BIM (Building Information Modeling)-based highway construction period traffic guide and change optimization system

Abstract

The invention discloses a BIM-based highway construction period traffic guide and change optimization system, and relates to the technical field of road traffic management. According to the BIM-based highway construction period traffic guide and change optimization system, traffic management in the construction period is dynamically adjusted in real time through space-time modeling, data fusion and optimization decision. The system generates a road network space-time model, matches vehicle trajectory data with lane space data, counts the number and speed of vehicles, and optimizes traffic flow. According to the optimization result, the system outputs the tailgating length, the number of lanes and the speed limit value and generates a corresponding control instruction and a speed limit issuing instruction, traffic management of the highway construction period is accurately optimized through space-time modeling and real-time data fusion based on BIM, the system can obtain vehicle track data in real time, the lanes and the speed limit strategy can be dynamically adjusted, and the speed limit efficiency is improved. Traffic delay in the construction period is minimized, and traffic safety and smoothness of a construction section are effectively guaranteed by generating an accurate lane control and speed limiting instruction.

Description

Technical Field

[0001] This invention relates to the field of road traffic management technology, specifically a BIM-based traffic diversion and optimization system for highway construction. Background Technology

[0002] During highway construction, traffic flow and speed are often affected by lane adjustments and changes in traffic facility layout. Temporary lane and traffic facility adjustments during the construction phase may lead to traffic flow instability and increase the risk of traffic congestion and delays.

[0003] Road traffic conditions change constantly during the construction period. How to respond to these changes in real time and effectively, and ensure the coordination between construction and traffic flow, is a key issue in construction management.

[0004] The limitations of existing technologies include at least the following problems: traditional traffic diversion schemes during construction are usually based on static design data and fail to fully consider the dynamic adjustment of temporary lanes and traffic facilities during construction, resulting in insufficient real-time management of traffic flow and vehicle speed.

[0005] Especially during the construction phase, changes in lane positions and traffic facility layouts are difficult to match and analyze with vehicle trajectory data in real time, leading to inaccurate traffic flow predictions and difficulty in adjusting traffic control strategies in a timely manner. As a result, traffic congestion and excessive delays may occur on construction sections, affecting road traffic efficiency, increasing traffic safety risks during the construction period, and delaying progress. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a BIM-based traffic diversion and optimization system for highway construction, which solves the problem that existing technologies struggle to cope with changes in lanes and traffic facilities during construction, leading to traffic congestion and delays.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a BIM-based traffic diversion and optimization system for highway construction, comprising: a spatiotemporal modeling unit, used to acquire the original design BIM model of the highway and construction organization plan data containing the coordinates of temporary lane boundaries and the locations of traffic facilities at each stage; based on the construction organization plan data, adding and associating the temporary lane boundaries and traffic facility models corresponding to each construction stage into the original design BIM model to generate a road network spatiotemporal model, the road network spatiotemporal model containing lane space definition data for defining the spatial location and geometric range of all lanes; and a data fusion unit, used to acquire real-time vehicle trajectory data and fuse the real-time vehicle trajectory data with... The system matches lane space definition data from the road network spatiotemporal model to obtain a set of vehicle trajectory points for each lane. Based on this set, it statistically generates the number of vehicles and average speed for each lane. An optimization decision unit, based on the number of vehicles and average speed for each lane, solves the optimization model with the objective of minimizing the total two-way delay time of the construction section, constrained by the lane space definition data and minimum safe sight distance from the road network spatiotemporal model. The model outputs the borrowing length, the number of borrowed lanes, and the recommended speed limit. A control execution unit generates lane control commands and speed limit issuance commands based on the borrowing length, the number of borrowed lanes, and the recommended speed limit, and sends them to the corresponding traffic control terminals to drive their execution.

[0008] Further, the specific steps for generating the spatiotemporal model of the road network are as follows: Based on the construction organization plan data, determine the locations of temporary lane boundaries and temporary traffic facilities that need to be set at each construction stage; in the original design BIM model, create corresponding temporary lane boundary models at the locations of temporary lane boundaries and corresponding temporary traffic facility models at the locations of temporary traffic facilities; associate the temporary lane boundary models and temporary traffic facility models with the original design BIM model; add corresponding construction stage time attributes to the temporary lane boundary models and temporary traffic facility models to generate the spatiotemporal model of the road network, and extract lane space definition data from it.

[0009] Furthermore, the specific steps for matching real-time vehicle trajectory data with lane space definition data in the road network spatiotemporal model are as follows: traverse every trajectory point in the real-time vehicle trajectory data; calculate the vertical distance from each trajectory point to the center line of each lane in the lane space definition data; assign the trajectory point to the lane with the smallest vertical distance; after completing the assignment calculation for all trajectory points, obtain the set of vehicle trajectory points contained in each lane.

[0010] Furthermore, the specific steps for generating the number of vehicles and average speed corresponding to each lane based on the set of vehicle trajectory points are as follows: For the set of vehicle trajectory points contained in each lane, deduplication is performed according to the unique vehicle identifier, and the number of deduplicated vehicle identifiers is counted to obtain the number of vehicles in that lane; For the set of vehicle trajectory points contained in each lane, the average value of the instantaneous speed of all trajectory points is calculated to obtain the average speed of that lane.

[0011] Furthermore, the minimum safe sight distance is obtained through the following steps: extracting the design speed and longitudinal profile of the construction section from the spatiotemporal model of the road network; calculating the theoretical parking sight distance according to the parking sight distance calculation formula corresponding to the design speed and longitudinal profile in the road design specifications; and correcting the theoretical parking sight distance based on the real-time collected pavement adhesion coefficient and visibility data of the construction section to obtain the minimum safe sight distance.

[0012] Furthermore, based on the real-time collected data on the road surface adhesion coefficient and visibility of the construction section, the specific steps for correcting the theoretical parking sight distance are as follows: Obtain the real-time collected data on the road surface adhesion coefficient and visibility of the construction section; determine the road surface correction coefficient corresponding to the current road surface adhesion coefficient based on the preset mapping relationship between the adhesion coefficient and the sight distance correction coefficient; determine the weather correction coefficient corresponding to the current visibility based on the preset mapping relationship between visibility and the sight distance correction coefficient; multiply the theoretical parking sight distance, the road surface correction coefficient, and the weather correction coefficient to obtain the corrected minimum safe sight distance.

[0013] Further, the specific steps for solving the optimization model and outputting the borrowing length, the number of borrowed lanes, and the recommended speed limit are as follows: Using the borrowing length, the number of borrowed lanes, and the recommended speed limit as optimization variables, and minimizing the total two-way delay time of the construction section as the objective function, an initial optimization problem is established. The road curvature and slope determined by the lane space definition data are added as the first type of constraint, and the minimum safe sight distance is added as the second type of constraint to the initial optimization problem, forming a constrained optimization model. A heuristic optimization algorithm based on traffic flow simulation is used to iteratively solve the constrained optimization model to obtain the combination of borrowing length, the number of borrowed lanes, and the recommended speed limit that minimizes the objective function.

[0014] Furthermore, the specific steps for iterative solution using a heuristic optimization algorithm based on traffic flow simulation are as follows: Set the initial solution and change step size for the borrowed lane length, the number of borrowed lanes, and the recommended speed limit; input the current solution combination into a preset microscopic traffic flow simulation model, run the simulation, and obtain the simulated value of the total two-way delay time of the construction section; adjust the current solution combination based on the optimization strategy, run the simulation again, and compare the simulation results; repeat the simulation and adjustment steps until the iteration termination condition is met, and use the optimal solution combination as the output for the borrowed lane length, the number of borrowed lanes, and the recommended speed limit.

[0015] Further, the specific steps for inputting the current solution combination into a preset microscopic traffic flow simulation model, running the simulation, and obtaining the simulated value of the total two-way delay time of the construction section are as follows: Based on the borrowing length and number of borrowed lanes in the current solution combination, the temporary passage lanes and closed construction areas are dynamically delineated in the road network spatiotemporal model; based on the number of vehicles and average speed corresponding to each lane, the simulated dynamic traffic demand is generated and loaded into the microscopic traffic flow simulation model; the microscopic traffic flow simulation model is run to simulate the following, lane changing, and merging behaviors of vehicles in the temporary passage lanes; the total travel time of all vehicles passing through the construction section and the total travel time under free flow conditions within the simulation period are statistically analyzed, and the difference is the simulated value of the total two-way delay time of the construction section.

[0016] Furthermore, the specific steps for generating lane control instructions and speed limit issuance instructions are as follows: Based on the borrowed lane length and the number of borrowed lanes, determine the lane space range and start and end positions that need to be closed or opened, and generate the corresponding lane control instructions; map the recommended speed limit value with the preset instruction coding rules to generate the speed limit issuance instructions.

[0017] The present invention has the following beneficial effects: (1) The BIM-based highway construction traffic diversion optimization system combines real-time vehicle trajectory data with the spatiotemporal model of the road network to dynamically track the adjustment of lanes and traffic facilities during the construction period. Unlike traditional solutions, this system does not rely on static design data, but matches lane changes and traffic flow at each construction stage with real-time data. This method significantly improves the accuracy of traffic flow prediction and can optimize lane arrangement and traffic facility layout in the construction section in real time, thereby minimizing traffic delays during construction. By automatically calculating and adjusting the number and length of lanes, the system provides scientific and reasonable speed limits, which can effectively avoid congestion caused by unreasonable lanes or inaccurate traffic flow prediction during the construction period and improve road traffic efficiency. In addition, the real-time updates of lane transfer control instructions and speed limit issuance instructions ensure more accurate and efficient traffic management in the construction section, thereby providing a reliable guarantee for the construction progress.

[0018] (2) The BIM-based traffic diversion and optimization system for highway construction can more accurately predict and manage traffic flow during construction by combining the BIM-based system model and vehicle trajectory data. During construction, traffic congestion often occurs due to the setting of temporary lanes and the adjustment of traffic facilities. Traditional solutions often cannot reflect these dynamic changes in real time. However, the system of this invention matches vehicle trajectory and lane definition data in real time. By accurately calculating the number of vehicles and average speed of each lane, it optimizes traffic flow during construction. When it is found that the traffic load of a certain lane is too high, the system can adjust the borrowing length and the number of lanes in a timely manner and reasonably allocate vehicle passage, thereby effectively alleviating congestion. At the same time, by accurately calculating and outputting recommended speed limits, it can reduce the risk of traffic accidents caused by excessive or slow speeds, and fundamentally avoid construction delays and traffic safety problems caused by improper traffic management.

[0019] (3) The BIM-based highway construction traffic diversion optimization system establishes a spatiotemporal model by combining the changes in temporary lanes and traffic facilities during the construction phase. It can accurately reflect the actual situation during the road construction phase. The system uses real-time vehicle trajectory data to dynamically optimize the traffic flow and speed of different lanes, ensuring that lane configuration and speed limit can be adjusted according to traffic conditions at each construction phase. This significantly improves the safety of the construction section. Especially in complex construction environments, the lane control instructions and speed limit release instructions automatically generated by the system can be sent to the traffic control terminal in real time and drive its execution, effectively avoiding the problems of untimely or inaccurate manual management.

[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0021] Figure 1 This is a block diagram of a BIM-based traffic diversion and optimization system for highway construction.

[0022] Figure 2 This is a flowchart illustrating the specific steps involved in generating a spatiotemporal model of a road network in a BIM-based traffic diversion and optimization system for highway construction.

[0023] Figure 3 This invention provides a flowchart outlining the specific steps involved in matching real-time vehicle trajectory data with lane space definition data in a BIM-based highway construction period traffic diversion and optimization system. Detailed Implementation

[0024] Please see Figure 1This invention provides a technical solution: a BIM-based traffic diversion and optimization system for highway construction, comprising: a spatiotemporal modeling unit, used to acquire the original highway design BIM model and construction organization plan data including the coordinates of temporary lane boundaries and the locations of traffic facilities at each stage; based on the construction organization plan data, adding and associating the temporary lane boundaries and traffic facility models corresponding to each construction stage into the original design BIM model to generate a road network spatiotemporal model, the road network spatiotemporal model including lane space definition data for defining the spatial location and geometric range of all lanes; and a data fusion unit, used to acquire real-time vehicle trajectory data and integrate the real-time vehicle trajectory data with the road network... The system matches lane space definition data in the spatiotemporal model to obtain a set of vehicle trajectory points for each lane. Based on the set of vehicle trajectory points, it statistically generates the number of vehicles and average speed for each lane. The optimization decision unit is used to solve the optimization model based on the number of vehicles and average speed for each lane, with the goal of minimizing the total two-way delay time of the construction section, and with lane space definition data and minimum safe sight distance in the spatiotemporal model of the road network as constraints. The optimization model outputs the borrowing length, the number of borrowed lanes, and the recommended speed limit. The control execution unit is used to generate lane control instructions and speed limit issuance instructions based on the borrowing length, the number of borrowed lanes, and the recommended speed limit, and sends them to the corresponding traffic control terminal to drive their execution.

[0025] The real-time vehicle trajectory data comes from video detectors, radar detectors, or vehicle-mounted GPS devices along the highway. The data is in the form of a structured data stream containing timestamps, longitude, latitude, elevation, instantaneous speed, and a unique vehicle identifier.

[0026] Traffic control terminals include variable message signs, lane indicators, and movable guardrail controllers. They are connected to the system via industrial Ethernet or private wireless networks and receive and execute control commands in text or binary format.

[0027] The total delay time in both directions of a construction section is defined as the sum of the differences between the actual travel time of all vehicles in both directions of the construction section and the travel time under free-flow conditions.

[0028] The optimization model is a multi-objective optimization model with geometric and safety constraints, and its decision variable is the bypass length. Number of lanes borrowed and recommended speed limit .

[0029] Lane space definition data includes the lane centerline coordinate sequence, lane width, and lane boundary equation.

[0030] Minimum safe line of sight It is obtained based on dynamic calculations of design speed, longitudinal slope, road surface adhesion coefficient, and visibility.

[0031] Specifically, the steps for generating the spatiotemporal model of the road network are as follows: Based on the construction organization plan data, the locations of temporary lane boundaries and temporary traffic facilities to be set up at each construction stage are determined as follows: Analyze the construction organization plan document and extract the first... Each construction phase ( =1, 2, ..., , The starting chainage (for the total number of construction stages) Termination chainage Road width And a temporary facility layout table; For example, in the first phase, a work area with a width of 3.5 meters on the outside needs to be set up in the section between chainage K1000 and K1100, and a temporary warning sign needs to be set up at K1050; In the original design BIM model, a corresponding temporary lane boundary model is created at the location of the temporary lane boundary, and a corresponding temporary traffic facility model is created at the location of the temporary traffic facility. Specifically: In the original design BIM model, based on the extracted stationing and width information, the offset command is used to offset the original lane edge line towards the roadside. A distance of 2 is used to generate a new 3D polyline as a temporary lane boundary model. ; Retrieve the "Construction Warning Sign" family from the BIM family library and position its insertion point at the road centerline coordinates corresponding to station K1050. The location is then rotated to face the oncoming traffic, generating a temporary traffic facility model. ; The temporary lane boundary model and temporary traffic facility model are linked with the original design BIM model, specifically as follows: Utilize the parameter association function of BIM software; Temporary lane boundary model The parent attribute is set to the "Right Lane of the Main Line" component in the original design BIM model; Temporary traffic facility model The reference attribute is set to the "central divider" component at K1050; for and Add a stage identifier attribute and set its value to ; Add corresponding construction phase time attributes to the temporary lane boundary model and temporary traffic facility model to generate a road network spatiotemporal model, and extract lane space definition data from it, specifically as follows: Each stage is identified as Add stage start time to the model components. and phase end time Attributes (e.g.) =2025-10-01 08:00:00 =2025-10-15 20:00:00). The collection of all BIM components assigned spatiotemporal attributes is called the road network spatiotemporal model. ; from Extract the centerlines of all lanes, representing them as a sequence of points: For the lanes ( =1, 2, ..., Its centerline is a sequence of points. ; in, For the first The number of points on the center line of each lane; Extract the width of each lane. ; gather This refers to the data defining the lane space. .

[0032] In this implementation plan, by combining construction organization plan data with the original design BIM model, a precise spatiotemporal model of the road network can be generated. This solves the problem of insufficient dynamic management of temporary lanes and traffic facilities in traditional solutions. By analyzing specific information of the construction phase, such as station numbers, road widths, and temporary facility layouts, the system can accurately create corresponding temporary lane boundaries and traffic facility models in the BIM model and effectively associate them with the original design model. The spatiotemporal attributes of each construction phase are assigned to the model components, ensuring that the changes in lane positions and traffic facilities at different stages can be reflected in real time during construction. This method improves the accuracy of lane and traffic facility layout during the construction phase. By extracting data such as the centerline and width of each lane, the system can construct detailed lane space definition data, thereby providing comprehensive and accurate basic information for traffic management and optimization during the construction period. This significantly improves the efficiency of traffic flow management and the coordination of construction progress during the construction period, avoiding traffic congestion and delays caused by untimely information updates during construction, and ensuring traffic safety and smoothness during construction.

[0033] Specifically, such as Figure 2 As shown, the specific steps for matching real-time vehicle trajectory data with lane space definition data in the road network spatiotemporal model are as follows: Iterate through each trajectory point in the real-time vehicle trajectory data, specifically as follows: set up The real-time vehicle trajectory data packets received at all times contain Vehicle information; For the first one vehicle ( =1, 2, ..., Its trajectory points are represented as quintuples. ; in, Longitude Latitude For elevation, Instantaneous velocity It serves as a unique identifier for the vehicle. Traverse all A trajectory point; Calculate the perpendicular distance from each trajectory point to the centerline of each lane in the lane space definition data, specifically as follows: trajectory points latitude and longitude coordinates The planar coordinates were obtained by transforming the model to the national 2000 coordinate system consistent with the BIM model using Gauss-Kruger projection. ; For the center line of lane Traverse all its line segments (consisting of adjacent center points) and calculate the points. The perpendicular distance to each line segment; Let the line segment originate from point A. and points Definition: Perpendicular distance from a point to a line segment The calculation formula is: Calculate vectors ; vector ; Calculate the projected scalar ; like The perpendicular is outside the starting point of the line segment. ; like The perpendicular foot is outside the endpoint of the line segment. ; like The foot of the perpendicular lies on the line segment. ; The minimum distance of all line segments is taken as the distance from the trajectory point to the lane. distance ; Assigning the trajectory point to the lane with the smallest vertical distance is as follows: Compare To all The set of distances for each lane Find the lane index corresponding to the minimum value. ; Will Marked as belonging to lane ,Right now ; For example, if a trajectory point is calculated to be 0.8 meters from the second lane and 2.1 meters from the third lane, then it is assigned to the second lane. After completing the assignment calculation for all trajectory points, the set of vehicle trajectory points contained in each lane is obtained, specifically as follows: For each lane Collect all that satisfy The trajectory points constitute the set of trajectory points for that lane. .

[0034] The specific steps for generating the number of vehicles and average speed for each lane based on the set of vehicle trajectory points are as follows: For each lane, the set of vehicle trajectory points is deduplicated based on the vehicle's unique identifier. The number of deduplicated vehicle identifiers is then counted to obtain the number of vehicles in that lane. Specifically: opposite lane The set of trajectory points Extract the unique vehicle identifiers (IDs) from all trajectory points to form a multiset; Perform deduplication on the multiset to obtain the set of identifiers of different vehicles that have appeared in that lane. ; Number of vehicles in this lane That is, a set The cardinality of is denoted as . ; For example, If the trajectory points include vehicles A, B, A, and C, then , ; For each lane, the average instantaneous velocity of the set of vehicle trajectory points is calculated to obtain the average velocity of that lane. Specifically: opposite lane The set of trajectory points Instantaneous velocity of all trajectory points Find the arithmetic mean; Let set The CCP For each trajectory point, the average velocity is... ; For example, If the speeds of the three trajectory points are 72 km / h, 75 km / h, and 69 km / h respectively, then... =72km / h.

[0035] In this implementation plan, by accurately matching real-time vehicle trajectory data with lane space definition data in the road network spatiotemporal model, the relationship between the position of each vehicle and its lane can be dynamically tracked. This method effectively avoids the drawback of traditional traffic management schemes that are difficult to adapt to lane changes during construction in real time. Through technologies such as Gauss-Kruger projection, trajectory points are accurately converted to a coordinate system consistent with the BIM model, and the vertical distance between the trajectory point and the lane centerline is calculated to ensure that each trajectory point can be correctly assigned to the most suitable lane.

[0036] Specifically, the minimum safe line of sight is obtained through the following steps: From the spatiotemporal model of the road network, the design speed and longitudinal profile of the construction section are extracted, specifically as follows: Query the spatiotemporal model of the road network In the middle, the design attributes of the road unit where the construction area is located are used to obtain its design speed value. (Unit: km / h); Extract the longitudinal profile data within the construction section, i.e., the station numbers of a series of slope change points. and elevation ( =1, 2, ..., The slope of each segment is obtained through differential calculation. ; Select the average slope of the section where the construction area is located. As a representative; If the construction area is located on an uphill section Take a positive value; If it is located on a downhill section Take the negative value; Based on the parking sight distance calculation formula corresponding to the design speed and longitudinal profile in the road design specifications, the theoretical parking sight distance is calculated as follows: According to the "Highway Route Design Specifications" (JTGD20-2017), the theoretical stopping sight distance is... The formula for calculating (unit: m) is: ; in, The driver's reaction time is set to 2.5 seconds. The longitudinal friction coefficient between the road surface and the tire is taken as 0.4; For longitudinal slope, use "+" for uphill and "-" for downhill; Based on real-time collected data on road surface adhesion coefficient and visibility in the construction section, the theoretical stopping sight distance is corrected to obtain the minimum safe sight distance, which is as follows: The road surface adhesion coefficient and visibility data are obtained from road surface condition sensors and weather stations deployed upstream of the construction section. The road surface condition sensors output dimensionless adhesion coefficient values ​​by measuring the road surface friction resistance. The weather station outputs visibility values ​​in meters via a forward scattering instrument. ; Let the real-time collected road surface adhesion coefficient be... (Dimensionless), visibility is (Unit: m); Define the adhesion coefficient correction factor ; in, =0.4 is the standard adhesion coefficient; Visibility correction factor ; The corrected minimum safe sight distance for: .

[0037] The specific steps for correcting the theoretical parking sight distance based on real-time collected data on road surface adhesion coefficient and visibility of the construction section are as follows: The real-time data on pavement adhesion coefficient and visibility of the construction section are obtained, specifically as follows: Real-time road surface adhesion coefficient is obtained by deploying road surface condition sensors on the construction section. ; Real-time visibility was obtained using a visibility meter. ; Based on the preset mapping relationship between the adhesion coefficient and the sight distance correction coefficient, the pavement correction coefficient corresponding to the current pavement adhesion coefficient is determined, specifically as follows: The default mapping is a piecewise function: ; Based on the preset mapping relationship between visibility and sight distance correction factors, the meteorological correction factor corresponding to the current visibility is determined, specifically as follows: The default mapping relationship is as follows: ; in, Theoretical parking sight distance; Multiplying the theoretical stopping sight distance, the road surface correction factor, and the weather correction factor yields the corrected minimum safe sight distance, which is as follows: calculate .

[0038] In this implementation plan, by combining the design speed, longitudinal profile, and real-time collected pavement adhesion coefficient and visibility data of the construction section, the minimum safe sight distance can be dynamically adjusted, thereby improving the traffic safety control capability during the construction period. Traditional traffic management schemes usually rely only on static design data, ignoring the impact of real-time factors such as pavement adhesion conditions and visibility during construction. However, this invention, through high-precision real-time data collection and analysis, enables the minimum safe sight distance to be dynamically adjusted according to real-time road conditions, thereby ensuring that the stopping sight distance of vehicles always meets safety requirements under different construction stages and environmental conditions. By correcting the pavement adhesion coefficient and visibility data, this scheme further optimizes the traffic safety performance of the construction section and effectively reduces the risk of traffic accidents caused by changes in road conditions during construction.

[0039] Specifically, the steps for solving the optimization model and outputting the borrowing length, the number of borrowed lanes, and the recommended speed limit are as follows: Using the borrowing length, the number of borrowed lanes, and the recommended speed limit as optimization variables, and minimizing the total two-way delay time of the construction section as the objective function, an initial optimization problem is established, which is as follows: Let the length of the borrowed passage be... (Unit: m), Number of lanes used: (Take a positive integer), recommended speed limit value is (Unit: km / h); The objective function is to minimize the total two-way delay time. ; The initial optimization problem is represented as: ; Satisfy constraints: ; in, and These are the minimum and maximum speed limits permitted by regulations; For example, =60km / h, =100km / h; The road curvature and slope determined by the lane space definition data are used as the first type of constraint, and the minimum safe sight distance is used as the second type of constraint. These are added to the initial optimization problem to form a constrained optimization model, which is as follows: Type I constraint: Based on the plane curvature radius in the lane space definition data (Unit: m) and longitudinal slope Speed ​​limits are recommended. satisfy: ; in, The lateral force coefficient is set to 0.15. Use "+" for uphill and "-" for downhill; Second type of constraint: Vehicles at speed Required stopping sight distance while driving It should not exceed the minimum safe sight distance. ,Right now: ; in, The driver's reaction time is set to 2.5 seconds. The longitudinal friction coefficient between the road surface and the tire is taken as 0.4; A heuristic optimization algorithm based on traffic flow simulation is used to iteratively solve the constrained optimization model, obtaining the combination of lane borrowing length, number of borrowed lanes, and recommended speed limit that minimizes the objective function. Specifically: A genetic algorithm is used to randomly generate a set of... As the initial population; For each individual, running a micro-traffic flow simulation yields... ; Calculate the penalty function based on the constraints to transform the constrained optimization problem into an unconstrained problem; A new generation of populations is generated through selection, crossover, and mutation; Repeat the iterations until the convergence condition is met, and output the optimal solution. .

[0040] The specific steps for iteratively solving the problem using a heuristic optimization algorithm based on traffic flow simulation are as follows: The initial solution and change step size for determining the borrowing length, the number of borrowed lanes, and the recommended speed limit are as follows: Optimize variables as vectors Initial solution ,in Take half the length of the construction section. , (Design speed); Change step size: =10m, =1, =5km / h; The current solution is input into a preset microscopic traffic flow simulation model, the simulation is run, and the simulated value of the total two-way delay time of the construction section is obtained, specifically as follows: The current solution As input, modify the length of the bypass area, the number of lanes, and the speed limit in the simulated road network; Run the simulation for a period of time (e.g., 1 hour); Extract the total travel time of all vehicles in both directions from the simulation results. Total travel time under free-flow conditions The total delay was obtained through comparison. ; The current solution combination is adjusted based on the optimization strategy, and simulation is performed again. The simulation results are then compared. Specifically: According to the rules of the genetic algorithm, new solutions are generated through crossover and mutation; Cross operation: Randomly select two parent individuals and Swap some variables with a certain probability, such as: ; in, A random number within the range [0, 1]; Mutation operation: To change the value of a variable randomly with a small probability, such as: ; in, A random number within the range [-1, 1]; The new solution is input into the simulation again to obtain a new result. Compare with the historical best solution; Repeat the simulation and adjustment steps until the iteration termination condition is met. The optimal solution combination from the simulation results is then used as the output for the lane borrowing length, the number of borrowed lanes, and the recommended speed limit. Specifically: The iteration termination condition is reaching the maximum number of iterations. (e.g., 100 generations) or consecutive The improvement in the optimal solution over a generation (e.g., 10 generations) is less than the threshold ϵ = 0.1%; Output of historical simulations Minimal combination of solutions .

[0041] The specific steps for inputting the current solution into a preset microscopic traffic flow simulation model, running the simulation, and obtaining the simulated value of the total two-way delay time of the construction section are as follows: Based on the borrowed lane length and number of borrowed lanes in the current solution combination, temporary traffic lanes and closed construction areas are dynamically delineated in the spatiotemporal model of the road network, specifically as follows: In the spatiotemporal model of road network In the middle, using the starting point chainage as a reference, a length is defined along the direction of traffic. The area for passing through; In this area, the original highway will be The opposite lane was converted to allow traffic in the same direction, and the lane in this direction was closed. One lane is designated as a construction area; For example, if =500m, =1, then in the model, the outermost lane in the opposite direction between chainage K1000 and K1500 is set as the temporary traffic lane in this direction, and the outermost lane in this direction is set as closed. Based on the number of vehicles and average speed in each lane, a simulated dynamic traffic demand is generated and loaded into a microscopic traffic flow simulation model, specifically as follows: Based on the number of vehicles in each lane obtained by the data fusion unit and average speed Calculate the hourly traffic volume of this lane. ,in The statistical period is in hours. In the simulation model, the traffic demand for each entrance lane is set as follows: (Unit: veh / h), the desired speed of the vehicle is (Unit: km / h); A microscopic traffic flow simulation model was run to simulate the following, lane changing, and merging behaviors of vehicles in temporary traffic lanes. Specifically: In the simulation model, the car-following behavior adopts the Wiedemann model, the lane-changing behavior is determined based on the minimum safe distance and expected benefits, and the merging behavior is based on the gap acceptance theory. The simulation step size is set to Δt = 0.1s; The difference between the total travel time of all vehicles passing through the construction section and the total travel time under free-flow conditions within the statistical simulation period is the simulated value of the total two-way delay time of the construction section. Specifically: Let the set of all vehicles passing through the construction section during the simulation period be denoted as . Each vehicle ∈ The travel time is The total travel time ; In free-flow conditions, the vehicle travels at its design speed Travel time for a single vehicle through the construction section at a constant speed. ,in The length of the construction section; Total delay ,in This represents the total number of vehicles.

[0042] In this implementation plan, by using the borrowed lane length, the number of borrowed lanes, and the recommended speed limit as optimization variables, and aiming to minimize the total two-way delay time of the construction section, the system can dynamically adjust traffic parameters to ensure traffic flow and construction safety. Compared with traditional traffic management methods, this scheme uses heuristic optimization techniques such as genetic algorithms to perform multi-objective optimization based on the actual situation and traffic flow of the construction section, finding the optimal combination of traffic parameters. Specifically, through microscopic traffic flow simulation, the system simulates the behavior of vehicles in the construction section and makes adjustments based on actual traffic data. It can not only calculate accurate traffic demand, but also predict and control traffic delays during the construction period in real time. Ultimately, the optimized traffic scheme effectively avoids the static scheduling problem in traditional methods, improves traffic capacity during construction, reduces traffic delays, and ensures efficient construction.

[0043] Specifically, such as Figure 3 As shown, the specific steps for generating lane control commands and speed limit issuance commands are as follows: Based on the length of the borrowed lane and the number of borrowed lanes, the spatial range and start and end positions of the lanes that need to be closed or opened are determined, and corresponding lane control instructions are generated, specifically as follows: The lane control command format is: CLOSE_LANE:start=K1000,end=K1500,lane=[1,2],time=2025-10-01 08:00:00, where lane numbers start from the left, and 1 represents the leftmost lane; This instruction indicates that lanes 1 and 2 are closed from chainage K1000 to K1500; The recommended speed limit value is mapped to a preset instruction encoding rule to generate a speed limit issuance instruction, specifically as follows: The format for issuing a speed limit command is: SPEED_LIMIT:value=80,start=K980,end=K1520,time=2025-10-01 08:00:00; This instruction indicates that the speed limit is set to 80 km / h from station K980 to K1520.

[0044] In this implementation plan, during construction, the system can accurately determine the range of lanes that need to be closed or opened based on the length of the borrowed lane and the number of lanes, and generate corresponding lane control instructions. These instructions specify the start and end points and time of lane closures, helping traffic control personnel to take timely measures and reduce traffic congestion and accident risks caused by unreasonable lane adjustments. At the same time, by mapping the recommended speed limit value with the instruction coding rules, the system can also generate speed limit issuance instructions to ensure that the speed of vehicles passing through the construction section is effectively controlled. The precise setting and real-time issuance of speed limit instructions help avoid traffic safety hazards caused by excessively high or low speed limits in the construction area.

[0045] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0046] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A BIM-based traffic diversion and optimization system for highway construction, characterized in that, include: The spatiotemporal modeling unit is used to acquire the original design BIM model of the highway and the construction organization plan data containing the coordinates of temporary lane boundaries and the location of traffic facilities at each stage. Based on the construction organization plan data, the unit adds and associates the temporary lane boundaries and traffic facility models corresponding to each construction stage in the original design BIM model to generate a road network spatiotemporal model. The road network spatiotemporal model contains lane space definition data used to define the spatial location and geometric range of all lanes. The data fusion unit is used to acquire real-time vehicle trajectory data, match the real-time vehicle trajectory data with the lane space definition data in the road network spatiotemporal model to obtain the set of vehicle trajectory points contained in each lane, and statistically generate the number of vehicles and average speed corresponding to each lane based on the set of vehicle trajectory points. The optimization decision unit is used to solve the optimization model based on the number of vehicles and average speed of each lane, with the goal of minimizing the total two-way delay time of the construction section, and with lane space definition data and minimum safe sight distance in the road network spatiotemporal model as constraints. The model outputs the borrowing length, the number of borrowed lanes, and the recommended speed limit. The control execution unit is used to generate lane control instructions and speed limit issuance instructions based on the borrowing length, the number of borrowed lanes and the recommended speed limit, and send them to the corresponding traffic control terminal to drive their execution.

2. The BIM-based traffic diversion and optimization system for highway construction as described in claim 1, characterized in that, The specific steps for generating the spatiotemporal model of the road network are as follows: Based on the construction organization plan data, determine the location of temporary lane boundaries and temporary traffic facilities that need to be set up at each construction stage; In the original design BIM model, create a corresponding temporary lane boundary model at the location of the temporary lane boundary, and create a corresponding temporary traffic facility model at the location of the temporary traffic facility. Link the temporary lane boundary model and temporary traffic facility model with the original design BIM model; Add corresponding construction phase time attributes to the temporary lane boundary model and temporary traffic facility model to generate a road network spatiotemporal model, and extract lane space definition data from it.

3. The BIM-based traffic diversion and optimization system for highway construction as described in claim 1, characterized in that, The specific steps for matching real-time vehicle trajectory data with lane space definition data in the road network spatiotemporal model are as follows: Iterate through every trajectory point in the real-time vehicle trajectory data; Calculate the vertical distance from each trajectory point to the centerline of each lane in the lane space definition data; Assign the trajectory point to the lane with the smallest vertical distance; After completing the assignment calculation for all trajectory points, the set of vehicle trajectory points contained in each lane is obtained.

4. The BIM-based traffic diversion and optimization system for highway construction as described in claim 3, characterized in that, The specific steps for generating the number of vehicles and average speed for each lane based on the set of vehicle trajectory points are as follows: For the set of vehicle trajectory points contained in each lane, deduplication is performed according to the unique vehicle identifier, and the number of vehicle identifiers after deduplication is counted to obtain the number of vehicles in that lane. For each lane, calculate the average instantaneous speed of the set of vehicle trajectory points, and obtain the average speed of that lane.

5. The BIM-based traffic diversion and optimization system for highway construction as described in claim 1, characterized in that, The minimum safe line of sight is obtained through the following steps: The design speed and longitudinal profile of the construction section are extracted from the spatiotemporal model of the road network. The theoretical stopping sight distance is calculated based on the stopping sight distance calculation formula corresponding to the design speed and longitudinal profile in the road design specifications. Based on real-time collected data on road surface adhesion coefficient and visibility of the construction section, the theoretical parking sight distance is corrected to obtain the minimum safe sight distance.

6. The BIM-based traffic diversion and optimization system for highway construction as described in claim 5, characterized in that, The specific steps for correcting the theoretical parking sight distance based on real-time collected data on road surface adhesion coefficient and visibility of the construction section are as follows: Obtain real-time data on road surface adhesion coefficient and visibility of the construction section; Based on the preset mapping relationship between the adhesion coefficient and the sight distance correction coefficient, determine the road surface correction coefficient corresponding to the current road surface adhesion coefficient; Based on the preset mapping relationship between visibility and sight distance correction coefficient, determine the meteorological correction coefficient corresponding to the current visibility; Multiply the theoretical parking sight distance, the road surface correction factor, and the weather correction factor to obtain the corrected minimum safe sight distance.

7. The BIM-based traffic diversion and optimization system for highway construction as described in claim 1, characterized in that, The specific steps for solving the optimization model and outputting the borrowing length, the number of borrowed lanes, and the recommended speed limit are as follows: The initial optimization problem is established with the length of the borrowed lane, the number of borrowed lanes, and the recommended speed limit as optimization variables, and the objective function is to minimize the total two-way delay time of the construction section. The road curvature and slope determined by the lane space definition data are used as the first type of constraint, and the minimum safe sight distance is used as the second type of constraint. These are added to the initial optimization problem to form a constrained optimization model. A heuristic optimization algorithm based on traffic flow simulation is used to iteratively solve the constrained optimization model to obtain the combination of lane borrowing length, number of borrowed lanes, and recommended speed limit value that minimizes the objective function.

8. The BIM-based traffic diversion and optimization system for highway construction as described in claim 7, characterized in that, The specific steps for iteratively solving the problem using a heuristic optimization algorithm based on traffic flow simulation are as follows: Set the initial solution and change step size for the borrowing length, the number of borrowed lanes, and the recommended speed limit; Input the current solution into the preset microscopic traffic flow simulation model, run the simulation and obtain the simulation value of the total two-way delay time of the construction section; Adjust the current solution combination based on the optimization strategy, perform simulation again, and compare the simulation results. Repeat the simulation and adjustment steps until the iteration termination condition is met, and use the optimal solution combination of the simulation results as the output of the borrowing length, the number of borrowed lanes, and the recommended speed limit.

9. The BIM-based traffic diversion and optimization system for highway construction as described in claim 8, characterized in that, The specific steps for inputting the current solution into a preset microscopic traffic flow simulation model, running the simulation, and obtaining the simulated value of the total two-way delay time of the construction section are as follows: Based on the borrowing length and number of borrowed lanes in the current solution combination, temporary traffic lanes and closed construction areas are dynamically delineated in the spatiotemporal model of the road network. Based on the number of vehicles and average speed in each lane, a simulated dynamic traffic demand is generated and loaded into a micro-traffic flow simulation model. Run a microscopic traffic flow simulation model to simulate the following, lane changing, and merging behaviors of vehicles in temporary traffic lanes; The difference between the total travel time of all vehicles passing through the construction section and the total travel time under free-flow conditions within the statistical simulation period is the simulated value of the total two-way delay time of the construction section.

10. The BIM-based traffic diversion and optimization system for highway construction as described in claim 1, characterized in that, The specific steps for generating lane control commands and speed limit issuance commands are as follows: Based on the length of the borrowed lane and the number of borrowed lanes, determine the range and start and end points of the lane space that needs to be closed or opened, and generate corresponding lane control instructions; The recommended speed limit value is mapped to the preset instruction encoding rules to generate a speed limit release instruction.

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