CN117636631A

CN117636631A - Rapid passing intelligent auxiliary method and system for emergency vehicle intersection

Info

Publication number: CN117636631A
Application number: CN202311591039.0A
Authority: CN
Inventors: 冯晓锋; 陈颖; 彭旭熠; 李丁龙; 王睿轩
Original assignee: Hunan Police Academy
Current assignee: Hunan Police Academy
Priority date: 2023-11-24
Filing date: 2023-11-24
Publication date: 2024-03-01

Abstract

The invention relates to the technical field of intelligent auxiliary traffic of vehicles, in particular to a rapid intelligent traffic auxiliary method and system for an emergency vehicle intersection. The method comprises the following steps: the method comprises the steps of utilizing vehicle-mounted communication equipment to collect real-time data of an emergency vehicle and generating real-time data of the emergency vehicle; planning the shortest path of the emergency vehicle according to the real-time data of the emergency vehicle, and generating planned path data; dividing the planned path section of the intersection position of the planned path data to generate divided section data; extracting vehicle circulation data from the divided road section data to obtain target vehicle circulation data; predicting the traffic time of the intersection on the target vehicle circulation data to generate predicted traffic time of the vehicle; and carrying out traffic signal lamp strategy design of the intersection based on the real-time data of the emergency vehicle and the predicted vehicle traffic time, and generating an intersection control signal lamp strategy. The invention realizes more efficient rapid passing of emergency vehicles.

Description

Rapid passing intelligent auxiliary method and system for emergency vehicle intersection

Technical Field

The invention relates to the technical field of intelligent auxiliary traffic of vehicles, in particular to a rapid intelligent traffic auxiliary method and system for an emergency vehicle intersection.

Background

Under the emergency condition that the emergency vehicle runs, every second is critical, and a great amount of time is lost due to the congestion condition of the intersection, auxiliary traffic is needed through a signal lamp controlling the intersection, the implementation of the auxiliary traffic can ensure that the emergency vehicle can quickly and safely pass through the intersection to reach the accident site, and rapid emergency actions are provided, so that casualties and property loss are reduced, the traffic signal and the traffic flow are dynamically regulated, the emergency vehicle can be ensured to pass through the intersection smoothly, and the overall efficiency of the urban traffic system is improved. With the continuous progress of technology, the development of intelligent traffic systems, automatic driving technologies and communication technologies can provide more intelligent solutions for emergency vehicle traffic, improve efficiency and safety, and enable emergency response to be more efficient. However, the conventional rapid transit assisting method for the intersection of the emergency vehicle requires a great deal of manpower to control the vehicles at the intersection by people, and may make the transit effect worse, and delay the arrival time of the emergency vehicle at the destination.

Disclosure of Invention

Based on the above, the invention provides a rapid passing intelligent auxiliary method and system for an emergency vehicle intersection, so as to solve at least one of the above technical problems.

In order to achieve the above purpose, a rapid passing intelligent auxiliary method for an emergency vehicle intersection comprises the following steps:

the method comprises the steps that S1, real-time data acquisition of an emergency vehicle is conducted through vehicle-mounted communication equipment, and real-time data of the emergency vehicle are generated, wherein the real-time data of the emergency vehicle comprise real-time position data of the emergency vehicle, real-time speed data of the emergency vehicle, destination position data of the emergency vehicle and running time period data; planning the shortest path of the emergency vehicle according to the real-time position data of the emergency vehicle and the target position data of the emergency vehicle, and generating planning path data;

s2, dividing the planned path section of the intersection position of the planned path data to generate divided section data; acquiring vehicle data of the divided road sections according to the data of the divided road sections to obtain the vehicle data of the divided road sections;

s3, analyzing the vehicle circulation data of the divided road sections to generate vehicle circulation data of the divided road sections; extracting vehicle circulation data of the target time node from the vehicle circulation data of the divided road sections to obtain target vehicle circulation data;

s4, collecting historical vehicle circulation data of the planned path according to the planned path data, and generating historical vehicle circulation data; based on a decision tree algorithm and historical vehicle circulation data, carrying out a relationship model for predicting the traffic time of the intersection, and generating a vehicle traffic time prediction model; predicting the traffic time of the intersection on the target vehicle circulation data by using the vehicle traffic time prediction model, and generating predicted traffic time of the vehicle;

And S5, designing a traffic signal lamp strategy of the intersection based on the real-time speed data of the emergency vehicle and the predicted vehicle traffic time, and generating an intersection control signal lamp strategy.

The invention utilizes the vehicle-mounted communication equipment to collect the real-time data of the emergency vehicle, generates the real-time data of the emergency vehicle, can accurately know the current state and the target of the emergency vehicle, is beneficial to planning the shortest path, ensures that the vehicle can reach the destination rapidly and safely, and greatly improves the rescue and emergency response efficiency. The shortest path planning is carried out according to the real-time position and the destination data of the emergency vehicle, so that unnecessary detours or congestion areas can be avoided, time and fuel cost are saved, the rapid traffic capacity of the emergency vehicle is improved, the residence time on a road is reduced, and potential danger is reduced to the greatest extent. The road section division of the intersection position is carried out on the planning path data, the whole route can be divided into a plurality of small sections, so that the position condition of the emergency vehicle on different road sections can be known more accurately, the position and the running progress of the vehicle can be monitored better, and the traffic management is more refined. The vehicle information is acquired according to the data of the divided road sections, so that the key data such as the number, the type and the speed of vehicles on each divided road section can be known in real time, important real-time information is provided for intersection traffic decision, and the traffic signal lamp, the traffic control and the road flow can be better regulated, so that the emergency vehicles can pass through the intersections smoothly. The analysis of the road segment dividing vehicle data generates road segment dividing vehicle circulation data which describe the flow conditions of vehicles on different road segments, and traffic managers can better grasp road conditions near intersections, including vehicle congestion, speed change, flow fluctuation and the like by knowing the vehicle circulation data, so that traffic strategies can be formulated more accurately. The vehicle circulation data extraction of the target time node is beneficial to monitoring the traffic situation of vehicles in a specific time period in real time, so that traffic management staff is helped to adjust traffic signal lamp strategies more flexibly to adapt to traffic demands of different time periods, and particularly in emergency situations, the smoothness of emergency vehicle traffic is ensured. Through careful analysis of vehicle circulation data, the traffic situation on a future intersection road section is predicted, measures are taken in advance to reduce occurrence of congestion or bottleneck, traffic fluidity is improved, traffic time is reduced, and rapid traffic efficiency of emergency vehicles is further improved. By collecting historical vehicle circulation data according to planned path data, historical data records of vehicle passing time can be established, the data records passing time information of vehicles on the same road section in the past, analysis of traffic modes and trends is facilitated, and a basis is provided for future passing time prediction. Based on methods such as decision tree algorithm, etc., a vehicle passing time prediction model is established, key factors and rules related to passing time are identified through historical vehicle circulation data, and the model can predict the vehicle passing time according to different conditions and traffic conditions, so that traffic management is more intelligent. The vehicle traffic time prediction model is utilized to predict the traffic time of the intersection on the traffic data of the target vehicle, and the traffic time of all vehicles corresponding to the road at the intersection can be predicted in advance, so that traffic signal lamp strategies and traffic control are coordinated better, and the emergency vehicles can pass through the intersection smoothly. According to the emergency vehicle passing time nodes and the vehicle passing time prediction, the intersection control signal lamp strategy can be dynamically adjusted, emergency situations can be responded timely, smoothness and no blockage of the intersection when the emergency vehicle passes by each intersection are guaranteed, waiting time is reduced to the greatest extent, and quick response of emergency rescue actions is guaranteed. Therefore, the intelligent auxiliary method for fast passing of the emergency vehicle intersection does not need to manually regulate and control vehicles at the intersection, saves a great deal of manpower management, and adjusts the intersection signal lamp in advance according to the time required for predicting the emergency vehicle to reach the intersection, so that the emergency vehicle just reaches the intersection smoothly without blockage, and reaches a destination on time.

Preferably, step S1 comprises the steps of:

s11, utilizing vehicle-mounted communication equipment to acquire real-time data of an emergency vehicle, and generating real-time data of the emergency vehicle;

step S12, analyzing passable routes of emergency lanes according to the real-time position data of the emergency vehicles and the destination position data of the emergency vehicles to generate passable path data;

and S13, extracting shortest traffic path data according to the traffic path data to obtain planning path data.

The invention utilizes the vehicle-mounted communication equipment to collect real-time data of the emergency vehicle, provides real-time and comprehensive vehicle data, enables the system to know the current state of the emergency vehicle, including the position, the speed, the driving direction and the like, is beneficial to timely responding to emergency conditions, and determines the optimal passing path so as to reduce rescue and emergency response time to the greatest extent. And carrying out the passable route analysis of the emergency lane according to the real-time position data of the emergency vehicle and the destination position data of the emergency vehicle, wherein the passable route analysis ensures that the emergency vehicle can select a passable route, avoids a possibly existing closed road or other obstacles, maximally improves the passing efficiency of the emergency vehicle, and ensures that the emergency vehicle can safely and quickly reach a destination. The shortest traffic path data extraction is carried out according to the traffic path data, and the planning path data extraction enables the system to provide detailed navigation guidance for emergency vehicles, so that the emergency vehicles can be ensured to advance along the shortest path in the traffic process, the traffic time is shortened, the risk is reduced, and the success rate of rescue and emergency response is improved.

Preferably, step S2 comprises the steps of:

s21, extracting intersection position data of a planned path according to the planned path data to obtain intersection position data;

s22, planning path section division is carried out on planning path data based on intersection position data, and division section data are generated;

and S23, collecting vehicle data of the divided road sections according to the data of the divided road sections to obtain the vehicle data of the divided road sections.

According to the invention, the intersection position data of the planned path is extracted according to the planned path data, the extraction of the intersection position data is beneficial to the system to better understand the intersection distribution condition in the planned path, key information is provided for subsequent traffic management decisions, and targeted measures can be taken at the intersection position, so that the traffic passing efficiency is improved. The road section division is carried out on the planned path data based on the intersection position data, the road section division simplifies the complex planned path into a series of small road sections, the flow condition of vehicles on different road sections can be monitored and managed more finely, and the accuracy and operability of traffic management are improved. The vehicle data acquisition of the divided road sections is carried out according to the data of the divided road sections, the acquisition of the vehicle data of the divided road sections is beneficial to knowing the road flow condition near the intersection and the traffic condition of the vehicle on each road section in real time, real-time data support is provided for traffic management, and traffic jam and flow change can be better dealt with.

Preferably, step S3 comprises the steps of:

s31, calculating the vehicle circulation efficiency of the divided road segments by using a road segment vehicle circulation efficiency algorithm to generate the circulation efficiency data of the divided road segments;

step S32, analyzing the vehicle circulation data of the divided road sections according to the vehicle data of the divided road sections and the circulation efficiency data of the divided road sections, and generating the vehicle circulation data of the divided road sections;

and S33, extracting the vehicle circulation data of the target time node from the vehicle circulation data of the divided road sections to obtain target vehicle circulation data.

According to the invention, the vehicle circulation efficiency calculation of the divided road sections is carried out on the vehicle data of the divided road sections, the circulation efficiency calculation of the divided road sections is beneficial to the quantitative analysis of the traffic fluency on different road sections by the system, the bottleneck road sections, the congestion points and the traffic barriers can be determined by measuring the circulation efficiency, the targeted data support is provided for traffic management decision, the vehicle circulation data of each road section in different time periods can be estimated, and the future vehicle data of each road in short time can be calculated approximately. The vehicle circulation data analysis of the division road sections is carried out according to the vehicle data of the division road sections and the circulation efficiency data of the division road sections, the future vehicle circulation data of short time can be predicted according to the current vehicle data of the division road sections and the circulation efficiency data of the division road sections, the actual situation of each road section can be better known, problems can be timely found, and effective measures can be formulated. And extracting vehicle circulation data of a target time node from the vehicle circulation data of the divided road sections, wherein the target time node corresponds to the time from each emergency vehicle to the intersection, and the vehicle condition when the emergency vehicle arrives at the intersection can be known from the vehicle circulation data of the divided road sections under the target time node, so that a traffic control decision can be more accurately made.

Preferably, the road section vehicle circulation efficiency algorithm in step S31 is as follows:

wherein P is represented as road vehicle circulation efficiency, L ₂ Expressed as the end position of the road segment, L ₁ The initial position of a road section is denoted as a starting position of the road section, alpha is denoted as an average outgoing vehicle of the road section, beta is denoted as an average incoming vehicle of the road section, x is denoted as a total length of the road section, ρ is denoted as an average queuing length of the road section, γ is denoted as a peak period adjustment factor, t is denoted as an outgoing time required for the road section to outgoing from the vehicle, ζ is denoted as a congestion influence adjustment coefficient, and v is denoted as an abnormal adjustment value of the circulation efficiency of the road section vehicle.

The invention utilizes a road sectionVehicle circulation efficiency algorithm which fully considers the end position L of road section ₂ Start position L of road segment ₁ The average outgoing vehicle α of the road section, the average incoming vehicle β of the road section, the total length x of the road section, the average queuing length ρ of the road section, the road section peak period adjustment factor γ, the required outgoing time t of the road section outgoing vehicle, the congestion influence adjustment coefficient ζ, and the interaction relationship between functions to form a functional relationship:

that is to say,the functional relation is used for dynamically calculating the circulation condition of the vehicle data of the divided road sections by calculating the vehicle data of the divided road sections and then analyzing the existing vehicle data of the divided road sections at different moments; the average outgoing vehicles and average incoming vehicles for the road segment reflect the average number of vehicles that leave and enter per unit time on the road segment. The average queuing length of the road segment reflects the quantification mode of the vehicle congestion status on the road segment. The road peak time adjustment factor is used for considering the efficiency adjustment during the peak traffic, and can be adjusted under different time distribution conditions; the average departure time required for the road segment to exit the vehicle reflects the speed and time relationship of the vehicle passing through the road segment. The congestion influence adjustment coefficient is used for considering the influence of congestion on the circulation efficiency of the road segments, and can be used for adjusting the congestion influence coefficient according to the vehicle circulation influence caused by the congestion. The functional relation comprehensively considers the influence of a plurality of factors on the circulation efficiency of the road section vehicles, including the entering and exiting speeds of the vehicles, the density of the vehicles, the peak traffic time, the congestion condition and other abnormal conditions, and the circulation condition of the road section vehicles can be more comprehensively evaluated through adjustment and synthesis of the parameters so as to be used for clearly knowing the number of the road section vehicles, the required communication time and the like at different time nodes. The function relation is adjusted and corrected by using the abnormal adjustment value tau of the road section vehicle circulation efficiency, so that the error influence caused by abnormal data or error items is reduced, the road section vehicle circulation efficiency P is more accurately generated, and the road section vehicle data division is improved Accuracy and reliability of vehicle circulation efficiency calculation of the split section. Meanwhile, the adjustment factors and the adjustment values in the formula can be adjusted according to actual conditions and are applied to different road section dividing vehicle data, so that the flexibility and applicability of the algorithm are improved.

Preferably, step S33 includes the steps of:

step S331, calculating a passing intersection time node of the emergency vehicle according to the real-time speed data of the emergency vehicle and the intersection position data, and generating the passing intersection time node of the emergency vehicle;

and S332, extracting vehicle circulation data of the target time node according to the time node of the traffic intersection and the vehicle circulation data of the divided road sections so as to obtain the target vehicle circulation data.

According to the invention, the time node calculation of the passing intersection of the emergency vehicle is carried out according to the real-time speed data of the emergency vehicle and the position data of the intersection, and the time node of the passing intersection is calculated excessively, so that the arrival time of the emergency vehicle at the intersection can be accurately predicted, the traffic signal lamp strategy can more accurately respond to the passing requirement of the emergency vehicle, the waiting time is reduced, and the rapidity of emergency response is ensured. And extracting vehicle circulation data of the target time node according to the vehicle circulation data of the divided road sections at the time node of the traffic intersection, wherein the vehicle circulation data of the target time node is extracted so that the system can concentrate on traffic conditions in a specific time period, and is beneficial to better knowing information such as vehicle flow, speed, density and the like of emergency vehicles at the traffic intersection, providing real-time data support for traffic control and signal lamp strategies and ensuring smoothness of traffic.

Preferably, step S4 comprises the steps of:

step S41, collecting historical vehicle circulation data of a planned path according to the planned path data, and generating historical vehicle circulation data;

s42, establishing a mapping relation of intersection vehicle passing time prediction by utilizing a decision tree algorithm, and generating an initial vehicle passing time prediction model;

step S43, transmitting historical vehicle circulation data to an initial vehicle passing time prediction model for model training, and generating a vehicle passing time prediction model;

and S44, predicting the traffic time of the intersection on the target vehicle circulation data by using the vehicle traffic time prediction model, and generating the predicted traffic time of the vehicle.

According to the invention, historical vehicle circulation data acquisition of the planned path is carried out according to the planned path data, the acquisition of the historical vehicle circulation data is helpful for establishing the basis of a vehicle passing time prediction model, and the traffic mode and trend can be identified by analyzing the past passing data, so that the basis is provided for future passing time prediction. The decision tree algorithm is utilized to establish a mapping relation of the intersection vehicle passing time prediction, and the vehicle passing time prediction model is established so that the system can predict the vehicle passing time according to different conditions and traffic conditions, thereby planning signal lamp strategies and traffic control better. The historical vehicle circulation data is transmitted to the initial vehicle passing time prediction model for model training, the model training enables the vehicle passing time prediction model to be more accurate and reliable, and the model is adjusted by using the historical data, so that different traffic conditions and changes can be better adapted, and the prediction accuracy is improved. The vehicle traffic time prediction model is utilized to predict the traffic time of the intersection on the traffic data of the target vehicle, and the generation of the vehicle predicted traffic time can predict the traffic time of the vehicle when approaching the intersection in advance, so that traffic signal lamp strategies and traffic control are better coordinated, and the emergency vehicle is ensured to pass through the intersection smoothly.

Preferably, step S43 comprises the steps of:

step S431, historical vehicle circulation data extraction of corresponding time nodes is carried out on the historical vehicle circulation data according to the time nodes of the emergency vehicle passage intersection, and historical target vehicle circulation data are generated;

step S432, designing a weighted loss function of the model of the initial vehicle passing time prediction model according to the historical target vehicle circulation data, and generating the weighted loss function of the initial vehicle passing time prediction model;

step S433, the historical vehicle circulation data is transmitted to an initial vehicle passing time prediction model, weight adjustment is carried out on the historical vehicle circulation data according to a weighted loss function of the initial vehicle passing time prediction model, and model training is carried out on the initial vehicle passing time prediction model by the historical vehicle circulation data after the weight adjustment, so that a vehicle passing time prediction model is generated.

According to the invention, historical vehicle circulation data extraction of the corresponding time node is carried out on the historical vehicle circulation data according to the time node of the emergency vehicle passage intersection, the historical target vehicle circulation data extraction is helpful for focusing on traffic conditions in a specific time period, real-time data support is provided for traffic management, and more accurate traffic control decisions can be made according to emergency conditions and prediction conditions. The model weighting loss function design is carried out on the initial vehicle passing time prediction model according to the historical target vehicle circulating data, so that the performance of the vehicle passing time prediction model can be estimated more accurately, the model can be adjusted better by considering the importance of different data points, the fitting degree of the model to the historical data is improved, and the prediction accuracy of the model is improved. The historical vehicle circulation data is transmitted to the initial vehicle passing time prediction model, the weight adjustment and model training of the historical data can be better adapted to the change and error in the historical data, and the fitting degree and accuracy of the model can be improved by adjusting the model according to the performance of the historical data, so that the model is more suitable for predicting future vehicle passing time.

Preferably, step S5 comprises the steps of:

step S51, analyzing the end time node of the control signal lamp based on the time node of the emergency vehicle passing intersection, and generating the end time node of the control signal lamp;

step S52, according to the predicted vehicle passing time and the control signal lamp ending time node, performing control signal lamp starting time node analysis to generate a control signal lamp starting time node;

and step S53, designing a control signal lamp strategy of the intersection according to the control signal lamp starting time node and the control signal lamp ending time node, and generating the control signal lamp strategy of the intersection.

The invention carries out the end time node analysis of the control signal lamp based on the time node of the emergency vehicle passing intersection, and can accurately determine the end time of the control state of the current intersection signal lamp through the end time node analysis so as to ensure that the emergency vehicle can pass smoothly at a proper time and the subsequent traffic management is not delayed. And according to the predicted vehicle passing time and the control signal lamp ending time node, the starting time node analysis of the control signal lamp is carried out, the time required by the common vehicle to pass through the traffic signal lamp is made clear by predicting the vehicle passing time, then the starting time of the control signal lamp is determined according to the required time and the control signal lamp ending time node, the common vehicle is made to pass first, then the emergency vehicle reaches the intersection and is just unblocked without blocking, and the passing condition of the emergency vehicle is ensured. The signal lamp strategy design is the core of traffic management, can balance different traffic demands, ensures the coordination traffic between emergent vehicles and other vehicles. Through reasonable signal lamp strategy, can improve the traffic efficiency at intersection to the maximum extent, reduce the emergence of jam and accident.

The present disclosure provides a fast-passing intelligent auxiliary system for an emergency vehicle intersection, configured to execute the fast-passing intelligent auxiliary method for an emergency vehicle intersection, where the fast-passing intelligent auxiliary system for an emergency vehicle intersection includes:

the emergency vehicle path planning module is used for acquiring real-time data of an emergency vehicle by using vehicle-mounted communication equipment to generate real-time data of the emergency vehicle, wherein the real-time data of the emergency vehicle comprise real-time position data of the emergency vehicle, real-time speed data of the emergency vehicle, destination position data of the emergency vehicle and operation time period data; planning the shortest path of the emergency vehicle according to the real-time position data of the emergency vehicle and the target position data of the emergency vehicle, and generating planning path data;

the system comprises a planned path vehicle data acquisition module, a data acquisition module and a data processing module, wherein the planned path vehicle data acquisition module is used for carrying out planned path section division of intersection positions on planned path data and generating division section data; acquiring vehicle data of the divided road sections according to the data of the divided road sections to obtain the vehicle data of the divided road sections;

the vehicle circulation data analysis module is used for analyzing the vehicle circulation data of the division road sections to generate the vehicle circulation data of the division road sections; extracting vehicle circulation data of the target time node from the vehicle circulation data of the divided road sections to obtain target vehicle circulation data;

The vehicle prediction passing time module is used for collecting historical vehicle circulation data of the planned path according to the planned path data and generating historical vehicle circulation data; based on a decision tree algorithm and historical vehicle circulation data, carrying out a relationship model for predicting the traffic time of the intersection, and generating a vehicle traffic time prediction model; predicting the traffic time of the intersection on the target vehicle circulation data by using the vehicle traffic time prediction model, and generating predicted traffic time of the vehicle;

and the intersection control signal lamp strategy analysis module is used for carrying out intersection traffic signal lamp strategy design based on the real-time speed data of the emergency vehicle and the predicted vehicle traffic time to generate an intersection control signal lamp strategy.

The method has the beneficial effects that key data of the emergency vehicle, including information such as position, destination and speed, are collected in real time through the vehicle-mounted communication equipment, and then complex path analysis is carried out by utilizing the data so as to find the shortest passing path. The method is beneficial to optimizing the driving route of the emergency vehicle, improving the response speed of the emergency vehicle, reducing the possibility of traffic jam and delay, improving the traffic safety and efficiency under emergency conditions, supporting the rapid passing of the emergency vehicle in cities, and playing an important role in the processing of emergency events. The traffic intersection situation on the path is better understood by extracting intersection position data, and the planned path is divided into a plurality of small road segments based on the position data to form divided road segment data. And finally, vehicle data acquisition is carried out on the divided road sections to acquire the vehicle data of the divided road sections, so that the traffic condition can be analyzed more accurately, the detail and accuracy of the road section circulation data can be improved, a more reliable data base can be provided for subsequent traffic management and emergency vehicle traffic, the traffic management and optimization can be effectively supported, and the efficiency and emergency response capability of a traffic system can be improved. The traffic efficiency data of the divided road sections are calculated and generated through the road section vehicle circulation efficiency algorithm, so that the traffic capacity and the congestion condition of different road sections can be evaluated, the vehicle circulation data of the divided road sections and the traffic efficiency data are comprehensively analyzed, the traffic condition is more comprehensively reflected, the vehicle circulation data at a specific moment are extracted according to the target time node, the knowledge of the traffic condition is improved, accurate information is provided for real-time traffic decision, more intelligent and refined traffic management and more effective emergency vehicle traffic are supported, and the overall operation efficiency of a road network is improved. The method comprises the steps of collecting historical vehicle circulation data, establishing a data base for knowing past traffic conditions and traffic time, applying a decision tree algorithm, creating an initial vehicle traffic time prediction model, transmitting the historical vehicle circulation data to the model for model training according to different factors, analyzing and predicting target vehicle circulation data by continuously optimizing model parameters, generating a vehicle traffic time prediction model, accurately predicting vehicle traffic time, providing powerful support for traffic management and emergency vehicle traffic, and being beneficial to improving the efficiency and reliability of a traffic system. Based on the time node of the emergency vehicle passing intersection, the end time node analysis of the control signal lamp is carried out, smooth passing of the emergency vehicle is ensured, the start time node analysis of the control signal lamp is carried out in combination with the predicted vehicle passing time and the end time node, so that traffic waiting time is reduced to the greatest extent, the control signal lamp strategy of the intersection is designed according to the time nodes, the emergency vehicle is ensured to pass preferentially, and the common vehicle can pass smoothly, so that the efficiency and the mobility of a traffic system are improved, the traffic management and the response capability under the emergency condition are enhanced, and the rapid passing of the emergency vehicle at the intersection is facilitated to be provided with higher efficiency.

Drawings

FIG. 1 is a schematic flow chart of steps of a fast traffic intelligent auxiliary method for an emergency vehicle intersection of the present invention;

FIG. 2 is a flowchart illustrating the detailed implementation of step S3 in FIG. 1;

FIG. 3 is a flowchart illustrating the detailed implementation of step S4 in FIG. 1;

FIG. 4 is a flowchart illustrating the detailed implementation of step S43 in FIG. 3;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 4, the present invention provides a fast traffic intelligent auxiliary method for an emergency vehicle intersection, comprising the following steps:

In the embodiment of the present invention, as described with reference to fig. 1, the step flow diagram of the intelligent rapid transit assistance method for an emergency vehicle intersection of the present invention is provided, and in the embodiment, the intelligent rapid transit assistance method for an emergency vehicle intersection includes the following steps:

in the embodiment of the invention, the real-time data of the emergency vehicle is acquired through the vehicle-mounted communication equipment equipped with the emergency vehicle, the data comprise the real-time position, speed, destination position and operation time period of the emergency vehicle, the accurate real-time position information of the emergency vehicle is acquired through the technologies of a Global Positioning System (GPS) and the like, the speed of the vehicle is monitored at the same time so as to know the current running state of the vehicle, and the destination of the emergency vehicle, namely the expected place of the emergency vehicle and the time period information of an emergency task are recorded and are all included in the real-time data of the emergency vehicle. The shortest path planning of the emergency vehicle is carried out based on the real-time data of the emergency vehicle, an advanced path planning algorithm is utilized in the planning process, the path reaching the destination at the highest speed is calculated according to the real-time position and the destination position of the emergency vehicle, the shortest distance is considered in the process, the factors such as the real-time speed of the vehicle, the road passability and the like are comprehensively considered, so that the generated path is optimal, the planned path data comprise detailed path information from the current position to the destination, and the information is used for predicting the traffic time of an intersection and formulating a signal lamp control strategy in the subsequent steps.

in the embodiment of the invention, the planned path section of the intersection position is divided for the planned path data, the planned path is divided into a series of relatively independent sections, each section corresponds to a specific intersection or crossing, the division considers the traffic topology structure, and each section is ensured to have a clearly defined starting point and end point, so that the running condition of the vehicle in each section can be monitored more accurately. The vehicle data acquisition of the divided road segments according to the data of the divided road segments comprises the steps of disposing a vehicle-mounted sensor on each divided road segment or collecting real-time data of common vehicles by using equipment such as a traffic monitoring camera and the like, wherein the data comprise information such as the number, speed, density, lane occupation condition and the like of the common vehicles, and the vehicle identification technology can be used for acquiring specific information of the vehicles, such as the types of the vehicles, license numbers and the like, and the detailed information about the running condition of the vehicles of each divided road segment can be acquired through the data acquisition.

in the embodiment of the invention, the vehicle data of each divided road section is analyzed, including statistics and calculation on the aspects of the number, speed, density, lane occupation condition and the like of the vehicles, so as to know the traffic circulation condition of each road section, including whether congestion, vehicle speed change and the like exist, and the vehicle circulation data of the divided road sections are generated, wherein the data comprises the information of the vehicle flow, average speed, lane utilization rate and the like of each road section, and are used for describing the traffic condition of each road section more comprehensively. And extracting vehicle circulation data of a target time node from the vehicle circulation data of the divided road sections, wherein the target time node is a corresponding time node for emergency vehicle running, and the relevant information is extracted from the vehicle circulation data of the divided road sections, and comprises information such as the number of vehicles passing through each road section in a target time point, speed distribution, congestion degree and the like.

in the embodiment of the invention, historical vehicle circulation data are started to be collected according to the planned path data, wherein the data comprise the information of vehicle flow, traffic time, congestion and the like of different road sections on the planned path in the past, and the information can be obtained through data sources such as traffic monitoring cameras, sensors, historical traffic records and the like, so that timeliness and accuracy of the data are required to be ensured, and a reliable prediction model is established. A machine learning method, such as a decision tree algorithm, is used for establishing a vehicle passing time prediction model based on historical vehicle circulation data, the historical vehicle circulation data is used for training the model, the vehicle passing time is used as a target variable, other factors such as road section attributes, time periods, traffic conditions and the like are used as characteristics, and a mathematical model for predicting the passing time of vehicles at intersections is established through model training and can be used for predicting the passing time of the vehicles at different road sections. The method comprises the steps of applying a vehicle passing time prediction model to target vehicle circulation data, inputting characteristics in the target vehicle circulation data, such as the number of vehicles, speed, congestion and the like, and calculating the passing time of each road section on a planned path by using the prediction model to generate the predicted passing time of the vehicle.

According to the embodiment of the invention, the traffic time node of the emergency vehicle reaching each intersection is determined according to the real-time speed data of the emergency vehicle and the running time of the emergency vehicle, then the traffic signal strategy design of the intersection is carried out according to the traffic time node of the emergency vehicle reaching each intersection and the predicted vehicle traffic time, for example, the end state of the control signal lamp of the intersection is designed according to the traffic time of the emergency vehicle reaching each intersection, if the emergency vehicle just passes through the signal lamp of one intersection, the control state of the signal lamp of the intersection is ended, the predicted time node of the end state is recorded, then the starting state of the control signal lamp of the intersection is designed according to the predicted time node of the end state and the predicted vehicle traffic time, the control time of the traffic signal lamp is designed according to the predicted time node of the starting state and the predicted vehicle traffic time, so that the traffic signal lamp strategy is designed according to the control time of the traffic signal lamp, for example, the control state of the control signal lamp of the intersection is started according to the traffic signal strategy, the common vehicle passes through the control signal lamp of the intersection, when the emergency vehicle just has no traffic jam when the emergency vehicle runs to the intersection, and then the control signal lamp of the intersection is ended when the emergency vehicle passes through the intersection.

Preferably, step S1 comprises the steps of:

In the embodiment of the invention, the real-time data acquisition of the emergency vehicle is carried out by using the vehicle-mounted communication equipment. This includes using sensors and communication devices inside the vehicle, such as Global Positioning System (GPS), vehicle speed sensors, communication modules, etc., to collect real-time data of the emergency vehicle, including information of the current location, speed, destination coordinates, and running time period of the vehicle. And analyzing the passable route of the emergency lane according to the real-time position data of the emergency vehicle and the destination position data of the emergency vehicle, and determining the passable route of the emergency vehicle according to the current position and the destination of the vehicle, wherein the route considers factors such as road limitation, road maintenance and the like so as to ensure that the emergency vehicle can reach the destination as soon as possible and safely, and passable route data is generated, including road sections along the way and navigation instructions. And extracting shortest traffic path data according to the passable path data, for example, further carrying out shortest path calculation through a path planning algorithm, screening out the shortest traffic path, namely, the optimal path from the current position to the destination, and determining the path reaching the destination at the highest speed by considering factors such as the length, the speed, the traffic flow and the like of the road section. The result is the generation of planned path data which will be used in subsequent steps for the prediction of intersection transit time and the design of traffic light control strategies.

Preferably, step S2 comprises the steps of:

The present invention analyzes the planned path data to identify intersection points therein, which are typically where roads intersect or meet, where traffic signals may be present, and obtains accurate geographic coordinates and identification information for each intersection using Geographic Information Systems (GIS) or map data. This includes longitude, latitude, intersection name, etc., and thus intersection location data. And dividing the planned path section of the planned path data based on the intersection position data, determining the sections among all intersections on the planned path, wherein each section consists of one intersection to the next intersection, and generating divided section data comprising the information of the starting point and the ending point of each section, the length of the section, the road type and the like. And acquiring vehicle data of the divided road sections according to the data of the divided road sections, and acquiring vehicle data of common vehicles through GPS (global positioning system), traffic monitoring cameras and the like for each road section, wherein the collected vehicle data comprises information such as the number of vehicles, the speed, the lane occupation condition, the vehicle type and the like so as to acquire the vehicle data of the divided road sections.

Preferably, step S3 comprises the steps of:

As an example of the present invention, referring to fig. 2, a detailed implementation step flow diagram of step S3 in fig. 1 is shown, where step S3 includes:

in the embodiment of the invention, the vehicle circulation efficiency calculation of the divided road section is carried out on the vehicle data of the divided road section by adopting a road section vehicle circulation efficiency algorithm, the algorithm can evaluate the circulation condition of the road section based on traffic flow theory or historical data, for example, the vehicle circulation efficiency of the vehicle data of the divided road section is calculated by analyzing vehicle behavior modes such as turning, straight running, parking and the like, and the historical vehicle average delay time, queuing length, passing rate and the like of the road section, and the vehicle circulation efficiency of the road section can evaluate whether the vehicle of the road section is increased or decreased for a period of time in the future so as to generate the circulation efficiency data of the divided road section, and the vehicle circulation efficiency of the road section can be calculated by the existing density flow velocity relation model.

In the embodiment of the invention, the common vehicle condition under the circulation condition on each divided road section is analyzed by combining the vehicle data of the divided road section and the circulation efficiency data of the divided road section so as to predict the vehicle data under the circulation condition of the road in a certain period of time, and the method is beneficial to evaluating the traffic smoothness degree of the road section by considering factors such as the flow of the circulated vehicle, thereby generating the vehicle circulation data of the divided road section.

In the embodiment of the invention, the time node of the passing intersection of the emergency vehicle is calculated according to the real-time speed data of the emergency vehicle and the position data of the intersection, and the calculation considers the current speed, the position and the position information of the intersection of the emergency vehicle so as to determine when the emergency vehicle reaches each intersection. Based on the calculated time nodes of the traffic intersections, vehicle circulation data related to the target time nodes are extracted from the vehicle circulation data of the divided road sections, and the vehicle circulation data comprises information such as the number of vehicles passing through each road section in a specific time point, speed distribution, congestion conditions and the like, so that the target vehicle circulation data are obtained.

wherein P is represented as road vehicle circulation efficiency, L ₂ Expressed as the end position of the road segment, L ₁ Expressed as the starting position of the road segment, alpha is expressed as the road segmentThe average outgoing vehicle, β is represented as the average incoming vehicle for the road segment, x is represented as the total length of the road segment, ρ is represented as the average queuing length for the road segment, γ is represented as the road segment rush hour adjustment factor, t is represented as the required outgoing time for the outgoing vehicle for the road segment, ζ is represented as the congestion influence adjustment factor, and τ is represented as the abnormal adjustment value for the road segment vehicle circulation efficiency.

The invention utilizes a road section vehicle circulation efficiency algorithm which fully considers the end position L of the road section ₂ Start position L of road segment ₁ The average outgoing vehicle α of the road section, the average incoming vehicle β of the road section, the total length x of the road section, the average queuing length ρ of the road section, the road section peak period adjustment factor γ, the required outgoing time t of the road section outgoing vehicle, the congestion influence adjustment coefficient ζ, and the interaction relationship between functions to form a functional relationship:

that is to say,the functional relation is used for dynamically calculating the circulation condition of the vehicle data of the divided road sections by calculating the vehicle data of the divided road sections and then analyzing the existing vehicle data of the divided road sections at different moments; the average outgoing vehicles and average incoming vehicles for the road segment reflect the average number of vehicles that leave and enter per unit time on the road segment. The average queuing length of the road segment reflects the quantification mode of the vehicle congestion status on the road segment. The road peak time adjustment factor is used for considering the efficiency adjustment during the peak traffic, and can be adjusted under different time distribution conditions; the average departure time required for the road segment to exit the vehicle reflects the speed and time relationship of the vehicle passing through the road segment. The congestion influence adjustment coefficient is used for considering the influence of congestion on the circulation efficiency of the road segments, and can be used for adjusting the congestion influence coefficient according to the vehicle circulation influence caused by the congestion. The functional relationship comprehensively considers the influence of a plurality of factors on the circulation efficiency of the road section vehicles, including the entering and exiting speeds of the vehicles, the density of the vehicles, the peak traffic time, the congestion condition and other abnormal conditions, and through the adjustment and the integration of the parameters, The vehicle circulation status of the road segment can be more comprehensively evaluated for later clear knowledge of the number of vehicles, required communication time, etc. of the road segment at different time nodes. And the function relation is adjusted and corrected by using the abnormal adjustment value tau of the road section vehicle circulation efficiency, so that the error influence caused by abnormal data or error items is reduced, the road section vehicle circulation efficiency P is more accurately generated, and the accuracy and the reliability of calculating the road section vehicle circulation efficiency by dividing road section vehicle data are improved. Meanwhile, the adjustment factors and the adjustment values in the formula can be adjusted according to actual conditions and are applied to different road section dividing vehicle data, so that the flexibility and applicability of the algorithm are improved.

Preferably, step S33 includes the steps of:

In the embodiment of the invention, the passing time node of the emergency vehicle reaching each intersection is calculated according to the real-time speed data of the emergency vehicle and the intersection position data, the current speed, the position and the position information of the intersection of the emergency vehicle are considered in the calculation process, and the time node of the passing intersection of the emergency vehicle is generated by determining when the emergency vehicle reaches each intersection through calculation. A target time node, which may be a specific time point or a specific time period, is determined using the emergency vehicle passing intersection time node, and data related to the traffic of the divided road section vehicle circulation data is extracted from the divided road section vehicle circulation data according to the target time node, which includes, for example, information of the number of vehicles passing each road section, speed distribution, congestion situation, etc. within the target time node.

Preferably, step S4 comprises the steps of:

As an example of the present invention, referring to fig. 3, a detailed implementation step flow diagram of step S4 in fig. 1 is shown, where step S4 includes:

according to the embodiment of the invention, historical vehicle circulation data related to a planned path is collected according to the planned path data, wherein the historical vehicle circulation data comprises data such as the number of vehicles, speed information, congestion conditions and the like of each road section passing through the planned path in a period of time, and the data can be acquired through a traffic monitoring system, sensor equipment or historical traffic records and the like, and are sorted, cleaned and stored for subsequent analysis and model training.

in the embodiment of the invention, the model is constructed through a decision tree algorithm. The decision tree model takes various factors into consideration to establish parameters and nodes of the model, such as traffic flow, road section attributes, time periods and the like, so as to establish a mathematical model for predicting the traffic time of the vehicle, generate an initial vehicle traffic time prediction model, help predict the traffic time of the intersection vehicle under different conditions, and provide important basis for the intersection signal lamp strategy.

in the embodiment of the invention, the historical vehicle circulation data is transmitted to the initial vehicle transit time prediction model, the model analyzes various factors in the historical data, such as road section characteristics, traffic flow, time factors and the like, so that the model is trained, and parameters are automatically adjusted and optimized according to a training set to establish a prediction model of the vehicle transit time.

In the embodiment of the invention, the vehicle passing time prediction model is utilized to predict the vehicle passing time of the intersection on the target vehicle circulation data, and the vehicle passing time prediction model can evaluate and predict the time required by the vehicle passing of the target vehicle circulation data, so that the time required by all vehicles passing on the road section is output, and the vehicle predicted passing time is generated.

Preferably, step S43 comprises the steps of:

As an example of the present invention, referring to fig. 4, a detailed implementation step flow diagram of step S43 in fig. 3 is shown, where step S43 includes:

according to the embodiment of the invention, corresponding time nodes in the historical vehicle circulation data are extracted according to the time nodes of the emergency vehicle passage intersection, for example, if one intersection of the time nodes of the emergency vehicle passage intersection is 6:00 pm, the data of the corresponding time nodes in the historical vehicle circulation data are extracted, and the historical target vehicle circulation data are generated.

in the embodiment of the invention, the weighted loss function of the initial vehicle passing time prediction model is designed according to the historical target vehicle circulating data, and the purpose of the weighted loss function is to adjust the weight of the model according to the historical target vehicle circulating data so as to better fit the historical data and improve the prediction accuracy, and different weights can be given to the data of different time periods and road sections so as to reflect the importance of the historical data, for example, the weight of the historical target vehicle circulating data is larger, and the weight of other common data is relatively smaller.

In the embodiment of the invention, the historical vehicle circulation data is transmitted to the initial vehicle passing time prediction model, the weight adjustment is carried out on the historical vehicle circulation data according to the weight loss function of the initial vehicle passing time prediction model, the historical target vehicle circulation data is more concerned in model training, and the adjusted data is used for further training the initial vehicle passing time prediction model so as to generate the vehicle passing time prediction model.

Preferably, step S5 comprises the steps of:

In the embodiment of the invention, the end time of the control signal lamp is determined according to the time node of the emergency vehicle passing intersection so as to ensure that the signal lamp is switched to a state favorable for passing when the emergency vehicle reaches the intersection, for example, the emergency vehicle is expected to reach the intersection at a certain time point to determine the time of the emergency lane passing the intersection, so that the optimal end time node is determined, and the emergency vehicle can pass smoothly. The start time node analysis of the control signal is performed according to the predicted vehicle passing time and the end time node of the control signal, and the predicted vehicle passing time is considered to determine when to start the signal to ensure the passing of the emergency vehicle, for example, the predicted vehicle passing time takes 5 minutes to pass the vehicle, and the signal can be started in advance before the end time node to create a passing condition for the emergency vehicle. And performing control signal lamp strategy design of the intersection according to the starting time node and the ending time node of the control signal lamp. The method comprises the steps of determining parameters such as switching time, color, mode and the like of the signal lamp so as to ensure safe traffic of emergency vehicles and other vehicles, and finally generating an intersection control signal lamp strategy by taking various factors such as traffic flow, road conditions, signal lamp types and the like into consideration in the design of the strategy so as to support smooth traffic of the emergency vehicles.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The intelligent rapid passing auxiliary method for the emergency vehicle intersection is characterized by comprising the following steps of:

2. The rapid transit intelligent assistance method of an emergency vehicle intersection according to claim 1, wherein step S1 comprises the steps of:

3. The rapid transit intelligent assistance method of an emergency vehicle intersection according to claim 1, wherein step S2 comprises the steps of:

4. The rapid transit intelligent assistance method for an emergency vehicle intersection according to claim 3, wherein step S3 comprises the steps of:

5. The rapid transit intelligent assistance method for an emergency vehicle intersection according to claim 4, wherein the road section vehicle circulation efficiency algorithm in step S31 is as follows:

6. The rapid transit intelligent assistance method of an emergency vehicle intersection according to claim 4, wherein step S33 comprises the steps of:

7. The rapid transit intelligent assistance method of an emergency vehicle intersection according to claim 6, wherein step S4 comprises the steps of:

8. The rapid transit intelligent assistance method of an emergency vehicle intersection according to claim 7, wherein step S43 comprises the steps of:

9. The rapid transit intelligent assistance method of an emergency vehicle intersection according to claim 6, wherein step S5 comprises the steps of:

10. A rapid-transit intelligent assistance system for an emergency vehicle intersection, for performing the rapid-transit intelligent assistance method for an emergency vehicle intersection as claimed in claim 1, the rapid-transit intelligent assistance system for an emergency vehicle intersection comprising: