizes major findings and recommendations.

3 Turner and Lasley BACKGROUND FHWA Pedestrian and Bicycle Data Collection Review Researchers with AMEC E&I, Inc. and Sprinkle Consulting, Inc. prepared a literature review for the Federal Highway Administration (FHWA) compiling information about pedestrian and bicycle data collection equipment, methods, and concerns (). The results of the review indicated that, although there are numerous types of technologies used, all are subject to error in some way. The review contained little information about data validation techniques, issues, and practice. However, researchers identified several studies testing the accuracy of certain types of equipment. The Norwegian government conducted a study () of several bicycle counter types, including several models of inductive loop equipment, pneumatic tubes, inductive loops (both for bicycles only and for bicycles, pedestrians, and motorized vehicles), and infrared. Researchers tested these technologies against parallel manual counts ranging from several hundred to several thousand observations. Accuracy ranged above % with pneumatic tubes being the most accurate (.%) followed by inductive loops for bicycles only (.%), other models of inductive loop equipment (.0%,.%, and one not reported), infrared equipment (.%), and inductive loops for bicycles, pedestrians and motorized vehicles (.%) being the least accurate. Researchers observed that most errors occurred in differentiating bicycles passing counters at the same time and bicycles passing near the edge of inductance loops. Researchers in New Zealand () examined two pneumatic tube counters (a bicycle classifier and a vehicle classifier) to count bicycles. The bicycle classifier was 00% accurate when exclusively counting bicycles and % accurate in a mixed roadway environment. The researchers commented that human error in the manual counts could have actually been the source of the inaccuracy. The vehicle classifier was 00% accurate with a tube shorter than 0 meters when bicycles traveled faster than 0 km/h ( mph) in a mixed roadway environment. Accuracy of a pedestrian and bicycle tracking and classification system tested in China proved to be % accurate (). The system uses video and image processing to correctly detect and classify different types of users. Problems arose when pedestrians stopped for long periods of time and when pedestrians stood too closely together for the imaging to detect the other person. Future research into the algorithms could make this a much more effective technology. Similarly, London Streets, a City of London transportation agency, found that video technology had an error rate of only.% compared to CCTV technology with an error rate of.% (). This study helped award a contract for 0 monitoring sites for three years to understand walking patterns in London. Other Accuracy Evaluation Results Several evaluations (both controlled and field evaluations) of automatic counter equipment have been conducted in the past ten years. The results indicate a wide variation of results and among counter equipment manufacturers. Researchers at the University of Minnesota () examined how data is collected and used for nonmotorized facilities in the Minneapolis, Minnesota, area. The study focused primarily on temporal and spatial patterns in data that can be used to estimate volumes throughout the year. Researchers found

4 Turner and Lasley that the managing city department collected and analyzed data from bicycle and pedestrian counters, but did not validate or calibrate the counters. Manual counts were compared to counts by different technologies in use, revealing that counts from some locations were either consistently high or consistently low when compared with manual benchmark counts. One location overestimated bicycle counts by %. Though calibration equations can be developed using the ordinary least squares (OLS) method, the difficulty rests in knowing how long an error has occurred and how long the equation is applicable. Additionally, understanding what caused the error can be helpful in creating correction factors. Therefore, periodic validation and calibration is crucial for estimating non motorized traffic volumes. Texas Transportation Institute (TTI) researchers have tested several different infrared trail counters in a variety of settings, including hiking trails and busy shared use paths (0). The results are summarized in Table, and indicate that two of the three infrared counters consistently undercounted, presumably due to occlusion of people traveling side by side. The undercounting was typically higher when nonmotorized traffic volumes were higher. Researchers at the University of California, Berkeley have also evaluated the accuracy of a passive infrared counter in several research projects (, ). Their evaluations (Table ) indicate a consistent undercounting bias, and these results were used to develop equipment adjustment factors. TTI researchers have also conducted controlled testing of trail counters (), in which test subjects travel through the counter detection zone in a prescribed manner and evaluate equipment performance in a wide range of possible counting scenarios. The results of controlled testing for four different trail counters are shown in Table. In the late 0s, the United States Forest Service tested eight different models of trail counters (). In their controlled testing, they looked at the effects of several variables: Group spacing Pedestrian weight (for seismic counters); and, Clothing color (i.e., light reflective clothing versus dark matte clothing). Their results (Table ) indicated a consistent undercounting bias, due mostly to group spacing, but also partly due to clothing color.

5 Location, Date, and Duration TTI Field Evaluations for National Park Service TABLE Summary of TTI Trail Counter Field Evaluations Usage Level Eco Counter PYRO TRAFx Infrared Counter TrailMaster TM0 Avg. Abs. Avg. Abs. Avg. Abs. Error Avg. Error Error Avg. Error Error Avg. Error Turner and Lasley McKittrick Canyon Trail, Guadalupe Mountains National Park, 0//, hours and total users Guadalupe Peak Trail, Guadalupe Mountains National Park, 0/0/, hours and total users San Antonio River Trail north of Brooklyn St, San Antonio Missions NHP, /0/, hours and total users San Antonio River Trail north of Brooklyn St, San Antonio Missions NHP, //, hours and total users Moderate ( users/hr) Low ( users/hr) Moderate ( users/hr) Low ( users/hr) % +% % % % % % +% % % 0% 0% 0% +% 0% % % % % +% 0% 0% % % Previous TTI Field Evaluations ( Texas A&M Recreation Center, College Station, Texas, //00, hours and 0 users Town Lake Trail on Pfluger Bridge, Austin, Texas, 0//0, hours and users High ( users/hr) High (0 users/hr) n.a. n.a. % % n.a. n.a. n.a. n.a. % % n.a. n.a. Overall Conclusions Source: Reference (0) Typically overcounted, but error fluctuates hour byhour. In general, seems to be more sensitive. Consistently undercounts, but error level depends on trail usage (~ 0% under on high use trails). Consistently undercounts, but appears to have higher error on trails with more side by side traffic.

6 Turner and Lasley Location, Date, and Duration TABLE Trail Counter Accuracy Evaluation Results from University of California, Berkeley Shattuck & Kitterdge (East Side), Berkeley CA, //0, hours and, users Durant & Fulton (South Side), Berkeley CA, //0, hours and users Durant & Bowditch (North Side), Berkeley CA, //0, hours and, users Broadway & th Street, Oakland CA, //0, hours and,0 users Broadway & th Street, Oakland CA, /0/0, hours and, users Broadway & th Street, Oakland CA, //0, hours and users MLK Jr. Way & th Street, Oakland CA, //0, hours and users Strawberry Creek Bridge, UC Berkeley CA, 0//, hours and, users Strawberry Creek Bridge, UC Berkeley CA, //, hour and users Strawberry Creek Bridge, UC Berkeley CA, //, hours and,0 users Strawberry Creek Bridge, UC Berkeley CA, //, 0. hour and users Source: References (,) Usage Level Very High ( users/hr) Moderate ( users/hr) Very High ( users/hr) Very High ( users/hr) Very High ( users/hr) Moderate ( users/hr) Low ( users/hr) Very High ( users/hr) Very High ( users/hr) Very High (,0 users/hr) High ( users/hr) Eco Counter Pyro Avg. Abs. Error Avg. Error % % % % % % % 0% % % % % % % % % % % % % % %

7 TABLE Summary of Results for Controlled Trail Counter Tests at Wolf Pen Creek Trail Test Condition Ground Truth Count Overall Error Rate (%) Jamar & Trafx Diamond Eco Counter Jamar TrafX Diamond Eco Std Eco Crowd Baseline Walking Walking % 0% 0% 0% % Baseline Biking 0 MPH 0 0 % 0% n.a. 0% 0% 0 ft % 0% 0% 0% % ft % 0% 0% % % Group Spacing ft % 0% % % % ft 0 0 % % n.a. 0% % ft 0 0 % % n.a. % % ft 0 0 0% % n.a. % % Stopped % 0% % 0% 0% to talk Pedestrian Speed Jogged % % % % 0% Running % 0% 0% 0% % mph 0 0 % 0% n.a. 0% 0% Bicyclist Speed mph % % n.a. 0% % 0 mph 0 00% 0% n.a. 0% 0% mph 00% 00% n.a. 0% n.a. 0 ft 0 0 % 00% 0% n.a. n.a. Detection Range 0 ft 0 0 % 00% 0% n.a. n.a. 0 ft 0 % 00% % n.a. n.a..0 ft 0 0% % n.a. n.a. n.a. Mounting Height.0 ft 0% % n.a. n.a. n.a.. ft 0 0% % n.a. n.a. n.a..0 ft 0 0% % n.a. n.a. n.a. *Jamar, TrafX, and Diamond: Tested July, 00 **Eco Counter: Tested April, 00, Eco Std=(Std, 0), Eco Crowd=(+,0), mounted at 0 cm ( in) height about ft from trail edge Source: Reference () Turner and Lasley

8 Model TABLE US Forest Service Accuracy Evaluation Test Results TOTAL GRP* BIG* SML* LIT* DARK* PERCENT Under Over Under Over Under Over Under Over Under Over Under Over Cuesta Systems RS Turner and Lasley Ivan Technologies Trail Traffic Counter Diamond Traffic TCS Compu Tech TR Counter, PIR 0 Sensor Diamond Traffic TT Counter, TT IR Sensor Compu Tech TR Counter, PR 0 Sensor Compu Tech TR Counter, TSS Sensor Diamond Traffic TT Counter, TT SS Sensor *GRP: Closely spaced groups; BIG: Heavier hiker than average (for seismic counters); SML: Lighter hiker than average (for seismic counters); LIT: Light, reflective clothing (for active infrared counters); DARK: Dark, matte clothing (for passive infrared counters). Source: Reference ()

9 Turner and Lasley KEY QUALITY ASSURANCE PRINCIPLES Principle : Quality Assurance Starts Before Data are Collected In some cases, quality assurance actions are limited to reviewing and removing suspect or erroneous data that have already been collected. However, one should recognize that data quality actions restricted only to removing low quality data after it has been collected (referred to as scrap andrework ) are ineffective in the long term because they address the symptom but not the root cause of poor data quality (). Instead, quality assurance should start before data are collected, and should include actions taken throughout the entire traffic monitoring program cycle. With this definition, quality assurance includes actions taken before data collection as well as after data summarization, such as the following: Routine staff training and professional development; Effective equipment procurement procedures; Bench testing new field equipment; Thorough inspection and acceptance testing of new equipment installations; Routine equipment testing and calibration; Scheduled maintenance activities; and Data customer feedback through various channels. For example, consider a portable automated trail counter that gets rotated among various shared use path locations. At a few of these locations, the counter reports unreasonably high trail usage. Upon review, the data analyst identifies the high trail counts and removes them from the data set. However, unless the root cause of these unreasonably high trail counts are determined, low quality data will continue to be collected, and removing the low quality data after it has been collected is only a bandage approach. In this example, an effective quality assurance process should:. Determine why the trail counter is reporting high volumes at certain locations;. Revise the procedures for identifying count locations and/or installing equipment;. Train data collection personnel in revised procedures; and,. Monitor future occurrences of unreasonably high trail counts and repeat above steps as necessary. Principle : Acceptable Data Quality Is Determined by Its Use A basic principle from the literature (,,) is that data quality is a relative concept that has different meaning(s) to different data consumers, depending upon how they intend to use the data. For example, data considered to have acceptable quality by one consumer may be of unacceptable quality to another consumer with more stringent use requirements. Thus it is important to consider and understand all intended uses of data before attempting to measure or prescribe acceptable data quality levels. For example, consider two different uses for pedestrian traffic counts:. Determining whether a traffic signal warrant has been met based on pedestrian volumes.. Identifying locations with the highest overall pedestrian flows.

10 Turner and Lasley In the first application (i.e., traffic signal warrant), the Manual on Uniform Traffic Control Devices (MUTCD) indicates that a minimum pedestrian flow (in pedestrians per hour) must be met to satisfy Warrant, Pedestrian Volume. In this application, hourly pedestrian counts are required, and a fixed absolute value of pedestrian volume must be collected. In the second application, pedestrian traffic counts are needed, but not for as detailed a time interval as the first (i.e., MUTCD) application (i.e., hourly counts versus daily/multi day counts). Further, the second application only requires relative magnitude of pedestrian flows among several locations. In this second application, undercounting due to occlusion may be less of an issue if the undercounting occurs among all locations being counted. Previous research on traffic data quality measures (,0) resulted in a data quality definition that is equally applicable for pedestrian and bicyclist count data: Data quality is the fitness of data for all purposes that require it. Measuring data quality requires an understanding of all intended purposes for that data. Principle : Measures Can Quantify Different Quality Dimensions It is usually not enough to say that data quality is acceptable or unacceptable. Instead, there are several dimensions of data quality that may be important, and there are different levels of acceptable and unacceptable data quality. Previous research on traffic data quality measures (,0) recommended six key measures for traffic data quality:. Accuracy: The measure or degree of agreement between a data value or set of values and a source assumed to be correct. Also, a qualitative assessment of freedom from error, with a high assessment corresponding to a small error.. Validity: The degree to which data values satisfy acceptance requirements of the validation criteria or fall within the respective domain of acceptable values.. Completeness (also referred to as availability): The degree to which data values are present in the attributes (e.g., volume and speed are attributes of traffic) that require them.. Timeliness: The degree to which data values or a set of values are provided at the time required or specified.. Coverage: The degree to which data values in a sample accurately represent the whole of that which is to be measured.. Accessibility (also referred to as usability): The relative ease with which data can be retrieved and manipulated by data consumers to meet their needs. These six quality measures are equally applicable to pedestrian and bicyclist count data. Given the current early stages of pedestrian and bicyclist count data collection, this paper will focus on the first two data quality measures: accuracy and validity.

11 Turner and Lasley PROCEDURES FOR EVALUATING DATA ACCURACY Data accuracy has been defined as the degree of agreement between a data value or set of values and a source assumed to be correct. () In other words, how close are the data measurements to true reality? Of course, the challenge with evaluating accuracy is establishing a source assumed to be correct, which is also called a benchmark, reference, or ground truth measurement. In regards to traffic counting, an accuracy benchmark can be established in a few ways: Manual counts conducted by multiple, independent human observers. Automated counts conducted by equipment that has been pre certified for accuracy (usually by the first method, manual counts). There have been several accuracy evaluations of automated pedestrian and bicyclist counters, and most of these evaluations have used manual counts by multiple, independent human observers. The Background section of this paper summarized the results of these accuracy evaluations. There have been two distinct types of accuracy evaluations to date: Controlled evaluations are conducted using test subjects in a prescribed manner and evaluate equipment performance in a wide range of possible counting scenarios. Controlled evaluations use detailed, person by person counting to determine accuracy in specific types of counting scenarios. For example, a controlled evaluation may perform 0 repetitions of people walking side by side to determine count accuracy when occlusion occurs. Also, a controlled evaluation may include a bicyclist traveling in different speed ranges (e.g., to mph in mph increments) to determine equipment accuracy in each speed range. Field evaluations are conducted with in place counter equipment and with the ambient user traffic (i.e., no test subjects are used). Field evaluations gauge how accurate the counter equipment is in the types of user traffic that are most common to the field evaluation location. Field evaluations typically consider longer time intervals (e.g., 0 minutes) in which random count error from short time intervals typically is canceled out. The primary purpose of a controlled evaluation is to determine the accuracy in a range of possible conditions, whereas a field evaluation determines the accuracy in conditions common to a specific location. For example, if most people walk single file on a hiking trail, then a field evaluation on that or a similar hiking trail will yield the most representative accuracy results. The accuracy results from a controlled evaluation that included pedestrians walking side by side would not be representative of the accuracy one could expect on a single file hiking trail. However, a controlled evaluation will be more instructive if the nature of the monitoring locations vary widely or are unknown, as a controlled evaluation includes a wide range of possible counting scenarios. Standardization and Consistency of Accuracy Evaluations There is a need to develop a uniform accuracy evaluation procedure for pedestrian and bicyclist counters, so that any counter testing done in the future by different groups is directly comparable and can be pooled to provide a comprehensive picture of counter equipment performance in a range of conditions. Similar accuracy evaluation procedures have already been developed for several other traffic monitoring data elements:

12 Turner and Lasley Motorized vehicle counts, speeds, classification, and axle counts () Motorized vehicle weights () Motorized vehicle travel times () As of mid 0, there is informal discussion about developing consensus based accuracy evaluation procedures for pedestrian and bicyclist counters. The evaluations that are summarized in the Background section of this paper could serve as the basis and framework for these consensus based evaluation procedures. The authors recommend that any consensus evaluation procedures distinguish between the two types of accuracy evaluations: Controlled evaluations that use test subjects in a prescribed manner and evaluate equipment performance in a wide range of possible counting scenarios. Field evaluations that are conducted with in place counter equipment and with the ambient user traffic (i.e., no test subjects are used). Further, it is recommended that controlled evaluations include the following test variables (most of which can be controlled in specified ranges): Group spacing: two pedestrians at 0 to ft. spacing, to test for occlusion Walking speed: test at normal walking, jogging, and running speeds Bicyclist speed: test at to mph ranges Average distance to detection zone (i.e., how far away is counter from pedestrians/bicyclists?) Equipment mounting height: use manufacturer s recommendation, but also experiment with slight variations that might occur in typical field conditions Ambient air temperature: test in very cold weather, or at temperatures near body temperature (for passive infrared equipment) With field evaluations, many of these variables cannot be controlled by the evaluators; however, evaluators should record these prevailing conditions as context for the accuracy results: Average user flow rate (users per or 0 minute period) Detection zone width (usable path or sidewalk width) Detection zone Ambient air temperature Precipitation type and levels Equipment calibration or sensitivity settings PROCEDURES FOR ASSESSING DATA VALIDITY Data validity has been defined as the degree to which data values satisfy acceptance requirements of the validation criteria or fall within the respective domain of acceptable values. () Data validity procedures are most commonly implemented by using automated rules or criteria within a spreadsheet or database application. These rules or criteria are known by several different terms: Quality control checks or rules; Validity criteria, checks, or rules; or, Business rules. Some data quality problems can be easily identified by visual review of the data. However, visual review of all collected data can be time consuming and repetitive, especially with extensive traffic monitoring databases. Automated data validity criteria are intended to be a first line of defense against quality

13 Turner and Lasley 0 0 problems in already collected data, as the criteria can automatically direct attention to those days and times in the data set when the data deviate most from expected patterns. Visual review can then be more focused, and traffic data analysts can make the final decision about whether to accept or reject the data that has been flagged by the validity criteria. There are several common types of data validity criteria (): Univariate and multivariate range criteria typically correspond to the minimum, maximum, or range of expected values for a single variable or combination of variables. Examples from motorized traffic data include: o Maximum traffic count of 00 vehicles per lane per minute period o Minimum traffic count of vehicle if lane occupancy is greater than 0% Spatial and temporal consistency criteria evaluate the consistency of traffic data as compared to nearby locations or previous time periods (days, months or years). Examples include: o Maximum hourly traffic directional ratio should be less than 0% o Maximum deviation of ±X% from previous hourly or daily traffic counts o Maximum ratio of X% for peak hour volume as compared to daily volume Detailed diagnostics These criteria require detailed diagnostic data from automatic counter equipment and typically cannot be performed with the aggregate traffic count data. Examples from motorized traffic data include: o Range and cumulative distribution of vehicle lengths o Sensor on time for different vehicle types

14 Turner and Lasley San Antonio Mission Reach Trail Case Study This example describes a practical application of data validity procedures to trail count data being collected in San Antonio, Texas. In this example, researchers working with the National Park Service and the San Antonio River Authority installed a permanent trail counter on the Mission Reach Trail just south of E. Theo Avenue. The counter records the number of pedestrians and bicyclists separately for each travel direction. But because of the west facing location of the passive infrared pedestrian sensor, researchers believe the afternoon sun trips the sensor, possibly explaining hundreds or even thousands of erroneous counts each day. If no directionality is measured by the counter, the counter s software will assign all unknown counts to the northbound direction. Though researchers pushed the infrared lens further into its shaded housing, the error still occasionally occurs during the late afternoon. Assumptions The counter error greatly skews the counting data, making it difficult to glean any meaningful bicycle and pedestrian statistics. Therefore, researchers implemented quality control checks to identify bad data points and reasonably estimate new data points that can be used to create trail use statistics. To do this, researchers made certain assumptions about the trail, the counter, and the data: The southbound pedestrian data collected by the counter are correct (because erroneous data does not have directionality assigned to it). Weekday and Weekday trail usage differ greatly from one another and cannot be combined. Extremely high counts in the northbound direction for multiple hours are not the result of a special event occurring in the area (though an effort was made to identify dates and times of special event). Identifying and Isolating Bad Data All data were downloaded from the counter via the website that manages the data collection from the counter and pasted into an excel spreadsheet for analysis. Identifying bad data involved finding outliers that did not fit reasonable trail usage patterns. Because the southbound data were assumed to be correct, they were subtracted from the northbound data. The difference is believed to be a more accurate identifier of bad data because it accounts for variation in counts on different days (i.e., if counts are particularly high for one day in both directions, since the southbound data is assumed to be correct, then it would be reasonable to believe the northbound counts should also be relatively high. Otherwise, the analysis would think high traffic days are errors). The difference was then separated by weekdays and weekends since trail usage differs greatly on the weekend. Any hour that had a zero count was excluded from the analysis (too many zero counts would skew processes further in the analysis). Once separated, the minimum value, first quartile, median, third quartile, and maximum value were calculated for both weekends and weekdays. The first and third quartiles were used to calculate the interquartile range (IQR), represented by the following equation:. The coefficient of. was used rather than the standard coefficient of. for both weekday and weekend analyses to provide a more conservative cutoff point for outliers.

15 Turner and Lasley The IQR produced a cutoff point for both weekdays and weekends: any difference between northbound and southbound found to be higher than this cutoff number was flagged as an error and isolated. These numbers would be excluded from any other data aggregations until a substitute number was calculated. As more data are collected, these cutoff points could change as they are recalculated to include new data points. Establishing New Data Points The data points that were below the cutoff points were then aggregated from hourly to daily counts in north and southbound directions. Note that a corresponding southbound count to a northbound count that fell above the cutoff point was also excluded for this segment. The northbound daily counts were then divided by the southbound counts to find a ratio of northbound to southbound traffic. These ratios were split between weekdays and weekends. In each category, the median ratio was found and used as a multiplier to establish a new data point. Currently, weekday northbound counts are.% greater than southbound counts and weekend northbound counts are.% greater than southbound counts. These ratios were then multiplied with the southbound data for all bad data points to establish new estimated counts for northbound traffic. Quality Control Results The IQR method used in San Antonio appears to have adequately revealed errors within the data set. The unadjusted data (Figure ) reveals many visually obvious errors. Site observations confirmed that the trail does not accommodate daily northbound pedestrian usage in the thousands. However, identifying the line between what is an error and what is not an error becomes the most difficult task for visually assessing quality. The IQR method used in San Antonio provides a statistically acceptable method for determining a cutoff point. Figure illustrates the same data set after using the IQR method to identify and eliminate errors. One will notice that many smaller variations in the data that may have been overlooked in a visual analysis were adjusted.

16 Turner and Lasley Obvious Error Unclear Error FIGURE San Antonio Mission Reach Trail Counts Before Quality Control.

17 Turner and Lasley 0 0 FIGURE San Antonio Mission Reach Trail Counts After Quality Control. Implementation Potential There are several motorized traffic database applications that already have automated validity criteria built into their data import process. Therefore, it is possible to use existing software applications to perform validity reviews of pedestrian and bicyclist count data. However, many of these existing validity criteria use thresholds and parameter values that have been developed and refined for typical motorized traffic patterns. Becausee non motorized traffic patterns can be highly variable over time, there is a need to develop appropriate deviation thresholds and other parameters for pedestrian and bicyclist traffic count data. For example, a common validity criterion for motorized traffic is the number of allowable time intervals in which a zero traffic count is reported (i.e., no vehicles being counted sometimes means the automatic counter has stopped working). However, this validity criterion would have to be modified to account for very low or no pedestrian and bicyclist counts in the overnight hours. Another common validity criterion is the maximum allowable percent difference from average weekday or weekend hourly traffic counts in the past month or year. Because non motorized have to be increased accordingly. counts can be more variable than motorized traffic counts, the threshold value would However, more extensive analyses of non motorized traffic count data is required before these and other dataa validity thresholds can be established.

18 Turner and Lasley 0 0 Use of Targeted Manual Visual Review Using automated quality checking processes will help to catch obvious data errors; however, targeted visual review by trained staff can be extremely valuable in identifying suspect data that may pass through the automated processes undetected. The intent should not be to review all collected data, but to visually review random samples of the incoming non motorized data to ensure that the automated quality control processes are functioning properly. Consider an analogy with a factory not all widgets rolling off an assembly line can be tested for quality standards. Automated processes are in place, and then quality control managers will randomly pull a small sample of widgets for more in depth testing or visual inspection. There are several chart graphics that can be used to visually review the non motorized traffic count data. It is important to review the data at a detailed enough level such that erroneous data values can be identified before they become averaged and washed out in summary statistics. At the same time, the review of extremely detailed data limits the number of locations that can be visually reviewed. In TTI s analysis of non motorized data, we have identified several chart graphics that have proven useful in visually reviewing the quality of non motorized traffic counts. These charts are shown as Figures and.

19 Turner an nd Lasley FIGURE Examp ple, Chart G Graphic Used to Visually R Review Non M Motorized Traaffic Counts. FIGURE Examp ple, Chart G Graphic Used to Visually R Review Non M Motorized Traaffic Counts.

20 Turner and Lasley CONCLUSIONS AND RECOMMENDATIONS As pedestrian and bicyclist monitoring increases among public agencies, it is critically important that data quality principles be included in the data collection practices. Pedestrian and bicyclist data quality is important because of the potential funding that may be at stake, as well as the credibility of this data among other transportation professionals, elected officials, and the general public. The main objective of this paper was to outline key quality assurance principles and their application to pedestrian and bicyclist traffic count data. Three key principles of quality assurance are:. Quality Assurance Starts Before Data are Collected: Quality assurance should start before data are collected, and should include actions taken throughout the entire traffic monitoring program cycle. Quality assurance actions include: Routine staff training and professional development; Effective equipment procurement procedures; Bench testing new field equipment; Thorough inspection and acceptance testing of new equipment installations; Routine equipment testing and calibration; Scheduled maintenance activities; and Data customer feedback through various channels.. Acceptable Data Quality Is Determined by Its Use: Data quality is a relative concept that has different meaning(s) to different data consumers, depending upon how they intend to use the data. For example, data considered to have acceptable quality by one consumer may be of unacceptable quality to another consumer with more stringent use requirements. Thus it is important to consider and understand all intended uses of data before attempting to measure or prescribe acceptable data quality levels.. Measures Can Quantify Different Quality Dimensions: Previous research on traffic data quality measures recommended six key measures: ) Accuracy ) Validity ) Completeness ) Timeliness ) Coverage ) Accessibility The authors outlined more specific procedures for quantifying the first two measures, accuracy and validity. In regards to accuracy, the authors recommend that uniform accuracy evaluation procedures for pedestrian and bicyclist counters be developed so that any counter testing done in the future by different groups is directly comparable and can be pooled to provide a comprehensive picture of counter equipment performance in a range of conditions. These consensus evaluation procedures should distinguish between the two types of accuracy evaluations: Controlled evaluations that use test subjects in a prescribed manner and evaluate equipment performance in a wide range of possible counting scenarios.

21 Turner and Lasley Field evaluations that are conducted with in place counter equipment and with the ambient user traffic (i.e., no test subjects are used). In regards to validity, the researchers recommended the adaptation of automated validity criteria that is already present in several motorized traffic database applications. However, many of these existing validity criteria use thresholds and parameter values that have been developed and refined for typical motorized traffic patterns. Because non motorized traffic patterns can be highly variable over time, there is a need to develop appropriate deviation thresholds and other parameters for pedestrian and bicyclist traffic count data. Finally, targeted visual review by trained staff can be extremely valuable in identifying suspect data that may pass through the automated processes undetected. The intent should not be to review all collected data, but to visually review random samples of the incoming non motorized data to ensure that the automated quality control processes are functioning properly. ACKNOWLEDGMENTS The information presented in this paper originated from several different data quality and trail counter research projects over the past ten years. The following persons and agencies are acknowledged for their contributions to the ideas and concepts presented in this paper: Mr. Ralph Gillmann in FHWA s Office of Highway Policy Information, who sponsored several projects on traffic data quality in the past 0 years. The Southwest Region University Transportation Center (SWUTC), which sponsored a research project on pedestrian sensors and trail counters. Dr. Jim Gramann and Ms. Diane Breeding of Texas A&M University and the National Park Service, who supported a research project on monitoring trail usage in national parks. Mr. Steven Abeyta, Mr. Mehdi Baziar, and Ms. Liz Stolz of the Colorado DOT, who funded a TTI project to develop a CDOT Strategic Plan for Non Motorized Data. Dr. Tongbin (Teresa) Qu, Mr. David Salgado, Mr. Gary Barricklow, Mr. Ryan Eurek, Dr. Dan Middleton, and other colleagues at TTI who assisted in several different trail counter evaluation projects. Mr. Jean Francois Rheault, Dr. Robert Schneider, Dr. Greg Lindsey, Dr. Luis Miranda Moreno, Ms. Krista Nordback, Mr. Scott Brady, Mr. Steven Jessberger, and other peers and colleagues with whom I have exchanged data, ideas, and insight on pedestrian and bicyclist count data.

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