Ravneet Bajwa
ABSTRACT
Truck weight data is used in many areas of transportation such as weight enforcement and pavement condition assessment. This paper describes a wireless sensor network (WSN) that estimates the weight of moving vehicles from pavement vibrations caused by vehicular motion. The WSN consists of: acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicle's arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The paper also describes a novel algorithm that estimates a vehicle's weight from pavement vibration and vehicle detection data, and calculates pavement deflection in the process. A prototype of the system has been deployed near a conventional Weigh-In-Motion (WIM) system on I-80 W in Pinole, CA. Weights of 52 trucks at different speeds and loads were estimated by the system under different pavement temperatures and varying environmental conditions, adding to the challenges the system must overcome. The error in load estimates was less than 10% for gross weight and 15% for individual axle weights. Different states have different requirements for WIM but the system described here outperformed the nearby conventional WIM, and meets commonly used standards in United States. The system also opens up exciting new opportunities for WSNs in pavement engineering and intelligent transportation.
- M. Arraigada, M. Partl, S. Angelone, and F. Martinez. Evaluation of accelerometers to determine pavement deflections under traffic loads. Materials and Structures, 42:779--790, 2009.Google Scholar
Cross Ref
- R. Bajwa, R. Rajagopal, P. Varaiya, and R. Kavaler. In-pavement wireless sensor network for vehicle classification. In Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on, pages 85 --96, april 2011.Google Scholar
- P. Burnos, J. Gajda, P. Piwowar, R. Sroka, M. Stencel, and T..Zegle'n. Accurate weighing of moving vehicles. Metrology and Measurement Systems, 14(4):507--516, 2007.Google Scholar
- R. Bushman and A. Pratt. Weigh in motion technology--economics and performance. In Presentation on the North American Travel Monitoring Exhibition and Conference (NATMEC). Charlotte, North Carolina, 1998.Google Scholar
- D. Cebon. Handbook of Vehicle-Road Interaction. Swets and Zeitlinger Publishers, 1999.Google Scholar
- M. Ceriotti, L. Mottola, G. Picco, A. Murphy, S. Guna, M. Corra, M. Pozzi, D. Zonta, and P. Zanon. Monitoring heritage buildings with wireless sensor networks: The Torre Aquila deployment. In Information Processing in Sensor Networks, 2009. IPSN 2009. International Conference on, pages 277--288. IEEE, 2009. Google ScholarDigital Library
- S. Y. Cheung, S. Coleri, B. Dundar, S. Ganesh, C.-W. Tan, and P. Varaiya. Traffic measurement and vehicle classification with single magnetic sensor. Transportation Research Record: Journal of the Transportation Research Board, 1917:173--181, 2006.Google ScholarCross Ref
- Colibrys, Inc, [email protected]. MEMS Capacitive Accelerometers Datasheet MS9000.D.Google Scholar
- A. Designation. E 1318--94, standard specifications for highway weigh-in-motion (wim) systems with user requirements and test methods. American Society for Testing and Materials, 1994.Google Scholar
- Federal Highway Administration, U.S. Department of Transportation. Traffic Monitoring Guide, May 2001.Google Scholar
- M. C. Fehler. Seismic Wave Propagation and Scattering in the Heterogenous Earth. Springer, 2009.Google Scholar
- M. Gindy, H. H. Nassif, and J. Velde. Bridge displacement estimates from measured acceleration records. Transportation Research Record: Journal of the Transportation Research Board, 2028(-1):136--145, 2007.Google Scholar
- A. Haoui, R. Kavaler, and P. Varaiya. Wireless magnetic sensors for traffic surveillance. Transportation Research C, 16(3):294--306, 2008.Google ScholarCross Ref
- S. Kim, S. Pakzad, D. Culler, J. Demmel, G. Fenves, S. Glaser, and M. Turon. Health monitoring of civil infrastructures using wireless sensor networks. In IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks, pages 254--263, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- A. N. Knaian. A wireless sensor network for smart roadbeds and intelligent transportation systems. Master's thesis, Massachusetts Institute of Technology, 2000.Google Scholar
- M. Li and Y. Liu. Underground structure monitoring with wireless sensor networks. In Proceedings of the 6th international conference on Information processing in sensor networks, pages 69--78. ACM, 2007. Google ScholarDigital Library
- C. Liu, L. Guo, J. Li, and X. Chen. Weigh-in-motion (wim) sensor based on em resonant measurements. In Antennas and Propagation Society International Symposium, 2007 IEEE, pages 561--564. IEEE, 2007.Google ScholarCross Ref
- R. B. Malla, A. Sen, and N. W. Garrick. A special fiber optic sensor for measuring wheel loads of vehicles on highways. Sensors, 8(4):2551--2568, 2008.Google ScholarCross Ref
- C. Merzbacher, A. Kersey, and E. Friebele. Fiber optic sensors in concrete structures: a review. Smart materials and structures, 5:196, 1996.Google Scholar
- D. Parl, N. Buch, and K. Chatti. Effective layer temperature prediction model and temperature correction via falling weight deflectometer deflections. Transportation Research Record: Journal of the Transportation Research Board, 1764:97--111, 2001.Google ScholarCross Ref
- R. Rajagopal. Large Monitoring Systems: Data Analysis, Deployment and Design. PhD thesis, University of California, Berkeley, 2009. Google ScholarDigital Library
- H. Sawant, J. Tan, and Q. Yang. A sensor networked approach for intelligent transportation systems. In Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on, volume 2, pages 1796 -- 1801 vol.2, 2004.Google ScholarCross Ref
- Sensys Networks, Inc, 2650 9th Street, Berkeley CA 94710. The Sensys Wireless™ Vehicle Detection System, 1.1 edition.Google Scholar
Index Terms
- In-pavement wireless weigh-in-motion
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