60 results about "Traffic flow modeling" patented technology

The invention provides an urban traffic dynamic deduction and real-time planning method and system based on multi-source position data fusion. Time-space references of signaling, MDT and MR data are unified through a dynamic rasterization mechanism, and the problem of data isomerism is solved by adopting anchor point selection of signal weighting and a triple criterion time expansion algorithm; generating a minute-level updated dynamic OD matrix based on the hierarchical road network constraint model; a METANET macroscopic traffic flow model and EKF extended Kalman filtering are fused to realize traffic state deduction, and parameter online calibration is supported; signal timing optimization, path induction and emergency control schemes are generated in combination with real-time deduction results, and a sensing-deduction-decision-evaluation closed loop is formed. The method can improve road network traffic efficiency, and is especially suitable for response of sudden traffic events.

The invention provides a bridge parallel steel wire pull sling corrosion fatigue residual life prediction method, and relates to the technical field of bridge safety assessment, and the method comprises the steps: obtaining first information and second information; establishing a random traffic flow model based on the first information; based on the second information and preset bridge pavement irregularity, cable force time history analysis considering vehicle-bridge dynamic response is carried out, and a response time history of a bridge system is obtained through analysis; processing the response time history based on a rain flow counting method to obtain all stress amplitudes of the bridge and corresponding cycle times; and calculating the corrosion fatigue duration of the bridge parallel steel wire pull sling by adopting a time increment method based on all the stress amplitudes and the corresponding cycle times, and outputting the fatigue life of the bridge parallel steel wire pull sling. The method is used for evaluating the residual life of the pull sling in the service period along with the change of the external environment, the evaluation model can be dynamically updated in combination with an actual inspection result, and effective combination of a theoretical model and actually measured data is realized.

The invention discloses a method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on actually measured traffic flow, which comprises the following steps of: establishing a finite element model of the prestressed concrete continuous box girder bridge according to a bridge structure design drawing, and calculating and defining the section attribute of each section in the longitudinal bridge direction; determining the most unfavorable section position of the bridge structure; the time-varying bearing capacity of the most unfavorable section is calculated through the constructed degradation model; preprocessing the data acquired by the dynamic weighing system; establishing the preprocessed dynamic weighing data into a numerical model to realize the construction of a traffic flow model; performing bridge dynamic analysis to obtain a load effect; calculating a time-varying reliability index in the service period of the bridge; and comparing the calculated time-varying reliable index with a target reliable index in the specification. According to the method, the time-varying reliability of the in-service prestressed concrete continuous box girder bridge in the service period can be accurately evaluated, and subsequent management and maintenance work is guided.

The invention discloses an electric vehicle charging scheduling method and system considering a power-carbon market, and the method comprises the steps: firstly constructing a power-carbon market cooperation model which comprises a power transmission network low-carbon operation model, a power distribution network cooperation operation model, a power transmission network carbon emission flow tracking model, and a power distribution network carbon emission flow tracking model; then, on the basis of a user equilibrium theory, a traffic flow model of the electric vehicle is constructed with the purpose of minimizing the total driving time, the charging time and the charging cost of the electric vehicle, and then a charging station planning and charging integrated management model of the electric vehicle is constructed on the basis of the electricity-carbon market cooperation model and the traffic flow model; and finally, solving the power-carbon market cooperation model, the traffic flow model and the charging station planning and charging integrated management model to obtain an electric vehicle charging scheduling strategy. By adopting the method, the charging strategy of the electric vehicle can be optimized, the carbon emission is reduced, the load pressure of a power grid is relieved, and the renewable energy consumption capability is improved.

The invention discloses a highway passenger and freight mixed traffic flow modeling method based on METANET model optimization. The method comprises the following steps: constructing a dynamic critical density correction function based on a freight car proportion; establishing a differentiated lane changing behavior decision-making mechanism, respectively setting lane changing probabilities based on a Logit utility function and a suppression coefficient for cars and trucks, and enhancing the dynamic simulation capability of lane transverse flow distribution; and constructing a ramp interaction model coupled with the truck proportion, and analyzing a traffic capacity attenuation mechanism of the confluence area. According to the method, the macroscopic modeling advantage of the METANET model is kept, and the method has good structural compatibility and generalizability. According to the method, the prediction precision of the flow, the speed and the density in a multi-vehicle mixed traffic scene is superior to that of a traditional model, and the method is suitable for modeling, simulation and control of an intelligent high-speed system.

The invention relates to the technical field of traffic scheduling, and discloses a multi-source data fusion smart city entrance and exit flow monitoring and intelligent regulation and control system, which comprises the following steps: collecting traffic flow data, environmental data, weather data and data of social activity data sources, carrying out fusion processing on the collected data, and carrying out data fusion processing on the fused data; establishing a non-linear traffic flow model based on the flow data and the environment data, then defining a flow optimization target, a safety optimization target, an environment optimization target and an energy efficiency optimization target, solving the optimization targets by adopting a multi-target optimization algorithm, and carrying out optimization on traffic signals according to an optimal regulation and control strategy. And dynamically adjusting the real-time traffic flow data, the meteorological data and the social activity data based on Bayesian reasoning. According to the invention, by introducing a multi-source heterogeneous data fusion mechanism, unified modeling of traffic flow, weather changes and social activities is realized, and a high-precision and full-time-space perception effect of traffic states at entrances and exits of a complex city is obtained.

The invention discloses a self-adaptive control method and platform for an intelligent traffic signal lamp, and relates to the technical field of intelligent control, and the method comprises the steps: collecting traffic flow data in real time through a multi-source sensing module, including the number of vehicles, the vehicle speed, the vehicle type and the pedestrian flow; according to the data, traffic condition prediction is carried out by using a traffic flow model, and traffic pressure values of roads in all directions are calculated and generated; and based on the traffic pressure value, determining the phase duration of the signal lamp in each direction, performing signal lamp control analysis, generating a corresponding signal control instruction, and executing adaptive control of the traffic signal lamp. The technical problems of low traffic efficiency and serious traffic jam caused by the fact that the existing traffic signal lamp control method cannot dynamically adjust the time length of the signal lamp according to the real-time traffic condition are solved, and the purposes of dynamically optimizing the phase time length of the signal lamp and giving consideration to the requirements of pedestrians are achieved. The traffic efficiency is improved; and the congestion is relieved.

The application relates to a traffic capacity estimation method based on traffic basic graph parameter calibration, belongs to the technical field of intelligent traffic systems and traffic flow modeling, and particularly relates to a traffic capacity estimation method based on traffic basic graph parameter calibration. The application aims to solve the problem that the existing method cannot obtain accurate traffic basic graph parameters, resulting in low traffic capacity estimation precision. The process is as follows: step one, obtaining historical traffic flow single vehicle data samples of a road section; processing the obtained single vehicle data samples to obtain a final aggregated data set; step two, obtaining adaptive weights based on kernel density estimation based on the obtained final aggregated data set; step three, obtaining an optimal parameter set of a traffic basic graph model by adopting a particle swarm optimization algorithm based on the adaptive weights; and step four, obtaining traffic capacity based on the optimal parameter set of the traffic basic graph model.

A device includes a memory and one or more processors coupled to the memory. The processor(s) are configured to obtain historical flight data that represents a plurality of flights associated with a plurality of airports and to generate a directed graph, based on the historical flight data, that includes a plurality of nodes and a plurality of edges connecting pairs of nodes. The plurality of nodes correspond to the plurality of airports and the plurality of edges correspond to the plurality of flights. The processor(s) are configured to perform a ranking operation on the directed graph to identify one or more target nodes of the plurality of nodes. The one or more target nodes correspond to one or more airports of the plurality of airports. The processor(s) are configured to output a graphical user interface that indicates the one or more airports as recommended airports for modeling predicted traffic flow.

The embodiment of the invention provides a vehicle lane changing influence prediction method and related equipment, and belongs to the technical field of traffic control. The method comprises the following steps: acquiring traffic environment data around a lane changing vehicle, wherein the traffic environment data comprises traffic flow data and vehicle-level data; according to the vehicle-level data, the influence of the lane changing vehicle on other vehicles when the lane changing vehicle is changed to the target lane is predicted through an improved car-following model, and a vehicle influence estimation index is obtained; according to the traffic flow data, the influence on the traffic flow when the lane changing vehicle is converted to the target lane is predicted through a traffic flow model, and a traffic flow influence estimation index is obtained; and performing lane changing benefit calculation according to the vehicle influence estimation index and the traffic flow influence estimation index to obtain a benefit index of changing the lane changing vehicle to the target lane. According to the embodiment of the invention, the accuracy of vehicle lane changing influence prediction can be improved, and an accurate reference is provided for the lane changing behavior of the lane changing vehicle.

The invention is suitable for the technical field of power distribution networks, and provides a flexible interconnected power distribution network and charging facility collaborative expansion planning method, which comprises the following steps: constructing a semi-dynamic traffic flow model according to obtained traffic network basic parameters and electric vehicle inventory data; based on the semi-dynamic traffic flow model, an electric vehicle charging and discharging space-time adjustment model is established in combination with electric vehicle charging and discharging adjustment constraints under the influence of the traffic flow; according to the obtained basic parameters of the power distribution network, a semi-dynamic traffic flow model and the electric vehicle charging and discharging space-time adjustment model, constructing a flexible interconnection power distribution network and charging facility collaborative expansion planning model; and converting and solving the collaborative expansion planning model to obtain a final planning scheme of the flexible interconnection power distribution network and the charging facility. The problems that power grid and traffic network coupling operation cannot be finely modeled and the multi-resource flexible adjustment capability cannot be fully excavated in power distribution network extension planning are solved, and the scientificity of a planning scheme and the power grid operation reliability are improved.

The invention provides an expressway lane level variable speed limit control method and device in severe weather, and is applied to the technical field of data processing. The method comprises the following steps: acquiring vehicle track and weather type data of each lane of a road section, and preprocessing to extract car-following and lane-changing data; a 2D-IDM and UDCL combined traffic flow model is constructed, parameters are calibrated based on a genetic algorithm, and driving behavior parameters are adjusted in combination with weather; an efficiency and safety double-target optimization model is established, and after multiple constraints are applied, an ant colony algorithm is used for solving a target speed limiting sequence; on the basis of a model predictive control framework, the traffic state is obtained in real time, evolution of 20-30 minutes in the future is predicted, target speed limit of each lane is output, closed-loop iteration is carried out, and a lane-level variable speed limit control model is formed; and finally, establishing different severe weather scenes, comparing with a non-control and integral control scheme, and combining macroscopic indexes and speed-time thermodynamic diagram analysis to generate a target speed limit control result.

The application discloses a kind of based on gradual large-scale short-time traffic flow prediction method, comprising: S1, the original data set collected by sensor is obtained, and the original data set is preprocessed;S2, construct based on gradual large-scale short-time traffic flow model, the processed multiple periodic time series data and driving distance adjacency matrix are used as the input of model, and the large-scale short-time traffic flow model is trained;S3, the current traffic data of input is predicted by the obtained model future traffic data.The method is based on the idea of gradual, time characteristics and spatial characteristics are fused by step-by-step fusion to more fully, more fully perform space-time fusion operation, effectively solve the problem of insufficient space-time fusion in existing method in large-scale traffic flow prediction, and greatly reduce the time overhead of calculation and space-dependent modeling;At the same time, the prediction effect is more accurate, can provide more accurate reference for urban traffic management.

This invention discloses a perception-driven intelligent multi-modal traffic signal optimization method and system, belonging to the field of traffic signal optimization technology. The method includes: collecting traffic flow data; constructing a multi-modal traffic flow model; connecting multiple traffic lights within a target area to obtain street light control signals; predicting traffic flow trends based on the traffic flow data and the street light control signals, using the multi-modal traffic flow model; and configuring a timing optimization scheme for multiple traffic lights within the target area based on the traffic flow trend prediction results. This invention solves the technical problem in the prior art where traffic light timing schemes cannot be accurately adjusted dynamically based on real-time traffic conditions and multi-modal traffic behavior interactions, leading to low traffic intersection efficiency. It achieves the technical effect of optimizing traffic light timing in a target area based on prediction results, effectively improving the traffic intersection efficiency.

The application discloses a macro dynamic traffic flow generation and evolution method and system for mixed traffic flow, and the method comprises the following steps: acquiring traffic flow basic data containing average speed, vehicle density, vehicle type ratio and automatic driving penetration rate; introducing a heterogeneous disturbance factor dynamically adjusted according to a heavy truck ratio in a continuity equation of a classical LWR model to obtain an improved traffic flow model to simulate fluctuation effects caused by mixed flow; calculating macro traffic flow by using the improved model; generating a virtual vehicle injection sequence based on a non-homogeneous Poisson process; detecting downstream density and speed changes in real time, dynamically correcting upstream injection intensity when approaching saturation, and suppressing reverse shock waves; deducing a start-end point matrix according to intersection flow by using a maximum entropy model; and finally outputting a virtual vehicle sequence with position, speed, vehicle type and target path, and injecting the virtual vehicle sequence into an automatic driving simulation platform. The application can accurately reflect congestion wave evolution characteristics under high heavy truck ratio mixed flow and improve test fidelity.

The present invention relates to a lane locking assisted lane changing method based on the combination of game lane changing and forced lane changing, and belongs to the field of traffic informatization. Aiming at the problem that the control strategy constructed based on game theory can complete the coordinated lane changing of vehicles, but the game benefits are difficult to support efficient lane changing when the traffic is deadlocked or stagnant, a method for combining game lane changing and forced lane changing to command efficient assisted lane changing in a cloud scenario is proposed. Prediction is performed according to the following steps: 1) traffic flow modeling; 2) description of the game lane changing process; 3) analysis of the game lane changing benefits; 4) division of game lane changing and forced lane changing; 5) construction of a combined lane changing model. Considering combining forced lane changing with game lane changing, when the distance between vehicles is too small, switching to a forced lane changing strategy can better improve the lane changing efficiency, thereby effectively reducing energy consumption, emissions and the effect of reducing the queue length upstream of the accident area on the highway.

The invention belongs to the technical field of traffic group travel prediction methods, and particularly relates to a multi-modal data fusion group travel intelligent analysis method based on a large model, and the method comprises the steps: collecting user historical travel track truth value data; performing time serialization processing on all switched base stations of the user from the starting time to the ending time of the travel to obtain base station zipper data of the user; in combination with a user travel track path reconstruction technology, filling up zipper data of the base station, and constructing full-chain base station data; carrying out vectorization and normalization on the discrete characteristics of the data of the whole-chain base station based on a Sichuan outflow line big model through an embedding technology to obtain multi-dimensional vector representation data of user travel; inputting the multi-dimensional vector representation data as a time sequence algorithm, and training to obtain a user travel prediction model; and reasoning and predicting a user travel mode based on the user travel prediction model. The technical problem that an existing user travel mode prediction method is not high in data accuracy is solved.

The present invention belongs to the field of intelligent transportation systems and relates to a multimodal spatiotemporal traffic flow modeling method that supports real-time prediction of large-scale road networks. The method first designs a spatiotemporal prediction framework for dynamic traffic networks and then performs training to obtain the final prediction model. The spatiotemporal prediction framework includes a data embedding layer, a spatiotemporal coding module, and a deep modeling and output module based on an expert mixture mechanism. The data embedding layer includes two parallel channels, namely time embedding and spectral-domain spatial embedding, and a spatiotemporal data fusion module. The spatiotemporal coding module includes a parallel block-level sparse temporal attention module and a spatial attention-message passing module and a weighted fusion layer. The deep modeling and output module based on the expert mixture mechanism includes a MoE dynamic expert modeling module, a fully connected mapping module, a skip connection layer, and an output layer. This design improves the model's response speed, prediction accuracy, and cross-regional adaptability in highly heterogeneous scenarios.

The invention belongs to the technical field of heterogeneous traffic flow modeling and intelligent traffic management, and relates to a heterogeneous traffic flow macroscopic fundamental diagram experimental modeling method considering takeover behaviors, which comprises the following steps of: firstly, performing stage division on driving behaviors of L3-level man-machine co-driving vehicles, and selecting a car-following model; integrating TOV permeability and takeover rate, four typical car-following modes in heterogeneous traffic flow and a function degradation mechanism of cooperative adaptive cruise control to deduce theoretical scale models of vehicles of various models in the traffic flow. A joint simulation platform is built to restore a heterogeneous traffic flow scene, and a two-stage multi-cycle experiment process is designed to collect comprehensive traffic flow data under different density levels. Whether the experimental vehicle model proportion is matched with the theoretical vehicle model proportion or not is checked, and if yes, the improved Underwood model is used for fitting experimental data to obtain an experimental model of the macroscopic fundamental diagram. The method has the advantage that an accurate model basis is provided for heterogeneous traffic flow characteristic analysis and CAV cooperative control strategy formulation.

The embodiment of the present application discloses a traffic control management method and system based on distributed architecture multi-source big data, which belongs to the field of smart transportation technology. It solves the problem that the existing technology has insufficient accuracy in predicting traffic conditions, which easily leads to inaccurate traffic control. It includes: constructing a dynamic traffic flow model based on the extracted feature data and the dynamic spatiotemporal graph corresponding to the traffic network; deducing the traffic situation through the central node in the traffic network to obtain a variety of traffic prediction scenarios, and matching the corresponding traffic control strategies; after detecting an abnormal traffic event, delineating the affected area according to the corresponding propagation impact trend of the abnormal traffic event on the spatial axis; dynamically combining multiple operating units corresponding to the traffic control strategy through the central node to generate a customized control plan; each edge node jointly optimizes the customized control plan based on the local traffic flow data using a distributed optimization algorithm to achieve traffic control management.

The application provides a signal system passing capacity evaluation method based on a sparse Gaussian process regression, and belongs to the field of intelligent traffic signal control. Specifically, the green signal ratio variable is included in the input feature and is simultaneously modeled to capture the mutual influence relationship between the signal configuration and the traffic state variable, so that a data-driven traffic flow model is developed by using the GP regression to reflect the complex nonlinear mapping between the high-dimensional variables, reduce the complexity of the calculation, obtain the uncertainty of the quantitative model, and accurately and dynamically evaluate the passing capacity of the intersection. The modified Gaussian kernel is used for modeling to capture the mutual influence relationship between the signal configuration and the traffic state variable, the data sparsification calculation is used to develop a data-driven traffic flow model by using the GP regression, the relationship that the signal timing (g / C) influences the traffic flow fluctuation with the change of the traffic flow is embodied, and the passing capacity of the intersection is dynamically evaluated.

The present invention discloses a traffic flow modeling method based on waveguide photon walks, comprising: dividing the traffic network into multiple sub-areas, extracting traffic flow data based on the sub-areas; generating driver dynamic probability patterns based on waveguide photon walks; screening the driver dynamic probability patterns for significant probability patterns to obtain significant probability patterns in each sub-area; realizing the mapping transformation between traffic flow data and driver significant probability patterns; mining the random features of the traffic network, defining the weighted average of the noise amplitude of the significant probability patterns as the driver randomness index; and defining the weighted average of the sum of the coupling strengths of the significant probability patterns as the traffic correlation index. The present invention innovatively applies the waveguide-based quantum walk system to the twin modeling of urban driver dynamics, effectively integrating driver randomness and traffic correlation in the traffic network, and providing a more accurate traffic volume modeling method.

The application belongs to the technical field of mixed traffic control, and discloses a mixed traffic multi-scale congestion suppression method mixed with slow-moving vehicles in a near-signal area, which comprises the following steps: S1. setting a three-lane mixed traffic scene mixed with network-connected automatic vehicles, human-driven vehicles and slow-moving vehicles in a near-signal area; S2. respectively analyzing the laws and influences of the red-green light interruption effect, the lane-changing behavior in the near-signal area and the slow-moving vehicle moving bottleneck effect; S3. establishing a mixed traffic flow model mixed with slow-moving vehicles in the near-signal area; S4. designing a network-connected automatic vehicle congestion feedback control method considering the flow difference; and S5. designing a speed guiding method of the network-connected automatic vehicle, so as to make speed suggestions and speed guidance for the network-connected automatic vehicle at the road section and vehicle scales. The application can effectively suppress the traffic congestion and parking problems caused by slow-moving vehicles and red-green lights, reduce fuel consumption, reduce the average travel and waiting time, improve the traffic efficiency, and provide a new idea for the traffic cooperative control problem in the near-signal area.

The invention relates to the field of campus intelligent systems, and discloses a campus intelligent pick-up and drop-off management method, which comprises the following steps of: obtaining real-time observation data of a target vehicle in a parking lot; updating a belief state of the traffic state in the parking lot based on the real-time observation data and a preset system dynamic model, wherein the system dynamic model is coupled with a traffic flow model used for simulating vehicle microscopic behaviors; and based on the updated belief state, adopting a partially observable Markov decision process optimization algorithm to decide a target adjacent berthing position and a preliminary guide path for the target vehicle. According to the method, a partial observable Markov decision process and cellular automaton microscopic traffic flow simulation are fused, and belief state updating is carried out in combination with a particle filter, so that dynamic highly-intelligent prediction and decision of the parking lot are realized; and the problems that the adaptability is insufficient in a complex dynamic environment, and local congestion or uneven resource allocation is easily caused are effectively solved.

The invention discloses an urban road traffic state intelligent estimation method based on microphysical representation, and the method comprises the steps: constructing a road section boundary condition based on the arrival and departure accumulated flow information of an intersection and a gate, and forming multi-source traffic perception input through combining with road section observation data; constructing a physical-data hybrid driving model, constructing a deep learning model in a data driving branch, learning a mapping relation between road section boundary cumulative flow and a traffic state in a road section, and converting an indistinguishable Newell traffic flow model into a distinguishable computational graph structure through function approximation and structural conversion in a distinguishable physical branch; and constructing a loss function of the hybrid drive model, so that data drive output and a physical model are kept coordinated, and traffic flow parameters are jointly estimated. Through deep embedding of the microphysical calculation graph, the dependence of a pure physical model on an ideal assumed condition is made up, and meanwhile, the problem that a pure data driving method is insufficient in generalization ability in a sensing blind area is solved.

The invention discloses a C-V2X-based traffic monitoring management method and a C-V2X-based traffic monitoring management system, relates to the technical field of path planning, and solves the problems of relatively single influence factor and insufficient individuation degree of path planning in the prior art. Various data including vehicle driving data and traffic flow data are collected, a traffic flow model is constructed based on the traffic flow data to predict the traffic flow, then a clustering algorithm is adopted to construct a vehicle driving behavior model, vehicles are classified based on the vehicle driving data, and finally a target function is established to predict the traffic flow. And calculating an optimal driving route for each vehicle based on the traffic flow model and the vehicle driving behavior model. According to the method, data of more dimensions, such as driving behaviors, driving habits and vehicle types of vehicles, can be introduced into path planning, so that more personalized and accurate path planning is realized, the requirements of different drivers are met, and the overall efficiency and safety of a traffic system are improved.

The present application relates to the field of traffic control, and particularly relates to a vehicle behavior recognition method and system based on an intelligent expressway system, the method comprising: collecting state information of all vehicles located on a set road section at a fixed frequency, the state information comprising a heading angle and a planar coordinate and a horizontal velocity vector of the vehicle in a planar rectangular coordinate system with the starting point of the set road section as the origin; calculating a real-time lane-changing probability of a target vehicle, and determining that the target vehicle has a high lane-changing risk when the real-time lane-changing probability is greater than or equal to a preset threshold value. The present application does not rely on fixed road markings such as physical lane lines at all, and only performs traffic flow modeling and behavior analysis based on the motion trajectory of the vehicle itself, has excellent all-weather and all-road section adaptability, and can timely identify lane-changing risks by calculating the lane-changing probability of the vehicle in real time, providing a longer reaction time for the traffic management system and the rear vehicle, and effectively improving road safety.

The application provides a networked vehicle cooperative control method based on road network traffic information, and relates to the technical field of networked vehicle cooperative control, and comprises the following steps: establishing a vehicle dynamics model and a road network traffic flow model; at the upper intersection control layer, designing a consistency controller based on a Lagrange traffic flow model, adjusting the expected speed of an approaching vehicle platoon according to the traffic density information of adjacent intersections, so that the traffic density entering each intersection is consistent, and determining the upper limit of the communication time delay based on a system time-delay differential equation to ensure the consistency of the road network; at the lower tracking control layer, based on the vehicle dynamics model, using a leader-follower communication topology, designing a distributed tracking controller for a follower vehicle, so that the follower vehicle tracks the expected speed of a leader vehicle at an expected inter-vehicle distance, and determining the upper limit of the communication time delay based on a subsystem characteristic equation to ensure the stability of the vehicle platoon; and verifying the internal stability of the vehicle platoon and the stability of the platoon.

The modeling method and following method of car-following model considering the influence of multiple vehicles in front and acceleration information belong to the technical fields of vehicle-road cooperation and traffic flow modeling. In order to solve the problem that the existing car-following model does not consider comprehensive factors, thereby the overall starting time is long and the operation efficiency of the team is affected. The application considers the influence of the position and speed of the front vehicle and the second front vehicle on the host vehicle, and establishes a car-following model considering the influence of the acceleration of the vehicle in front, and then describes the car-following behavior based on the established car-following model. The application is suitable for modeling and following process of car-following model.

The application relates to the field of traffic management and control, and discloses a safety facility system suitable for a highway flat intersection with large traffic volume, which comprises a data sensing module, a traffic flow modeling module, an optimal control module, a dynamic execution module and a feedback monitoring module; the data sensing module is used for collecting the traffic flow speed, density and position information of the intersection in real time; the traffic flow modeling module establishes a traffic flow potential field model to describe the traffic flow density and speed distribution state; the optimal control module generates an optimal control strategy; the dynamic execution module dynamically regulates and controls the signal lamp duration and the yellow flashing lamp flicker frequency according to the optimal control strategy; and the feedback monitoring module is used for monitoring the traffic flow state and system performance and feeding back the feedback data to the data sensing module and the optimal control module. The application realizes real-time dynamic regulation and control of the traffic flow of the flat intersection, improves the passing efficiency, reduces the conflict point accident rate, and guarantees the safety and smoothness of the highway flat intersection.