A warning analysis method for different levels of traffic accidents based on emergency location service information

Abstract

The application relates to a warning analysis method for different levels of traffic events based on emergency location service information, which comprises the following steps: according to two dimensions of a mobile phone number unique identifier and time, event integration is carried out on emergency location information actively reported by a mobile phone to obtain an event integration list; data in the event integration list is subjected to coordinate reverse geographic coding analysis to obtain relationship structured data of coordinates and roads; event clustering analysis based on spatial distance and time dimensions is carried out according to the relationship structured data to obtain a warning result of different levels of traffic events.

Description

Technical Field

[0001] This invention relates to the field of traffic early warning technology, and in particular to an early warning analysis method for traffic events of different levels based on emergency location service information. Background Technology

[0002] Currently, frontline traffic management departments are directly responsible for managing abnormal events such as road accidents and road obstructions. However, due to various limitations, these departments focus primarily on handling road incidents, with relatively insufficient efforts in disseminating information to the public. The main challenges are as follows.

[0003] 1. Lack of technical means to quickly obtain accurate geographic coordinates. For frontline traffic management departments, the main source of information about incidents is the location information described by the parties involved over the phone. This general location information (such as near xx road xx bridge) is only sufficient for frontline personnel to get to the accident site, and does not provide the accurate coordinate information needed to release the incident to the public and dynamically adjust navigation routes.

[0004] 2. Difficulty in scientifically and effectively assessing event severity. Traffic management departments at all levels face a large number of traffic anomalies of varying response levels daily. Since the location information for these anomalies is typically a general telephone location description, it's difficult to assess the risk level using clustering techniques based on time and range. This necessitates manual judgment on which events to report and which not to, or more commonly, choosing not to report at all. This directly impacts other drivers' access to effective safety warnings.

[0005] Because of the aforementioned two problems, frontline traffic management departments are still relying on issuing textual descriptions and having internet map providers manually mark the incident locations on maps based on the information in the announcements, as well as other comprehensive information such as road congestion, before issuing the official warnings. This severely impacts the timeliness and accuracy of incident dissemination.

[0006] The fundamental problem with the two issues mentioned above is the lack of accurate location information for abnormal events at the frontline level. To address this, emergency location service information obtained from the Ministry of Industry and Information Technology (MIIT) at the government level can supplement the missing location information. However, due to personal information protection reasons, currently only the location and time information of the event are available online, with data samples as follows: Figure 1As shown in the figure, the data is arranged in reverse chronological order. It contains accurate location information actively reported by mobile phones when dialing specific numbers, as well as the called number, unique mobile phone number identifier, and coordinate system. This information is stored on the internet by the Ministry of Industry and Information Technology and transmitted to government user units via the internet. However, due to its data attributes, it must be integrated with the existing telephone call center systems of user units to be used. Further, the location information of the mobile phone can be determined by calculating and matching the mobile phone number obtained from the call center with the unique mobile phone number identifier field in the data. However, since the integration process is all within the intranet, the final integrated data cannot be transmitted to the internet. Therefore, traffic management departments still cannot release the data to the public during the handling of road incidents. In summary, the above data has the following characteristics: (1) The data contains the accurate location information of the party's mobile phone; (2) The data reported by the party’s mobile phone is repeated. Each brand has different characteristics. Some mobile phones will repeatedly report location information during the call, while some mobile phones will report location information after detecting a change in location. (3) The unique identifier of a mobile phone number can be considered as a unique code corresponding to a mobile phone number. According to the standard, its specific function expression is as follows: The unique identifier for a mobile phone number (M) = SM3("+86"+phone_num); Where M and phone_num have a one-to-one mapping relationship, phone_num is the mobile phone number string, SM3 is the SM3 algorithm encryption calculation performed on the string in parentheses, and ("+86"+phone_num) means adding the three characters "+86" before the phone_num string.

[0007] (4) The coordinate systems of the location information reported by different mobile phones of users are different; (5) The emergency call type can only be obtained in coarse granularity from the called number, as shown in Table 1; Table 1 (6) Data can be linked to the internal system data of government departments through phone_num, combining specific police information with location coordinates. However, due to the isolation of the internal system data network, information such as the type of police incident cannot be transmitted to the Internet, and the conditions for publication are not available.

[0008] (7) The same mobile phone number with a unique identifier (M) cannot be simply regarded as an independent event. It is also possible that the same mobile phone makes multiple emergency calls to report different traffic incidents within a short time interval.

[0009] Considering the need for traffic management departments to promptly release information about road incidents to the public, emergency location service information cannot be used directly. There are several issues that need to be addressed through data processing and filtering.

[0010] (1) Event differentiation. It is necessary to integrate the duplicate data reported by the same mobile phone at different times according to the algorithm, so as to ultimately reflect that an event occurred at a certain location coordinate.

[0011] (2) Type differentiation. All traffic incidents that need to be released to the public are on the road, so non-road data need to be eliminated one by one.

[0012] (3) Format unification. Coordinate data in different formats need to be converted to a unified format.

[0013] (4) Calculation of the level. An algorithm needs to be designed to integrate the event data reported by different mobile phones according to spatial and temporal dimensions, so as to calculate how many different mobile phones made alarm calls at a certain location, and thus calculate the event level. If a large number of people call the police at the same location in a short period of time, a major event must have occurred on the road. On the one hand, this directly prompts the police to release the information as soon as possible, allowing the police to pre-set the content to be released. On the other hand, it serves as a judgment threshold to promptly identify which locations' events need to be released.

[0014] Therefore, based on the aforementioned data characteristics, in the application scenario of information dissemination to the public, using emergency location information to support early warning of traffic incidents of different levels requires, first and foremost, large-scale data preprocessing. Summary of the Invention

[0015] The purpose of this invention is to propose an early warning analysis method for traffic events of different levels based on emergency location service information, so as to solve the problems existing in the prior art.

[0016] To achieve the above objectives, the present invention provides the following solution: A method for early warning analysis of traffic incidents of different levels based on emergency location service information, comprising: Based on the unique identifier of the mobile phone number and the time, emergency location information actively reported by mobile phones is integrated to obtain an event integration list. The data in the event integration list is parsed using inverse geocoding to obtain structured data on the relationship between coordinates and roads; Based on the structured data of the relationships, event clustering analysis based on spatial distance and time dimensions is performed to obtain early warning results for traffic events of different levels.

[0017] Optionally, the event integration of emergency location information proactively reported by mobile phones includes: Set an event threshold T0; where the event threshold T0 is the time interval between the party making the emergency call. If the event is not processed within the time window Data_T0 corresponding to the event threshold T0, making another call will also correspond to the same event. Extract the unique identifiers of all mobile phones from the reported emergency location information; For each identifier, determine whether the corresponding event already exists in the event list within the time window Data_T0; If the data already exists, it indicates a duplicate report, and this data will be skipped. If this is the first time the data is received, all event data corresponding to the identifier will be sorted in reverse chronological order, and the earliest one will be stored in the event integration list.

[0018] Optionally, obtaining the event aggregation list also includes: The data stored in the event integration list is processed using unified coordinates.

[0019] Optionally, inverse geocoding parsing of coordinates is performed to obtain structured data on the relationship between coordinates and roads, including: The data in the event integration list is subjected to reverse coordinate parsing to obtain the parsing results associated with the road, that is, to obtain geocoding information; Based on the acquired geocoding information, it is determined whether the coordinate location is on a road. If it is on a road, the corresponding geocoding information is retained and stored together with the relevant road segment information.

[0020] Optionally, determining whether the coordinates are on a road based on the acquired geocoding information includes: Based on the error range of mobile phone positioning, set key dynamic parameters; Once the system obtains the inverse geographic information of a location, it extracts the distance and road segment name from the road field. For each road segment record, it will be determined whether its distance is less than the preset key dynamic parameter. If the distance is less than the key dynamic parameter, the coordinate point is considered to fall within the reasonable road network range.

[0021] Optionally, event clustering analysis based on spatial distance and time dimensions includes: Set a data validity time threshold; Set event space association threshold; Based on the data validity time threshold and event space association threshold, the relational structured data is analyzed and processed to obtain clustering analysis results; The event level is determined based on the number of emergency location information entries reported in each traffic event in the cluster analysis results.

[0022] Optionally, the analysis and processing of the relational structured data based on the data validity time threshold includes: By setting a validity time threshold T1 for traffic events, only data within the threshold T1 range before the current time point is read.

[0023] Optionally, the analysis and processing of the relational structured data based on the data validity time threshold includes: By setting a spatial correlation threshold D1 for the data and using D1 as the neighborhood radius parameter in the DBSCAN algorithm, different traffic events will be represented by the values ​​of clusters. The number of data entries in each cluster will represent how many different emergency location information entries are in that event.

[0024] The beneficial effects of this invention are as follows: This technical solution describes the classification and grading of early warning information based on emergency location information. Its practical benefits are mainly as follows.

[0025] 1. Improve the efficiency and effectiveness of rapid detection of traffic incidents and internet information services based on the internet.

[0026] Current situation: While traffic management departments can obtain emergency information immediately through emergency calls and relay it to frontline officers, their workflow primarily revolves around the on-site handling of traffic incidents. However, as the demand for socialized services from traffic management departments continues to increase, these departments need to promptly release information about on-site incidents to the public.

[0027] Problem: Since all police incidents are processed on the internal network, frontline officers need to relay the information to officers at the command center. These officers then manually select and mark locations on a map in the internet-based business system and publish the information. This requires officers on the ground to provide location information to officers at the command center, which is inefficient and difficult to guarantee in terms of accuracy.

[0028] Advantages: This technical solution is based on real-time analysis technology of Internet data, and its advantages include two aspects.

[0029] 1) High accuracy. It can provide police officers at the command center with accurate coordinate information of traffic incidents reported by the caller's mobile phone, and its advantage in road location accuracy is irreplaceable.

[0030] 2) High efficiency. Since emergency location information is obtained through the Ministry of Industry and Information Technology's internet platform, the data transmission is highly real-time, and the data analysis results can be automatically displayed to the police officers in the command center. This allows them to quickly grasp the location of the incidents on the road and facilitates immediate communication with the officers on the road, thereby completely changing the working mode of traffic incident release and effectively improving the efficiency of releasing traffic incident information to the public.

[0031] 2. Intelligent traffic incident classification effectively helps improve the ability to allocate resources for traffic incident handling and issue early warnings.

[0032] Current situation: Although traffic management departments can obtain information about road incidents by phone in the first instance, it is in the form of single emergency calls. The description information of each call is not structured and accurate. Specifically, it is necessary for the police to manually determine whether the calls are in the same area, and it is even more difficult to determine whether they belong to the same incident based on their specific location. In practice, the police simply transfer the incidents to the front line according to procedures, and the front-line police officers make specific judgments on how to handle them.

[0033] Problem: Under the current model, the command center struggles to determine whether each emergency call falls into the same category, requiring manual experience for data integration and lacking objective criteria. This also hinders the overall deployment of police resources on the ground. To some extent, for major incidents, the lack of timely objective assessment of the incident response level affects the efficiency of response to such incidents.

[0034] Advantages. This technical solution, through accurate location coordinates of the alarm user and hierarchical analysis using clustering, can determine the response level of each event at the system level in real time, offering the following two advantages.

[0035] 1) Effectively enables traffic management departments to quickly make response decisions, optimize the allocation of police forces on the road, and especially to deploy diversion and control measures for upstream and downstream areas and carry out road rescue work.

[0036] 2) To provide data support for subsequent event releases, traffic management departments at all levels can set up contingency plans in advance, clarifying which levels require release and which levels can be approved for release. This can provide clear quantitative indicators to support information release, enabling scientific decision-making on information release and effectively improving the quality of information release by traffic management departments. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram illustrating a sample of data obtainable via the internet. Figure 2 This is a schematic diagram of the event integration method based on two dimensions: unique mobile phone number identifier and time, according to an embodiment of the present invention. Figure 3This is a schematic diagram of the unified coordinate system method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the method for determining whether a coordinate location is on a road based on the acquired geocoding information according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a method for early warning analysis of traffic events of different levels based on emergency location service information, according to an embodiment of the present invention. Figure 6 This is an example diagram of coordinate transformation code in an embodiment of the present invention; Figure 7 This is an example diagram of coordinate inverse analysis according to an embodiment of the present invention; Figure 8 This is an example diagram of the coordinate reverse parsing code in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] like Figure 5 As shown, this embodiment proposes an early warning analysis method for traffic events of different levels based on emergency location service information, including: Based on the unique identifier of the mobile phone number and the time, emergency location information actively reported by mobile phones is integrated to obtain an event integration list. The data in the event integration list is parsed using inverse geocoding to obtain structured data on the relationship between coordinates and roads; Based on the structured relational data, event clustering analysis based on spatial distance and time dimensions is performed to obtain early warning results for traffic events of different levels. Furthermore, the integration of emergency location information proactively reported by mobile phones includes: Set an event threshold T0; where the event threshold T0 is the time interval between the party making the emergency call. If the event is not processed within the time window Data_T0 corresponding to the event threshold T0, making another call will also correspond to the same event. Extract the unique identifiers of all mobile phones from the reported emergency location information; For each identifier, determine whether the corresponding event already exists in the event list within the time window Data_T0; If the data already exists, it indicates a duplicate report, and this data will be skipped. If this is the first time the data is received, all event data corresponding to the identifier will be sorted in reverse chronological order, and the earliest one will be stored in the event integration list.

[0042] Specifically, in this embodiment, the event integration method based on the unique identifier of the mobile phone number and the time dimension includes: This method is mainly aimed at situations where the same person makes an emergency call and their mobile phone repeatedly reports location information. If this data is used directly, it will affect subsequent cluster analysis, misclassifying data reported by one person multiple times as data reported by multiple people, leading to inaccurate event rating.

[0043] The specific method is as follows: 1) A manually set event threshold T0 is defined as the time interval between when a party makes an emergency call. This data is manually set by the developers and can be adjusted as needed. Generally, when a person is involved in an accident on the road, they will call the emergency number. If the accident is not resolved, they will call again for the same incident. If the accident is resolved and the person encounters another incident while driving, they may call the emergency number again. Therefore, in this embodiment, the setting of T0 can be adjusted according to the average accident handling time for different regions and road types. Generally, it can be set to 30 minutes.

[0044] 2) Logical processing methods such as Figure 2 As shown, this processing flow starts immediately upon the system receiving a batch of emergency location service information and is completed before the data is officially entered into the database.

[0045] First, the system will extract the unique identifier of each mobile phone in this batch of data; Next, for each identifier (such as M1), the system will determine whether the corresponding event already exists in the event list within the "Data_T0" time window.

[0046] If the data already exists, it indicates a duplicate report, and the system will skip this data. If it is the first time the system receives the data, it will sort all the event data corresponding to the identifier in reverse chronological order and store the earliest record in the DB_alarm_event table.

[0047] The entire process emphasizes real-time and proactiveness—that is, before data enters the final database, it must be deduplicated, sorted, and filtered to ensure that the data entering the database is cleaned, free of redundancy, and represents the earliest valid events, thereby ensuring the accuracy and timeliness of subsequent alarm analysis.

[0048] Furthermore, obtaining the event aggregation list also includes: The data stored in the event integration list is processed using unified coordinates.

[0049] Specifically, in this embodiment, the unified coordinate system includes: Using existing technical solutions, the coordinate system of the data stored in DB_alarm_event is unified before being stored in the database. To facilitate subsequent deployment, since Baidu Maps uses the BD09 coordinate system and online map providers such as Gaode Maps / Tencent Maps use the GCJ02 coordinate system, this embodiment processes the coordinate system according to the following flowchart to simultaneously convert the received data coordinates to both the BD09 and GCJ02 coordinate systems. The method logic is as follows: Figure 3 Its core technical feature is that the coordinates are uniformly processed into two coordinate systems: BD09 and GCJ02.

[0050] In this embodiment, the overall technical solution for the unified coordinate system is described as follows: This embodiment involves converting WGS84 and GCJ02 coordinate systems to BD09. Because many traffic management departments collaborate with Baidu Maps and use its maps, it's necessary to convert these different coordinate systems to BD09. Furthermore, some local traffic management departments use maps from companies like Gaode Maps, Tianditu, or PGIS, requiring the unified use of the GCJ02 coordinate system.

[0051] Since emergency location service information is obtained from the mobile phone's positioning chip, the reported data, according to standards, can only be in two coordinate systems: WGS84 and GCJ02. Therefore, traffic management departments should handle the data according to the following principles: (1) When the local business system uses the bd09 coordinate system, the coordinate system type of each piece of data should be determined for the received emergency location information.

[0052] 1) When the coordinate system type is wgs84, call the wgs84 to bd09 conversion method to perform the conversion.

[0053] 2) When the coordinate system type is gcj02, call the gcj02 to bd09 conversion method to perform the conversion.

[0054] (2) When the local business system uses the gcj02 coordinate system, the data type of each emergency location information received should be determined.

[0055] 1) When the coordinate system type is wgs84, call the wgs84 to gcj02 conversion method to perform the conversion.

[0056] 2) When the coordinate system type is gcj02, there is no need to call the conversion method; the data of the gcj02 coordinate system can be directly retained.

[0057] Optionally, for ease of work, during data processing, all data can be processed uniformly and saved simultaneously in both the BD09 and GCJ02 coordinate systems. Figure 3 The process is performed according to the flowchart shown.

[0058] The specific technical methods for unifying the coordinate system are as follows: In practical data processing code development examples, mature code libraries (such as the xyconvert library) can be called for data transformation. These libraries have encapsulated the coordinate system transformation data processing into functions. An example of the transformation code is shown below. Figure 6 As shown, a coordinate system transformation is performed. While coordinate system transformation introduces some technical error, this error is very small and does not affect practical use.

[0059] Similarly, map companies such as Baidu and Gaode have also encapsulated standard interfaces for coordinate transformation. Coordinate system transformation can be performed through interface calls. The specific method does not require local code calls to libraries such as xyconvert. Instead, the original coordinate system data is input into the interface in the form of an HTTP request to obtain the transformed coordinate data.

[0060] Furthermore, inverse geocoding and parsing of coordinates are performed to obtain structured data on the relationship between coordinates and roads, including: The data in the event integration list is subjected to reverse coordinate parsing to obtain the parsing results associated with the road, that is, to obtain geocoding information; Based on the acquired geocoding information, it is determined whether the coordinate location is on a road. If it is on a road, the corresponding geocoding information is retained and stored together with the relevant road segment information.

[0061] Furthermore, based on the acquired geocoding information, it is determined whether the coordinates are on the road, including: Based on the error range of mobile phone positioning, set key dynamic parameters; Once the system obtains the inverse geographic information of a location, it extracts the distance and road segment name from the road field. For each road segment record, it will be determined whether its distance is less than the preset key dynamic parameter. If the distance is less than the key dynamic parameter, the coordinate point is considered to fall within the reasonable road network range.

[0062] The coordinate inverse analysis scheme in this embodiment is as follows: The reverse coordinate parsing scheme relies on standardized third-party interfaces provided by companies such as Baidu and Gaode, such as... Figure 7As shown, the method for obtaining reverse parsing requires relying on the interface service provided by the map company. Without the necessary geospatial information, it is impossible to calculate it locally.

[0063] The code for coordinate reverse parsing is as follows Figure 8 As shown, the `roads` field contains the following information: the `name` field represents the name of a road near the given coordinate; `distance` is the straight-line perpendicular distance from the given coordinate to the road; `direction` indicates the relative direction; and `location` represents the road surface coordinates of the perpendicular projection point of the given coordinate onto the road segment. The `roads` field is a list format, and its data count indicates the number of roads surrounding the given coordinate point.

[0064] The above data example is the data format for calling Baidu's API service. The overall pattern is the same for calling Gaode's API service, but the data format is different and needs to be determined by referring to its API documentation.

[0065] Specifically, in this embodiment, the structured processing of the relationship between coordinates and roads obtained by coordinate inverse geocoding parsing can be divided into two steps: Obtaining inverse geocoding information of coordinates based on existing technologies: This step can directly utilize the existing technical interfaces of Baidu and Gaode, or you can use other existing technical means to reverse-parse the coordinates. After parsing the coordinates, you can obtain data samples similar to the following. It should be noted that coordinate reverse geocoding parsing involves various different results and data types. This embodiment requires the parsing results associated with roads. If using Baidu Maps, you need to force the extensions_road option to be enabled on the interface to obtain the data; otherwise, the obtained reverse geocoding data will be essentially no different from the "location information" field in the emergency location service information example, which is unusable for traffic management departments.

[0066] The coordinate reverse geocoding parsing is shown below: Based on the obtained geocoding information, determine whether the coordinates are on the road: Through the aforementioned reverse geocoding process, this embodiment can obtain the correspondence between coordinates and nearby roads. This embodiment shows which roads a coordinate is close to, their respective distances in meters, and the coordinates of the intersection points of the perpendicular lines to the road segments. For traffic management work, specifically for calculating traffic incident warning levels and issuing incident notices, this embodiment requires information specific to the roads. Therefore, this embodiment constructs the following algorithm, such as... Figure 4 As shown, the calculation determines whether it is on the road and obtains its specific location description information on the road.

[0067] In this processing flow, the system requires manual pre-setting of a key dynamic parameter D0, typically recommended to be 10 meters—a value stemming from the generally present error range (approximately ±10 meters) in mobile phone positioning technology. After acquiring the inverse geographic information of a location (such as address text and feature values), the system extracts data such as distance and name from the "roads" field. Then, for each road segment record, the system determines whether its distance is less than the preset D0 value. If the distance is less than D0, the coordinates are considered to fall within a reasonable road network range, and the system retains the data, merging it with relevant road segment information (including fields such as name, distance, direction, and location) for storage. Conversely, if the distance is greater than or equal to D0, the point is determined to be outside the road surface, an invalid or drifting location, and the system automatically ignores the data, preventing it from entering subsequent storage stages. By introducing the D0 parameter, the system achieves intelligent filtering of positioning noise, ensuring the spatial accuracy and business availability of the data entering the database.

[0068] The core idea of ​​this embodiment is to set a threshold D0, which is used to determine the deviation between the coordinates and the road, according to the following formula: Based on the above formula and the introduction of the D0 variable, this embodiment can determine whether the received emergency location information is on the road. Data that is not on the road can be directly deleted. In this technical method, only the data that is on the road needs to be retained.

[0069] Furthermore, event clustering analysis based on spatial distance and time dimensions includes: Set a data validity time threshold; Set event space association threshold; Based on the data validity time threshold and event space association threshold, the relational structured data is analyzed and processed to obtain clustering analysis results; The event level is determined based on the number of emergency location information entries reported in each traffic event in the cluster analysis results.

[0070] Specifically, in this embodiment, the event clustering analysis based on spatial distance and time dimensions includes: The specific steps of event clustering analysis based on emergency location information are as follows.

[0071] 1) Set a data validity time threshold. Each emergency location information, after being filtered and processed through the above steps, corresponds to a specific road traffic event. For traffic events, this embodiment differs from traditional clustering algorithms by setting a traffic event validity time threshold T1, only reading data within the threshold T1 range before the current time point.

[0072] 2) Set an event spatial correlation threshold. Since different people may call to report a traffic incident at the same time after it occurs, the coordinates will be concentrated in space. Therefore, by setting a spatial correlation threshold D1, and using this parameter as the neighborhood radius parameter in the DBSCAN algorithm commonly used in the industry, different traffic incidents will be represented by the value of clusters. The number of data entries in each cluster will represent how many different emergency location information entries are in that incident.

[0073] 3) Event Severity Determination. The cluster analysis above yielded the number of emergency location information entries reported by the public for each traffic incident. This number will serve as the basis for further determining the traffic incident response level; that is, the more entries, the more people reported the incident, and the more urgent the incident. Specific settings can be flexibly adjusted based on the specific circumstances of each region. Similarly, congestion data can be introduced as an auxiliary factor in the level determination to construct an event handling level assessment matrix.

[0074] This technical solution describes the classification and grading of early warning information based on emergency location information. Its practical benefits are mainly as follows.

[0075] 1. Improve the efficiency and effectiveness of rapid detection of traffic incidents and internet information services based on the internet.

[0076] Current situation: While traffic management departments can obtain emergency information immediately through emergency calls and relay it to frontline officers, their workflow primarily revolves around the on-site handling of traffic incidents. However, as the demand for socialized services from traffic management departments continues to increase, these departments need to promptly release information about on-site incidents to the public.

[0077] Problem: Since all police incidents are processed on the internal network, frontline officers need to relay the information to officers at the command center. These officers then manually select and mark locations on a map in the internet-based business system and publish the information. This requires officers on the ground to provide location information to officers at the command center, which is inefficient and difficult to guarantee in terms of accuracy.

[0078] Advantages: This technical solution is based on real-time analysis technology of Internet data, and its advantages include two aspects.

[0079] 1) High accuracy. It can provide police officers at the command center with accurate coordinate information of traffic incidents reported by the caller's mobile phone, and its advantage in road location accuracy is irreplaceable.

[0080] 2) High efficiency. Since emergency location information is obtained through the Ministry of Industry and Information Technology's internet platform, the data transmission is highly real-time, and the data analysis results can be automatically displayed to the police officers in the command center. This allows them to quickly grasp the location of the incidents on the road and facilitates immediate communication with the officers on the road, thereby completely changing the working mode of traffic incident release and effectively improving the efficiency of releasing traffic incident information to the public.

[0081] 2. Intelligent traffic incident classification effectively helps improve the ability to allocate resources for traffic incident handling and issue early warnings.

[0082] Current situation: Although traffic management departments can obtain information about road incidents by phone in the first instance, it is in the form of single emergency calls. The description information of each call is not structured and accurate. Specifically, it is necessary for the police to manually determine whether the calls are in the same area, and it is even more difficult to determine whether they belong to the same incident based on their specific location. In practice, the police simply transfer the incidents to the front line according to procedures, and the front-line police officers make specific judgments on how to handle them.

[0083] Problem: Under the current model, the command center struggles to determine whether each emergency call falls into the same category, requiring manual experience for data integration and lacking objective criteria. This also hinders the overall deployment of police resources on the ground. To some extent, for major incidents, the lack of timely objective assessment of the incident response level affects the efficiency of response to such incidents.

[0084] Advantages. This technical solution, through accurate location coordinates of the alarm user and hierarchical analysis using clustering, can determine the response level of each event at the system level in real time, offering the following two advantages.

[0085] 1) Effectively enables traffic management departments to quickly make response decisions, optimize the allocation of police forces on the road, and especially to deploy diversion and control measures for upstream and downstream areas and carry out road rescue work.

[0086] 2) To provide data support for subsequent event releases, traffic management departments at all levels can set up contingency plans in advance, clarifying which levels require release and which levels can be approved for release. This can provide clear quantitative indicators to support information release, enabling scientific decision-making on information release and effectively improving the quality of information release by traffic management departments.

[0087] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for early warning analysis of traffic incidents of different levels based on emergency location service information, characterized in that, include: Based on the unique identifier of the mobile phone number and the time, emergency location information actively reported by mobile phones is integrated to obtain an event integration list. The data in the event integration list is parsed using inverse geocoding to obtain structured data on the relationship between coordinates and roads; Based on the structured data of the relationships, event clustering analysis based on spatial distance and time dimensions is performed to obtain early warning results for traffic events of different levels.

2. The early warning analysis method for traffic events of different levels based on emergency location service information according to claim 1, characterized in that, Event integration of emergency location information proactively reported by mobile phones includes: Set an event threshold T0; where the event threshold T0 is the time interval between the party making the emergency call. If the event is not processed within the time window Data_T0 corresponding to the event threshold T0, making another call will also correspond to the same event. Extract the unique identifiers of all mobile phones from the reported emergency location information; For each identifier, determine whether the corresponding event already exists in the event list within the time window Data_T0; If the data already exists, it indicates a duplicate report, and this data will be skipped. If this is the first time the data is received, all event data corresponding to the identifier will be sorted in reverse chronological order, and the earliest one will be stored in the event integration list.

3. The early warning analysis method for traffic events of different levels based on emergency location service information according to claim 1, characterized in that, Retrieving the event aggregation list also includes: The data stored in the event integration list is processed using unified coordinates.

4. The early warning analysis method for traffic events of different levels based on emergency location service information according to claim 1, characterized in that, Performing coordinate reverse geocoding parsing to obtain structured data on the relationship between coordinates and roads includes: The data in the event integration list is subjected to reverse coordinate parsing to obtain the parsing results associated with the road, that is, to obtain geocoding information; Based on the acquired geocoding information, it is determined whether the coordinate location is on a road. If it is on a road, the corresponding geocoding information is retained and stored together with the relevant road segment information.

5. The early warning analysis method for traffic events of different levels based on emergency location service information according to claim 4, characterized in that, Determining whether a location is on a road based on the acquired geocoding information includes: Based on the error range of mobile phone positioning, set key dynamic parameters; Once the system obtains the inverse geographic information of a location, it extracts the distance and road segment name from the road field. For each road segment record, it will be determined whether its distance is less than the preset key dynamic parameter. If the distance is less than the key dynamic parameter, the coordinate point is considered to fall within the reasonable road network range.

6. The early warning analysis method for traffic events of different levels based on emergency location service information according to claim 1, characterized in that, Event clustering analysis based on spatial distance and time dimensions includes: Set a data validity time threshold; Set event space association threshold; Based on the data validity time threshold and event space association threshold, the relational structured data is analyzed and processed to obtain clustering analysis results; The event level is determined based on the number of emergency location information entries reported in each traffic event in the cluster analysis results.

7. The method for early warning analysis of traffic incidents of different levels based on emergency location service information according to claim 6, characterized in that, Based on the data validity time threshold, the analysis and processing of the relational structured data includes: By setting a validity time threshold T1 for traffic events, only data within the threshold T1 range before the current time point is read.

8. The early warning analysis method for traffic events of different levels based on emergency location service information according to claim 6, characterized in that, Based on the data validity time threshold, the analysis and processing of the relational structured data includes: By setting a spatial correlation threshold D1 for the data and using D1 as the neighborhood radius parameter in the DBSCAN algorithm, different traffic events will be represented by the values ​​of clusters. The number of data entries in each cluster will represent how many different emergency location information entries are in that event.

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