In-pavement wireless Weigh-In-Motion

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.

[1]  David Cebon,et al.  Handbook of vehicle-road interaction , 1999 .

[2]  Hani Nassif,et al.  Bridge Displacement Estimates from Measured Acceleration Records , 2007 .

[3]  Sukun Kim,et al.  Health Monitoring of Civil Infrastructures Using Wireless Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[4]  Alan D. Kersey,et al.  Fiber optic sensors in concrete structures: a review , 1996 .

[5]  X. Chen,et al.  Weigh-in-motion (WIM) sensor based on EM resonant measurements , 2007, 2007 IEEE Antennas and Propagation Society International Symposium.

[6]  Pravin Varaiya,et al.  Wireless magnetic sensors for traffic surveillance , 2008 .

[7]  Pravin Varaiya,et al.  Large Monitoring Systems: Data Analysis, Design and Deployment , 2009 .

[8]  Michael Fehler,et al.  Seismic Wave Propagation and Scattering in the Heterogeneous Earth , 2012 .

[9]  Norman W. Garrick,et al.  A Special Fiber Optic Sensor for Measuring Wheel Loads of Vehicles on Highways , 2008, Sensors.

[10]  Piotr Piwowar,et al.  Accurate weighing of moving vehicles , 2007 .

[11]  Karim Chatti,et al.  Effective Layer Temperature Prediction Model and Temperature Correction via Falling Weight Deflectometer Deflections , 2001 .

[12]  Ara N. Knaian,et al.  A Wireless Sensor Network for Smart Roadbeds and Intelligent Transportation Systems , 2000 .

[13]  Pravin Varaiya,et al.  In-pavement wireless sensor network for vehicle classification , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[14]  Andrew J. Pratt,et al.  Weigh In Motion Technology - Economics and Performance , 1998 .

[15]  Amy L. Murphy,et al.  Monitoring heritage buildings with wireless sensor networks: The Torre Aquila deployment , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[16]  M. Arraigada,et al.  Evaluation of accelerometers to determine pavement deflections under traffic loads , 2009 .

[17]  Jindong Tan,et al.  A sensor networked approach for intelligent transportation systems , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[18]  Yunhao Liu,et al.  Underground Structure Monitoring with Wireless Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[19]  Sinem Coleri,et al.  Traffic Measurement and Vehicle Classification with a Single Magnetic Sensor , 2004 .