Freeway Detector Assessment- Aggregate Data fromthe Remote Traffic Microwave Sensor (RTMS)
Benjamin Coifman, PhDAssistant Professor, Civil and Environmental Engineering and Geodetic ScienceAssistant Professor, Electrical and Computer EngineeringOhio State UniversityHitchcock Hall 4702070 Neil AveColumbus, OH 43210
http://www.ceegs.ohio-state.edu/~coifman
614 292-4282
ABSTRACT
Loop detectors have been the preeminent detection technology for several decades, but theyrequire closing the right of way during installation and potentially undermine the integrity of thepavement surface if they are not installed prior to paving. As a result there is great interest inemerging technologies that promise traffic detection without the liabilities of loop detectors,many of which have already been deployed in large numbers. The Remote Traffic MicrowaveSensor (RTMS) is among the widest deployed non-invasive traffic detector. This study evaluatesthe performance of the RTMS in side-fire mode relative to loop detectors in freewayapplications. First by comparing the aggregated data reported by the RTMS using its internalcontroller emulation in comparison to data from nearby dual loop detectors. It is shown that theRTMS measures are noisier than loop detectors for occupancy (percentage time the detector isoccupied by vehicles) and flow (number of vehicles per unit time), though the RTMS velocityestimates are almost as good as those from single loop detectors (while being inferior to directmeasurement from dual loop detectors). Secondly, the study considers aggregate measurementsfrom contact closure data, comparing the RTMS against the dual loop detectors. For reference,the work also compared one loop against another in a dual loop detector, with the spacingbetween loops being greater than the spacing between the reference loops and the RTMSdetection zone.
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INTRODUCTION
Loop detectors have been the preeminent detection technology for several decades, but theyrequire closing the right of way during installation and potentially undermine the integrity of thepavement surface if they are not installed prior to paving. As a result there is great interest inemerging technologies that promise traffic detection without the liabilities of loop detectors,many of which have already been deployed in large numbers. The Remote Traffic MicrowaveSensor (RTMS) manufactured by Electronic Integrated Systems (EIS) [1] is among the widestdeployed non-invasive traffic detector. But performance varies from one detector technology tothe next and it is important that a given detector performs as expected. To this end, there havebeen numerous studies comparing emerging detector technologies against loop detectors ormanual validation, e.g., [2-5]. The present study continues in this approach, examining theRTMS in side-fire mode, with particular care to synchronize the data across the differentdetectors.
Using Station 7 in the Berkeley Highway Laboratory (BHL) on I-80, north of Oakland, CA [6],(Figure 1) the study collected contact closure data from the two detector systems, recording thestate at 60 Hz, using a model 170 controller running software developed by Caltrans andpreviously deployed in [6-7]. For this study these stations were equipped with Peek sensors thathad been meticulously calibrated using the tools presented in [8-9]. The research also collectedthe data recorded by the RTMS using its internal controller emulation, this mode allows theRTMS to operate independently, providing aggregate traffic data without the need of aconventional traffic controller (the exact details of the controller emulation are proprietary).
The RTMS unit was mounted on a CCTV pole in late 1999 in accordance with themanufacturer's specifications and the RTMS was hardwired to the controller input file. It isworth noting that the calibration software was difficult to control for novice users; however, it isbelieved that this deficiency could be overcome with training. To ensure optimal RTMSperformance, representatives of EIS aligned and calibrated the unit. The EIS representative inCalifornia conducted the initial calibration on October 21, 1999. The EIS representative stronglyrecommended having a trained professional calibrate the RTMS units, stating that he believes hecould calibrate approximately four devices per day. While at the site, he commented on two site-specific features that were likely to reduce the unit's performance. First, the mounting angle ofthe unit was such that it would reduce performance in the closest lane (lane 5). Second, the sitedoes not have any shoulders in the median, which he expected would reduce the performance onthe inside lane (lane 1) in each direction. This degradation is due to echoes off of the concretebarrier on the near side of the median and due to the microwave "shadow" of the barrier on thefar side. The EIS representative said that the shadow would impact performance in the first twolanes on the far side of the roadway and upon his suggestion, it was decided to limit the RTMS tomonitoring traffic on the near side of the median. Earlier studies came to a similar conclusionabout this shadowing and [2] noted the need for one RTMS unit for each direction in mostsituations. Although the median may impact performance on the near side, many sites inCalifornia and elsewhere do not have median shoulders and the results for lane 1 should berepresentative of these locations. The analysis includes lane 1 for completeness, but the readermay choose to ignore it based on these comments. The president of EIS (who lead the originaldevelopment of the RTMS) conducted the final calibration on November 3, 1999 and realignedthe RTMS unit to eliminate the problems in lane 5. Finally, according to the EIS representative
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who did the initial calibration, the RTMS delays the end of each pulse in the contact closureoutput by a fixed 0.15 sec to prevent erroneous dropouts. The installation did not use the EISInterface Card, which reportedly corrects for this extension to replicate the detection zone of aloop. The data used in this study were collected in late 1999 and mid 2000.
The remainder of this paper examines the performance of the RTMS relative to loop detectors,closing with a discussion and conclusions.
RTMS PERFORMANCE
The analysis compares data from the RTMS against the downstream loop in the given lane forthe detector station shown in Figure 1. This choice to use loops for the reference was made forseveral reasons, first, loop detectors are the de facto standard in most states. Second, theperformance of the loops used in this study has been validated using microscopic data analysistools [8-10]. Third, other research has demonstrated the reproducibility of the loopmeasurements from this detector station with those at neighboring stations in the process ofreidentifying vehicle measurements between stations [11-12]. The reidentification work requiresaccurate measurement of individual vehicle passages, on times, velocities and lengths in eachlane, independently at two different detector stations. Fourth, as noted above, earlier studiesfound that properly installed loop detectors provided very accurate count measurements [2-4].After estimating the detector spacing, the analysis uses the aggregated data reported by theRTMS using its internal controller emulation, then the focus shifts to aggregate measurementsfrom the RTMS contact closure data. For reference, the work also compared one loop againstanother in the dual loop detectors.
Detector SpacingThroughout this study the RTMS performance is compared to the downstream loop detector inthe given lane and the two loops in the dual loop detector are compared against one another. Ofcourse the detection zones are spatially separated, e.g., Figure 2A shows an example in the time-space plane of a vehicle passing through the three detection zones of a single lane from Figure 1.The contact closure data records a pulse from each detector, consisting of a rising edge andfalling edge, the difference between the two is simply the on-time, denoted in the figure as OT1,OT2, OTR, respectively for the upstream loop, downstream loop and RTMS zone. Based on thespatial constraints shown, it is clear that a vehicle must pass the upstream loop, then thedownstream loop, then the RTMS. The leading edge of the loops in each lane is a known 20 ftapart but the exact location of the RTMS detection zone varied from lane to lane. To deduce thedistance between the downstream loop and the RTMS, relative to the upstream loop, all pulses atthe upstream loop are matched to the subsequent pulse at the downstream loop and all pulses atthe RTMS are matched to the preceding pulse at the downstream loop. In the event that a pulseat the downstream loop is matched to exactly one pulse at the upstream loop and one pulse at theRTMS, we can measure the traversal time between the three detectors, denoted TTL and TTR inFigure 2. For these measurements the spacing between the downstream loop and the RTMSdetection zone, DR [ft], can be estimated using the following equation:
DR = 20 ⋅TTR
TTL
(1)
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As shown in [10], there are some lane change maneuvers over these detectors, but their numbersare small. Applying Equation 1 to a large number of uniquely matched pulses across the threedetectors, ranging between 31,000 and 99,000 cases across the lanes, the median value for DR forthe rising edge ranged between 6 and 11 ft across the five lanes while DR for the falling edgeranges between 13 and 20 ft, with the effective RTMS detection zone being 3 to 13.5 ft largerthan the loops', increasing as one moves from the lane closest to the RTMS towards the median.These distributions show that the RTMS detection zone ends within 20 feet downstream of theend of the downstream loop detection zone in each lane. The actual size of the loop detectionzone should be approximately 6 feet, but it was not measured (see [10] for more details). Notethat this analysis did not correct for the fact that the RTMS delays the end of each pulse in thecontact closure output by a fixed 0.15 sec. So the trailing edge of the RTMS detection zoneshould be even closer to the downstream loop than value reported above. In other words, theRTMS detection zone is closer to the downstream loop than the two loops are to one another.
Aggregated Data Reported by the RTMSThis first effort examines the aggregated data reported by the RTMS using its internal controlleremulation. Four data collection runs were conducted, though the first two are omitted from thispaper due to the aforementioned potential for calibration errors prior to the final alignment. Theremaining two runs, presented below, occurred after the final calibration. The specific timing ofthe runs were arbitrary, selected ahead of time based on convenience of accessing the site andsuch that each run would likely include both free flow and congested traffic conditions.
Run 3
This run collected data from the RTMS using conventional 30 sec sample periods, the studyperiod lasted 10 hrs, four of which were congested. The RTMS unit was calibrated by thepresident of EIS and as a result, these data should be representative of the best possiblecalibration for the location. According to the EIS representative, the RTMS unit estimatesvelocity in manner similar to what is conventionally used at single loop detectors,
estimated velocity =flow ⋅ vehicle length
occupancy(2)
However, the RTMS unit excludes all vehicles that occupy the detector for more than three timesthe current average on-time. Presumably, this exclusion will eliminate most long vehicles fromthe sample (see [13-16] for further discussion of these impacts). Another difference is that theRTMS unit continually updates the vehicle length estimate in each zone, to adjust to changingtraffic conditions. Figure 3 shows the scatter plots comparing the velocity reported by the RTMSinternal controller against the measured velocity from the dual loop detectors in the same lane.All of the points would fall on the diagonal axis if the two detectors provided identical data,while any difference will cause the points to fall off of the axis. According to the dual loopdetector data, the two inside lanes are characterized by few vehicles over 25 feet whileapproximately 10 percent of the vehicles in the other lanes are over 25 feet. The presence oflong vehicles may have skewed the estimated "vehicle length" estimate used by the RTMS in theouter three lanes. In contrast, Figure 4 shows the estimated velocity using flow and occupancyfrom the downstream loop detector via Equation 2. So this figure shows estimated versusmeasured velocity from loop data. The average effective vehicle length was assumed to be 21feet in all lanes for the loop estimate. Comparing Figures 3 and 4, the RTMS appears to be
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noisier than the single loop estimates in lane 1 and 2, tighter in lanes 3 and 4 (presumably due toexcluding long on-times), and biased in lane 5.
To quantify the magnitude of errors the work used the root mean squared error (RMSE) and biasof as given parameter over n samples as follows:
RMSE =
ˆ x i − xi*( )
2
i=1
n
∑n
(3)
Bias =
ˆ x i − xi*( )
i=1
n
∑n
(4)
where xi* is the measured value from the dual loop detector for the i-th sample and ˆ x i is the
corresponding measurement or estimate from the RTMS (or in the case of Figure 4, from thesingle loops). The resulting RMSE and Bias for each lane are reported in the first five columnsof Tables 1 and 2, respectively. Note that the measures are reported for velocity, flow andoccupancy. The latter two will be discussed shortly. The table also includes a comparisonagainst the upstream loop detector in the given lane. For reference, the next five columns showthe auto-correlation between successive measurements from the dual loop detector and the finalfive columns show the auto-correlation between successive RTMS measurements. For flow andoccupancy, Table 1 shows that the RMSE of RTMS versus Loop is less than either of the auto-correlations in this run.
Run 4L
With the RTMS sampling every 10 sec, this run attempted to analyze the performance of theRTMS unit running at its fastest sampling period. Once more the study period lasted 10 hrs, fourof which were congested. The results are noisy and given the separation between detectors,comparison might not be merited at such a short sampling period. Instead, after collecting thedata, the flow and occupancy data were aggregated up to 300 sec samples and the analysisapplied to Run 3 is now used for longer samples. Figure 5 presents scatter plots of flow, clearlyshowing that the longer sampling period brought most of the observations to the central axis.Moving to occupancy in Figure 6, relative to the loop detector, the RTMS overestimatesoccupancy for high values in lane 2 and underestimates in lanes 4 and 5. Overestimation in lane2 may be due to occlusion from lanes 3-5, but it is not clear why the unit is underestimating thisparameter in the outside lanes unless the change in detection zone size impacts the RTMSperformance. The RMSE and Bias statistics for this run are reported in Tables 1-2. Twoapproaches were used to aggregate the velocity data up to the 300 sec sample period. First, 30successive 10 sec samples of average velocity were averaged together. This approach does notreplicate precisely what the RTMS would report for a 300 sec sample, rather, it should be takenas an lower bound on performance when comparing the RTMS to velocity measured by the dualloops over the 300 sec sample. Nevertheless, this estimate was comparable to or better than theconventional single loop estimate for lanes 3-5. The second approach used Equation 2 applieddirectly to the RTMS flow and occupancy after aggregating up to the 300 sec sample period. Itis clear that these results are worse than the other approach; however, the estimates from
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Equation 2 are presented because they are an indication of how accurately the RTMS measuresflow and occupancy.
Contact Closure Data Reported by the RTMSThis section considers aggregate measurements from the contact closure data from the RTMSand dual loop detector collected over a five day period starting on May 25, 2000. Figure 7compares the cumulative distribution function (CDF) of the flow difference relative to thedownstream loop using 5 min samples, q2-qR and q2-q1, (continuing the use of subscripts, 1, 2, R,denoting respectively the upstream loop, downstream loop, and RTMS). The mean and medianfor these distributions are close to zero in all lanes, but the RTMS has a larger variance comparedto that of the upstream loop, as evident in the larger tails in the distributions. The first fewcolumns of Table 3 show the average absolute percent error for all samples that had a non-zeroflow at the downstream loop, |(q2-qR)/q2| and |(q2-q1)/q2|. Except for lane 1, the RTMS flow iswithin 10 percent of the downstream loop, while the upstream loop is within three percent for alllanes. There were two periods of congestion during the data collection and one period wherelanes 1-3 were closed for about six hours due to maintenance activity downstream of the detectorlocation. The data from these lane closures were excluded from the results in Table 3 becausethe downstream loop reported zero values during these periods. The remaining columns of Table3 subsample the data by a flow threshold of 1000 vph and then separately by the velocitythreshold of 45 mph. The RTMS error increases at low velocity in lanes 2 and 5 while droppingin lanes 3 and 4 at lower velocities. The RMTS error increases in all lanes at low flow. Similartrends are evident in the loop data, though the relative change is much smaller for the loops.
Repeating this analysis using occupancy (occ), Figure 8 shows the CDF of the difference relativeto the downstream loop, occ2-occR and occ2-occ1. Before measuring occR, the recorded OTR wasshortened by 0.15 sec to remove the extension delay on the falling transition (the results from theRTMS degrade in all but lane 5 if the extension delay is retained). The mean and median inlanes 1-2 are similar for the upstream loop and RTMS, but again the variance is larger for theRTMS. As one moves to the outside lanes, closer to the RTMS unit, there is a clear bias in theRTMS measurement. The RTMS underestimates occupancy relative to the downstream loop. Incontrast, the upstream loops only show a slight increase in variance as one moves to the outerlanes. Some of this error is due to the spatial separation between the detection zones, as isevident in the upstream loop distribution. Recall that from the downstream loop, the upstreamloop is further away than the RTMS detection zones. Again, the first few columns of Table 3show the average absolute percent error for all samples that had a non-zero occupancy at thedownstream loop, |(occ2-occR)/occ2| and |(occ2-occ1)/occ2|. The average error for loop 1 rangesbetween 1.5 percent and 2.7 percent across the lanes. The average error from the RTMS is bestin lane 3, at 8.8 percent and degrades as one moves away from this lane, reaching a maximum of57.4 percent error in lane 1. As shown in the table, after subsampling the data by the flowthreshold and separately by the velocity threshold, at low velocities the RTMS performanceimproves in the lanes closest to the sensor unit but degrades in lanes further from the sensor. Incontrast, the upstream loops show slightly higher error at low velocities compared to theirperformance at higher velocities, with a maximum of 6.0 percent. Returning to the RTMS, onesees a slight increase in the occupancy percent error at lower flows for the near lanes, withsignificant degradation in lanes 4 and 5. These results are important, not only did the RTMSsensor show large errors relative to the downstream loop, but the magnitude of those errors
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depend on the lane. These results suggest that it would be difficult to derive a simple correctionfactor to map RTMS occupancy to loop occupancy.
This analysis was repeated using 30 sec samples with similar results, as shown at the bottom ofTable 3. Highlighting the differences compared to the 30 sec data, 5 min flow from the upstreamloop improves by roughly a factor of three. The RTMS performance improves slightly in thelanes closest to the RTMS, but remains roughly the same in lane 2 and has degrades in lane 1.The 5 min loop occupancy performance improves by roughly a factor of two while the RTMSperformance does not change significantly.
The analysis in this section has focused on aggregating the contact closure output from theRTMS collected through a traffic controller, concurrent 30 sec aggregate data were also reportedby the RTMS unit. We were not able to perfectly synchronize the RTMS and controller clocksover this extended study because the RTMS sample period was slightly longer than 30 sec,making one-to-one comparisons difficult between the two outputs at a microscopic scale.Presumably the flow and occupancy measurements reported directly from the RTMS should beidentical to what was derived from the contact closure output. The loop and RTMS flows arealready close, but significant discrepancies were found between the loop and RTMS occupancymeasured by the controller. To verify that these results are representative of the RTMS, theanalysis compared the occupancy measured directly by the RTMS unit and the occupancymeasured from the RTMS contact closure data via the controller. Comparing the two time seriesover 24 hours, the two data sets appear to fall on top of one another [10]. Although the sampleperiods are not identical, taking the difference between the two series, an error in one sample dueto being slightly out of phase should be offset by an opposite error in the next sample. The meandifference, or bias, is roughly 0.5 percent occupancy in each lane (ranging between 0.33 and 0.57across the lanes), exactly what one would expect if the data reported directly from the RTMStruncates occupancy to integer percent. Figure 9 shows three different hour long periods fromlane 2 (note that the vertical scales change from plot to plot). The top plot shows the periodbetween 11-12 hrs, which exhibited high free flow occupancies. The middle plot shows theperiod between 15-16 hrs, which exhibited high congestion occupancies. The bottom plot showsthe period between 23-24 hrs, which exhibited low free flow occupancies, and the truncatedoccupancies are clearly evident. These plots are typical of all five lanes. The strong correlationbetween the two time series occupancies, direct from the RTMS and via the controller, verifiesthe EIS representative's statement that the RTMS delays the end of each pulse in the contactclosure output by a fixed 0.15 sec. Reportedly, the most recent revision of the RTMS does nottruncate occupancy to integer percent.
CONCLUSIONS
This study examined the performance of the RTMS sensor deployed in side-fire mode and thefollowing conclusions only apply to this mode of operation. First, the manufacturer's generalperformance claims are over-ambitious for the RTMS sensor, most measures are not as clean asloop detectors; still, the RTMS promises significant cost savings and could be used to (relatively)cheaply provide information where none was previously available. Although the study site wasreportedly less than ideal from the manufacturer's standpoint, it is representative of commonfreeway geometry and the RTMS was not able to monitor the opposing lanes at this location.While the RTMS occupancy and flow measures are noisier than loops, the velocity estimates are
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almost as good as those from single loops (while being inferior to direct measurement from dualloops). Although many operating agencies use single loop detectors estimate velocity, ourresearch has shown that there are several ways to improve upon the quality of these conventionalestimates (and presumably the RTMS by extension) [13-16]. In the mean time, it may beadvisable not to use the RTMS to estimate velocity for samples less than 5 minutes long (theauthor would make a similar argument for conventional velocity estimation from single loopdetectors). Of course, if a higher sampling rate is desired, a moving average or exponential filtercould be used on the flow and occupancy measurements.
For reference, the work also compared one loop against another in a dual loop detector. Theanalysis consisted of aggregate data comparisons. The accuracy of the subject loop detectorstation has been verified elsewhere in the literature, e.g., [10]. The work used the redundanciesof the dual loop detector to verify the performance of each loop in a given lane. It also showedthat the two loops were further apart than the RTMS detection zone was from the closest loop.Hence, the loop comparison should provide an upper bound on the expected errors in the RTMSdata due to the spatial separation.
Consistent with earlier studies, the RTMS flow measurements are within 10 percent of loops,though the two loops were within 3 percent of one another. These flow measurements should begood enough for many real-time operations applications but may not be good enough forcalculating average annual daily travel (AADT). The RTMS occupancy measurements showedsignificant discrepancies relative to the loops, ranging between 13 and 40 percent across lanes 2-5, showing an apparent lane dependency. These results suggest that it would be difficult toderive a simple correction factor. Even if one excludes the results from both lane 1 and lane 5,the occupancy measurements may not be good enough for applications such as traffic responsiveramp metering.
These findings are consistent with those of the Caltrans Traffic Operations Program DataCollection Functional Requirements Task Force. The task force noted that in areas withoutexisting loops, the RTMS can be deployed in side fire mode to, "quickly fill in gaps wheredetection is poor, non existent, or out due to construction. It must be accepted that this data issignificantly less than perfect, but some data is always better than none." [17]
Of course all of the figures are presented in this document, allowing the reader to assess theperformance of the RTMS and reach their own conclusions. After all, the appropriateness of thedetector depends on the applications, detection network and a host of other factors that are farbeyond the scope of this work.
ACKNOWLEDGEMENTS
This work was performed as part of the California PATH (Partners for Advanced Highways andTransit) Program of the University of California, in cooperation with the State of CaliforniaBusiness, Transportation and Housing Agency, Department of Transportation. The Contents ofthis report reflect the views of the authors who are responsible for the facts and accuracy of thedata presented herein. The contents do not necessarily reflect the official views or policies of theState of California. This report does not constitute a standard, specification or regulation.
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REFERENCES
[1] Wang, J., Case, E., Manor, D., "The Road Traffic Microwave Sensor (RTMS)", Proc. of the3rd Vehicle Navigation and Information Systems Conference, IEEE, 1992, pp 82-91.
[2] Klein, L, Kelley, M [Hughes Aircraft Company], Detection Technology for IVHS: FinalReport, FHWA, 1996, FHWA-RD-95-100.
[3] MNDOT Field Test of Monitoring of Urban Vehicle Operations Using Non-IntrusiveTechnologies, FHWA, 1997,FHWA-PL-97-018
[4] Middleton, D, Jasek, D., Parker, R., Evaluation of Some Existing Technologies for VehicleDetection, Texas Transportation Institute, 1999, FHWA/TX-00/1715-S.
[5] Wald, W., Microwave Vehicle Detection, Final Report, Caltrans, 2004,http://www.dot.ca.gov/hq/traffops/elecsys/reports/FinalMVDS104.pdf
[6] Coifman, B., Lyddy, D., Skabardonis, A., "The Berkeley Highway Laboratory- Building onthe I-880 Field Experiment", Proc. IEEE ITS Council Annual Meeting, 2000, pp 5-10,
[7] Skabardonis, A., Petty, K., Noeimi, H., Rydzewski, D., Varaiya, P., "I-880 Field Experiment:Data-Base Development and Incident Delay Estimation Procedures", Transportation ResearchRecord 1554, TRB, 1996, pp 204-212.
[8] Coifman, B., "Using Dual Loop Speed Traps to Identify Detector Errors", TransportationResearch Record no. 1683, Transportation Research Board, 1999, pp 47-58.
[9] Coifman, B., Dhoorjaty, S. "Event Data Based Traffic Detector Validation Tests", ASCEJournal of Transportation Engineering, Vol 130, No 3, 2004, pp 313-321.
[10] Coifman, B., An Assessment of Loop Detector and RTMS Performance, AutomatedDiagnostics of Loop Detectors and the Data Collection System in the Berkeley HighwayLaboratory- Part II, PATH research report, University of California, 2004.
[11] Coifman, B., Cassidy, M. "Vehicle Reidentification and Travel Time Measurement onCongested Freeways", Transportation Research: Part A, vol 36, no 10, 2002, pp. 899-917.
[12] Coifman, B. "Identifying the Onset of Congestion Rapidly with Existing Traffic Detectors",Transportation Research: Part A, vol 37, no 3, 2003, pp. 277-291.
[13] Coifman, B., Dhoorjaty, S., Lee, Z. "Estimating Median Velocity Instead of Mean Velocityat Single Loop Detectors", Transportation Research: Part C, vol 11, no 3-4, 2003, pp 211-222.
[14] Neelisetty, S., Coifman, B., "Improved Single Loop Velocity Estimation in the Presence ofHeavy Truck Traffic" Proc. of the 83rd Annual Meeting of the Transportation Research Board,2004.
[15] Coifman, B. "Improved Velocity Estimation Using Single Loop Detectors", TransportationResearch: Part A, vol 35, no 10, 2001, pp. 863-880.
[16] Jain, M., Coifman, B., "Improved Speed Estimates from Freeway Traffic Detectors" Proc.of the 83rd Annual Meeting of the Transportation Research Board, 2004.
[17] Caltrans Traffic Operations Program Data Collection Functional Requirements Task Force,Draft Data Collection Functional Requirements, 2001.
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LIST OF FIGURES
Figure 1, (A) Photo showing the configuration of Station 7 in the BHL. (B) Schematic ofthe data collection site at the station.
Figure 2, One vehicle passing over a dual-loop-detector and the RTMS from the previousfigure, (A) the three detection zones and the vehicle trajectory as shown in thetime space plane. The height of the vehicle's trajectory reflects the non-zerovehicle length. (B) The recorded response at each detector.
Figure 3, Lane by lane comparison of velocity reported by the RTMS and measured by thedual loop detectors, T=30 sec, November 3, 1999.
Figure 4, Lane by lane comparison of velocity estimated by the downstream loop andmeasured by the dual loop detectors, T=30 sec, November 3, 1999.
Figure 5, Lane by lane comparison of flow reported by the RTMS and measured by the dualloop detectors, T=5 min, November 9, 1999.
Figure 6, Lane by lane comparison of occupancy reported by the RTMS and measured bythe dual loop detectors, T=5 min, November 9, 1999.
Figure 7, Cumulative distribution by lane of the flow difference relative to the downstreamloop for the RTMS and upstream loop for each 5 min sample.
Figure 8, Cumulative distribution by lane of the occupancy difference relative to thedownstream loop for the RTMS and upstream loop for each 5 min sample.
Figure 9, Details of 30 sec occupancy reported directly by the RTMS, 'x', and as calculatedfrom the RTMS contact closure output reported to the controller, 'o' in lane 2.These results were typical of all five lanes. Note the three plots are at differentscales.
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LIST OF TABLES
Table 1, RMSE for the various runs. First five columns are RTMS or upstream loop versusthe downstream loop, the next five columns are loop(n+1) versus loop(n) and thefinal five columns are RTMS(n+1) versus RTMS(n). These last ten columns areincluded for reference.
Table 2, Bias for the various runs. The columns correspond to those of the previous table.For the first five columns, a positive value indicates the RTMS, on average,overestimated the given parameter and a negative value indicates the RTMSunderestimated it.
Table 3, Average absolute percent error in aggregated flow and occupancy from thecontact closure data for the upstream loop and RTMS relative to the downstreamloop.
median barrierlane 1lane 2lane 3lane 4lane 5
Eastbound
Westbound
upst
ream
loop
sdo
wns
tream
loop
s
RTMS detection zone
CCTV pole withRTMS unit
Figure 1, (A) Photo showing the configuration of Station 7 in the BHL. (B) Schematic of the data collection site at the station.
A)
B)
CCTV
RTMSupstream
loopsdownstream
loops
Controller cabineteastbound
Downstream Loop's Detection Zone
Upstream Loop's Detection Zone
Second Loop's Response
First Loop's Response
dist
ance
time
time
20 ft
(A)
(B)
Vehicle
Traject
ory
on
off
on
off
Figure 2, One vehicle passing over a dual-loop-detector and the RTMS from the previous figure, (A) the three detection zones and the vehicle trajectory as shown in the time space plane. The height of the vehicle's trajectory reflects the non-zero vehicle length. (B) The recorded response at each detector.
TTL
TTR
OT1
OT2
OTR
RTMS Detection Zone
RTMS Responseon
off
0 20 40 60 800
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dual loop velocity (mph)
RTM
S ve
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ph)
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S ve
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ty (m
ph)
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Figure 3, Lane by lane comparison of velocity reported by the RTMS and measured by the dual loop detectors, T=30 sec, November 3, 1999.
0 20 40 60 800
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ocity
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Figure 4, Lane by lane comparison of velocity estimated by the downstream loop and measured by the dual loop detectors, T=30 sec, November 3, 1999.
0 50 100 150 2000
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downstream loop flow (veh/5min)
RTM
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w (v
eh/5
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)lane 1
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min
)
lane 2
0 50 100 150 2000
50
100
150
200
downstream loop flow (veh/5min)
RTM
S flo
w (v
eh/5
min
)
lane 3
0 50 100 150 2000
50
100
150
200
downstream loop flow (veh/5min)
RTM
S flo
w (v
eh/5
min
)
lane 4
0 50 100 150 2000
50
100
150
200
downstream loop flow (veh/5min)
RTM
S flo
w (v
eh/5
min
)
lane 5
Figure 5, Lane by lane comparison of flow reported by the RTMS and measured by the dual loop detectors, T=5 min, November 9, 1999.
0 20 400
10
20
30
40
50
downstream loop occupancy (%)
RTM
S oc
cupa
ncy
(%)
lane 1
0 20 400
10
20
30
40
50
downstream loop occupancy (%)
RTM
S oc
cupa
ncy
(%)
lane 2
0 20 400
10
20
30
40
50
downstream loop occupancy (%)
RTM
S oc
cupa
ncy
(%)
lane 3
0 20 400
10
20
30
40
50
downstream loop occupancy (%)
RTM
S oc
cupa
ncy
(%)
lane 4
0 20 400
10
20
30
40
50
downstream loop occupancy (%)
RTM
S oc
cupa
ncy
(%)
lane 5
Figure 6, Lane by lane comparison of occupancy reported by the RTMS and measured by the dual loop detectors, T=5 min, November 9, 1999.
-20 -10 0 10 20count (# vehicles)
CD
F
0
0.2
0.4
0.6
0.8
1
-20 -10 0 10 20count (# vehicles)
CD
F
0
0.2
0.4
0.6
0.8
1
-20 -10 0 10 20count (# vehicles)
CD
F
0
0.2
0.4
0.6
0.8
1
-20 -10 0 10 20count (# vehicles)
CD
F
0
0.2
0.4
0.6
0.8
1
-20 -10 0 10 20count (# vehicles)
CD
F
0
0.2
0.4
0.6
0.8
1
lane 1 lane 2
lane 3 lane 4
lane 5
Figure 7, Cumulative distribution by lane of the flow difference relative to the downstream loop for the RTMS and upstream loop for each 5 min sample.
upstream loop
upstream loop
RTMS
RTMS
upstream loopRTMS
upstream loop
RTMS
RTMSupstream loop RTMS
upstream loop
RTMS
lane 1 lane 2
lane 3 lane 4
lane 5
Figure 8, Cumulative distribution by lane of the occupancy difference relative to the downstream loop for the RTMS and upstream loop for each 5 min sample.
upstream loop
upstream loop
RTMS
RTMS
upstream loop
upstream loop
RTMS
RTMS
upstream loop RTMS upstream loop RTMS
upstream loop
RTMS
0.2
0.4
0.6
0.8
1C
DF
0.2
0.4
0.6
0.8
1
CD
F
0
0.2
0.4
0.6
0.8
1
CD
F
0
0
0.2
0.4
0.6
0.8
1
CD
F
0.2
0.4
0.6
0.8
1
CD
F
0
0
occupancy (percent) occupancy (percent)
occupancy (percent)
occupancy (percent) occupancy (percent)
-10 -5 0 5 10 15 -10 -5 0 5 10 15
-10 -5 0 5 10 15
-15
-15
-10 -5 0 5 10 15 -10 -5 0 5 10 15-15
-15
-15
11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 120
5
10
15
20
25
30
time of day (h)
occu
panc
y (%
)
15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 1610
20
30
40
50
60
70
time of day (h)
occu
panc
y (%
)
23 23.1 23.2 23.3 23.4 23.5 23.6 23.7 23.8 23.9 240
2
4
6
8
10
time of day (h)
occu
panc
y (%
)
Figure 9, Details of 30 sec occupancy reported directly by the RTMS, 'x', and as calculated from the RTMS contact closure output reported to the controller, 'o' in lane 2. These results were typical of all five lanes. Note the three plots are at different scales.
Table 1,
units run T (sec) 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5RTMS measured flow
(vph) 3 30 234.4 253.0 217.5 228.1 261.7 432.8 452.8 401.0 395.0 429.4
upstream loop flow (vph) 3 30 41.5 68.9 73.0 74.3 74.1 392.9 439.7 394.1 404.2 424.7
RTMS measured occupancy
(%) 3 30 2.5 6.7 3.8 4.4 6.9 4.3 9.9 8.0 6.6 6.2
upstream loop occupancy
(%) 3 30 0.3 4.2 2.2 2.4 2.7 3.2 8.3 7.2 6.3 7.0
RTMS reported velocity
(mph) 3 30 13.2 10.2 5.9 4.8 10.7 13.5 9.9 8.0 7.3 9.6
loop estimated velocity (a)
(mph) 3 30 6.8 3.7 7.5 7.3 5.9 10.6 7.1 11.4 11.3 9.6
loop measured velocity (b)
(mph) 3 30 5.3 5.4 4.4 4.5 5.3
RTMS measured flow
(vph) 4 L 300 74.3 62.0 51.8 67.5 90.7 117.4 136.1 115.2 122.6 155.5
upstream loop flow (vph) 4 L 300 8.1 11.7 16.6 16.6 14.2 116.6 139.3 120.3 131.2 153.0
RTMS measured occupancy
(%) 4 L 300 0.8 2.8 0.9 2.3 4.7 1.2 2.4 2.1 2.0 2.0
upstream loop occupancy
(%) 4 L 300 0.1 0.6 0.6 0.7 1.0 1.1 2.0 2.0 2.3 2.2
RTMS estimated velocity (c)
(mph) 4 L 300 20.0 18.1 10.5 13.7 44.3 15.2 11.7 12.8 10.5 19.6
RTMS average reported velocity (d)
(mph) 4 L 300 24.6 10.4 8.0 8.4 8.8 8.8 5.8 5.7 5.2 7.6
loop estimated velocity (a)
(mph) 4 L 300 5.9 2.5 8.2 14.1 8.6 7.9 3.9 7.8 5.9 7.3
loop measured velocity (b)
(mph) 4 L 300 4.9 3.3 3.0 2.9 3.1
(a) Loop estimated velocity via Equation 2.(b) The loop measured velocity is from dual loops, not just the downstream loop.(c) RTMS estimated velocity via Equation 2 after aggregating 10 sec flow and occupancy over 30 successive samples.(d) The average of 30 successive 10 sec samples of reported velocity from the RTMS.
RMSE for the various runs. First five columns are RTMS or upstream loop versus the downstream loop, the next five columns are loop(n+1) versus loop(n) and the final five columns are RTMS(n+1) versus RTMS(n). These last ten columns are included for reference.
RMSE versus downstream Loop (or dual loop for velocity), lane #
RMSE downstream Loop(n+1) versus downstream Loop(n), lane #
RMSE RTMS(n+1) vs. RTMS(n), lane #
Table 2,
units run T (sec) 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5RTMS measured flow
(vph) 3 30 39.7 57.5 -10.5 -30.7 110.2 -0.5 -1.2 -1.0 -0.6 -1.3
upstream loop flow (vph) 3 30 -2.3 -2.2 -0.5 -3.5 7.0 -0.7 -1.1 -0.9 -1.0 -1.1
RTMS measured occupancy
(%) 3 30 0.2 2.4 -0.8 -3.0 -5.7 0.0 0.0 0.0 0.0 0.0
upstream loop occupancy
(%) 3 30 0.0 0.0 0.5 0.3 0.7 0.0 0.0 0.0 0.0 0.0
RTMS reported velocity
(mph) 3 30 -8.5 -6.9 -2.5 1.1 7.5 0.4 0.0 0.0 0.0 0.0
loop estimated velocity (a)
(mph) 3 30 2.4 1.7 -0.9 -0.9 1.6 0.0 0.0 0.0 0.0 0.0
loop measured velocity (b)
(mph) 3 30 0.0 0.0 0.0 0.0 0.0
RTMS measured flow
(vph) 4 L 300 47.9 25.6 -12.5 -37.0 74.4 -0.8 0.2 0.0 -1.2 -2.9
upstream loop flow (vph) 4 L 300 -1.6 -1.0 -1.3 -1.5 7.8 -1.5 -0.6 -0.6 -1.7 -2.9
RTMS measured occupancy
(%) 4 L 300 0.3 0.8 -0.5 -2.0 -3.8 0.0 0.0 0.0 0.0 0.0
upstream loop occupancy
(%) 4 L 300 0.0 0.0 0.3 0.3 0.6 0.0 0.0 0.0 0.0 0.0
RTMS estimated velocity (c)
(mph) 4 L 300 -5.6 12.4 1.9 1.9 37.3 0.0 0.0 0.0 0.0 0.0
RTMS average reported velocity (d)
(mph) 4 L 300 -19.2 -9.2 -6.0 -4.1 1.1 0.0 0.0 0.0 0.0 0.0
loop estimated velocity (a)
(mph) 4 L 300 1.2 1.0 -4.8 -9.8 -4.6 0.0 0.0 0.0 0.0 0.0
loop measured velocity (b)
(mph) 4 L 300 -0.2 0.0 0.0 0.0 0.0
(a) Loop estimated velocity via Equation 2.(b) The loop measured velocity is from dual loops, not just the downstream loop.(c) RTMS estimated velocity via Equation 2 after aggregating 10 sec flow and occupancy over 30 successive samples.(d) The average of 30 successive 10 sec samples of reported velocity from the RTMS.
Bias for the various runs. The columns correspond to those of the previous table. For the first five columns, a positive value indicates the RTMS, on average, overestimated the given parameter and a negative value indicates the RTMS underestimated it.
Bias versus downstream Loop (or dual loop for velocity), lane #
Bias downstream Loop(n+1) versus downstream Loop(n), lane # Bias RTMS(n+1) vs. RTMS(n), lane #
Table 3,
lane# of
samplesupstream
loop RTMS# of
samplesupstream
loop RTMS# of
samplesupstream
loop RTMS# of
samplesupstream
loop RTMS# of
samplesupstream
loop RTMSFlow 5 min 1 1172 0.8% 31.5% 988 0.7% 30.1% 69 0.3% 27.5% 174 0.5% 4.8% 998 0.8% 36.8%Flow 5 min 2 1186 0.7% 8.9% 1003 0.6% 4.3% 109 1.5% 12.7% 715 0.7% 4.2% 471 0.8% 17.4%Flow 5 min 3 1194 0.9% 5.1% 1027 0.9% 4.6% 108 0.8% 4.3% 793 0.8% 4.4% 401 1.2% 6.6%Flow 5 min 4 1195 1.1% 5.9% 1086 1.1% 6.1% 109 0.9% 3.6% 852 0.9% 4.6% 343 1.4% 9.0%Flow 5 min 5 1195 1.0% 6.5% 1079 1.0% 6.3% 116 0.7% 8.6% 824 0.9% 4.9% 371 1.2% 9.9%Occupancy 5 min 1 1172 1.8% 57.4% 988 1.6% 59.1% 69 2.0% 22.3% 174 1.2% 9.0% 998 1.9% 66.9%Occupancy 5 min 2 1186 1.2% 12.9% 1003 0.8% 6.8% 109 2.7% 19.6% 715 1.0% 7.1% 471 1.4% 23.3%Occupancy 5 min 3 1194 2.6% 8.8% 1027 2.5% 9.4% 108 3.6% 3.4% 793 2.5% 8.6% 401 2.8% 9.4%Occupancy 5 min 4 1195 1.5% 22.1% 1086 1.4% 22.8% 109 2.2% 14.7% 852 1.5% 20.7% 343 1.4% 25.4%Occupancy 5 min 5 1195 2.7% 40.0% 1079 2.6% 41.1% 116 3.7% 29.8% 824 2.6% 41.3% 371 2.9% 37.3%Flow 30 sec 1 9275 1.9% 17.4% 7880 1.7% 17.6% 598 2.7% 13.6% 1945 1.9% 9.2% 7330 1.9% 19.9%Flow 30 sec 2 10920 2.0% 8.1% 9629 1.8% 7.1% 1031 3.9% 17.4% 6531 2.0% 7.1% 4388 2.0% 9.7%Flow 30 sec 3 11420 2.6% 7.0% 10070 2.4% 6.4% 1067 4.1% 12.5% 7356 2.5% 6.1% 4062 2.9% 8.8%Flow 30 sec 4 11830 3.1% 8.0% 10700 2.9% 7.9% 1108 3.8% 8.1% 8227 2.8% 6.6% 3606 3.5% 11.0%Flow 30 sec 5 11860 2.7% 10.0% 10540 2.5% 9.6% 1280 3.4% 12.1% 7828 2.4% 6.8% 4036 3.1% 16.1%Occupancy 30 sec 1 9275 4.2% 35.4% 7880 4.0% 36.5% 598 3.7% 16.0% 1945 2.7% 17.0% 7330 4.7% 40.8%Occupancy 30 sec 2 10920 3.3% 15.6% 9629 2.9% 14.8% 1031 5.2% 23.2% 6531 2.6% 13.7% 4388 4.3% 18.7%Occupancy 30 sec 3 11420 4.2% 13.3% 10070 3.9% 13.7% 1067 6.0% 8.9% 7356 3.6% 11.6% 4062 5.4% 16.6%Occupancy 30 sec 4 11830 3.5% 22.8% 10700 3.3% 23.6% 1108 4.4% 15.1% 8227 3.1% 21.2% 3606 4.4% 26.4%Occupancy 30 sec 5 11860 4.1% 41.7% 10540 3.9% 42.9% 1280 5.2% 31.2% 7828 3.5% 41.7% 4036 5.3% 41.6%
Average absolute percent error in aggregated flow and occupancy from the contact closure data for the upstream loop and RTMS relative to the downstream loop
q < 1000 vphall v > 45 mph v < 45 mph q > 1000 vph
