Method for identifying spatial-temporal load distribution of bridge group based on multi-source data fusion

A method for identifying the spatial-temporal load distribution of a bridge group based on multi-source data fusion, comprising the following steps:

• S1. performing GIS topological network matching on a vehicle based on vehicle positioning data to obtain vehicle trajectory data;
• S11. selecting vehicle load detection points in an area, and extracting and collecting vehicle load detection data and vehicle positioning data;
• wherein
• the vehicle load detection points comprise an overload control point, a source overload control detection point, a high-speed weight toll detection point and a bridge dynamic weighing detection point, vehicle load detection data and vehicle positioning data in the same selected time are selected in a selected area, the vehicle load detection data comprises a license plate number, detection time, a vehicle load and an ID, a longitude and a latitude of a section where the vehicle load is located, and the vehicle positioning data comprise a vehicle length, a vehicle model, a vehicle type, a license plate number, time, a driving speed and longitude and latitude coordinates;
• S12. preprocessing the vehicle positioning data, and collecting GIS topological network section information of a road network;
• S13. collecting bridge group information, and matching the vehicle positioning data with the GIS topological network section information;
• S14. according to the GIS topological network section information, obtaining vehicle positioning data matched with bridge traveling sections in the bridge group to form vehicle trajectory data;
• S2. segmenting the vehicle trajectory based on the vehicle load detection points, and obtaining a path segment where the bridge is located in combination with the section where the bridge is located;
• S21. dividing all vehicle trajectories into a plurality of path subsegments according to the ID of the section where the vehicle load detection points are located;
• S22. extracting path subsegments where bridge travelings of each bridge in the bridge group are located according to a section where the bridge traveling in bridge group information is located;
• S3. according to the vehicle positioning data and vehicle load data, identifying vehicle loads matched with start points and end points of traveling path segments of each bridge in the bridge group, and calculating an actual vehicle load passing through the bridge;
• S31. respectively identifying a start point vehicle load detection point and an end point vehicle load detection point corresponding to a vehicle entering segment and a vehicle exiting segment of each path subsegment of vehicle load segment sets matched with bridge travelings, and calculating detection time and vehicle load passing through the start point vehicle load detection point and the end point vehicle load detection point;
• S32. selecting a specific vehicle load detection point, and calculating predicted time of the specific vehicle load detection point, predicted time of the vehicle passing through the start point vehicle load detection point and predicted time of the vehicle passing through the end point vehicle load detection point based on the vehicle positioning data;
• S33. matching the vehicle positioning data with vehicle load data of a load segment, and obtaining the actual vehicle load, wherein the load segment is a segment from the start point vehicle load detection point to the end point vehicle load detection point;
• S4. according to the vehicle positioning data and vehicle load detection point detection data, identifying the vehicle loads matched with the start points and the end points of the bridge traveling path segments, and obtaining the vehicle loads passing through the bridge;
• S41. selecting vehicle positioning data of a section where the bridge traveling direction is located, and constructing a lane-level GIS topological network of the section where the bridge traveling direction is located;
• S42. determining a matching priority according to sampling frequency, constructing a vehicle positioning sequence set with different matching priorities for the vehicle positioning data of the section where the bridge traveling is located, and calculating possibility scores of vehicle positioning data points matching lanes;
• S43. fitting by adopting a Gaussian distribution model, identifying a possibility of vehicle lane changing, calculating a probability of vehicle lane changing, and constructing a multi-mode vehicle lane changing probability model;
• S44. constructing an optimal matching model between the vehicle positioning data based on the matching priority and the lanes of the section where the bridge is located according to constraint condition 1 and constraint condition 2;
• wherein the constraint condition 1 is lane change constraint, the vehicle lane changing is constrained in a matching process;
• the constraint condition 2 is vehicle position constraint, a vehicle position conflict at the same moment is constrained;
• S45. performing lane matching on vehicle positioning according to different priorities, solving vehicle positioning point matching results of different sampling frequencies by adopting the optimal matching model, and integrating to obtain a point set for vehicle trajectory correction;
• S46. performing lane matching on the vehicle positioning data of the sections where bridge travelings of each bridge in the bridge group are located, and obtaining a matching result of the vehicle positioning data of the sections where bridge travelings are located;
• S5. obtaining the spatial-temporal distribution of the vehicles on the bridge deck based on a bridge group lane-level road network simulation model;
• S51. constructing the bridge group lane-level road network simulation model based on bridge design parameters and a microscopic traffic simulation model;
• S52. obtaining a matching result of vehicle positioning data on the section where the bridge group is located and lanes of GIS road network topology by adopting the optimal matching model for positioning points based on the matching priority, calculating time and speed of a single vehicle entering the bridge, and correcting position conflicts of the vehicles;
• S53. extracting a driving path of the vehicle on the bridge deck according to the matching result, and setting simulation model parameters in a bridge lane-level road network simulation model;
• S54. running the lane-level road network simulation model to obtain the spatial-temporal distribution of vehicles, simulating each bridge in the bridge group, and integrating the spatial-temporal distribution of the vehicles on all the bridges in the bridge group to obtain the spatial-temporal distribution of the vehicles on the bridge deck;
• and
• S6. matching the vehicle load of the vehicle passing through the bridge with the spatial-temporal distribution of the vehicles on the bridge deck by the vehicle positioning data to obtain the spatial-temporal distribution of the loads of each bridge in the bridge group, and integrating to obtain the spatial-temporal distribution of the loads of the bridge group.