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A Digital Road Profile (DRP) is a digital representation of road surface topography or terrain in the longitudinal direction. The need for accurate DRP is vital in two stages; before the road construction starts and after the road construction finished where the verification of its geometrical characteristics is essential for engineering safety purposes. Classical surveying techniques are traditionally used for the DRP generation with limitation of high-cost and time-waste. Kinematic DGPS or Real Time Kinematic DGPS positioning can provide accurate enough results for such application. This paper presents an assessment study of using kinematic GPS technique for DRP generation comparing with classical survey in south Egypt. The results shows that, vehicle-GPS system used in combination with post processing kinematic DGPS gave satisfactory accuracy for nearly all points for a distance of nearly 2 km. with max. and min. difference not more than 7.7 cm, a mean value of 0.10 cm and a Root Mean Square RMS value of 4.11 cm. Keywords: 1. Digital Road Profile 1. INTRODUCTION Profile is the representation of something in outline. When applied to roads, this means that a profile is a longitudinal-section view of the earth along the centerline, and it is always viewed perpendicular to the centerline. A Digital Road Profile (DRP) is a digital representation of road surface topography or terrain in the longitudinal direction. Accurate DRP is needed before the road construction starts and after the road construction finished where the verification of its geometrical characteristics is essential for engineering safety purposes. This subject has to do with the compliance of the construction to the design data and is very important for the safety of all the vehicles using the road. Classical survey techniques are traditionally used for RDP generation with limitation factors such as high cost and time-waste. Unfortunately, due to the high cost of data collection with the above mentioned method, the control of the new road can never be complete. The problem becomes more urgent when many kilometers of newly constructed roads have to be quickly checked for geometrical accuracy and, then, be left to common use. GPS technology could be utilized effectively in this domain. Using vehicle-based GPS receivers for mapping a road network is a common task in many applications, such as mobile mapping (ElSheimy, 2001), map matching (Taylor & Blewitt, 1999) or real-time mapping (Lakakis, 2000). GPS positional and time data have been used in several occasions for the estimation of traffic conditions along an urban road network (Savvaidis P. et al., 2000). The method usually employed 2. Kinematic 3.GPS 96 is running along the roads in a vehicle equipped with a GPS receiver. Kinematic DGPS or Real Time Kinematic DGPS positioning can provide accurate enough results in most applications (Zhao, 1997). This paper presents accuracy assessment study for using kinmetaic GPS for RDP generation for nearly 2 km road length where a GPS system were utilized over a vehicle. The idea for using this scheme was to get the first ideas about its functionality in real conditions and have enough data to study the possible absorption of inclinations from the damping system of the vehicle. In order to test the functionality of the system, classical geodetic methods were used for the accurate measurement of a completed approx. 2 Km road at the Aswan city, Egypt. 2. MEASURING SYSTEM AND FIELD OBSERVATIONS Figure 1 shows the vehicle preparation used for collecting GPS observations along the centerline of 1800 m road length in a rural area of the city of Aswan, Egypt. The RDP was generated using classical survey (TOPCON GTS-712 total station) (Topcon manual, 2000). During this the road was divided into segments with 50 m separation distance, though the number of observed points was 37 points. The RDP of the road was also generated using post processing DGPS kinematic technique where the baseline length between base station and observed trajectory was between (200-750) m. The system used during GPS collecting observations process was ProMark3 GPS system ProMark3 system is L1 C/A code and carrier with Kinematic Survey Performance Horizontal: 0.012 m + 2.5 ppm (0.039 ft + 2.5 ppm) Vertical: 0.015 m + 2.5 ppm (0.049 ft + 2.5 ppm) The average vehicle speed was 18 km/hr with 5 seconds recording interval of GPS observations. Fig. 1. A picture of the measuring system on the vehicle equipped with GPS system 97 3. KINEMATIC GPS Collecting field observations were achieved using PrMark3 single-frequency GPS system whose specifications are shown in table 1. The observations collected from reference and rover points were post-processed using GNSS Solutions software (GNSS Solutions manual, 2007). Table 1. ProMark3.0 GPS system specifications (ProMark3.0 manual, 2005) Parameter GPS survey mode supported Survey accuracy (RMS) - Static Survey accuracy (RMS) Stop-and-go Specification Static, Stop-and-go, Kinematic Horizontal: 0.005m + 1 ppm Vertical: 0.010m + 2 ppm Horizontal: 0.012m + 2.5 ppm Vertical: 0.015m + 2.5 ppm SBAS (WAAS/EGNOS) RMS: Horizontal < 1 meter (3 feet) DGPS (Beacon or RTCM) RMS: Horizontal < 1 meter (3 feet) Up to 20 kilometers Up to 10 kilometers 4 to 40 minutes typical, depending upon vector length 15 seconds typical 15 seconds on known points 5 minutes on initializer bar GPS satellite channels SBAS satellite channels GPS satellite elevation mask Recording interval 12 2 10 degrees 1 30 seconds Real-Time Performance Survey point spacing Static (vector length) Survey point spacing Stop and- go (vector length) Observation time - Static Observation time Stop-and go Initialization time Stop-and go 4. STUDY OUTPUTS Table 2 presents the road height from classical survey and from kinematic GPS. Note that five points were eliminated from the original 37 points for the lake of kinematic GPS derived height. Figure 2 shows Elevation differences between classical surveying measurements and GPS data for the road centerline. 98 Table 2. The Road Profile height in meters from classical survey and kinematic GPS Station No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Accumulated Distance 0 50 150 200 250 400 450 500 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1800 Total Station Height (m) 169.838 170.349 171.051 171.526 172.247 174.375 175.233 176.040 177.354 177.924 178.544 179.032 179.367 179.821 180.662 181.435 182.199 182.563 182.787 183.271 183.609 183.897 184.048 184.334 184.398 184.467 184.507 184.600 185.257 186.157 186.396 186.043 Kinematic GPS Height (m) 169.897 170.362 171.020 171.449 172.233 174.362 175.238 176.063 177.342 177.847 178.502 179.013 179.328 179.795 180.611 181.370 182.149 182.560 182.801 183.281 183.646 183.907 184.012 184.362 184.417 184.471 184.521 184.653 185.279 186.230 186.460 186.118 Height Difference (cm) = Total H-GPS H -5.8 -1.3 3.0 7.7 1.3 1.3 -0.5 -2.2 1.2 7.6 4.1 1.8 3.8 2.6 5.0 6.4 4.9 0.3 -1.3 -1.0 -3.7 -1.0 3.5 -2.7 -1.9 -0.3 -1.3 -5.3 -2.1 -7.3 -6.3 -7.4 Road Profile using Total station instrument height (m) Distance (m) Figure 2. Road Profile using Total Station instrument Road Profile using Kinematic GPS technique height (m) Distance (m) Figure 3. Road Profile using Kinematic GPS technique Elevation differences between classical surveying measurements and GPS data for the road centerline height difference (cm) Distance (m ) -5 -10 Figure 4. Road Profile-Elevation differences between classical surveying measurements (Total Station) and Kinematic GPS technique Table 3 presents statistical analysis for the Elevation differences between classical surveying measurements and GPS data for the road centerline. Table 3. Statistical analysis for the Elevation differences between classical surveying measurements and GPS data for the road centerline. Parameter Maximum Height Diff. (cm) Minimum Height Diff. (cm) Mean Height Diff. (cm) Standard Deviation Height Diff. (cm) 4.11 RMS Height Diff. (cm) Value (cm) -7.4 5. CONCLUSIONS During this research RDP could be generated using vehicle-GPS system with post processing DGPS. The Accuracy of this RDP was tested against classical survey measurements. The following conclusions can be drawn: 101 1. The computed road profiles from real surveying data and the GPS were in good agreement. 2. The vehicle-GPS system used in combination with post processing kinematic DGPS gave satisfactory accuracy for nearly all points measured with the GPS system. 3. The max. height difference was not more than 7.7 cm with a min. value of 7.4 cm. The standard deviation value of height difference is 4.11 cm. The RMS value for height difference was 4.05 cm with a mean value of less than 0.1 cm. 4. The results obtained from the pilot project show that the vehicle-GPS system can be used for quick road surveying. 5. Future research could be done to improve the system by adding more receivers to test the slope along the road and the super-elevations across the road. 6. REFERENCES Ellun, C.M., & El-Sheimy, N. (2001): A mobile mapping system for the survey community. Proceedings of the 3rd International Symposium on Mobile Mapping Technology. Cairo, Egypt. GNSS Solution (2007): "GNSS Solution Software", Version 3.00.07, Magellan Navigation Company Copy right, 2007. Lakakis, K. (2000): Land vehicle navigation in an urban area by using GPS and GIS technologies. PhD Thesis, Aristotle University of Thessaloniki, Department of Civil Engineering. Thessaloniki, Greece. Promark 3.0 manual (2005): "Promark 3.0 Receivers Reference Manual", Copyright Thales Navigation company, 2005. Savvaidis, P., Ifadis, I., & Lakakis, K. (2000): Use of a fleet management system for monitoring traffic conditions after a major earthquake in an urban area. Presented at the 22nd Urban and Regional Data Management Symposium. Delft, The Netherlands. Taylor, G., & Blewitt, G. (1999): Virtual differential GPS and road reduction filtering by map matching. Proceedings of ION GPS-99 (pp. 1675-1684). Nashville: The Institute of Navigation. TOPCON Manual (2000): "TOPCON GTS-710 Series Instruction Manual", Zhao Y. (1997): Vehicle Location and Navigation Systems, Artech House, p. 345. Received: 2009-11-02, Reviewed: 2010-01-25, by M. Figurski, Accepted: 2010-01-25.
Artificial Satellites – de Gruyter
Published: Jan 1, 2009
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