Bridge Load Identification via GPS and Traffic Simulation
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Solution Overview
Problem
Existing methods for identifying bridge group load distribution require additional equipment, are costly, time-consuming, and struggle to accurately determine spatial-temporal vehicle load distribution, especially at night.
Innovation Solution
A method utilizing multi-source data fusion, including GIS topological network matching, vehicle load detection points, and microscopic traffic simulation, to identify spatial-temporal load distribution without additional equipment, by integrating vehicle positioning data and load detection points, and constructing a lane-level road network simulation model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic weighing systems and radar equipment are installed on the bridge deck, then vehicle load detection accuracy is improved, but system cost and complexity increase
Solution Approach 1:
The patent uses GPS positioning data as an intermediary to connect vehicle location information with bridge load detection. Instead of directly installing complex detection equipment on the bridge deck, the system uses existing GPS data from vehicles combined with bridge geometric information to calculate load distribution, thereby avoiding the need for complex radar and weighing systems while maintaining detection accuracy.
Solution Approach 2:
The patent replaces mechanical detection systems (radar, dynamic weighing systems) with information processing methods. By using GPS positioning data, vehicle trajectory calculation, and influence line theory, the system substitutes physical measurement equipment with computational methods to determine vehicle load distribution, thereby reducing system complexity and cost.
2Measurement precision
If multiple cameras are mounted on the bridge deck for image-based vehicle detection, then spatial distribution of vehicles is improved, but cost and operational complexity increase
Solution Approach 1:
The patent uses GPS positioning data as an intermediary to obtain vehicle spatial distribution information. Instead of mounting multiple cameras on the bridge deck, the system uses existing GPS data from vehicles to determine their positions and trajectories, combining this with bridge geometric information to calculate load distribution, thereby avoiding the complexity of camera systems.
Solution Approach 2:
The patent creates a virtual copy of the physical detection system by using GPS data to represent vehicle positions and trajectories. This digital representation allows the system to analyze vehicle distribution and calculate loads without the need for physical camera equipment, reducing complexity while maintaining measurement capability.
3Measurement precision
If structural health monitoring systems are installed to obtain vehicle load data, then load identification accuracy is improved, but installation cost and time consumption increase
Solution Approach 1:
The patent enables the system to use existing GPS data from vehicles' own navigation systems to obtain positioning information. Instead of requiring external monitoring infrastructure, the vehicles themselves provide the necessary data through their built-in GPS systems, eliminating the need for time-consuming installation of structural health monitoring systems.
Solution Approach 2:
The patent uses pre-existing GPS positioning data that is already being collected by vehicles for navigation purposes. By leveraging this preliminary data collection that occurs independently of the bridge monitoring system, the patent avoids the need for simultaneous installation and configuration of complex monitoring infrastructure during bridge operations.
4Measurement precision
If video identification algorithms are used to analyze vehicle images, then vehicle recognition accuracy is improved, but computational power requirements and operation difficulty increase
Solution Approach 1:
The patent replaces complex video identification algorithms with simpler GPS-based positioning and trajectory calculation methods. Instead of using computational vision to recognize vehicles from images, the system uses mathematical calculations based on GPS coordinates, vehicle speeds, and bridge geometry to determine vehicle positions and loads, thereby reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent creates a simplified digital model of vehicle positioning and trajectory that replaces the need for complex video processing. By using GPS data to construct virtual vehicle paths and positions, the system avoids the computational burden of analyzing video images while maintaining the ability to accurately track and identify vehicle locations and movements.
Data Source
AI summary
The present application discloses a method for identifying the spatial-temporal load distribution of a bridge group based on multi-source data fusion, and belongs to the technical field of bridge group load distribution identification. Based on the vehicle correlation and time correlation between vehicle positioning data and vehicle load detection data and combining the characteristics of the vehicle load being unchanged within a certain period of time, the present application takes a vehicle load detection point as a node, divides a vehicle trajectory into a plurality of load segments, obtains the load segment where the bridge is located, and simultaneously performs correlation matching on the vehicle positioning data and the vehicle load detection point data in the load segment where the bridge is located according to the vehicle ID and the vehicle load point detection time to obtain the actual load data when the vehicle passes through the bridge.