Submitted for Presentation 94th Annual Meeting of the Transportation Research Board January 11-15, 2015, Washington, D.C.

Transcription

1 Wegh-In-Moton Staton Montorng and Calbraton usng Inductve Loop Sgnature Technology Shn-Tng (Cndy) Jeng (Correspondng Author) CLR Analytcs Inc 8885 Research Drve Sute 15 Irvne, CA Tel: , Fax: Emal: URL: Lanyu Chu CLR Analytcs Inc 8885 Research Drve Sute 15 Irvne, CA Tel: , Fax: Emal: URL: Mect Cetn Cvl and Envronmental Engneerng Old Domnon Unversty 135 Kaufman Hall Norfolk, VA Tel: E-mal: Word Count: 3, Fgures and 1 Tables = 7,95 words Submtted for Presentaton 94th Annual Meetng of the Transportaton Research Board January 11-15, 215, Washngton, D.C.

2 Jeng, Chu, and Cetn 2 ABSTRACT Despte heavy vehcles representng a small porton of vehcles on the roads, they have sgnfcant nfluences on pavement, safety, envronment, energy consumpton, and the performance of traffc system. Wegh-In-Moton (WIM) s the major technology employed to collect truck data on the freeways over three decades. However, WIM statons usually are not calbrated n a tmely fashon and the calbraton s manly performed usng fve-axle sngle-traler trucks once every half a year to three years. A potental soluton s to adopt a comprehensves remote calbraton montorng system. Therefore, ths study proposed an nductve loop sgnature-wim based approach, whch utlzed both nductve loop sgnatures and WIM data to track heavy vehcles at WIM statons and generated "Matched Vehcle Pars (MVPs)" for WIM staton montorng and calbraton. The algorthm was establshed based on a prevously developed truck trackng algorthm, RTREID-2MT, and ntegrated wth a Bayesan redentfcaton model to flter out the MVPs that were most lkely ncorrectly matched by the system. The MVPs were then utlzed for WIM staton montorng and temporary approxmate calbraton applcatons. Case study showed that the upstream staton reported low weghts, whle the downstream staton reported hgh axle spacngs. The average offsets of the drve tandem axle spacng, Gross Vehcle Weght (GVW), and steer axle weght between the statons were thus derved from MVPs on a per lane bass and successfully appled to calbrate the problematc statons.

3 Jeng, Chu, and Cetn 3 INTRODUCTION Despte heavy vehcles representng a small porton of vehcles on the roads, they have sgnfcant nfluences on pavement, safety, envronment, energy consumpton, and the performance of traffc system. Wegh-In-Moton (WIM) s the major technology employed to collect truck data on the freeways over three decades. A WIM system requres sophstcated data collecton sensors and costly equpment for setup and calbraton. Both of whch wll functon properly only under a controlled envronment. A man challenge/lmtaton that has been dentfed whle deployng WIM technologes s that WIM statons suffer from data accuracy ssues due to the vehcle dynamcs affected by the pavement condtons over tme of day and season of year (1). Thus, WIM statons need to be calbrated n order to provde accurate data and the calbraton needs to be repeated regularly. Although the ASTM E s a wdely used calbraton procedure, dfferent states are found to follow dfferent procedures and have dfferent calbraton requrements (2). It s also found that the calbraton of WIM statons s not performed n a tmely fashon. Dependng on the states, a WIM staton s usually calbrated usng fve-axle sngle-traler trucks (3) once every half a year to three years (4). In addton, onste vst, road testng, and parameter fne-tunng at a WIM ste are very expensve. Therefore, how to effectvely utlze the valuable WIM data, montor WIM statons' performance and dentfy out of calbraton statons are especally mportant to government agences. A potental soluton s to adopt a remote calbraton montorng system. A remote calbraton montorng system can assst n performng data qualty checks and approxmate calbraton tunng. The objectves of the calbraton montorng procedures (5) nclude: Mantan system calbraton throughout the lfe of the system. Verfy the desred effects of calbraton factor adjustments on WIM weght, axle spacng, and vehcle length outputs. Identfy wegh sensors that are ntermttently and/or subtly malfunctonng. Adjust calbraton factors for a wegh sensor exhbtng calbraton drft pendng onste recalbraton usng test trucks. Temporarly assgn calbraton factors for a wegh sensor replacement pendng onste recalbraton usng test trucks. Schedule onste calbratons/valdaton for stes wth most need when fundng and/or resources for runnng test trucks s lmted. In addton to montor the gross vehcle weght dstrbuton and average drve tandem axle spacng, other useful statstcs may nclude average steer axle weght, steer axle's average rght and left wheel weghts, and standard devaton for the steer axle's average rght and left wheel weghts (5). A prelmnary study from Cetn and Monsere (6) ndcated that sensor calbraton problems and some sensor falures would be consstent across all observatons, causng a shft n the densty plot rather than outlers. They also found that the means and standard devatons of the weght and spacng plots were most lkely ndcators of the dfference n sensor calbraton parameters. For example, large standard devatons n the axle-weght error are most lkely attrbuted to the pavement profle at the WIM statons, dfferent sensor types, poor calbraton, or a falng sensor. In addton, the cause of ncreased standard devatons n the axle spacng could be falng axle-sensor detectors. It was because when a sensor faled, t would sometmes mss lght-weght axles, causng axle-spacng data and classfcaton data to be erroneous. Wth a comprehensves remote calbraton montorng system, data analysts can promptly dentfy out of calbraton WIM statons and the possble causes. Temporary approxmate calbraton tunng can then be performed offste so that reasonable performance can be mantaned at the problematc WIM statons before an onste re-calbraton can be a performed. Therefore, we propose an Inductve Loop Sgnature-WIM based montorng system for exstng WIM statons n ths research. The system utlzes both Inductve Loop Sgnatures and WIM data to track heavy vehcles at WIM statons (7) to generate "Matched Vehcle Pars (MVPs)." The MVPs are then utlzed for WIM staton montorng and calbraton.

4 Jeng, Chu, and Cetn 4 Ths paper s organzed nto fve man sectons ncludng ths ntroductory secton. The second secton dscusses truck trackng based on WIM and nductve loop sgnature technologes. The thrd secton presents the proposed approach for WIM staton montorng and calbraton. The fourth secton presents the results and fndngs. The last secton concludes ths study and outlnes future works. TRUCK TRACKING BASED ON WIM AND INDUCTIVE LOOP SIGNATURE TECHNOLOGIES Over the last decade, researchers have attempted to track trucks usng WIM data. Cetn et al. nvestgated dfferent methods for solvng the truck redentfcaton problem and explored some of the factors that affected the accuracy of the results (6, 8, and 9). In ther studes, archved data from WIM statons n Oregon were used for developng, calbratng, and testng the Bayesan redentfcaton algorthms. A comprehensve analyss was amed to nvestgate the key factors affectng the accuracy of the results. The analyses were performed by employng the Bayesan redentfcaton algorthm to match commercal vehcles that crossed upstream and downstream pars of WIM stes that were separated by long dstances rangng from 7 to 214 mles. Data from 14 dfferent pars of WIM stes were used to evaluate how the matchng accuracy was affected by varous factors such as the dstance between two stes, travel tme varablty, truck volumes, and sensor accuracy or consstency of measurements. It was found that sensor accuracy and volumes had the greatest mpacts on matchng accuracy whereas the dstance alone dd not have a sgnfcant mpact. Although these studes show good matchng rates usng WIM data, the Bayesan redentfcaton algorthms requre sample data for tranng and calbraton. Researchers have also focused on developng vehcle trackng systems usng other technologes (e.g., Inductve Loop Detecton (ILD), Bluetooth, Global Postonng System (GPS), cell phone, mage processng, Automatc Vehcle Identfcaton (AVI) tags, etc.). Recently, Jeng and Chu developed a truck trackng algorthm, RTREID-2MT, whch combnes the use of both WIM data and the nductve loop sgnature data (7). The nductve loop sgnature data are the analog waveform outputs from advanced detector cards, whch measure and output the nductance change (.e., the analog waveform) n an nductve loop sensor. Ths analog waveform outputs generated by each traversng vehcle are referred to as the nductve loop sgnatures or nductve vehcle sgnatures. The RTREID-2MT was developed and tested wth three basc scenaros (7): 1) Scenaro 1: Redentfcaton usng vehcle sgnature data. 2) Scenaro 2: Redentfcaton usng vehcle sgnature and WIM vehcle class and axle spacngs data. 3) Scenaro 3: Redentfcaton usng vehcle sgnature and WIM vehcle class, axle spacngs, and weght data. The performance was demonstrated between two WIM statons 19 mles apart from SR-57 SB Orange Staton to I-5 SB Irvne Staton, as shown n FIGURE 1. It was found that the proposed approach was able to work wth dfferent types of data and generate promsng trackng performance wth both vehcle sgnature and WIM data appled. The best results were obtaned from Scenaro 3 wth about 61.5% Correct Match Rate (CMR) and 5.5% Error Rate (ER). The output from the RTREID-2MT can be utlzed to montor the performance of WIM statons, dentfy off-calbrated WIM statons, and perform temporary approxmate calbraton. Possble out of calbraton WIM statons can be dentfed by evaluatng the axle spacng and axle weght nformaton of the MVPs. However, the CMR should reach a desrable level so that satsfactory system relablty can be acheved. Therefore, the RTREID-2MT was modfed to ntegrate wth the Bayesan redentfcaton model (8) to further mprove the CMR, as dscussed n the followng secton.

5 Jeng, Chu, and Cetn 5 FIGURE 1 Study ste: SR-57 SB Orange and I-5 SB Irvne WIM statons (7). THE PROPOSED TRUCK TRACKING ALGORITHM FOR WIM STATION MONITORING AND CALIBRATION Based on the current results from the RTREID-2MT, the CMRs are about 61%. To further mprove the CMRs, the Bayesan redentfcaton algorthm (8) developed by Cetn et al. was ntegrated nto the RTREID-2MT to flter out the MVPs that were most lkely ncorrectly matched by the system, as shown n FIGURE 2.

6 Jeng, Chu, and Cetn 6 Vehcle Sgnature Data WIM Data Extract vehcle sgnature features (PSRs) WIM vehcle class Match vehcles usng sgnature features Tme wndow estmaton No Matched by RTREID-2MT? No Yes Matchng Probablty >.9? Bayesan redentfcaton model Yes Target vehcle not matched Target vehcle matched FIGURE 2 Framework of RTREID-2MT ntegrated wth Bayesan redentfcaton model. The dea s to further narrow down the MVPs usng the probablty generated by the Bayesan redentfcaton model. By dong so, only vehcles wth probablty over a preferred threshold (e.g.,.9~.95) wll be selected to help ncrease the CMR to be at least over 85%~9% for FHWA Class 9 vehcles (as defned n TABLE 1). Then, the MVPs wll be used for WIM staton montorng and calbraton applcatons. TABLE 1 FHWA Class Defnton by FHWA Vehcle Types (3) FHWA Class FHWA Vehcle Types FHWA Class 1 Motorcycles FHWA Class 2 Passenger Cars FHWA Class 3 Other Two-Axle, Four-Tre Sngle Unt Vehcles FHWA Class 4 Buses FHWA Class 5 Two-Axle, Sx-Tre, Sngle-Unt Trucks FHWA Class 6 Three-Axle Sngle-Unt Trucks FHWA Class 7 Four or More Axle Sngle-Unt Trucks FHWA Class 8 Four or Fewer Axle Sngle-Traler Trucks FHWA Class 9 Fve-Axle Sngle-Traler Trucks FHWA Class 1 Sx or More Axle Sngle-Traler Trucks FHWA Class 11 Fve or fewer Axle Mult-Traler Trucks FHWA Class 12 Sx-Axle Mult-Traler Trucks FHWA Class 13 Seven or More Axle Mult-Traler Trucks The objectve was to nvestgate the threshold of the probablty level. The selected results are shown n FIGURE 3, and FIGURE 4. It can be seen that better performances are obtaned when vehcles from FHWA Class 6 and above are consdered, and the CMRs range from 73% to 88% wth the

7 Jeng, Chu, and Cetn 7 probablty level >.9. The CMRs of FHWA Class 9 vehcles are about the same for Scenaros 2 and 3, but larger number of correct match vehcles are observed for probablty level = % Number of Common Vehcles % 8% 7% 6% 5% 4% 3% 2% 1% # of Correct match - FHWA Class 4-13 # of Correct match - FHWA Class 6-13 # of Correct match - FHWA Class 9 CMR - FHWA Class 4-13 CMR - FHWA Class 6-13 CMR - FHWA Class 9 Scenaro 1 Scenaro 2 Scenaro 3 Scenaro 1 Scenaro 2 Scenaro 3 %.9.95 Probablty Level FIGURE 3 System performance by probablty and scenaros. In addton, when the probablty level s <.9, the number of msmatch vehcles of FHWA Class 9 ncreases gradually, as shown n FIGURE 4. Therefore, the threshold of probablty level s set to.9, and the redentfcaton results from Scenaro 2 are selected. Scenaro 2: Selecton of Probablty Level Number of Vehcles % 9.% 8.% 7.% 6.% 5.% 4.% 3.% 2.% 1.%.% % Accuracy Correct Match Msmatch % Accurancy Probablty Level FIGURE 4 Probablty level by number of correct match vehcle, number of msmatch vehcles, and CMR for FHWA Class 9: Scenaro 2.

8 Jeng, Chu, and Cetn 8 WIM STATION MONITORING AND CALIBRATION The WIM data used for evaluaton nclude vehcle length, axle spacng 2-3, gross vehcle weght (GVW), axle weght 1, axle weght 2, axle weght 3, axle weght 4, and axle weght 5. FIGURE 5 compares the selected varables at SR-57 SB Orange Staton (upstream) and I-5 SB Irvne Staton (downstream) based on Matched Vehcle Pars (MVPs). Length Axle Spacng 2-3 GVW Axle Weght Axle Weght 2 Axle Weght 3 Axle Weght 4 Axle Weght FIGURE 5 Comparsons of WIM data at SR-57 SB Orange Staton and I-5 SB Irvne Staton. It can be observed that ether the WIM sensors at the downstream staton grossly overestmate the length and weght, or the WIM sensors at the upstream staton grossly underestmate the length and weght. FHWA Class 9 GVW Dstrbuton and Axle Spacngs A FHWA Class 9 GVW dstrbuton graph and drve tandem axle spacng hstograms of the MVPs were prepared to examne and dentfy the problematc staton, as shown n FIGURE 6 and FIGURE 7. The nformaton presented n FIGURE 6 and FIGURE 7 are obtaned from the same vehcles (FHWA Class 9 MVPs) but at dfferent locatons (.e., SR-57 SB Orange Staton and I-5 SB Irvne Staton). Therefore, reasonable and smlar dstrbutons between the two WIM statons should be found n FIGURE 6 and FIGURE 7 f the two statons are under normal condtons. It s known that the empty truck dstrbuton wll typcally peak at "3~35" kps and the loaded truck dstrbuton wll typcally peak at "7~8" kps (1). However, FIGURE 6 shows that the WIM sensors at SR-57 SB Orange Staton (plotted as red dot lne) reported lower weghts for both the empty and loaded truck dstrbuton peaks. The empty truck dstrbuton peaks at "27.5~3" kps and the loaded truck dstrbuton peaks at "65~67.5" kps, whch are both lower than ther correspondng typcal values.

9 Jeng, Chu, and Cetn < Number of Vehcles Irvne_gvw Orange_gvw Gross Vehcle Weght (kps) FIGURE 6 FHWA Class 9 GVW dstrbuton by staton and count for the MVPs. For the axle spacngs, the average drve tandem axle spacng (.e., axle spacng 2-3) for FHWA Class 9 vehcles s about 4.3 feet (5 and 1). However, as shown n FIGURE 7, the WIM sensors at I-5 SB Irvne Staton (plotted as blue bar) reported hgh drve tandem axle spacngs. The majorty of FHWA Class 9 vehcles at ths staton have the drve tandem axle spacng at 4.5 ft and 4.4 ft, whch s above the average value. Number of Vehcles Irvne_spc2-3 Orange_spc Drve Tandem Axle Spacng (ft) FIGURE 7 Drve tandem axle spacng hstograms by staton and count for the MVPs. Temporary Approxmate Calbraton of Drve Tandem Axle Spacngs The drve tandem axle spacngs were found too hgh at the I-5 SB Irvne staton. Snce the drve tandem axle spacng hstograms observed from the SR-57 SB Orange Staton seems normal, the average offsets for each lane can be calculated and appled to the I-5 SB Irvne Staton. The lane-based average offsets can be drectly derved from the MVPs. FIGURE 8 shows that the calbrated drve tandem axle spacng at the I-5 SB Irvne Staton has the peak at 4.3 ft after applyng the average offsets for each lane to the MVPs.

10 Jeng, Chu, and Cetn 1 FIGURE 8 Calbrated drve tandem axle spacng hstograms by staton and count for the MVPs. The lane based average offsets for the drve tandem axle spacng can be appled to the whole dataset collected at the I-5 SB Irvne Staton. FIGURE 9 shows the FHWA Class 9 drve tandem axle spacng hstograms usng WIM data collected on 3/21/13 (7). It can be seen that lane 4 (plotted as red bar) s the most problematc one, whch has the peak at 4.5 ft. FIGURE 1 shows the drve tandem axle spacng hstograms before and after the calbraton at the I-5 SB Irvne Staton for the 3/21/13 WIM data. The SR-57 SB Orange Staton s also ncluded for comparson. Number of Vehcles Drve Tandem Axle Spacng (ft) FIGURE 9 Drve tandem axle spacng hstograms at I-5 SB Irvne Staton by lane and count.

11 Jeng, Chu, and Cetn 11 Number of Vehcles Irvne_spc2-3 Irvne_spc2-3_new Orange_spc Drve Tandem Axle Spacng (ft) FIGURE 1 Calbrated drve tandem axle spacng hstograms by staton and count. Temporary Approxmate Calbraton of Steer Axle Weght and Gross Vehcle Weght For weght calbraton, t was known from FIGURE 6 that the SR-57 SB Orange staton reported weghts that were too low. Ths nformaton was further nvestgated on a per-lane bass usng the 3/21/13 WIM data. As shown n FIGURE 11 and FIGURE 12, t was found that lane 4 reported lower weghts whle lane 5 actually reported hgher weghts at the SR-57 SB Orange staton. Number of Vehcles Steer Axle Weght (kps) Lane 3 Lane 4 Lane 5 Lane 4 Lane 5 Irvne Orange FIGURE 11 Steer axle weght hstograms of the MVPs FIGURE 12 FHWA Class 9 GVW dstrbuton at SR-57 SB Orange Staton Gross Vehcle Weght (kps) Lane 4 Lane 5 Orange_All

12 Jeng, Chu, and Cetn 12 Agan, the lane-based average offsets can be drectly derved from the MVPs. Therefore, the average offsets (.e., ADJ gvw& axw1 ) of the GVW and the steer axle weght for each lane were calculated and appled usng the followng equatons: ADJ gvw& axw1 avgoffsetgvw avgoffsetgvw + avgoffsetaxw1 = (1) 2 = m n = 1 j= 1 avggvw Veh t j avggvw m n = 1 j= 1 = 1 j= 1 Veh j j c m n j avgaxw1 Veh t j avgaxw1 = 1 j= 1 c m n avgoffsetaxw1 = (3) where, avgoffsetg VW Veh j : average offset of GVW for lane at staton c that needs adjustment j avggvw t : average GVW for lane j at staton t that does not need adjustment avggvw : average GVW for lane at staton c that needs adjustment c avgoffsetaxw 1 : average offset of axle weght 1 for lane at staton c j avgaxw1 t : average of axle weght 1 for lane j at staton t avgaxw1 c : average of axle weght 1 for lane at staton c Veh : number of vehcles travelng from lane at staton c to lane j and staton t j m : number of lanes at staton c n : number of lanes at staton t FIGURE 13 shows the calbrated GVWs of the MVPs at the SR-57 SB Orange Staton generally match the trend of the GVWs at the I-5 SB Irvne Staton. (2)

13 Jeng, Chu, and Cetn Number of Vehcles < Irvne_gvw Orange_gvw Orange_gvw_new Gross Vehcle Weght (kps) FIGURE 13 FHWA Class 9 GVW dstrbuton for the MVPs at SR-57 SB Orange Staton: Before and after calbraton. The lane based average offsets for the GVW can be appled to all data collected at the SR-57 SB Orange Staton. FIGURE 14 shows the FHWA Class 9 GVW dstrbuton by statons before and after calbraton appled at the SR-57 SB Orange Staton usng the 3/21/13 WIM data. Number of Vehcles Irvne Orange Orange_new FIGURE 14 FHWA Class 9 GVW dstrbuton: Before and after calbraton. CONCLUSIONS AND FUTURE RESEARCHES In ths study, an nductve loop sgnature-wim based approach for WIM staton montorng and calbraton was proposed. A prevously developed truck trackng algorthm, RTREID-2MT was modfed to ntegrate a Bayesan redentfcaton model to flter out the matched vehcle pars (MVPs) that were most lkely ncorrectly matched by the system. Only the matched FHWA Class 9 vehcle pars wth probablty over.9, whch would ncrease the CMR to 89%, were selected for WIM staton montorng Gross Vehcle Weght (kps)

14 Jeng, Chu, and Cetn 14 and temporary approxmate calbraton applcatons. It was found from the MVPs that the upstream staton reported weghts that were too low, whle the downstream staton reported the axle spacngs that were too hgh. The average offsets of the drve tandem axle spacng, GVW, and steer axle weght between the statons were thus derved from the MVPs on a per lane bass and appled to approxmately calbrate the problematc statons. The proposed approach has potental to fundamentally change the way WIM statons are operated and montored n practces. Because exstng WIM statons requre sophstcated data collecton sensors, perodc and proper calbraton s crtcal to ther performance. The ntegraton of nductve loop sgnature technology offers a low-cost soluton to keep track of truck movement, provde truck movement data for WIM calbraton, montor performance of WIM statons, dentfy out-of-calbraton statons, and perform temporary calbraton. Therefore, the proposed approach wll not only reduce the cost of WIM calbraton, but also provde the ablty to montor system performance contnuously. Future researches wll focus on developng a comprehensves remote WIM performance evaluaton and calbraton montorng system. In addton, to mplement the proposed approach n the real world, a poneer research s undergong to verfy the performance of the proposed approach wth data collected from well calbrated WIM statons. Once the performance of the proposed approach s valdated, the federal and state agences can dscuss and approve the use of the proposed approach. ACKNOWLEDGEMENT The research was supported by Federal Hghway Admnstraton's SBIR program. The contents of ths paper reflect the vews of the authors, who are responsble for the facts and the accuracy of the data presented. Ths paper does not consttute a standard, specfcaton, or regulaton. The authors thank to the FHWA Contractng Offcer Techncal Representatve, Mr. Steven Jessberger, for hs gudance on our research. We also would lke to thank Prof. Andrew Nchols of West Vrgna Unversty for hs nputs on WIM data qualty control and Mr. Stan Norkane of Calforna Department of Transportaton (Caltrans) for hs supports on the use of Caltrans WIM facltes n the research. REFERENCES 1. FHWA. Traffc Montorng Gude. FHWA-PL-13-15, Accessed July 3, ASTM Standard E1318-2, 29. ASTM Standard Specfcaton for Hghway Wegh-n-Moton (WIM) Systems wth User Requrements and Test Methods. ASTM Internatonal, West Conshohocken, PA, 29, DOI: 1.152/E Accessed July 3, Federal Hghway Admnstraton, USDOT. FHWA Vehcle Types. Accessed July 3, Schultz, Grant G., and Luke Seegmller. Utah Commercal Motor Vehcle Wegh-n-Moton Data Analyss and Calbraton Methodology. Utah Department of Transportaton Research and Development Dvson/Plannng Dvson, Report No. UT-6.1, Nchols, A. and D. M. Bullock. Qualty Control Procedures for Wegh-n-Moton Data. Publcaton FHWA/IN/JTRP-24/12. Jont Transportaton Research Program, Indana Department of Transportaton and Purdue Unversty, West Lafayette, Indana, Cetn, M., and C. M. Monsere. Exploratory Methods for Truck Re-dentfcaton n a Statewde Network Based on Axle Weght and Axle Spacng Data to Enhance Freght Metrcs: Phase 2. Fnal report, OTREC-RR-12-4, Oregon Transportaton Research and Educaton Consortum (OTREC), Jeng, S.-T. and L. Chu. Trackng Heavy Vehcles Based on Wegh-In-Moton and Inductve Loop Sgnature Technologes. In IEEE Transactons on Intellgent Transportaton Systems, Vol. PP, Issue: 99, 214, pp. 1-1, DOI: 1.119/TITS

15 Jeng, Chu, and Cetn Cetn, M., C. M. Monsere, and A. P. Nchols. Bayesan Models for Re-dentfcaton of Trucks over Long Dstances on the Bass of Axle Measurement Data. In Journal of Intellgent Transportaton Systems: Technology, Plannng, and Operatons, Vol. 15, Issue 1, 211, pp Cetn, M., C. M. Monsere, A. P. Nchols, and I. Ustun. Investgatng the Key Factors Affectng the Accuracy of Re-dentfcaton of Trucks over Long Dstances Based on Axle Measurement Data. In Transportaton Research Record: Journal of the Transportaton Research Board, No. 2243, Transportaton Research Board of the Natonal Academes, Washngton, D.C., 211, pp FHWA. WIM Data Analyst's Manual. Report No. FHWA-IF-1-18, Accessed July 3, 214.