Traffic monitoring using optical sensors
A sensor network comprises at least one lateral optical fiber and at least one longitudinal optical fiber. The lateral fiber comprises optical sensors coupled to a pavement in a transverse orientation relative to a direction of vehicle travel along the pavement. The longitudinal fiber comprises optical sensors coupled to the pavement in a longitudinal orientation relative to the direction of vehicle travel. The optical sensors are configured to produce wavelength shift signals comprising one or more lateral strain signals associated with the lateral fiber and one or more tangential strain signals associated with the longitudinal fiber. A processor is operatively coupled to the sensor network and configured to determine a weight of vehicles moving along the pavement based on the lateral and tangential strain signals. A transmitter is operatively coupled to the processor and configured to transmit the weight of vehicles to a predetermined location.
1. A system, comprising:
a sensor network comprising:
at least one lateral fiber comprising optical sensors coupled to a pavement in a transverse orientation relative to a direction of vehicle travel along the pavement;
at least one longitudinal fiber comprising optical sensors coupled to the pavement in a longitudinal orientation relative to the direction of vehicle travel;
the optical sensors configured to produce wavelength shift signals comprising one or more lateral strain signals associated with the lateral fiber and one or more tangential strain signals associated with the at least one longitudinal fiber;
a processor operatively coupled to the sensor network and configured to determine a weight of vehicles moving along the pavement based on the lateral and tangential strain signals; and
a transmitter operatively coupled to the processor and configured to transmit the weight of vehicles to a predetermined location.
2. The system of claim 1 , wherein the at least one longitudinal fiber is positioned along a center of a lane of the pavement.
3. The system of claim 1 , wherein the at least one longitudinal fiber is positioned along a lane of the pavement at an angle relative to the direction of vehicle travel.
4. The system of claim 1 , wherein the one or more longitudinal fibers comprises a plurality of longitudinal fibers and wherein the plurality of longitudinal fibers are positioned within a lane of the pavement.
5. The system of claim 1 , wherein at least one optical sensor of the lateral fiber is positioned on the pavement a predetermined distance from at least one optical sensor of the at least one longitudinal fiber.
6. The system of claim 1 , wherein the processor is configured to:
produce spatial-temporal strain measurements based on the lateral and tangential strain signals; and
determine one or both of traffic monitoring attributes and vehicle attributes based on the spatial-temporal strain measurements.
7. The system of claim 6 , wherein:
the traffic monitoring attributes comprise one or more of traffic volume, traffic speed, and road occupancy; and
the vehicle attributes comprise one or more of number of axles, axle groups, vehicle type, and axle weight.
8. The system of claim 1 , wherein the processor is configured to determine an axle group weight for vehicles moving along the pavement using the tangential strain signal.
9. The system of claim 1 , wherein the processor is configured to determine an axle group weight for vehicles moving along the pavement using the lateral strain signal and the tangential strain signal.
10. The system of claim 1 , wherein the processor is configured to:
determine a location of each axle of each vehicle passing by the lateral fiber using the lateral strain signals obtained from optical sensors of the lateral fiber;
determine, for each axle, a peak value of the lateral strain signals and one or more peak values of the longitudinal strain signals; and
estimate a weight of each axle using the peak values of the lateral and longitudinal strain signals, a lateral equivalent stiffness estimation associated with the lateral fiber, and a longitudinal equivalent stiffness estimation associated with the at least one longitudinal fiber.
11. The system of claim 1 , wherein the processor is configured to:
sum lateral strain measurements acquired using the optical sensors of the lateral fiber;
differentiate the lateral strain measurements with respect to time to identify timestamps of each axle of each vehicle passing by the lateral fiber;
pick peak values in the lateral strain measurements at corresponding timestamps to identify wheel locations;
pick peak values of mid-lateral strain signals acquired from a mid-lateral optical sensor of the lateral fiber between wheel locations;
pick peak values in the longitudinal strain signals acquired from optical sensors of the at least one longitudinal fiber; and
estimate a weight of each axle group using a subset of the tangential strain signals and mid-lateral strain signals, a lateral equivalent stiffness estimation associated with the lateral fiber, and a longitudinal equivalent stiffness estimation associated with the at least one longitudinal fiber.
12. A method, comprising:
receiving one or more lateral strain signals in the form of first wavelength shift signals from at least one lateral fiber comprising optical sensors coupled to a pavement in a transverse orientation relative to a direction of vehicle travel along the pavement;
receiving one or more longitudinal strain signals in the form of second wavelength shift signals from at least one longitudinal fiber comprising optical sensors coupled to the pavement in a longitudinal orientation relative to a direction of vehicle travel;
determining a weight of vehicles moving along the pavement based on the lateral and longitudinal strain signals; and
transmitting the weight of vehicles to a predetermined location.
13. The method of claim 12 , wherein the at least one longitudinal fiber is positioned along a center of a lane of the pavement.
14. The method of claim 12 , wherein the one or more longitudinal fibers comprises a plurality of longitudinal fibers and wherein the plurality of longitudinal fibers are positioned within a lane of the pavement.
15. The method of claim 12 , wherein the at least one longitudinal fiber is positioned along a lane of the pavement at an angle relative to the direction of vehicle travel.
16. The method of claim 12 , wherein at least one optical sensor of the lateral fiber is positioned on the pavement a predetermined distance from at least one optical sensor of the at least one longitudinal fiber.
17. The method of claim 12 , comprising:
producing spatial-temporal strain measurements based on the lateral and tangential strain signals; and
determining one or both of traffic monitoring attributes and vehicle attributes based on the spatial-temporal strain measurements.
18. The method of claim 17 , wherein:
the traffic monitoring attributes comprise one or more of traffic volume, traffic speed, and road occupancy; and
the vehicle attributes comprise one or more of number of axles, axle groups, vehicle type, and axle weight.
19. The method of claim 12 , comprising determining an axle group weight for vehicles moving along the pavement using the tangential strain signal.
20. The method of claim 12 , comprising determining an axle group weight for vehicles moving along the pavement using the lateral strain signal and the tangential strain signal.
21. The method of claim 12 , comprising:
determining a location of each axle of each vehicle passing by the lateral fiber using the lateral strain signals obtained from optical sensors of the lateral fiber;
determining, for each axle, a peak value of the lateral strain signals and one or more peak values of the longitudinal strain signals; and
estimating a weight of each axle using the peak values of the lateral and longitudinal strain signals, a lateral equivalent stiffness estimation associated with the lateral fiber, and a longitudinal equivalent stiffness estimation associated with the at least one longitudinal fiber.
22. The method of claim 12 , comprising:
summing lateral strain measurements acquired using the optical sensors of the lateral fiber;
differentiating the lateral strain measurements with respect to time to identify timestamps of each axle of a each vehicle passing by the lateral fiber; and
picking peak values in the lateral strain measurements at corresponding timestamps to identify wheel locations;
picking peak values of mid-lateral strain signals acquired from a mid-lateral optical sensor of the lateral fiber between wheel locations;
picking peak values of the longitudinal strain signals acquired from optical sensors of the at least one longitudinal fiber; and
estimating a weight of each axle group using a subset of the tangential strain signals and mid-lateral strain signals, a lateral equivalent stiffness estimation associated with the lateral fiber, and a longitudinal equivalent stiffness estimation associated with the at least one longitudinal fiber.