Traffic early warning device and emergency guidance system

Abstract

The invention relates to the technical field of traffic emergency guidance, and discloses a traffic early warning device and an emergency guidance system, and the device comprises a data collection unit, a data processing unit, a congestion early warning unit, and a path guidance unit. Wherein the data acquisition unit is used for collecting historical congestion data, environmental data and real-time traffic data; the data processing unit is used for identifying a congestion risk road section and a congestion risk time period, and calculating a traffic index of each road section; the congestion early warning unit is used for performing congestion prediction and identifying a congestion road section and a congestion time period; the path guiding unit plans an alternative guiding path for each congested road section based on the digital map, and selects an emergency guiding path from the alternative guiding paths. According to the invention, the accuracy of congestion prediction is improved, and more scientific and effective detouring suggestions can be provided for vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic emergency guidance, and in particular to a traffic early warning device and an emergency guidance system. Background Art

[0002] In terms of traffic congestion warning, traditional methods mainly rely on fixed-position sensors, such as geomagnetic sensors, cameras, etc., to monitor traffic flow and vehicle speed on local sections of roads. Although these sensors can provide certain real-time traffic information, they have obvious limitations. First, their coverage is limited, and they can only obtain traffic data around the installation location, and cannot fully reflect the traffic conditions of the entire urban road network. This makes it difficult to accurately predict the occurrence and spread of congestion in advance when facing regional or large-scale traffic congestion. For example, at the intersection of the main road and branch roads in the city, if only relying on sensor data at the entrance of the branch road, it may not be possible to timely detect the potential impact of the impending congestion on the main road on the branch road traffic.

[0003] Traditional traffic monitoring data lacks in-depth analysis and integration. It only focuses on real-time traffic flow and vehicle speed information, but does not fully explore the laws and trends contained in historical traffic data. There are significant differences in traffic congestion patterns on different sections of roads at different time periods and on different dates, but existing systems often fail to effectively use this historical information to make more accurate congestion predictions. For example, a section of road is usually congested during the morning and evening rush hours on weekdays, but the traffic flow is relatively stable during holidays. If it is not combined with historical data for analysis, it is easy to cause misjudgment or delay in congestion warning for the section during peak hours on weekdays. Most of the existing traffic warning systems do not effectively integrate weather data with traffic data for analysis, and cannot adjust the congestion warning strategy in time according to weather changes. In terms of emergency guidance path planning, existing vehicle navigation systems and traffic guidance equipment usually provide navigation information based on static map data and preset route planning algorithms, and cannot quickly and accurately replan the optimal emergency guidance path based on real-time traffic congestion conditions.

[0004] For example, a Chinese patent application with publication number CN110648533A discloses a traffic control method, device, system and storage medium. Among them, the method includes: receiving roadside detection information; analyzing the roadside detection information to determine whether there is traffic anomaly in the roadside detection section; if there is traffic anomaly, generating a guidance and warning strategy based on these roadside detection information; and performing relevant traffic control operations. Compared with the prior art that uses large-area traffic flow information to control large-area traffic, the use of roadside detection information can guide and control small-scale traffic more accurately, thereby realizing all-round traffic control of local, near-end and far-end. The information acquisition, analysis and decision-making processes in this method are all implemented locally, which greatly reduces the delay caused by information transmission. In addition, the generation of guidance and warning strategies using roadside detection information has a predictive function, which can eliminate risks through early warning prompts when traffic anomalies are about to form.

[0005] For example, a Chinese patent application with publication number CN116311896A discloses a vehicle guidance system, method and device, which relates to the field of road condition monitoring technology and can solve the problems of difficulty in signal reception of traffic guidance devices and low efficiency in adjusting the state of signal lights. The system includes: a road traffic device, an information communication device and a monitoring and early warning device; the road traffic device includes an information collection unit and a vehicle guidance unit; the road traffic device is used to collect target real-time data of the monitoring point through the information collection unit; the road traffic device is also used to send target real-time data to the monitoring and early warning device through the information communication device; the monitoring and early warning device is used to receive target real-time data and determine the target guidance strategy based on the target real-time data; the monitoring and early warning device is also used to send the target guidance strategy to the vehicle guidance unit through the information communication device; the road traffic device executes the target guidance strategy through the vehicle guidance unit. This application can improve the efficiency of information transmission and improve the efficiency of changing strategies of the vehicle guidance system.

[0006] The above patents all have the problem raised by this background technology: it is impossible to quickly and accurately re-plan the optimal emergency guidance path according to the real-time traffic congestion situation.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0008] The technical problem to be solved by the present invention is to overcome the defects of the prior art, provide a traffic warning device and an emergency guidance system, improve the accuracy of congestion prediction, and provide more scientific and effective detour suggestions for vehicles.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In one aspect, the present invention provides a traffic warning device, comprising a data collection unit, a data processing unit, a congestion warning unit, and a path guidance unit; wherein:

[0011] The data collection unit is used to collect historical congestion data, environmental data, and real-time traffic data;

[0012] The data processing unit identifies the congestion risk road section and the congestion risk period of each congestion risk road section based on the historical congestion data; the data processing unit also calculates the traffic index of each road section based on the real-time traffic data;

[0013] The congestion warning unit performs congestion prediction based on the historical congestion data and the environmental data, identifies the congested road sections and the congested time period of each congested road section, and modifies the congested time period of any congested road section based on the real-time traffic data;

[0014] The path guidance unit plans an alternative guidance path for each congested road section based on the digital map, and selects an emergency guidance path from the alternative guidance paths based on the traffic index of each road section included in each alternative guidance path.

[0015] As a preferred solution of the traffic warning device of the present invention, wherein: the data collection unit includes a query subunit and a monitoring subunit; wherein the query subunit is used to collect historical congestion data and environmental data; wherein the historical congestion data includes the traffic status of any road section at each time of each day in the past M days, and the traffic status includes congestion and smooth traffic; the environmental data includes weather characteristics and date characteristics;

[0016] The query subunit is also used to connect with the database of the geographic information system to obtain a digital map;

[0017] The monitoring subunit is used to monitor the real-time traffic data of each road section; the real-time traffic data of any road section includes the number of vehicles, average vehicle speed, and average braking frequency.

[0018] As a preferred solution of the traffic warning device of the present invention, wherein: the data processing unit includes a congestion calculation subunit; the congestion calculation subunit includes a congestion identification strategy for identifying congestion risk sections and congestion risk time periods of each congestion risk section; the congestion identification strategy is specifically as follows:

[0019] For any road section, count the frequency of traffic congestion at each time point on any day in the past M days, and record it as the congestion frequency at each time point in the day;

[0020] If the congestion frequency at m consecutive time points is higher than the preset congestion frequency threshold, and m is greater than m0 , then the m time points are marked as the congestion risk periods of the corresponding road sections; m 0 is a preset congestion duration threshold; any road section with a congestion risk period is marked as a congestion risk section; the starting time points of the m time points are recorded as the starting time of the corresponding congestion risk period, and the duration of the congestion risk period is recorded as the estimated duration of the congestion.

[0021] As a preferred solution of the traffic warning device of the present invention, the data processing unit further includes a traffic calculation subunit; the traffic calculation subunit is used to calculate the traffic index of each road section; the traffic index includes path length and estimated travel time; wherein the path length of any road section is obtained based on a digitized map; the formula for calculating the estimated travel time by the traffic calculation subunit is as follows:

[0022]

[0023] Among them, t represents the estimated travel time of any road section; L represents the path length of the corresponding road section, and V represents the average vehicle speed of the corresponding road section.

[0024] As a preferred solution of the traffic warning device of the present invention, the traffic index also includes a safety index; the traffic calculation subunit calculates the safety index in the following manner:

[0025] Based on the monitoring subunit, the number of vehicles and average speed of any road section are obtained; the maximum speed limit and the maximum number of vehicles passing the corresponding road section are obtained; and the safety index is calculated. The formula is as follows:

[0026]

[0027] Among them, S represents the safety index of any road section; V represents the average speed of the corresponding road section; V m Indicates the maximum speed limit of the corresponding road section; N c Indicates the number of vehicles on the corresponding road section; N m Indicates the maximum number of vehicles on the corresponding road section; w 1 、w 2 are all weight coefficients.

[0028] As a preferred solution of the traffic warning device of the present invention, wherein: the congestion warning unit includes a prediction subunit; the prediction subunit is configured with a prediction model for identifying congested sections and congested periods of each congested section; the prediction model is any one of logistic regression, support vector machine, and decision tree;

[0029] The input of the prediction model is the congestion status of any congestion risk section at each moment of each day in the past M days, and a feature vector encoded by weather features and date features; the output of the prediction model is the congestion prediction result of any congestion risk period of the corresponding congestion risk section; the congestion prediction result is whether congestion will occur today or not today in the corresponding congestion risk period;

[0030] If the congestion prediction result of any congestion risk period is that congestion will occur today, the corresponding congestion risk period will be marked as a congestion period, and any congestion risk road section including the congestion period will be marked as a congested road section.

[0031] As a preferred solution of the traffic warning device of the present invention, the congestion warning unit further includes a correction subunit; the correction subunit is configured with a congestion correction strategy for correcting the congestion period of any congested road section; the congestion correction strategy is specifically as follows:

[0032] For any congested road section, when the current time point does not belong to any corresponding congested time period, if the number of vehicles in the corresponding congested road section is greater than the preset vehicle number threshold, and the average vehicle speed is less than the preset average vehicle speed threshold, and the average braking frequency is higher than the preset braking frequency threshold, and the number of vehicles keeps increasing for at least n consecutive time points, then the start time of the congested time period closest to the current time point will be corrected to the current time point.

[0033] As a preferred solution of the traffic warning device of the present invention, the path guidance unit includes a path generation subunit; the path generation subunit is configured with a graph theory algorithm for planning an alternative guidance path for each congested road section; specifically as follows:

[0034] The road network in the digital map is represented as a graph structure, with any road intersection as a node and any road section as an edge; attributes are added to each edge, including the path length, estimated travel time, and safety index corresponding to each road section; the edges corresponding to all congested road sections are marked as unavailable; a node is selected at one end of the edge corresponding to the target congested road section as the starting point of the alternative guidance path, and a node is selected at the other end of the edge corresponding to the target congested road section as the end point of the alternative guidance path; a shortest path algorithm is used to generate an alternative guidance path from the selected starting point to the selected end point; and the shortest path algorithm is repeatedly used to generate at least N alternative guidance paths.

[0035] As a preferred solution of the traffic warning device of the present invention, the path guidance unit further includes an evaluation subunit; the evaluation subunit is configured with a path evaluation function for selecting an emergency guidance path from the alternative guidance paths; the formula of the path evaluation function is as follows:

[0036]

[0037] Where F represents the evaluation factor of any alternative guidance path; L S represents the total length of the corresponding alternative guidance path, which is obtained by summing the path lengths of all sections included in the corresponding alternative guidance path; L max Indicates the maximum total length of all candidate guidance paths; t i represents the estimated travel time of the i-th road section included in the corresponding alternative guidance path, and the value range of i is 1, 2, ..., h, where h is the total number of road sections included in the corresponding alternative guidance path; L i represents the path length of the i-th road segment included in the corresponding alternative guidance path; S i represents the safety index of the i-th road section included in the corresponding alternative guidance path; α 1 , α 2 , α 3 All are weight factors;

[0038] An evaluation factor of each candidate guidance path is calculated, and the candidate guidance path with the largest evaluation factor is selected as the emergency guidance path.

[0039] In a second aspect, the present invention provides an emergency guidance system, comprising a display module, a push module, and the traffic warning device described in the present application; wherein:

[0040] The traffic warning device is used to identify congested road sections and the congested time period of each congested road section, and generate an emergency guidance path for each congested road section;

[0041] The display module is used to display congestion information at both ends of each congested road section; the congestion information includes the start time of the congestion period and the estimated duration of the congestion;

[0042] The push module is used to send congestion information and emergency guidance routes to the target vehicle.

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0044] By collecting historical congestion data, environmental data and real-time traffic data, the risk sections and time periods of congestion are identified, and the congested sections and time periods are predicted and corrected in combination with environmental data and real-time traffic data. The accuracy of congestion prediction is improved by integrating multiple factors, and early warning of congested sections can be issued in advance. Digital maps and graph theory algorithms are used to plan multiple alternative guidance paths for congested sections, and based on traffic indicators such as path length, expected travel time, and safety indicators, the optimal emergency guidance path is selected from them through the path evaluation function, providing vehicles with more scientific and effective detour suggestions, which helps to alleviate traffic congestion and improve road traffic efficiency. The device can be installed at key intersections of urban roads, entrances and exits of highways, transportation hubs and other locations. By interconnecting with surrounding vehicles, traffic facilities and management centers, it can realize the real-time collection, analysis, early warning and guidance functions of traffic information, effectively respond to various complex situations in urban traffic, and ensure smooth and safe traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0046] Figure 1 A schematic structural diagram of a traffic warning device provided by the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0048] Example 1

[0049] This embodiment introduces a traffic warning device. Figure 1 , the device includes a data acquisition unit, a data processing unit, a congestion warning unit, and a path guidance unit; wherein:

[0050] The data collection unit is used to collect historical congestion data, environmental data, and real-time traffic data;

[0051] The data collection unit includes a query subunit and a monitoring subunit; wherein the query subunit is used to collect historical congestion data and environmental data; wherein the historical congestion data includes the traffic status of any road section at each time of each day in the past M days, and the traffic status includes congestion and smooth traffic; the environmental data includes weather characteristics and date characteristics;

[0052] The query subunit is also used to connect with the database of the geographic information system to obtain a digitized map; the digitized map provided by the geographic information system can provide geospatial data such as road map information, terrain data, intersection information, etc., providing a basis for subsequent route planning and identification and positioning of congested sections.

[0053] The monitoring subunit is used to monitor the real-time traffic data of each road section; the real-time traffic data of any road section includes the number of vehicles, average vehicle speed, and average braking frequency.

[0054] The data processing unit identifies the congestion risk road section and the congestion risk period of each congestion risk road section based on the historical congestion data; the data processing unit also calculates the traffic index of each road section based on the real-time traffic data;

[0055] The data processing unit includes a congestion calculation subunit and a traffic calculation subunit; wherein the congestion calculation subunit includes a congestion identification strategy for identifying congestion risk sections and congestion risk time periods of each congestion risk section; the congestion identification strategy is specifically as follows:

[0056] For any road section, the frequency of congestion at each time point on any day in the past M days is counted, and recorded as the congestion frequency at each time point in the day; for example, if a road section was congested at 8:00 a.m. 200 days in the past year, then for this road section, the congestion frequency at 8:00 a.m. is 200 divided by 365, which is approximately 0.55.

[0057] If the congestion frequency at m consecutive time points is higher than the preset congestion frequency threshold, and m is greater than m 0 , then the m time points are marked as the congestion risk periods of the corresponding road sections; m 0 is a preset congestion duration threshold; any road section with a congestion risk period is marked as a congestion risk section; the starting time points of the m time points are recorded as the starting time of the corresponding congestion risk period, and the duration of the congestion risk period is recorded as the estimated duration of the congestion.

[0058] The traffic calculation subunit is used to calculate the traffic index of each road section; the traffic index includes path length, safety index, and estimated travel time; wherein the path length of any road section is directly obtained based on the digital map; the traffic calculation subunit calculates the safety index in the following manner:

[0059] Based on the monitoring subunit, the number of vehicles and average speed of any road section are obtained; the maximum speed limit and the maximum number of vehicles passing the corresponding road section are obtained; and the safety index is calculated. The formula is as follows:

[0060]

[0061] Among them, S represents the safety index of any road section; V represents the average speed of the corresponding road section; V m Indicates the maximum speed limit of the corresponding road section; N c Indicates the number of vehicles on the corresponding road section; N m represents the maximum number of vehicles on the corresponding road section, which is set by technicians in this field based on experience; 1 、w 2 They are all weight coefficients, which are set by technicians in this field based on actual needs.

[0062] In the calculation formula of the safety index, This item indicates the speed saturation of the corresponding road section, that is, the ratio of the average vehicle speed to the maximum speed limit specified for the road section. The lower the speed saturation, the more serious the congestion on the road and the lower the safety index. This item indicates the traffic saturation of the corresponding road section, that is, the ratio of the current number of vehicles on the road section to the maximum number of vehicles that the road section can carry; the higher the traffic saturation, the more serious the congestion level of the road section and the lower the safety index. 1 、w 2 It is set based on actual needs. For example, urban roads pay more attention to speed saturation, while expressway sections pay more attention to flow saturation. Therefore, the sections of urban roads can be set to w 1 is 0.6, w 2 is 0.4.

[0063] The formula used by the travel calculation subunit to calculate the estimated travel time is as follows:

[0064]

[0065] Among them, t represents the estimated travel time of any road section; L represents the path length of the corresponding road section.

[0066] The congestion warning unit performs congestion prediction based on the historical congestion data and the environmental data, identifies the congested road sections and the congested time period of each congested road section, and modifies the congested time period of any congested road section based on the real-time traffic data;

[0067] The congestion warning unit includes a prediction subunit and a correction subunit; wherein the prediction subunit is configured with a prediction model for identifying congested sections and the congested time periods of each congested section; the input of the prediction model is the congestion status of any congestion risk section at each moment of each day in the past M days, and a feature vector encoded by weather features and date features; for example, 0 is used to represent normal weather, 1 is used to represent rain, 2 is used to represent fog, etc., and the weather features are input into the prediction model as part of the feature variables. The dates are divided into categories such as weekends, weekdays, holidays, etc., and are encoded with one-hot encoding. For example, the date feature is encoded as a three-dimensional vector, such as [0,1,0] for weekdays, [1,0,0] for weekends, and [0,0,1] for holidays.

[0068] The output of the prediction model is the congestion prediction result of any congestion risk period of the corresponding congestion risk section; the congestion prediction result is whether congestion will occur today or not today during the corresponding congestion risk period;

[0069] If the congestion prediction result of any congestion risk period is that congestion will occur today, the corresponding congestion risk period will be marked as a congestion period, and any congestion risk road section including the congestion period will be marked as a congested road section.

[0070] The prediction model is any one of logistic regression, support vector machine, and decision tree. The logistic regression model is simple and easy to understand, with high computational efficiency, the support vector machine has high prediction accuracy, and the decision tree model can intuitively display the importance of features and the decision-making process, all of which are applicable to binary classification problems, that is, predicting whether a congestion risk section actually occurs during the congestion risk period. According to historical data and real-time traffic conditions, it is analyzed that a certain section of road is likely to be congested within a specific time period, and congestion warnings can be issued for the section in advance, and detour routes can be planned in advance to avoid worsening congestion. Considering road environmental factors, such as bad weather (such as heavy rain, fog, ice and snow, etc.) will reduce road capacity. Considering date characteristics, such as weekends, weekdays, holidays, etc., will also affect the occurrence of congestion.

[0071] The correction subunit is configured with a congestion correction strategy for correcting the congestion period of any congested road section; the congestion correction strategy is specifically as follows:

[0072] For any congested road section, when the current time point does not belong to any corresponding congested time period, if the number of vehicles in the corresponding congested road section is greater than the preset vehicle number threshold, and the average vehicle speed is less than the preset average vehicle speed threshold, and the average braking frequency is higher than the preset braking frequency threshold, and the number of vehicles keeps increasing for at least n consecutive time points, then the start time of the congested time period closest to the current time point will be corrected to the current time point.

[0073] Since the input of the prediction model cannot cover all factors that affect whether congestion actually occurs on a congestion risk section, the prediction results of the congestion period are corrected in combination with actual traffic data to improve the accuracy of identifying congestion. For any congested section, the start time of the congestion period is based on the statistics of historical data and may not be accurate; the above correction strategy can be used to determine whether congestion is about to occur or is occurring, thereby correcting the start time of the congestion period.

[0074] The path guidance unit plans an alternative guidance path for each congested road section based on the digital map, and selects an emergency guidance path from the alternative guidance paths based on the traffic index of each road section included in each alternative guidance path.

[0075] The path guidance unit includes a path generation subunit and an evaluation subunit; wherein the path generation subunit is configured with a graph theory algorithm for planning an alternative guidance path for each congested road section; specifically as follows:

[0076] The road network in the digital map is represented as a graph structure, with any road intersection as a node and any road section as an edge; attributes are added to each edge, including the path length, estimated travel time, and safety index corresponding to each road section; the edges corresponding to all congested sections are marked as unavailable; a node is selected at one end of the edge corresponding to the target congested section as the starting point of the alternative guidance path, and a node is selected at the other end of the edge corresponding to the target congested section as the end point of the alternative guidance path; the purpose is to guide vehicles to bypass the target congested section through the alternative guidance path. When planning an alternative guidance path for any congested section, the corresponding congested section is the target congested section. Using the shortest path algorithm, an alternative guidance path is generated from the selected starting point to the selected end point; the shortest path algorithm is repeatedly used to generate at least N alternative guidance paths.

[0077] Taking the Dijkstra algorithm as an example, the method of generating an alternative guidance path is as follows:

[0078] Initialization: Create a distance array to store the temporary shortest distance from the starting point to each node, with the initial value set to infinity (except the starting point is set to 0); create a visited node set, which is initially empty.

[0079] Iteration: Select the node u with the smallest distance from the unvisited nodes and mark it as visited. For any adjacent node v of u, calculate the distance from the starting point through u to v. If the distance from the starting point through u to v is less than the currently stored temporary shortest distance, update the distance array.

[0080] Termination condition: When the end point is marked as visited or all reachable nodes have been visited, the algorithm terminates. At this time, the distance to the end point stored in the distance array is the shortest path length. By backtracking, the nodes and sections passed by the shortest path can be found, which is a feasible alternative guide path.

[0081] Searching for multiple paths: You can adjust certain parameters in the algorithm (for example, consider changing the starting point and the end point each time you select the node with the minimum distance) and run the algorithm multiple times, each time excluding the previously found alternative guidance paths, thereby searching for multiple alternative guidance paths.

[0082] The evaluation subunit is configured with a path evaluation function for selecting an emergency guidance path from the alternative guidance paths; the formula of the path evaluation function is as follows:

[0083]

[0084] Where F represents the evaluation factor of any alternative guidance path; L S represents the total length of the corresponding alternative guidance path, which is obtained by summing the path lengths of all sections included in the corresponding alternative guidance path; L max Indicates the maximum total length of all candidate guidance paths; t i represents the estimated travel time of the i-th road section included in the corresponding alternative guidance path, and the value range of i is 1, 2, ..., h, where h is the total number of road sections included in the corresponding alternative guidance path; L i represents the path length of the i-th road segment included in the corresponding alternative guidance path; S i Represents the safety index of the i-th road segment included in the corresponding alternative guidance path. 1 , α 2 , α 3 are all weight factors, which are set by technicians in this field based on actual needs; α 1 , α 2 , α 3 It is also used to normalize and eliminate the dimension of the corresponding multiplier terms.

[0085] The path evaluation function comprehensively considers the total length and estimated travel time of each alternative path, and weights the safety index based on the path length. The longer the road section, the higher the requirement for the safety index. Therefore, the priority of each alternative guidance path is comprehensively evaluated in multiple dimensions, which is conducive to selecting the best alternative path.

[0086] An evaluation factor of each candidate guidance path is calculated, and the candidate guidance path with the largest evaluation factor is selected as the emergency guidance path.

[0087] In practical applications, the traffic warning device described in this application can be installed at key intersections of urban roads, highway entrances and exits, transportation hubs and other locations, and interconnected with surrounding vehicles, traffic facilities and management centers to achieve real-time collection, analysis, warning and guidance functions of traffic information. For example, during the morning rush hour in the city, the device predicts that a certain trunk road is about to be congested based on historical data and real-time traffic conditions, and displays congestion warning information on the display screen around the road section in advance, and pushes detour suggestions and emergency guidance paths to passing vehicles through the on-board navigation system and mobile phone APP. When a traffic accident occurs, the device can also quickly detect the location and severity of the accident, and calculate the optimal emergency guidance path with reference to the congested guidance route planning method, and promptly release it to the affected vehicles to guide the vehicles to evacuate quickly and avoid further expansion of traffic congestion.

[0088] Example 2

[0089] This embodiment is the second embodiment of the present invention; based on the same inventive concept as the first embodiment, this embodiment introduces an emergency guidance system, including a display module, a push module, and the traffic warning device as described in the first embodiment; wherein:

[0090] The traffic warning device is used to identify congested road sections and the congested time period of each congested road section, and generate an emergency guidance path for each congested road section;

[0091] The display module is used to display congestion information at both ends of each congested road section; the congestion information includes the start time of the congestion period and the expected duration of the congestion; by setting display screens near both ends of the road section where congestion often occurs, the congestion information is displayed in the form of intuitive graphics, text or voice to achieve congestion warning and remind passing vehicles to plan routes in advance.

[0092] The push module is used to send congestion information and emergency guidance routes to target vehicles; using communication means such as 4G / 5G networks, it realizes real-time data transmission and communication between traffic warning devices and vehicles, traffic management centers, and other road infrastructure (such as display screens, etc.), guiding target vehicles to avoid congested sections and follow recommended emergency guidance routes; in addition, it can also provide drivers with real-time traffic guidance and warning prompts through the voice broadcast function to improve driving safety.

[0093] One way to detect the target vehicle to which congestion information and emergency guidance routes are to be pushed is as follows: based on the average speed of the road section where the vehicle is located, calculate the time when the vehicle arrives at the congested section; if the time of arriving at the congested section is within the congested period, the vehicle is a target vehicle and congestion information and emergency guidance routes need to be pushed to it.

[0094] The specific functions of the above modules are implemented by referring to the relevant contents of the traffic warning device described in Example 1 and will not be elaborated here.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the purpose and scope of protection of the present invention, which are all within the protection of the present invention.

Claims

1. A traffic warning device, characterized in that: It includes a data collection unit, a data processing unit, a congestion warning unit, and a path guidance unit; wherein: The data collection unit is used to collect historical congestion data, environmental data, and real-time traffic data; The data processing unit identifies the congestion risk road section and the congestion risk period of each congestion risk road section based on the historical congestion data; the data processing unit also calculates the traffic index of each road section based on the real-time traffic data; The congestion warning unit performs congestion prediction based on the historical congestion data and the environmental data, identifies the congested road sections and the congested time period of each congested road section, and modifies the congested time period of any congested road section based on the real-time traffic data; The path guidance unit plans an alternative guidance path for each congested road section based on the digital map, and selects an emergency guidance path from the alternative guidance paths based on the traffic index of each road section included in each alternative guidance path.

2. A traffic warning device as claimed in claim 1, characterized in that: The data collection unit includes a query subunit and a monitoring subunit; wherein the query subunit is used to collect historical congestion data and environmental data; wherein the historical congestion data includes the traffic status of any road section at each time of each day in the past M days, and the traffic status includes congestion and smooth traffic; the environmental data includes weather characteristics and date characteristics; The query subunit is also used to connect with the database of the geographic information system to obtain a digital map; The monitoring subunit is used to monitor the real-time traffic data of each road section; the real-time traffic data of any road section includes the number of vehicles, average vehicle speed, and average braking frequency.

3. A traffic warning device as claimed in claim 2, characterized in that: The data processing unit includes a congestion calculation subunit; the congestion calculation subunit includes a congestion identification strategy for identifying congestion risk sections and congestion risk time periods of each congestion risk section; The congestion identification strategy is as follows: For any road section, count the frequency of traffic congestion at each time point on any day in the past M days, and record it as the congestion frequency at each time point in the day; If there are m consecutive time points where the congestion frequency is higher than the preset congestion frequency threshold, and m is greater than m0, the m time points are marked as the congestion risk period of the corresponding road section; m0 is the preset congestion duration threshold; Mark any road section with congestion risk period as a congestion risk section; The starting time points of the m time points are recorded as the starting time of the corresponding congestion risk period, and the duration of the congestion risk period is recorded as the estimated duration of the congestion.

4. A traffic warning device as claimed in claim 3, characterized in that: The data processing unit also includes a traffic calculation subunit; the traffic calculation subunit is used to calculate the traffic index of each road section; the traffic index includes path length and estimated travel time; wherein the path length of any road section is obtained based on a digitized map; the traffic calculation subunit calculates the estimated travel time of the corresponding road section based on the path length of any road section and the average vehicle speed of the corresponding road section.

5. A traffic warning device as claimed in claim 4, characterized in that: The traffic index also includes a safety index; the traffic calculation subunit calculates the safety index in the following manner: Based on the monitoring subunit, the number of vehicles and the average speed of any road section are obtained; the maximum speed limit and the maximum number of vehicles passing the corresponding road section are obtained; The safety index of a road section is calculated based on the ratio of the average vehicle speed to the maximum speed limit and the ratio of the number of vehicles on the road section to the maximum number of vehicles.

6. A traffic warning device as claimed in claim 5, characterized in that: The congestion warning unit includes a prediction subunit; the prediction subunit is configured with a prediction model for identifying congested sections and congested periods of each congested section; the prediction model is any one of logistic regression, support vector machine, and decision tree; The input of the prediction model is the congestion status of any congestion risk section at each moment of each day in the past M days, and a feature vector encoded by weather features and date features; the output of the prediction model is the congestion prediction result of any congestion risk period of the corresponding congestion risk section; the congestion prediction result is whether congestion will occur today or not today in the corresponding congestion risk period; If the congestion prediction result of any congestion risk period is that congestion will occur today, the corresponding congestion risk period will be marked as a congestion period, and any congestion risk road section including the congestion period will be marked as a congested road section.

7. A traffic warning device as claimed in claim 6, characterized in that: The congestion warning unit further includes a correction subunit; the correction subunit is configured with a congestion correction strategy for correcting the congestion period of any congested road section; the congestion correction strategy is specifically as follows: For any congested road section, when the current time point does not belong to any corresponding congested time period, if the number of vehicles in the corresponding congested road section is greater than the preset vehicle number threshold, and the average vehicle speed is less than the preset average vehicle speed threshold, and the average braking frequency is higher than the preset braking frequency threshold, and the number of vehicles keeps increasing for at least n consecutive time points, then the start time of the congested time period closest to the current time point will be corrected to the current time point.

8. A traffic warning device as claimed in claim 7, characterized in that: The path guidance unit includes a path generation subunit; the path generation subunit is configured with a graph theory algorithm for planning an alternative guidance path for each congested road section; specifically as follows: The road network in the digital map is represented as a graph structure, with any road intersection as a node and any road section as an edge; attributes are added to each edge, including the path length, estimated travel time, and safety index corresponding to each road section; and edges corresponding to all congested road sections are marked as unavailable; Selecting a node at one end of the edge corresponding to the target congested road section as the starting point of the alternative guidance path, and selecting a node at the other end of the edge corresponding to the target congested road section as the end point of the alternative guidance path; Using the shortest path algorithm, starting from the selected starting point and ending at the selected end point, an alternative guidance path is generated; Repeat the shortest path algorithm to generate at least N candidate guidance paths.

9. A traffic warning device as claimed in claim 8, characterized in that: The path guidance unit further includes an evaluation subunit; the evaluation subunit is configured with a path evaluation function for selecting an emergency guidance path from the alternative guidance paths; the path evaluation function calculates an evaluation factor of the corresponding alternative guidance path based on the estimated travel time, path length, and safety index of each road section included in any alternative guidance path; The evaluation subunit calculates an evaluation factor of each candidate guidance path, and selects the candidate guidance path with the largest evaluation factor as the emergency guidance path.

10. An emergency guidance system, characterized in that: The device comprises a display module, a push module, and a traffic warning device as claimed in any one of claims 1 to 9; wherein: The traffic warning device is used to identify congested road sections and the congested time period of each congested road section, and generate an emergency guidance path for each congested road section; The display module is used to display congestion information at both ends of each congested road section; the congestion information includes the start time of the congestion period and the estimated duration of the congestion; The push module is used to send congestion information and emergency guidance routes to the target vehicle.

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