Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
NCHRP 07â19(02) Final Report 31 ChapterÂ 3:Â CombinedÂ FindingsÂ fromÂ PhasesÂ 1Â andÂ 2 A major component of the research involved field testing a variety of commercially available pedestrian and bicycle counting technologies by comparing the counts produced by the technologies with manual counts taken from video footage. The manual counts were assumed to represent correct, or âground truth,â counts. Counting technologies were then evaluated for accuracy (average error rate across all time periods) and consistency (degree to which similar accuracy rates are repeated for different time periods) based on comparison to the ground truth data. The term precision is also used to describe counting consistency. All of the manual counts were observed from videos taken at each test site. Videos were typically recorded for two periods of up to a week at each of the sites in the study, in an effort to test the technologies under a diverse set of environmental conditions, within the limitations of the project budget. For example, a set of test sites were selected in Davis, CA in Phase 1 because it has hot weather and high bicycle volumes, and a set of test sites were selected in Minneapolis because it has high volumes and cold temperatures. Digital copies of the videos were shipped from the test sites on DVDs and flash drives to the data reduction team in Berkeley. DATAÂ ANALYSISÂ Data analysis involved three phases: graphical (exploratory) analysis, accuracy calculations, and correction functions. GraphicalÂ AnalysisÂ The first phase of the data analysis process involved plotting manual (ground truth) versus automated counts for each technology. For example, the initial plots depict the manual count values on the xâaxis versus the automated count values on the yâaxis (at 1âhour resolution, which has been used for all of the analysis). Figure 3â1 provides an example. The âovercountingâ region in this figure shows hours where the automated count exceeded the ground truth. The âundercountingâ region shows hours when the automated count was less than the ground truth. The diagonal dashed âperfect accuracyâ line indicates hours when the automated and ground truth counts matched each other. Note that when a data point falls on this line, undercounting and overcounting could be occurring that cancel each other out (e.g., four missed detections and four false positives), resulting in an automated count that matches the ground truth count.
FigureÂ 3â The graph example, adjusted intended multiuse tubes ove FigureÂ 3â 1.Â Â Exam ical analysi the pneuma after the firs for a mixed path site. Pr rcounted, bu 2.Â Â Befor Adjus pleÂ ofÂ aÂ Gra s also shows tic tubes on t two days o traffic situat ior to the adj t overall cou eÂ andÂ AfterÂ tmentÂ phicalÂ Displ patterns in the Midtown f data collect ion, which re ustment, the nt accuracy Comparison 32 ayÂ ofÂ CountÂ the data in te Greenway i ion during P quires diffe tubes unde improved. F Â ofÂ Pneuma N DataÂ rms of accu n Minneapo hase 1. Thes rent sensitiv rcounted. Fo igure 3â2 de ticÂ TubeÂ Ac CHRP 07â19 racy and con lis had their e tubes wer ity settings t llowing the picts this ef curacyÂ with (02) Final R sistency. As sensitivity e initially han for a adjustment, fect. Â SensitivityÂ eport an the
NCHRP 07â19(02) Final Report 33 AccuracyÂ CalculationsÂ Four accuracy and precision measures were calculated for each technology. Average percentage deviation (APD) and weighted average percentage deviation (WAPD) are both measures of accuracy, while average of the absolute percentage deviation (AAPD) and Pearsonâs Correlation Coefficient (r) are measures of precision. These measures are described in more detail below. All analyses were carried out using the Python programming language. Average Percentage Deviation (APD) Average Percentage Deviation (APD) represents the overall divergence from perfect accuracy across all data collected. This is calculated as: Ü£Ü²Ü¦ àµ 1Ýà· Ü£à¯§ àµ Ü¯à¯§ Ü¯à¯§ à¯¡ à¯§àà¬µ where At is the automated count for time period t, Mt is the manual (ground truth) count in period t, and n is the total number of periods analyzed. This metric has the advantage of providing insight into what correction factors can be used for a given technology (as discussed in greater detail below), but does not provide as much detail on overall accuracy. In particular, overcounts and undercounts in different time periods can cancel each other out. Weighted Average Percentage Deviation (WAPD) To account for the lowâvolume bias of the APD measure, a volumeâweighted accuracy measure is also calculated, as: Ü¹Ü£Ü²Ü¦ àµ à· áÜ£à¯§ àµÜ¯à¯§â Ü¯à¯à¯¡à¯àà¬µ á à¯¡ à¯§àà¬µ WAPD is considered to be more reliable than APD, as it is not sensitive to deviations in lowâvolume hours. For example, consider two hours with ground truth volumes of 1 and 100 people. If there is a single false positive and it occurs during the first time period, the APD would be 50%, and the WAPD would be 0.99%. If the same false positive occurred in the second time period, the APD would be 0.5% and the WAPD would still be 0.99%. Thus, APD is highly sensitive to stochastic variation during lowâvolume periods, and is primarily included here to allow comparisons with other studies. Average of the Absolute Percentage Deviation (AAPD) AAPD helps to remedy the undercount/overcount cancellation problem with the APD. Ü£Ü£Ü²Ü¦ àµ 1Ýà· à¸¬ Ü£à¯§ àµ Ü¯à¯§ Ü¯à¯§ à¸¬ à¯¡ à¯§àà¬µ
NCHRP 07â19(02) Final Report 34 By taking the absolute values, over and undercounts of the same magnitude no longer balance each other out, but rather both count toward the total accuracy. However, this measure has the difficulty that percentage errors at low volumes can bias the results, as for example an overcount of 1 on a ground truth volume of 1 is calculated as a 100% overcount, whereas an overcount of 1 on a ground truth volume of 100 is calculated as a 1% overcount. Pearsonâs Correlation Coefficient (r) Pearsonâs r tells how correlated two variables are with each other, where r = +1 is total positive correlation, r = â1 is total negative correlation, and r = 0 is no correlation. With automated counters, the value of r will ideally be +1 between the ground truth volume and the automated count. That is, one perfectly predicts the other, although the two counts do not necessarily have to be equal. A correlation coefficient close to r = +1 suggests that one can reasonably estimate the volume by multiplying the automated count by a multiplicative adjustment factor. Pearsonâs coefficient is calculated as: Ý àµ â áºÜ¯à¯§ àµÜ¯à´¥á»áºÜ£à¯§ àµ Ì Ü£á» à¯¡à¯§àà¬µ à¶¥â áºÜ¯à¯§ àµ Ü¯à´¥á»à¬¶à¯¡à¯§àà¬µ à¶§â áºÜ£à¯§ àµ Ì Ü£á»à¬¶à¯¡à¯§àà¬µ CorrectionÂ FunctionsÂ A number of accuracy correction functions were estimated for each technology. As the value being modeled is an integer variable, correction functions are modeled using Poisson regression estimated using maximumâlikelihood estimation. A logâlink function is considered, and the automated count is used as an âexposureâ variable. This is expressed mathematically as: Ü¯à¯ ~ Poissonáºß¤à¯á» lnáºß¤à¯á» àµ lnáºÜ£à¯á» àµ ßà¯ àµ à¢¼à¢à¢ where ï· Mi is the manual (i.e. ground truth) count for observation hour i ï· ß¤à¯ is the (conditional) Poisson rate for the observation hour i ï· Ai is the automated count for the observation hour i ï· ßà¯ is the intercept, an estimated parameter corresponding to the baseline miscount rate ï· à¢¼ is a vector of additional estimated parameters corresponding to the effects of various factors on counter accuracy ï· à¢à¢ is a vector of values for additional factors, such as precipitation or lighting conditions, thought to potentially effect the accuracy of the counter The effect of modeling the accuracy in this way is that, by exponentiation, the Poisson rate can be expressed as:
NCHRP 07â19(02) Final Report 35 ß¤à¯ àµ Ü£à¯Ýà°à³à¬¾à¢¼à¢à¢ àµ Ü£à¯Ýà°à³Î à¯Ýà°à³à¯à³à³ That is, the rate is assumed to vary linearly with the automated count. If only an intercept (ßà¯á» is included in the model, the resulting value of Ýà°à³ is a simple multiplicative factor by which the automated count should be adjusted. In this formulation, any additional factors included in Xi will also have a multiplicative effect on the accuracy. For example, in studying the passive infrared technology, one of the estimated models includes an intercept and a fixed effect for the specific product with which the observation was collected. The intercept point estimate is 0.0388, with a value of 0.276 for the Product B term. The resulting correction function can thus be interpreted as Ü¯à¯ àµ Ü£à¯Ýà¬´.à¬´à¬·à¬¼à¬¼ àµ 1.040 àµ Ü£à¯ for Product A (the base), and Ü¯à¯ àµ Ü£à¯Ýà¬´.à¬´à¬·à¬¼à¬¼Ýà¬´.à¬¶à¬»à¬º àµ 1.040 àµ 1.318 àµ Ü£à¯ àµ 1.37 àµ Ü£à¯ for Product B. Similar interpretations can be made for other effects. For each dataset, models were compared on the basis of the Akaike Information Criterion (AIC). The AIC is a measure of model fit which penalizes models based on the number of estimated parameters. For a given dataset, models with lower AIC values can be interpreted as fitting the data better. However, the AIC does not provide any information on absolute goodnessâofâfit (as measures such as R2 do). R2 is not used in this evaluation because it is not reliable for nonâGaussian models. Models were also evaluated based on the significance (compared against 0 using a tâtest) of individual parameter estimates. ANALYSISÂ BYÂ TECHNOLOGYÂ TYPEÂ RadarÂ Qualitative Experience For this study, a single radar unit was tested in a bicycle lane at the Oakland site. The bicycle lane is adjacent to a parking lane on the right and to two parallel vehicular travel lanes on the left, as shown previously in Figure 2â6. In addition, the site is located on a slight curve in the roadway. An analysis of the data indicates the tested device overcounts at this site. Motorized vehicles were observed encroaching the bicycle lane (âcutting the cornerâ); parking activity will also be registered as a bicycle detection. As the counter is buried in the pavement, its detection zone was estimated from the deviceâs technical specifications, which required some subjectivity on the part of the data collector to determine whether a given bicyclist traversed the detection zone or skirted its edge. It could not be determined with certainty whether the sensorâs detection area extended beyond the bicycle lane (in which case motorized vehicles at the edge of their lane could be detected) or fell short of the edge of the bicycle lane (in which case a bicycle riding along the edge of the lane might not be detected). If a bicyclist riding along the lane line (a frequent occurrence, likely due to the wellâutilized onâstreet parking at the site) was detected by the sensor, but the data collector misidentified this event as being outside the detection zone, it would result in an apparent
overcoun eliminate Based on motor ve understa character the detec passes th either mo where th In additio repeater the repea Accuracy In Figure count of b truth to b encroach apparent One outli automate would be removing (a)Â Bicy FigureÂ 3â t, when in fa these error this experie hicles are lik nding of the istics of the tion zone. M rough the de stly segrega ere is not mu n to conside and receiver ter can be lo and Consis 3â3, two acc icyclists pas e a count of ments into t accuracy, su er was obser d count data an unreason this outlier clistsÂ PassingÂ 3.Â Accur ct the error s, but there i nce, care sho ely to freque technology, reflected rad isidentificati tection zone ted facilities ch lane chan ration of the component cated from b tency uracy plots a sing through bicyclists pa he bicycle la ggesting tha ved at coord . This data p ably high nu dramatically ThroughÂ Dete acyÂ PlotsÂ fo was in the m s some possi uld be taken ntly encroac it classifies v ar pulse, wh ons appear t , so this tech (e.g., cycle t ging behavi sensor loca s of the syste oth the sens re shown. T the detecti ssing throug ne. The inclu t the sensor inates (1, 76 oint is from mber for th improves th ctionÂ Zone rÂ RadarÂ 36 anual valida bility of thei when insta h the senso ehicles as bi ich is govern o occur mos nology is lik racks, offâstr or, so that m tion, site sel m can be loc or and the r he plot on th on zone. The h the detect sion of these may in fact b ) that would 6:00 a.m. on is time perio e results. (b)Â Bicy N tion. All pos r occurrence lling radar c râs detection cycles or car ed in part b t frequently ely to work eet paths), o otor vehicle ection also n ated, as the eceiver. e left consid plot on the ion zone, plu car encroac e detecting likely be fla a rainy Sun d at this loca clistsÂ PlusÂ Car CHRP 07â19 sible efforts . ounters to av zone. From s based on t y the crossâs when a sma best for coun r in shared l s are identif eeds to cons re are limits ers the grou right consid s a count of hments imp these events gged in a qu day, when 76 tion. As sho Â Encroachmen (02) Final R were made t oid sites wh the authorsâ he ectional ma ll section of ting bicycle anes at loca ied correctly ider where t on how far a nd truth to b ers the grou car roves the . ality check o bicyclists wn in Table tsÂ inÂ Detection eport o ere ss in a car s in tions . he way e a nd f the 3â1, Â ZoneÂ
NCHRP 07â19(02) Final Report 37 TableÂ 3â1.Â AccuracyÂ andÂ ConsistencyÂ ValuesÂ forÂ RadarÂ SensorÂ DatasetÂ APDÂ AAPDÂ WAPDÂ rÂ NÂ AverageÂ HourlyÂ VolumeÂ BicyclistsÂ 256%Â 261%Â 18%Â 0.89Â 32Â 70Â BicyclistsÂ (noÂ outlier)Â 23%Â 28%Â 14%Â 0.92Â 31Â 72Â BicyclistsÂ +Â perceivedÂ carÂ encroachmentsÂ 30%Â 62%Â â12%Â 0.94Â 32Â 93Â Notes:Â Â APDÂ =Â averageÂ percentageÂ deviation,Â AAPDÂ =Â averageÂ ofÂ theÂ absoluteÂ percentÂ difference,Â WAPDÂ =Â weightedÂ averageÂ percentageÂ deviation,Â rÂ =Â PearsonâsÂ CorrelationÂ Coefficient.Â Effects of Environmental Conditions The radar sensor tested in this study was installed in Oakland, California and data were collected at times of year when there is relatively little variation in environmental conditions. Due to the limited amount of data collected in this study for this device, it is not possible to identify any effects of environmental conditions. The only observation of note is the one outlier hour discussed above, which was collected during heavy rain; however, this is a single observation, and therefore does not provide any significant statistical evidence of performance under these conditions. Other observations collected in the rain by this device were highly accurate, but there were only three rainy hours total in the dataset. Correction Functions Using the Poisson formulation above yields a multiplicative correction factor of 0.851 (95% CI: 0.816, 0.887). However, it is important to note that this result is based on just one site at which the particular mechanics of interaction with the adjacent traffic lane appear to have a substantial impact on the results (resulting in more overcounting than would generally be expected). Siteâ specific factors should be estimated for any new installation, and additional research is needed to generate more general adjustment factors for this technology. ThermalÂ ImagingÂ CameraÂ Qualitative Experience One thermal imaging device was tested, at one location. This device required hardwiring into the electrical grid, which may limit its applicability to locations with access to electrical power. While this technology is designed to collect a variety of data types, including multimodal counts, this project only tested its ability to count bicyclists in a dedicated bicycle facility â specifically, a twoâ way cycle track segment with minimal intrusion by motor vehicles. This technology offers the ability to selectively identify events of interest to monitor. For this application, two overlapping bounding boxes were defined within the cameraâs field of view, with specified directionality. A count was recorded when a bicyclist passed through a bounding box in
the direct the data c than the b bounding sources o differenti Accuracy The therm The obse r=0.912 w FigureÂ 3â Regressi Based on with the c there app multiplic develope AIC, and h ion specified ollector to m icycle or pe boxes that w f error. Futu ate bicyclist and Consis al imaging rved accurac ith a sampl 4.Â Accur on Correctio visual inspe aveat that th ears to be li ative factor. d; as shown ad significa for that box itigate bypa destrian faci ere defined re studies co s in mixed tr tency counter has y and precis e size of N=2 acyÂ PlotÂ forÂ ns ction, it appe is result is b mited utility However, m in Table 3â2 nt pâvalues. . This featur ss errors by lity alone. H only extend uld vary thi affic or to di fairly high ac ion metrics 8. ThermalÂ Im ars that the ased on a si to developin odels that in ,these show 38 e allows for defining the owever, due ed to the ed s configurati fferentiate b curacy with were APD=5 agingÂ Senso thermal infr ngle counter g correction cluded rain a ed marginal N directional bounding b to the limite ge of the cyc on to test th icyclists from high consist .5%, AAPD= rÂ ared camera at a single s functions m nd nighttim improvemen CHRP 07â19 counting, an ox to cover a d scope of th le track, to a e deviceâs ab pedestrian ency, as sho 22.5%, WAP âs accuracy ite. In light o ore comple e (as binary ts to overal (02) Final R d may also a wider exten is testing, th void conflat ility to s. wn in Figure D=2.7%, and function is li f this findin x than a simp variables) w l fit, based on eport llow t e ing 3â4. near, g, le ere the
NCHRP 07â19(02) Final Report 39 TableÂ 3â2.Â CorrectionÂ FunctionsÂ forÂ ActiveÂ InfraredÂ SensorÂ InterceptÂ NightÂ RainÂ AICÂ â0.0263Â (0.163)Â âââÂ âââÂ 400Â â0.0398Â (0.045)Â âââÂ 0.146Â (0.021)Â 397Â â0.00567Â (0.786)Â â0.105Â (0.0295)Â âââÂ 397Â Notes:Â Â AICÂ =Â AkaikeÂ InformationÂ Criterion.Â NumbersÂ inÂ parenthesesÂ areÂ PÂ values;Â coefficientsÂ withÂ aÂ PÂ valueÂ ofÂ 0.050Â orÂ lessÂ areÂ significantÂ atÂ aÂ 95%Â confidenceÂ level.Â NightÂ isÂ anÂ indicatorÂ variableÂ thatÂ isÂ 1Â whenÂ theÂ startingÂ timeÂ forÂ theÂ countingÂ periodÂ isÂ betweenÂ sunsetÂ andÂ sunrise,Â andÂ 0Â otherwise.Â Effects of Different Conditions Because only one thermal imaging sensor was tested in the research, there was not enough variation in the data to ascertain whether any siteâlevel factors had an impact on the accuracy. The following factors were hypothesized to have an effect: False Positives in Heavy Precipitation The modeling suggests that controlling for rain can yield a slight improvement in overall correction function fit. However, the estimated parameter values counterintuitively suggest that rainy hours are better expressed as undercounting than overcounting. This result suggests the need for further testing in inclement weather, which might best be accomplished with a disaggregate study design. Temperature The hypothesized effect of temperature on the operation of this technology is a âmaskingâ of objects when the ambient temperature approaches the object temperatures, or approximately 98oF. The maximum observed temperature for this technology during this study was 83oF, so this effect could not be evaluated. PassiveÂ InfraredÂ Qualitative Experience Passive infrared sensors made up a significant proportion of the data in the study. According to the practitioner survey conducted during Phase 1 (Ryus et al. 2014b), these sensors are in wide use for collecting pedestrian volume data in singleâmode environments (e.g., sidewalks) and for collecting combined bicycle and pedestrian volume data in mixedâmode environments. As documented by the Phase 1 literature review (Ryus et al. 2014b), multiple studies have evaluated the accuracy of passive infrared sensors. The degree of difficulty associated with installing passive infrared sensors varied by product but was generally fairly simple. Temporary installations involved either bracketing a box with the sensor inside to an existing fixed object, such as a street sign pole, or screwâmounting a small sensor into a wooden surface. Permanent passive infrared sensor installations typically involve
NCHRP 07â19(02) Final Report 40 sinking a wooden post into the ground adjacent to the facility being counted, as is common when used as part of a combination sensor unit. Due care should be taken with the site design and installation for passive infrared sensors to avoid directing the sensor toward backgrounds that are likely to trigger false detections. Examples of problematic backgrounds include heavy foliage, windows, or background traffic or other movements. One example of an installationârelated issue from the testing experience involved the sensor being directed toward a planter box below a window, resulting in reflective interference that inflated counts. This problem was exacerbated with high ambient air temperatures that likely heated the foliage to a temperature approaching that of a human body. Another difficulty with passive infrared sensors is undercounting due to occlusion. These counters were observed to perform worse at higher volumes, especially Product B. Accuracy and Consistency The findings on passive infrared sensors corroborate the findings of previous studies. The passive infrared sensors demonstrated an overall average undercount rate (APD) of 10.0% and an AAPD of 17.0%. Figure 3â4 provides a combined graph of the accuracy of all three tested passive infrared products, as well as accuracy graphs for the individual products. A less accurate count rate was observed with one product compared to the other two, as demonstrated in Figure 3â4 and Table 3â2. All three products appear to follow a roughly linear profile, but one seems to have a lower slope (i.e., farther from 45Â°), suggesting that errors for this product propagate with volumes at a higher rate than the other products. Note that Product C, which is the new addition from Phase 2, was only tested at relatively low traffic volumes at one site (as part of a combination counter on a multiuse path), so direct comparison with the other two products is somewhat limited.
FigureÂ 3â The fact t technolog installatio However counts ov obtained correctio needed to correctio DeviceâSp Evaluatin these tech calculate 5.Â Accur hat the prod y plays an im n are impor , as can be in er a variety from any of n factor need adjust the o n factors. ecific Accur g accuracy a nologies, bu separate fac (a)Â acyÂ PlotsÂ of ucts had diff portant rol tant factors ferred from of volumes, the tested pr ed to adjust ther produc acy and Con nd consisten t given the v tors for each CombinedÂ Acc (b)Â AccuracyÂ Â PassiveÂ Infr erent error e in the coun to investigat Figure 3â5, a which mean oducts by si one produc tsâ counts, it sistency cy in aggreg aried result device prod 41 uracyÂ ofÂ AllÂ Th ofÂ IndividualÂ P aredÂ Senso rates indicat terâs accura e prior to pu ll three prod s that reason mply applyi tâs counts is is recomme ate is useful s for this spe uct and dev N reeÂ Products roductsÂ rsÂ es that a ven cy and thus rchasing an ucts produc ably accurat ng a correcti substantially nded that us for evaluati cific techno ice tested. Ta CHRP 07â19 dorâs implem product sele d deploying ed relatively e volume es on factor. Be different th ers develop ng the overa logy, it is imp ble 3â3 show (02) Final R entation of ction and this technolo consistent timates coul cause the an the facto their own lo ll performan ortant to s the result eport this gy. d be rs cal ce of s.
NCHRP 07â19(02) Final Report 42 TableÂ 3â3.Â Â AccuracyÂ andÂ ConsistencyÂ MetricsÂ onÂ aÂ SiteâÂ andÂ DeviceâSpecificÂ BasisÂ forÂ PassiveÂ InfraredÂ SensorsÂ SiteÂ ProductÂ APDÂ AAPDÂ WAPDÂ rÂ NÂ Average HourlyÂ VolumeÂ OverallÂ AverageÂ âÂ â10%Â 17% â11%Â 0.96Â 392Â 261Â OverallÂ ProductÂ AÂ A â1%Â 12% â4%Â 0.98Â 238Â 284Â LÂ StÂ A â2%Â 10% â3%Â 0.94Â 39Â 537Â FourÂ MileÂ RunÂ AÂ 2%Â 8%Â 3%Â 0.90Â 42Â 171Â FellÂ StreetÂ A â5%Â 10% â3%Â 0.97Â 26Â 43Â 15thÂ AvenueÂ A â5%Â 5% â4%Â 0.99Â 16Â 367Â KeyÂ BridgeÂ A â12%Â 13% â13%Â 0.99Â 48Â 393Â BerkeleyÂ A â14%Â 14%Â 14%Â 0.96Â 6Â 151Â SycamoreÂ AÂ 14%Â 15%Â 12%Â 0.99Â 24Â 160Â LoyolaÂ A â18%Â 19% â18%Â 0.86Â 12Â 64Â OverallÂ ProductÂ BÂ B â26%Â 26% â27%Â 0.98Â 115Â 263Â MidtownÂ GreenwayÂ B â26%Â 26% â27%Â 0.97Â 24Â 436Â FourÂ MileÂ RunÂ B â21%Â 22% â21%Â 0.91Â 42Â 171Â KeyÂ BridgeÂ B â29%Â 29% â30%Â 0.98Â 31Â 351Â SycamoreÂ B â33%Â 33% â31%Â 0.85Â 18Â 96Â OverallÂ ProductÂ CÂ C â14%Â 14% â14%Â 0.99Â 39Â 113Â FourÂ MileÂ RunÂ C â14%Â 14% â14%Â 0.99Â 39Â 113Â Notes:Â APDÂ =Â averageÂ percentageÂ deviation,Â AAPDÂ =Â averageÂ ofÂ theÂ absoluteÂ percentÂ difference,Â WAPDÂ =Â weightedÂ averageÂ percentageÂ deviation,Â rÂ =Â PearsonâsÂ CorrelationÂ Coefficient,Â NÂ =Â numberÂ ofÂ detectors,Â AverageÂ volumeÂ =Â hourlyÂ averageÂ pedestrianÂ andÂ bicycleÂ countsÂ basedÂ onÂ videoÂ observation.Â Correction Functions The various correction function forms that were tested for passive infrared counters are presented in Table 3â4. Including a product fixed effect yields significantly improved results, further confirming that the particular implementation of the technology can affect results. Controlling for the product, significant effects on accuracy are found due to lighting conditions (ânightâ), rain, and hot weather. The positive signs on all of these parameters suggest that these conditions lead to
NCHRP 07â19(02) Final Report 43 greater degrees of undercounting, as the adjustment factor is positive. Similarly, Products B and C appear to have lower overall accuracy than Product A, again as suggested by the positive signs. TableÂ 3â4.Â RegressionÂ CorrectionÂ FunctionsÂ TestedÂ forÂ PassiveÂ InfraredÂ SensorsÂ InterceptÂ NightÂ RainÂ Temperature ColdÂ HotÂ ProductÂ BÂ ProductÂ CÂ AICÂ 0.117Â (0.000)Â âââÂ âââÂ âââÂ âââÂ âââÂ âââÂ âââÂ 6265Â â0.0391Â (0.055)Â âââÂ âââÂ 0.00219Â (7.13eâ15)Â âââÂ âââÂ âââÂ âââÂ 6205Â 0.0388Â (0.000)Â âââÂ âââÂ âââÂ âââÂ âââÂ 0.276Â (0.000)Â 0.107Â (0.000)Â 4737Â 0.0375Â (0.000)Â âââÂ âââÂ âââÂ âââÂ 0.0972Â (0.000)Â 0.276Â (0.000)Â 0.0926Â (0.000)Â 4723Â 0.0372Â (0.000)Â âââÂ âââÂ âââÂ 0.00828Â (0.66)Â 0.0973Â (0.000)Â 0.276Â (0.000)Â 0.0929Â (0.000)Â 4725Â 0.0346Â (0.000)Â âââÂ 0.03Â (4.46eâ3)Â âââÂ âââÂ âââÂ 0.279Â (0.000)Â 0.111Â (0.000)Â 4731Â 0.033Â (0.000)Â âââÂ 0.0317Â (2.69eâ3)Â âââÂ âââÂ 0.0999Â (0.000)Â 0.28Â (0.000)Â 0.096Â (0.000)Â 4716Â 0.0363Â (0.000)Â 0.0397Â (4.09eâ3)Â âââÂ âââÂ âââÂ âââÂ 0.277Â (0.000)Â 0.108Â (0.000)Â 4731Â 0.0298Â (0.000)Â 0.0443Â (0.0014)Â 0.0338Â (0.0014)Â âââÂ âââÂ 0.103Â (0.000)Â 0.282Â (0.000)Â 0.097Â (0.000)Â 4708Â Notes:Â Â AICÂ =Â AkaikeÂ InformationÂ Criterion.Â NumbersÂ inÂ parenthesesÂ areÂ PÂ values;Â coefficientsÂ withÂ aÂ PÂ valueÂ ofÂ 0.050Â orÂ lessÂ areÂ significantÂ atÂ aÂ 95%Â confidenceÂ level.Â Hot,Â cold,Â rain,Â andÂ darkÂ areÂ indicatorÂ variablesÂ thatÂ areÂ 1Â whenÂ theÂ conditionÂ isÂ metÂ andÂ 0Â otherwise.Â NightÂ isÂ definedÂ asÂ theÂ startingÂ timeÂ forÂ theÂ countingÂ periodÂ beingÂ betweenÂ sunsetÂ andÂ sunrise.Â âColdâÂ representsÂ temperaturesÂ belowÂ 30Â Â°FÂ andÂ âhotâÂ representsÂ temperaturesÂ overÂ 90Â Â°F.Â Environmental Condition Effects For passive infrared counters, the following effects of weather and other environmental conditions were hypothesized, with the conclusions enumerated below: Worse performance at higher volumes, due to a higher incidence of occlusion This effect appears to be more of a problem with Product B than Products A and C. Product A actually has negative coefficients in the automated count, which suggests that its performance is slightly better at high volumes. The magnitude of this term is small, however. Product B, on the other hand, demonstrates a stronger adverse effect of high volumes. This is a problem that has been documented for passive infrared counters in previous literature (Schneider et al. 2012, Ozbay et al. 2010), but it appears that it is a surmountable problem, given Product Aâs high accuracy even with high volumes.
Worse pe distinguis There is m 3â6, there than in th temperat problem most of th difficult t On the ot at very hi very cold attributed current r enough c recomme winter te FigureÂ 3â Worse pe The data Figure 3â rformance at hing people f inimal evid are limited e overall da ures above 9 is that high t e data in th o conclusive her hand, th gh volumes. temperatur to heavily i esearch beca lothing to ha nded that pr mperatures 6.Â Passiv rformance in collected by 7. temperatur rom the bac ence of this data in the â taset. In this 0 ï°F, and âM emperatures e high tempe ly discern an e testing fou This is in co es and high v nsulating clo use the tem ve an effectâ actitioners u monitor dev eÂ InfraredÂ A heavy rain a NCHRP Proj es approachi kground effect sugges hotâ temper plot, âColdâ idâ refers to have a depr rature regim y difference nd no effect nflict with re olumes, wh thing. It is s peratures w temperatu sing passive ice performa ccuracyÂ asÂ nd/or snow ect 07â19 we 44 ng that of a ted in Table ature catego refers to tem anywhere in essing effec e did not ha s in accuracy of freezing te cent researc ere many mi uspected tha itnessed wer res did not d infrared se nce during c aÂ FunctionÂ o due to false p re sparse fo N human body, 3â4. Howev ry, with a m peratures b between. O t on nonmot ve very high . mperatures h (Andersen ssed detecti t this effect e not low en rop much be nsors in loca oldest cond fÂ Temperat ositives r heavy rain CHRP 07â19 due to diffic er, as demon uch smaller v elow 30 ï°F, â ne difficulty orized volum volumes. It on detectio et al. 2014) ons occurred was not obs ough to war low 10 ï°F. I tions with e itions to ens ureÂ and snow, a (02) Final R ulties strated in F olume rang Hotâ refers t in assessing es, such tha is therefore n accuracy, e conducted , which was erved in the rant heavyâ t is xtreme cold ure accuracy s indicated i eport igure e o this t ven at . n
FigureÂ 3â However observed detection However undercou The testin whether operate b ActiveÂ I Qualitati The testin device ap with prev The activ transmitt 7.Â Accur Range , during peri . On the surf s on passive , in the regre nting, as inc g was not a snow affects y detecting b nfraredÂ ve Experien g only inclu peared to fu ious experie e infrared se er and recei acyÂ Compar Â ods of rain, n ace, this wou infrared sen ssion analys luding the ex ble to captur device perfo ody heat. ce ded one acti nction fairly nce with the nsor is mod ver have to b isonÂ forÂ Pas o substantia ld appear to sors, but fur is, it appears istence of ra e significant rmance. It s ve infrared s accurately, w technology erately easy e installed s 45 siveÂ Infrare l overcount refute the p ther testing that rain m in as a facto snow event eems unlike ensor, a loan ith very hig (Lindsey et to install (no eparately an N dÂ SensorsÂ b s or other pe roposition t may be need ight actually r does impr s, to allow a ly, given tha from the U h consisten al., 2012). ground cut d aligned w CHRP 07â19 yÂ RainÂ andÂ T rformance i hat rain trig ed to confir lead to a gr ove model fi determinati t passive infr niversity of M cy. This expe ting required ith each othe (02) Final R emperatur ssues were gers false m this findin eater degree t. on about ared sensor innesota. T rience fits w ), although r. eport eÂ g. of s his ell the
Accuracy The activ 3â8. In pa WAPD = â hours of d FigureÂ 3â Regressi Based on again wit finding, th simple m were test fit was w TableÂ 3â5 In 0.079 0.0 0.079 0.09 Notes:Â Â AI aÂ 9 Ni and Consis e infrared se rticular, volu 7.62%, r = 0 ata. 8.Â Accur on Correctio visual inspe h the caveat ere appears ultiplicative ed, both sep orse as indic . Corre terceptÂ 3Â (6.21eâ17)Â 816Â (0.17)Â 5Â (1.75eâ16)Â 06Â (0.242)Â CÂ =Â AkaikeÂ Inform 5%Â confidenceÂ le ghtÂ isÂ anÂ indicator tency nsor has fair me estimat .9979) with acyÂ PlotÂ forÂ ns ction, it appe that this is o to be limite factor. Mode arately and a ated using A ctionÂ Functi â0.005 â0.01 ationÂ Criterion.Â Nu vel.Â Â variableÂ thatÂ isÂ 1Â ly high accu es were foun a gradually i ActiveÂ Infra ars that the nly based on d utility to d ls including s a linear su IC, as seen in onsÂ forÂ Acti NightÂ âââÂ âââÂ 96Â (0.909)Â 21Â (0.857)Â mbersÂ inÂ parenth whenÂ theÂ starting 46 racy with ve d to be very ncreasing un redÂ Sensor active infrar a single cou eveloping co temperature m, but no sig Table 3â5. veÂ InfraredÂ esesÂ areÂ PÂ values Â timeÂ forÂ theÂ coun N ry high cons precise (AP dercount. T ed accuracy nter at a sin rrection fun and night t nificant val SensorÂ Temper âââ â3.11eâ05 âââ â0.000142 ;Â coefficientsÂ with tingÂ periodÂ isÂ bet CHRP 07â19 istency, as s D = â6.61%, A hese values function is e gle site. In li ctions more ime (as a bin ues were fou atureÂ Â Â (0.968)Â Â Â (0.885)Â Â aÂ PÂ valueÂ ofÂ 0.050 weenÂ sunsetÂ andÂ (02) Final R hown in Figu APD = 7.33 are based on xtremely lin ght of this complex tha ary variable nd and over Â orÂ lessÂ areÂ signif sunrise,Â andÂ 0Â oth eport re %, 34 ear, n a ) all AICÂ 266Â 268Â 268Â 270Â icantÂ atÂ erwise.Â
NCHRP 07â19(02) Final Report 47 Effects of Different Conditions Because only one active infrared sensor was tested in the research, there was not enough variation in the data to ascertain whether any siteâlevel factors have an impact on the accuracy. Further, the inclementâweather data for this counter were quite sparse. The following factors were hypothesized to have an effect: Occlusion effects that increase with increasing volumes Occlusion does not appear to be a factor with increased volumes, given that undercount rates do not increase with volume, despite the observed site including relatively high volumes. False positives in heavy precipitation No time periods with heavy rain or snow were captured on camera for the site with the active infrared sensor, so no conclusions can be drawn on this topic from this study. While previous work has suggested that these events may trigger false positives, this effect has not been documented (Bu et al., 2007). Temperature Temperature does not appear to affect accuracy for the active infrared sensor. PneumaticÂ TubesÂ Qualitative Experience Pneumatic tubes were tested primarily on multiuse paths or bicycle lanes in this study. Bicycleâ specific pneumatic tubes, thinner and smaller than vehicle counting tubes, were primarily used, although a set of standard tubes (traditional motor vehicle tubes) was used at one site (Oakland) after the bicycleâspecific tubes were dislodged early in the study. The researchers experienced issues with the smaller bicycleâspecific tubes being dislodged at multiple sites during Phases 1 and 2, with various combinations of products and installation teams. The Oakland bicycleâspecific tubes were therefore replaced with standard tubes to reduce the chance of the tubes coming loose a second time. The tests of the third pneumatic tubes product (Product C) at onâstreet locations (Oakland and 15th Street NW in Washington, D.C.) included installing the pneumatic tubes across both the bicycle facility (lane/cycle track) and the adjacent general purpose travel lanes in the same direction to test the ability of bicycleâspecific tube counters to distinguish bicycles from motor vehicle traffic. During Phase 1, only one mixed traffic site produced usable data for pneumatic tubes, and the bicycle volumes on that facility were higher than the motor vehicle volumes. The two onâstreet facilities used in Phase 2, on the other hand, had substantially higher motor vehicle volumes than bicycle volumes, which is expected to be a more common situation for observing bicycles in mixed traffic. One site (15th Avenue in Minneapolis) proved problematic in multiple ways. First, during the initial data collection phase, the tube fasteners failed, causing the tubes to become free. After the tubes were reinstalled, they did not appear to function well, as shown in Figure 3â9. These data are from
two sets o tubes we longer pr inaccurat occasiona However counting. overall da FigureÂ 3â Accuracy Figure 3â Figure 3â C with bic and site, a f pneumatic re damaged operly funct e counts is t lly occluded , this does no The data fro ta plot in Fi 9.Â Accur and Consis 10 plots accu 11 plots accu ycleâspecifi nd Table 3â tubes, one i during the p ional when t hat 15th Ave the camera t seem like t m these sen gure 3â10) a acyÂ PlotÂ forÂ tency racy results racy results c and standa 5 provides a nstalled in e eriod when t hey were re nue has fair âs view of the he sole expl sors have be s the cause o PneumaticÂ for all tubes by the three rd tubes atta ccuracy and 48 ach bicycle l hey became installed. An ly high bus a counter for anation, give en omitted f f the errors TubesÂ atÂ 15 that were t products te ched). Figur consistency N ane. One pos free to the e other possib nd truck tra 1â2 minute n the severi rom all follo is uncertain. thÂ AvenueÂ S ested (minus sted (includ e 3â12 plots statistics for CHRP 07â19 sible explan xtent that th le explanati ffic, and thes s in a 15âmin ty of both un wing analys iteÂ the 15th Av ing a compa accuracy re all tubes. (02) Final R ation is that ey were no on for the e large vehic ute count p derâ and ov is (and the enue site). rison of Prod sults by prod eport the les eriod. erâ uct uct
FigureÂ 3â The pneu counter t work. It c accuracy Universit were test tubes we sometime tubes or n crossed c 10.Â Accur matic tube d echnologies. an be seen in line are Fell y in Montrea ed, and it als re installed w s difficult fo ot because lose to the e acyÂ PlotÂ forÂ ata present This result s Figure 3â12 Street in San l (Product B o has the hig ithin the bi r data collec the tubes we nd of the tub PneumaticÂ a number of uggests that that the sit Francisco a ). Rue Milton hest observ cycle lane bu tors to deter re at the edg es. 49 TubesÂ Â distinct patt there are st es with the h nd Rue Milto is a mixed t ed bicycle vo t not into th mine wheth e of the field N erns, in cont rong siteâ an ighest diver n in Montre raffic site on lumes. On F e sharedâuse er the bicycl of view, an CHRP 07â19 rast with th d deviceâsp sion from th al (Product which pneu ell Street, th lane. For th ist rode ove d many bicy (02) Final R e other teste ecific effects e perfect A), and Rue matic tubes e pneumatic is site, it wa r the pneum clistsâ traject eport d at s atic ories
FigureÂ 3â11.Â AccuracyÂ PlotÂ forÂ PneumaticÂ 50 TubesÂ byÂ Pr N oductÂ CHRP 07â19(02) Final Report
FigureÂ 3â12.Â AccuracyÂ PlotÂ forÂ PneumaticÂ 51 TubesÂ byÂ Pr N oductÂ andÂ S CHRP 07â19 iteÂ (02) Final Report
NCHRP 07â19(02) Final Report 52 TableÂ 3â6.Â AccuracyÂ andÂ ConsistencyÂ ValuesÂ forÂ PneumaticÂ TubesÂ byÂ ProductÂ andÂ SiteÂ SiteÂ ProductÂ APDÂ AAPDÂ WAPDÂ rÂ NÂ AverageÂ VolumeÂ OverallÂ Average âÂ â14.0%Â 16.6%Â â14.1%Â 0.986Â 259Â 166Â BicycleâspecificÂ tubes â14.0%Â 16.6%Â â14.0%Â 0.986Â 242Â 173Â OverallÂ ProductÂ AÂ AÂ â9.5%Â 10.8%Â â11.1%Â 0.993Â 172Â 203Â UniversityÂ AÂ 0.5%Â 1.2%Â 0.6%Â 0.998Â 17Â 206Â KeyÂ BridgeÂ AÂ â11.1%Â 11.3%Â â12.4%Â 0.981Â 48Â 97Â LÂ StreetÂ AÂ â6.7%Â 10.8%Â â10.4%Â 0.991Â 45Â 62Â FellÂ StreetÂ AÂ â20.2%Â 20.2%Â â23.1%Â 0.987Â 23Â 248Â ClarendonÂ AÂ â21.1%Â 21.1%Â â22.9%Â 0.999Â 3Â 23Â MiltonÂ AÂ â7.7%Â 7.7%Â â9.2%Â 0.997Â 36Â 503Â OverallÂ ProductÂ BÂ BÂ â52.6%Â 52.6%Â 39.9%Â 0.970Â 28Â 99Â UniversityÂ BÂ â34.3%Â 34.3%Â â34.8%Â 0.805Â 11Â 199Â EastbankÂ BÂ â64.4%Â 64.4%Â 59.4%Â 0.781Â 17Â Â 34Â OverallÂ ProductÂ CÂ CÂ â7.3%Â 16.6%Â â9.4%Â 0.920Â 43Â 76Â FourÂ MileÂ CÂ 8.9%Â 9.2%Â 7.6%Â 0.996Â 22Â 75Â 15thÂ StreetÂ NWÂ CÂ â24.2%Â 24.2%Â â26.6%Â 0.976Â 20Â 81Â OaklandÂ CÂ â28.6%Â 28.6%Â â28.6% 1Â 14Â StandardÂ TubesÂ CÂ â15.2%Â 17.1%Â â17.9%Â 0.936Â 17Â 62Â OaklandÂ CÂ â15.2%Â 17.1%Â â17.9%Â 0.936Â 17Â 62Â Notes:Â APDÂ =Â averageÂ percentageÂ deviation,Â AAPDÂ =Â averageÂ ofÂ theÂ absoluteÂ percentÂ difference,Â WAPDÂ =Â weightedÂ averageÂ percentageÂ deviation,Â Â rÂ =Â PearsonâsÂ CorrelationÂ Coefficient,Â NÂ =Â numberÂ ofÂ detectors,Â AverageÂ volumeÂ =Â hourlyÂ averageÂ pedestrianÂ andÂ bicycleÂ countsÂ basedÂ onÂ videoÂ observation.Â Effects of Environmental Conditions Frozen tubes The research team hypothesized that pneumatic tubes will become less sensitive in very cold temperatures, as the rubber in the tubes hardens. However, very little data were available in cold temperatures (below 30 ï°F), so these conditions were not extensively tested. It bears mentioning that in many cases, these conditions should not be experienced anyway, as pneumatic tubes are generally not intended to be used during winter conditions. Reduced accuracy due to aging tubes The rubber components of a pneumatic tube sensor system are a consumable that must be periodically replaced as they wear out. Tubes can develop cracks, holes, and weak spots that result in miscounts. Cases of total failures are fairly obvious to detectâno counts are produced. However, it was suspected that tubes might lose some accuracy as they age. To test this, one set of bicycleâ
NCHRP 07â19(02) Final Report 53 specific tubes was left installed on the Midtown Greenway for the duration of the study (~5 months). Accuracy rates were not substantially worse for this set of tubes than for others in the study. However, tubes installed in mixed traffic likely experience more rapid degradation based on the size and frequency of vehicles traveling across the tubes. Reduced accuracy in mixed traffic Two field tests of Product A were conducted in Montreal at the Rue Milton site, as well as field tests of Product C on Grand Avenue in Oakland and on 15th Street NW in Washington, D.C. At the Rue Milton site, tubes were deployed across a bicycle lane in one direction and across a shared lane in the opposite direction. At the Grand Avenue and Washington, D.C. test sites, the tubes were installed across a bicycle facility the two adjacent motor vehicle travel lanes to simulate the effects of a shared travel lane while allowing multiple counters to be tested at the same location. In the regression table below (Table 3â7), it can be seen that including an indicator variable for a counter being installed on a shared lane does not improve model fit, suggesting that there is no significant effect on accuracy. Regression Corrections Table 3â7 provides the parameters associated with the various regression models that were tested. Significant differences were found between Product A (the base), Product B, and Product C using standard tubes (but not Product C using bicycleâspecific tubes). Mixed traffic lanes, as discussed above, do not appear to have an effect. Temperature (when included as a linear term) does appear to have a significant effect on accuracy when controlling for differences between products, although this finding is somewhat equivocal as the mechanism behind the effect is not clear. However, a substantially better inâsample fit (measured by AIC) is seen for this model.Â TableÂ 3â7.Â CorrectionÂ FunctionsÂ forÂ PneumaticÂ TubesÂ Â InterceptÂ SharedÂ LaneÂ TemperatureÂ TubesÂ BÂ TubesÂ CÂ TubesÂ CÂ StandardÂ AICÂ 0.152Â (0.000)Â âââÂ âââÂ âââÂ âââÂ âââÂ 4485 0.168Â (0.000)Â âââÂ â0.000233Â (0.693)Â âââÂ âââÂ âââÂ 4486 0.117Â (0.000)Â âââÂ âââÂ 0.484Â (0.000)Â â0.0189Â (0.301)Â 0.0795Â (0.0113)Â 3782 0.117Â (0.000)Â âââÂ âââÂ 0.484Â (0.000)Â 0.00406Â (0.801)Â âââÂ 3788 0.121Â (0.000)Â â0.00669Â (0.515)Â âââÂ 0.481Â (0.000)Â â0.0197Â (0.281)Â 0.0826Â (9.22eâ3)Â 3784 0.507Â (0.000)Â âââÂ â0.00566Â (0.000)Â 0.529Â (0.000)Â â0.0792Â (0.000)Â 0.052Â (0.0991)Â 3711 Notes:Â Â AIC=Â AkaikeÂ InformationÂ Criterion.Â NumbersÂ inÂ parenthesesÂ areÂ PÂ values;Â coefficientsÂ withÂ aÂ PÂ valueÂ ofÂ 0.050Â orÂ lessÂ areÂ significantÂ atÂ aÂ 95%Â confidenceÂ level.Â SharedÂ laneÂ isÂ anÂ indicatorÂ variableÂ thatÂ isÂ 1Â whenÂ bothÂ motorizedÂ vehiclesÂ andÂ bicyclesÂ crossÂ theÂ tubesÂ andÂ 0Â otherwise.Â
NCHRP 07â19(02) Final Report 54 RadioÂ BeamÂ Qualitative Experience The research team faced significant difficulties evaluating the accuracy of one of the radio beam sensors. The product that distinguished pedestrians from bicyclists (Product A) had to be mounted on both sides of the facility, with a maximum separation of 10 feet, which severely limited the choice of sites where the counter could be tested. The product specifications available online, used in selecting test sites, had indicated that the maximum separation was 12 feet, but the material that arrived with the product specified only 10 feet, which required the team to locate new test sites for the product, which ended up being narrow sections of multiuse paths. The narrower paths were also associated with lower pedestrian and bicycle volumes, which reduced the volume range over which the device could be tested. Product A also defaulted to beginning its count immediately when initiated, rather than aggregating into bins beginning on the hour, creating difficulty synchronizing the data collection intervals with the ground truth video. This was a setting that could be altered, but required going into an âadvanced settingsâ menu, which most of the installers did not realize. This meant that the automated counts collected corresponded to different time periods than the manual counts, as all other devices counted in 15âminute or 1âhour periods that began on the hour. However, this setup issue is a minimal problem in terms of collecting volume data in general, and in fact this device had far more flexibility than the others tested (e.g., time bins of any integer number of minutes, delayed count starts). Accordingly, counts had to be repeated for a number of radio beam counters, for example counting from 12:05â12:19, 12:20â12:34, 12:35â12:49, and 12:50â13:04. One counter used in this study was inadvertently set to count in 61âminute intervals, so manual counts for this site were redone using intervals corresponding to the same time periods. Finally, substantial data errors were encountered with the installations of Product A, and therefore results for Product A have been removed from the analysis. It is suspected that these data errors were due to the devices being installed on bridges containing large metal components, which was not a condition identified in the product instructions, but which the research team learned retrospectively may affect this technology. Accuracy and Consistency Figure 3â13 shows the accuracy plot for radio beam Product B, which provides a total of all passersâ by. As shown in Table 3â8, the device had high accuracy and precision over a large range of traffic volumes, and the results were similar across all sites in the study. The results for Product B presented in the Phase 1 report were based on raw count data that had not been adjusted for daylight savings time; this problem has been corrected in the results presented below.
FigureÂ 3â Â TableÂ 3â8 DatasetÂ OverallÂ AverageÂ FourÂ Mil Midtown 5thÂ Aven Notes:Â Â AP de Effects of The prim as is the c lighting, a variation radio bea between 13.Â Accur .Â Accur Pro eÂ RunÂ Â ueÂ DÂ =Â averageÂ perc viation,Â rÂ =Â Pearso Environme ary hypothe ase with all nd rain wer in the weath m sensors in modes. acyÂ PlotÂ forÂ acyÂ andÂ Con ductÂ AP âÂ â10 BÂ â6. BÂ â11 BÂ â12 entageÂ deviation,Â nâsÂ CorrelationÂ Co ntal Conditi sized source screenline se e not suspec er to evalua adverse we RadioÂ Beam sistencyÂ Va DÂ AA .0%Â 10 0%Â 6 .0%Â 11 .0%Â 12 AAPDÂ =Â averageÂ o efficientÂ .Â ons of error for nsors. Radi ted to be pro te any poten ather condit 55 Â ProductÂ B luesÂ forÂ Rad PDÂ W .0%Â â .0%Â â .0%Â â .0%Â â fÂ theÂ absoluteÂ per radio beam o beams are blems. Duri tial effect of ions, and for N ioÂ BeamÂ Pr APDÂ 11.0%Â 6.0%Â 12.0%Â 13.0%Â centÂ difference,Â W sensors prio not optical d ng testing, th rain. Furthe radio beam CHRP 07â19 oductÂ BÂ rÂ 0.99Â 1.00Â 0.98Â 0.98Â APDÂ =Â weightedÂ r to this stud evices, so te ere was ins r research is counters th (02) Final R NÂ Ave Ho Vo 56Â 3 20Â 1 24Â 4 12Â 3 averageÂ percenta y was occlu mperature, ufficient warranted at distinguis eport rageÂ urlyÂ lumeÂ 21Â 56Â 36Â 66Â geÂ sion, for h
Inductiv Qualitati Inductive Both perm When eva addresse always co which is l and insta eliminate First, con 14. The lo is located located in maneuve the loop s Fell Stree also be co FigureÂ 3â Second, c Minneapo zones. Tw surface lo between testing w errors for eÂ LoopsÂ ve Experien loops were anent and t luating indu d. Bypass err ver the enti ikely a resul llation and h d. sider the em op is install immediatel the bicycle rs between b ensor. Simil t and are con nsidered a b 14.Â Poten onsider the lis. This site o sets of ind ops. Both se their edges a here the stri eastbound ce tested at a n emporary in ctive loops, ors arise be re width of t t of traffic pa elp minimiz bedded loop ed on a green y after a bicy lane. High bi icyclists, wh arly, cyclists tinuing stra ypass error tialÂ BypassÂ embedded a is a very wi uctive loops ts of inducti nd the sides pe was shift bicyclists us umber of sit ductive loop a special typ cause of the he facility. Cy tterns and m e this error, installation âpainted bic cle route tur cycle volum ich can resu who have m ight) someti . ErrorÂ Source nd surface lo de multiuse were instal ve loops are of the path, ed 1 to 2 feet ing the edge 56 es during th s were teste e of error, by sensorâs spa clists somet icroâdesign but in most o on Fell Stre ycle lane on ns left onto es on this fac lt in bypass ade a right t mes ride on sÂ onÂ FellÂ St op detectors path, with m led at this sit centered in a result of th into the ped line as a gui N is project, bo d. pass error, tially limited imes ride ar elements of nâstreet cas et in San Fra the left side Fell Street. T ility result i errors if the urn onto Fel the right sid reetÂ installed on arked separ e: a set of pe the bicycle fa e restriping estrian zon de; the bypa CHRP 07â19 th onâ and o needs to be detection z ound the ed the facility. es it cannot ncisco, show of a oneâwa he inductiv n frequent p passing man l Street (or w e of the stre Midtown G ated pedestr rmanent loo cility, but h of the edgel e. This gap r sses seemed (02) Final R ffâstreet fac specifically one: loops d ge of the loo Good site de be fully n in in Figur y street. Thi e loops are assing euvers occu ere already et, which can reenway in ian and bicy ps and a set ave small ga ine just prio esulted in by to occur mo eport ilities. o not ps, sign e 3â s site r at on cle of ps r to pass re
frequentl estimatin difficult t Finally, co Arlington The path path. Hen FigureÂ 3â Accuracy Figure 3â volume a plots rep Midtown of nearly lower vol volume a slightly a y at high vol g the accura o determine nsider the e , Virginia, sh has a constr ce, this site 15.Â Induc and Consis 16 compares nd the overa resent two s Greenway d 800 bicyclis umes, the fa ppears to inc ffected. umes. Note t cy of the loo the exact ed mbedded lo own in Figu ained width does not exp tiveÂ LoopÂ Te tency the differen ll facility vol lightly differ ata collectio ts per hour), cility counts rease with v hat this effe ps when bicy ge of the det op detector re 3â15. The (8 to 10 feet erience subs stingÂ onÂ Ke ces in accur ume (includ ent data sets n process. Th which likely are underco olume, whe 57 ct was also p clists were ection zone on the Key B facility is a m ), and the lo tantial bypa yÂ BridgeÂ acy and cons ing bypass e , due to the a is is a very h biases the a unted. As ex reas detectio N roblematic f riding direct in the camer ridge betwe ultiuse path ops cover ro ss errors. istency base rrors). It is i forementio ighâvolume ccuracy dow pected, und n zone accu CHRP 07â19 or data colle ly over them a footage. en Washingt on the side ughly the en d on the det mportant to ned difficult site (peak v nwards. Ho ercounting o racy appear (02) Final R ction in term , in that it w on, D.C. and of the bridg tire width of ection zone note that th ies with the olumes obse wever, even f the facility s to be only v eport s of as e. the ese rved at ery
FigureÂ 3â Table 3â9 (i.e., cons table, ind substanti TableÂ 3â9 Notes:Â AP de DatasetÂ AllÂ dataÂ AllÂ surfa LoyolaÂ Universit AllÂ embe Sycamor Clarendo KeyÂ FellÂ (a)Â Detect 16.Â Accur provides the idering only uctive loop s al difference . Accur DÂ =Â averageÂ perc viation,Â rÂ =Â Pearso Ty â ceÂ S S yÂ S ddedÂ E eÂ E nÂ E E E ionÂ ZoneÂ OnlyÂ acyÂ PlotsÂ fo accuracy an those bicycl ensors are b between th acyÂ andÂ Con entageÂ deviation,Â nâsÂ CorrelationÂ Co peÂ AP Â 1.5 Â 0.3 Â 7.9 Â â5.0 Â 1.9 Â 6.6 Â â27.4 Â 7.5 Â â9.6 rÂ InductiveÂ L d consistenc es that passe oth very acc e surface ind sistencyÂ Va AAPDÂ =Â averageÂ o efficient.Â DÂ AA %Â 8 %Â 7 %Â 10 %Â 5 %Â 8 %Â 7 %Â 27 %Â 7 %Â 9 58 oopsÂ y statistics f d through a urate and ve uctive loops luesÂ forÂ Ind fÂ theÂ absoluteÂ per PDÂ W .3%Â .6%Â .8%Â .3%Â .5%Â .5%Â .4%Â â .7%Â .6%Â N (b)Â By or inductive loopâs detec ry precise. T and the emb uctiveÂ Loop centÂ difference,Â W APDÂ â0.6%Â â3.1%Â 6.2%Â â4.7%Â 0.1%Â 5.4%Â 29.7%Â 6.2%Â â9.5%Â CHRP 07â19 passÂ ErrorsÂ In loops, witho tion zone). A here does n edded indu sÂ (Detection APDÂ =Â weightedÂ rÂ 0.990Â 0.997Â 0.974Â 0.988Â 0.989Â 0.997Â 1.000Â 0.994Â 0.998Â (02) Final R cludedÂ ut bypass err s shown in ot appear to ctive loops. Â ZoneÂ Accu averageÂ percenta NÂ A H V 134Â 29Â 12Â 17Â 105Â 24Â 3Â 48Â 26Â eport ors the be a racy)Â geÂ verageÂ ourlyÂ olumeÂ 136Â 145Â 51Â 211Â 133Â 141Â 25Â 97Â 193Â
NCHRP 07â19(02) Final Report 59 Table 3â10 presents accuracy and consistency statistics for inductive loops when considering facility volumes (i.e., including bypasses) as the âground truth.â The facility volume was defined on a siteâbyâsite basis. For multiuse paths, all bicyclists were counted while riding on the path. For bicycle lanes, the volume included all bicyclists riding along the street. Accordingly, the facilityâlevel accuracy and consistency values presented here should not be taken as general truths, but rather as documentation of the range of values encountered for the sites on which inductive loops were installed in this study. While these are important performance considerations, they will vary substantially by site and therefore need to be evaluated on a caseâbyâcase basis when using devices that do not cover the entire width of a facility. Overall consistency rates are fairly high (Pearsonâs r > 0.95 for all sites), while accuracy rates varied substantially between sites. The multiuse path sites and cycletrack site had high accuracy rates, particularly when the inductive loops spanned the entire path width. The Midtown Greenwayâs inductive loops were both narrower than the path, with the surface loops being narrower than the embedded loops, and hence these loops undercounted the total facility volume. Similarly, the onâ street sites were subject to bypass errors from bicyclists riding outside of the bike lane. TableÂ 3â10.Â AccuracyÂ andÂ ConsistencyÂ ValuesÂ forÂ InductiveÂ LoopsÂ (FacilityâLevelÂ Accuracy)Â Notes:Â EÂ =Â embedded,Â SÂ =Â surface.Â APDÂ =Â averageÂ percentageÂ deviation,Â AAPDÂ =Â averageÂ ofÂ theÂ absoluteÂ percentÂ difference,Â WAPDÂ =Â weightedÂ averageÂ percentageÂ deviation,Â rÂ =Â PearsonâsÂ CorrelationÂ Coefficient,Â NÂ =Â numberÂ ofÂ detectors,Â AverageÂ volumeÂ =Â hourlyÂ averageÂ pedestrianÂ andÂ bicycleÂ countsÂ basedÂ onÂ videoÂ observation.Â Â Inductive loop bypass error can be mitigated in a number of ways, including selecting and designing sites with widths compatible with the loop detector parameters and avoiding locations that are unconstrained, allowing bicycle traffic to travel outside the intended travelway. However, these measures cannot necessarily be applied to onâstreet facilities where travel paths cannot be easily constrained and bicyclists may choose and be permitted to travel outside the marked bicycle lane. DatasetÂ TypeÂ APDÂ AAPDÂ WAPDÂ rÂ NÂ AverageÂ HourlyÂ VolumeÂ AllÂ dataÂ âÂ 90.4%Â 123.8%Â â19.4%Â 0.950Â 202Â 184Â AllÂ surfaceÂ SÂ â10.8%Â 49.6%Â â35.3%Â 0.980Â 66Â 186Â LoyolaÂ SÂ â1.6%Â 8.2%Â â3.3%Â 0.977Â 12Â 56Â LÂ StreetÂ SÂ 25.6%Â 98.6%Â â31.6%Â 0.500Â 20Â 79Â MidtownÂ SÂ â35.4%Â 35.4%Â â38.1%Â 0.991Â 34Â 295Â AllÂ embeddedÂ EÂ 139.5%Â 159.7%Â â11.5%Â 0.971Â 136Â 183Â SycamoreÂ EÂ 6.6%Â 7.5%Â 5.4%Â 0.997Â 24Â 141Â ClarendonÂ EÂ â46.6%Â 46.6%Â â48.0%Â 0.996Â 3Â 33Â KeyÂ EÂ 7.5%Â 7.7%Â 6.2%Â 0.994Â 48Â 97Â MidtownÂ EÂ â17.9%Â 17.9%Â â19.4%Â 0.996Â 31Â 321Â FellÂ EÂ â25.6%Â 25.6%Â â26.2%Â 0.991Â 26Â 236Â
NCHRP 07â19(02) Final Report 60 For these installations, siteâspecific correction factors should be developed to account for bypass errors and to improve the estimation of facility volumes. Effects of Environmental Conditions The age of inductive loops has been suggested as a potential issue for loop accuracy. However, this was not detected in the testing. One set of embedded loops that was tested was 3Â½ years old and another was 2 years old. However, as shown in Figure 3â15, which includes data from all of the counters, no data seem to be of especially bad quality despite these older systems being included in the study. Correction Functions As the graphical exploration and accuracy metrics discussed above suggest, inductive loops work very well when considering the accuracy through the âdetection zone.â This is additionally borne out by estimating correction functions, which when only an intercept was concluded showed both a very low parameter estimate (0.0065) and a 95% confidence interval on either side of 0, suggesting that no correction is needed. Correction functions are not separately considered for the facility counts, because the rates of this are largely a function of siteâspecific conditions, and therefore should be evaluated on a caseâbyâ case basis. General findings on the degree of bypass are an area for potential further study. PiezoelectricÂ StripsÂ Qualitative Experience Two piezoelectric strip sensors were tested in this study, both located at the Four Mile Run site. One was a preâexisting installation. The other was installed during Phase 1, but experienced technical problems that were fixed by the productâs new vendor in time for Phase 2. Accuracy and Consistency Figure 3â17 presents accuracy plots for the two tested devices. The results for Product A that were published in the Phase 1 reports failed to correct for daylight savings time; this error has been corrected in the results presented below, and the data for the product have been supplemented with additional data collection during Phase 2.
FigureÂ 3â Both of th consisten are some are prom TableÂ 3â1 ProductÂ OverallÂ ProductÂ ProductÂ Notes:Â AP de co Effects of While the precipita condition Regressi As noted are neede tested bu 17.Â Accur e piezoelect cy over the r what limited ising for this 1. Accur AP â4.0 AÂ â3.4 BÂ â5.2 DÂ =Â averageÂ perc viation,Â rÂ =Â Pearso untsÂ basedÂ onÂ vid Environme re was limit tion on accur s. on Correctio above, the o d, and parti t was not fou acyÂ PlotÂ forÂ ric strip sen ange of volu , to the exten technology. acyÂ andÂ Con DÂ AA 0%Â 4.5 0%Â 3.7 0%Â 6. entageÂ deviation,Â nâsÂ CorrelationÂ Co eoÂ observation.Â ntal Conditi ed adverse w acy. Further ns verall high a cularly not fo nd to be sig Piezoelectr sors tested i mes observ t that this t sistencyÂ Va PDÂ 0%Â 0%Â 10%Â AAPDÂ =Â averageÂ o efficient,Â NÂ =Â num ons eather obse research sh ccuracy of th r environm nificant. The 61 icÂ StripsÂ n this study ed, as indicat est was only luesÂ forÂ Pie WAPDÂ â4.10%Â â3.40%Â â5.80%Â fÂ theÂ absoluteÂ per berÂ ofÂ detectors, rved, there ould be cons is technolog ental conditi pooled mod N showed very ed in Table conducted o zoelectricÂ St rÂ 0.995Â 0.997Â 0.994Â centÂ difference,Â W Â AverageÂ volume did not appe idered on th y suggests t ons. A fixed el with just CHRP 07â19 high accura 3â11. While n a single m ripsÂ NÂ Av 120Â 81Â 39Â APDÂ =Â weightedÂ =Â hourlyÂ averageÂ ar to be any e effects of hat no subst effect for the an intercept (02) Final R cy and these finding ultiuse path erageÂ Hourl VolumeÂ 105Â 112Â 91Â averageÂ percenta pedestrianÂ andÂ bi effect of very cold antial correc product wa is estimated eport s , they yÂ geÂ cycleÂ tions s to
NCHRP 07â19(02) Final Report 62 have an intercept value of 0.0415 (95% CI: 0.024, 0.059), suggesting a multiplicative adjustment factor of 1.04 (1.03, 1.06). CombinationÂ CounterÂ (PedestrianÂ Estimates)Â Qualitative Experience Combination counters use multiple technologies to generate separate estimates of pedestrian and bicycle volumes. In this study, two combination counter technologies were tested. Both technologies used passive infrared detection to get aggregate counts, but one used inductive loops to get separate bicycle counts and the other used piezoelectric strips. The tested devices output estimates of pedestrian and bicycle volumes by default. These sensors have been evaluated separately in the previous sections, considering the sum of the automated and pedestrian count as the passive infrared sensorâs ground truth and considering the bicycle count as the inductive loopâs ground truth volume. Qualitative experiences have already been discussed, but here the accuracy and consistency of the countersâ inferred pedestrian volumes are discussed explicitly. It should be noted that the error in the pedestrian count depends in part on the errors in the bicycle and total counts (as the pedestrian volume is estimated by subtracting the bicycle count from the total count), but the focus here is on evaluating the need to correct the count data produced by a combination counter. Accuracy Description Figure 3â18 shows the pedestrian volume estimate accuracy based on data for both sites. Figure 3â 19 distinguishes the data between the two products, while Table 3â12 gives calculated accuracy and consistency metrics. These devices appear to work well on the whole, with a high consistency rate (Pearsonâs r = 0.992). The Sycamore Park detector and the piezoelectric stripsâbased detector both had fairly poor accuracy and precision metrics, but these are largely attributable to the relatively low pedestrian volumes observed at these sites. As previously discussed, a small number of detection errors at a lowâvolume site results in a large percentage deviation.
FigureÂ 3â FigureÂ 3â The insta due to oc 18.Â Â Accur 19.Â Accur Count llation at the clusion effec acyÂ PlotÂ forÂ acyÂ andÂ Con ersÂ Compar Key Bridge ts) with a hi PedestrianÂ sistencyÂ Plo ingÂ TwoÂ Pro site, on the o gh rate of co 63 VolumesÂ Es tsÂ forÂ Pede ductsÂ ther hand, s nsistency, as N timatedÂ fro strianÂ Volum hows net un indicated in CHRP 07â19 mÂ Combina esÂ fromÂ Co dercounting Table 3â12. (02) Final R tionÂ Counte mbinationÂ (as expecte eport rsÂ d
NCHRP 07â19(02) Final Report 64 TableÂ 3â12.Â AccuracyÂ andÂ ConsistencyÂ MetricsÂ forÂ PedestrianÂ VolumesÂ fromÂ CombinationÂ CountersÂ Regression Corrections Table 3â13 shows the correction functions developed for pedestrian volume estimates using combination counters. The analysis was restricted to considering an interceptâonly model with a binary factor representing the product type. It appears that considering the two products separately produces substantially better results. Caution is advised with these results, however, due to the limited range of conditions under which observations were taken, particularly the limited volume range for the piezoelectricâbased sensor. TableÂ 3â13.Â CorrectionÂ FunctionsÂ EstimatedÂ forÂ PedestrianÂ VolumesÂ fromÂ CombinationÂ CountersÂ InterceptÂ PiezoÂ SensorÂ AICÂ 0.199Â (0.000)Â âââÂ 1412Â 0.179Â (0.000)Â 0.424Â (0.000)Â 1283Â DatasetÂ APDÂ AAPDÂ WAPDÂ rÂ NÂ AverageÂ HourlyÂ VolumeÂ AllÂ dataÂ 8.8%Â 54.8%Â â18.0%Â 0.992Â 111Â 140Â CombinationÂ passiveÂ infrared/inductiveÂ loopsÂ 36.6%Â 61.4%Â â16.4%Â 0.991Â 72Â 204Â SycamoreÂ 145.7%Â 148.3%Â 60.9%Â 0.751Â 24Â 19Â KeyÂ BridgeÂ â17.9%Â 17.9%Â â18.9%Â 0.982Â 48Â 296Â CombinationÂ passiveÂ infrared/piezoelectricÂ â42.7%Â 42.7%Â â45.3%Â 0.648Â 39Â 22Â
NCHRP 07â19(02) Final Report 65 SUMMARYÂ Table 3â14 provides a combined comparison of accuracy and consistency values by site, product, and counting technology. TableÂ 3â14.Â AccuracyÂ andÂ ConsistencyÂ ValuesÂ forÂ allÂ TechnologiesÂ byÂ SiteÂ andÂ ProductÂ TechnologyÂ SubsetÂ APDÂ AAPDÂ WAPDÂ rÂ NÂ AverageÂ HourlyÂ VolumeÂ PassiveÂ InfraredÂ AllÂ dataÂ â3.5%Â 22.5%Â â9.5%Â 0.938Â 398Â 258Â ProductÂ AÂ 8.7%Â 22.2%Â â1.6%Â 0.949Â 244Â 279Â ProductÂ BÂ â26.0%Â 26.4%Â â27.0%Â 0.982Â 115Â 263Â ProductÂ CÂ â13.5%Â 13.5%Â â13.6%Â 0.988Â 39Â 113Â FourÂ MileÂ (A)Â 2.1%Â 8.0%Â 2.9%Â 0.897Â 42Â 171Â FourÂ MileÂ (B)Â â21.1%Â 22.1%Â â20.9%Â 0.913Â 42Â 171Â FourÂ MileÂ (C)Â â13.5%Â 13.5%Â â13.6%Â 0.988Â 39Â 113Â KeyÂ (A)Â â12.3%Â 12.6%Â â12.7%Â 0.988Â 48Â 393Â KeyÂ (B)Â â28.6%Â 28.6%Â â30.1%Â 0.982Â 31Â 351Â LÂ StreetÂ 53.1%Â 63.2%Â 4.5%Â 0.865Â 45Â 474Â LoyolaÂ â18.5%Â 19.5%Â â18.1%Â 0.863Â 12Â 64Â SycamoreÂ (A)Â 14.1%Â 14.7%Â 12.0%Â 0.993Â 24Â 160Â SycamoreÂ (B)Â â32.8%Â 32.8%Â â30.8%Â 0.852Â 18Â 96Â 15thÂ AveÂ â4.9%Â 5.4%Â â4.1%Â 0.995Â 16Â 367Â 5thÂ AveÂ 10.0%Â 22.4%Â 1.4%Â 0.872Â 31Â 294Â FellÂ â4.6%Â 9.6%Â â3.0%Â 0.968Â 26Â 43Â ActiveÂ InfraredÂ AllÂ dataÂ â6.6%Â 7.3%Â â7.6%Â 0.998Â 34Â 327Â ThermalÂ CameraÂ AllÂ dataÂ 5.5%Â 22.5%Â 2.7%Â 0.912Â 28Â 101Â RadarÂ AllÂ dataÂ 23.0%Â 28.0%Â 14.0%Â 0.920Â 31Â 72Â PneumaticÂ TubesÂ Â AllÂ dataÂ â15.3%Â 17.7%Â â14.8%Â 0.986Â 279Â 160Â ProductÂ AÂ â9.5%Â 10.8%Â â11.1%Â 0.993Â 172Â 203Â ProductÂ BÂ â45.9%Â 45.9%Â â41.9%Â 0.889Â 47Â 117Â ProductÂ CÂ Â (bikeâspecificÂ tubes)Â â5.4%Â 14.8%Â â7.5%Â 0.938Â 43Â 76Â ProductÂ CÂ (standardÂ tubes)Â â15.2%Â 17.1%Â â17.9%Â 0.936Â 17Â 62Â ClarendonÂ â21.1%Â 21.1%Â â22.9%Â 0.999Â 3Â 23Â KeyÂ â11.1%Â 11.3%Â â12.4%Â 0.981Â 48Â 97Â LÂ StreetÂ â6.7%Â 10.8%Â â10.4%Â 0.991Â 45Â 62Â MidtownÂ 1â Â 4.3%Â 33.3%Â 30.0%Â 0.901Â 30Â 298Â MidtownÂ 2â Â 39.3%Â 39.3%Â 46.9%Â 0.982Â 9Â 12Â UniversityÂ (A)Â 0.5%Â 1.2%Â 0.6%Â 0.998Â 17Â 206Â UniversityÂ (B)Â â34.3%Â 34.3%Â â34.8%Â 0.805Â 11Â 199Â EastbankÂ â49.4%Â 49.4%Â â46.6%Â 0.789Â 36Â 92Â FellÂ â20.2%Â 20.2%Â â23.1%Â 0.987Â 23Â 248Â MiltonÂ 1Â â6.6%Â 8.9%Â 9.8%Â 0.979Â 18Â 497Â MiltonÂ 2Â â8.2%Â 8.2%Â 9.9%Â 0.996Â 18Â 497Â Â 15thÂ StÂ NWÂ â19.9%Â 20.3%Â â22.7%Â 0.981Â 20Â 81Â Â OaklandÂ â15.2%Â 17.1%Â â17.9%Â 0.936Â 17Â 62Â Â FourÂ MileÂ 8.9%Â 9.2%Â 7.6%Â 0.996Â 22Â 75Â
NCHRP 07â19(02) Final Report 66 TechnologyÂ SubsetÂ APDÂ AAPDÂ WAPDÂ rÂ NÂ AverageÂ HourlyÂ VolumeÂ InductiveÂ LoopsÂ AllÂ dataÂ 0.6%Â 8.9%Â 7.6%Â 0.994Â 108Â 128Â SurfaceÂ loopsÂ 0.3%Â 7.6%Â 5.7%Â 0.997Â 29Â 145Â EmbeddedÂ loopsÂ 0.6%Â 9.4%Â 8.4%Â 0.993Â 79Â 122Â SycamoreÂ 7.9%Â 8.2%Â 7.7%Â 0.973Â 18Â 81Â LoyolaÂ 7.9%Â 10.8%Â 9.8%Â 0.974Â 12Â 51Â Clarendon â27.4%Â 27.4%Â 29.7%Â 1.000Â 3Â 25Â KeyÂ 8.0%Â 8.1%Â 6.0%Â 0.996Â 31Â 92Â University â5.0%Â 5.3%Â 5.0%Â 0.988Â 17Â 211Â Fell â9.5%Â 9.5%Â 9.5%Â 0.998Â 27Â 194Â AllÂ data* â14.1%Â 17.6%Â 23.6%Â 0.965Â 165Â 200Â SurfaceÂ loops* â20.1%Â 21.6%Â 29.3%Â 0.942Â 59Â 222Â EmbeddedÂ loops* â10.7%Â 15.4%Â 19.9%Â 0.990Â 106Â 187Â Sycamore*Â 7.9%Â 8.2%Â 7.7%Â 0.973Â 18Â 81Â Loyola* â1.6%Â 8.2%Â 8.4%Â 0.977Â 12Â 56Â Clarendon* â27.3%Â 27.3%Â 29.7%Â 1.000Â 3Â 25Â Key*Â 8.0%Â 8.1%Â 6.0%Â 0.996Â 31Â 92Â MidtownÂ (surface)* â37.5%Â 37.5%Â 41.3%Â 0.994Â 30Â 298Â MidtownÂ (embedded)*Â â20.6%Â 20.6%Â 23.0%Â 0.998Â 27Â 330Â University* â2.4%Â 2.8%Â 2.8%Â 0.992Â 17Â 206Â Fell* â25.5%Â 25.5%Â 26.1%Â 0.990Â 27Â 237Â PiezoelectricÂ AllÂ data â4.0%Â 4.5%Â â4.1%Â 0.995Â 120Â 105Â ProductÂ A â3.4%Â 3.7%Â â3.4%Â 0.997Â 81Â 112Â ProductÂ B â5.2%Â 6.1%Â â5.8%Â 0.994Â 39Â 91Â RadioÂ BeamÂ AllÂ dataÂ (ProductÂ B) â9.6%Â 9.7%Â â11.1%Â 0.991Â 56Â 321Â Midtown â11.1%Â 11.4%Â â11.5%Â â0.982Â 24Â 436Â 5thÂ Ave â12.2%Â 12.2%Â â13.5%Â 0.977Â 12Â 366Â FourÂ MileÂ Run â6.2%Â 6.2%Â â6.4%Â 0.999Â 20Â 156Â CombinationÂ (ped)Â AllÂ dataÂ 8.8%Â 54.8%Â â18.0%Â 0.992Â 111Â 140Â InductiveÂ loopâbasedÂ 36.6%Â 61.4%Â â16.4%Â 0.991Â 72Â 204Â SycamoreÂ 145.7%Â 148.3%Â 60.9%Â 0.751Â 24Â 19Â KeyÂ BridgeÂ â17.9%Â 17.9%Â â18.9%Â 0.982Â 48Â 296Â PiezoelectricâbasedÂ â42.7%Â 42.7%Â â45.3%Â 0.648Â 39Â 22Â Notes:Â Â *DenotesÂ valuesÂ calculatedÂ usingÂ facilityÂ countsÂ (i.e.,Â includingÂ bypassÂ errors).Â â DenotesÂ valueÂ notÂ includedÂ inÂ overallÂ accuracyÂ calculationsÂ dueÂ toÂ identifiedÂ sensorÂ problems.Â (A),Â (B),Â (1),Â andÂ (2)Â representÂ differentÂ productsÂ implementingÂ aÂ givenÂ sensorÂ technology.Â APDÂ =Â averageÂ percentageÂ deviation,Â AAPDÂ =Â averageÂ ofÂ theÂ absoluteÂ percentÂ difference,Â WAPDÂ =Â weightedÂ averageÂ percentageÂ deviation,Â rÂ =Â PearsonâsÂ CorrelationÂ Coefficient,Â NÂ =Â numberÂ ofÂ detectors,Â AverageÂ volumeÂ =Â hourlyÂ averageÂ pedestrianÂ andÂ bicycleÂ countsÂ basedÂ onÂ videoÂ observation.Â Table 3â15 provides sample adjustment factors for each technology, based on the experience of the field testing. The factors are simple multiplicative factorsâthat is, a multiplier that is applied to the raw count to estimate the true count. For example, if the raw count was 100 bicycles in an hour and the counting technology in use has an adjustment factor of 1.20, the estimate of the true count would be 120. Although a number of different types of models for correcting counts were tested by the research, multiplicative adjustment factors provided the best combination of prediction accuracy and simplicity of application. The implication is that count errors increased at a linear rate
NCHRP 07â19(02) Final Report 67 for the technologies tested. Where multiple products representing a given technology were tested, Table 3â15 also provides productâspecific, anonymized results. TableÂ 3â15.Â CounterÂ SampleÂ AdjustmentÂ FactorsÂ DevelopedÂ byÂ NCHRPÂ ProjectÂ 07â19Â SensorÂ TechnologyÂ InterceptÂ AdjustmentÂ FactorÂ HoursÂ ofÂ DataÂ ActiveÂ infrared*Â 0.079Â 1.082Â 34Â ThermalÂ imagingÂ camera* â0.026Â 0.974Â 28Â PassiveÂ infraredÂ 0.100Â 1.106Â 398Â Â ProductÂ AÂ 0.016Â 1.016Â 244Â Â ProductÂ BÂ 0.314Â 1.369Â 115Â Â ProductÂ CÂ 0.146Â 1.157Â 39Â Radar â0.162Â 0.851Â 31Â RadioÂ beamÂ (ProductÂ B)Â 0.118Â 1.125Â 56Â InductionÂ loops â0.034Â 0.967Â 165Â Â ProductÂ A â0.046Â 0.955Â 136Â Â Â Â Â ProductÂ BÂ 0.032Â 1.032Â 29Â PiezoelectricÂ stripsÂ 0.042Â 1.042Â 120Â Â ProductÂ AÂ 0.035Â 1.035Â 81Â Â ProductÂ BÂ 0.060Â 1.061Â 39Â PneumaticÂ tubesÂ 0.160Â 1.173Â 279Â Â ProductÂ AÂ (bikeâspecificÂ tubes)Â 0.117Â 1.124Â 172Â Â ProductÂ BÂ (bikeâspecificÂ tubes)Â 0.543Â 1.721Â 47Â Â ProductÂ CÂ (bikeâspecificÂ tubes)Â 0.078Â 1.081Â 43Â Â StandardÂ tubesÂ 0.197Â 1.217Â 17Â Note:Â *FactorÂ isÂ basedÂ onÂ aÂ singleÂ sensorÂ atÂ oneÂ site;Â useÂ cautionÂ whenÂ applying.Â