Traffic road congestion risk identification method, system and equipment
By building a road congestion risk identification model and using historical vehicle flow data to deeply analyze the congestion risks of traffic roads, the problem that existing technology is difficult to reflect the essential characteristics of road congestion, and effective identification and governance of traffic road congestion risks are achieved.
Patent Information
- Application Number
- CN202411932535.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-04-11
AI Technical Summary
The existing technology is difficult to deeply reflect the essential characteristics of urban road traffic congestion, which leads to the lack of operation in the traffic management department to control congestion, and people cannot avoid routes that may be congested in advance during their daily travel.
By obtaining historical traffic data of all traffic road sections, a road congestion risk identification model is constructed, and multi-dimensional evaluation indicator data is determined based on the model and the historical traffic data of each pending section, so as to identify and evaluate the congestion risk of traffic roads and determine the high-risk target congestion section.
In-depth analysis and identification of traffic road congestion risks have been achieved, traffic management departments have helped targeted congestion control, and provided the people with convenience to avoid congestion routes in advance.
Smart Images
Figure CN119811076A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of traffic engineering, and specifically to a method, system and device for identifying traffic road congestion risks. Background Art
[0002] The number of motor vehicles in cities continues to grow, and urban traffic pressure has increased dramatically. Traffic congestion often occurs in some areas. Traffic congestion is the primary problem facing urban traffic management at present, and traffic congestion management is also the key task and main work of urban traffic management departments. In the prior art, the three colors of "red, yellow, and green" are usually used on maps to intuitively indicate the congestion of traffic roads to help drivers plan their driving routes reasonably. Among them, red usually represents congested sections on the map; yellow indicates that the road section is driving slowly, but has not yet reached the level of congestion; green represents unobstructed roads on the map. In actual life and work, the people and traffic management departments can often only understand the congestion conditions of various points and sections on urban traffic roads through the "red, yellow, and green" reflected on the map.
[0003] However, the "red, yellow and green" colors on the map that reflect the degree of road congestion generally change dynamically in real time based on the real-time congestion index or average vehicle speed. Although this can directly reflect the instantaneous congestion status of each point on the road, it is difficult to reflect some deeper essential characteristics of congestion. There is a problem that the characteristics of urban road traffic congestion are not clear, which makes it difficult for traffic management departments to deal with congestion and for people to avoid routes that may be congested in advance during their daily travel. Summary of the invention
[0004] In view of this, the embodiments of the present application hope to provide a method, system and device for identifying traffic congestion risks to at least solve the above-mentioned technical problems.
[0005] To achieve the above purpose, the technical solution of this application is implemented as follows:
[0006] According to one aspect of an embodiment of the present application, a method for identifying traffic road congestion risk is provided, the method comprising:
[0007] Obtaining historical traffic flow data of all traffic road sections, and constructing a road congestion risk identification model based on the historical traffic flow data;
[0008] Obtain at least one road segment to be processed;
[0009] Determine multidimensional evaluation index data corresponding to each road section to be processed according to the road congestion risk identification model and the historical traffic flow data corresponding to each road section to be processed;
[0010] According to the multi-dimensional evaluation index data corresponding to each of the to-be-processed road sections, a target congested road section is determined from the at least one to-be-processed road section.
[0011] Optionally, determining a target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed includes:
[0012] According to the multidimensional evaluation index data corresponding to the at least one road section to be processed, respectively determine the mean value corresponding to the evaluation index data of each dimension;
[0013] From the at least one road section to be processed, a road section to be processed in which the evaluation index data of all dimensions exceeds the average is determined as a target congested road section.
[0014] Optionally, determining a target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed includes:
[0015] According to the multidimensional evaluation index data corresponding to the at least one to-be-processed road section, respectively determine the quartiles corresponding to the index data of each dimension, and determine the value corresponding to the preset quartile according to the quartiles;
[0016] From the at least one road section to be processed, a road section to be processed whose evaluation index data in all dimensions exceeds the numerical value corresponding to the preset percentile is determined as a target congested road section.
[0017] Optionally, determining a target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed includes:
[0018] According to the multi-dimensional evaluation index data corresponding to the at least one road section to be processed, the evaluation index data of each dimension is sorted to obtain a sorting result;
[0019] According to the sorting result, a preset number of to-be-processed sections are selected to form a set of candidate sections corresponding to each dimension;
[0020] A road section to be processed in a set of candidate road sections in all dimensions is determined as a target congested road section.
[0021] Optionally, determining a target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed includes:
[0022] Determine the congestion risk assessment value of each dimension of the evaluation index data corresponding to each road section to be processed;
[0023] Determine the total congestion risk assessment value corresponding to each road section to be processed according to the congestion risk assessment value of the evaluation index data of each dimension;
[0024] According to the total congestion risk assessment value corresponding to each of the to-be-processed road sections, a target congested road section is determined from the at least one to-be-processed road section.
[0025] Optionally, constructing a road congestion risk identification model based on the historical traffic flow data includes:
[0026] Calculate the historical multi-dimensional evaluation index data corresponding to each traffic road section according to the historical traffic flow data;
[0027] The road congestion risk identification model is constructed based on the historical multi-dimensional evaluation index data corresponding to all traffic road sections.
[0028] Optionally, the historical multi-dimensional evaluation index data includes:
[0029] The average congestion index data of each traffic road section at each time; the congestion time interval of each traffic road section throughout the day; the congestion impact range of each traffic road section in each congestion time interval; the congestion duration of each traffic road section in each congestion time interval; whether each traffic road section is the starting point of congestion in each congestion time interval; whether the road traffic flow change of each traffic road section in each congestion time interval is acceptable.
[0030] According to a second aspect of the present application, a traffic road congestion risk identification system is provided, the system comprising:
[0031] An identification model building module is used to obtain historical traffic flow data of all traffic road sections and build a road congestion risk identification model based on the historical traffic flow data;
[0032] A road section acquisition module to be processed, used to acquire at least one road section to be processed;
[0033] A congestion risk status assessment module, used to determine multi-dimensional evaluation index data corresponding to each road section to be processed based on the road congestion risk identification model and the historical traffic flow data corresponding to each road section to be processed;
[0034] The target congested road section determination module is used to determine the target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed.
[0035] Optionally, the target congested road section determination module includes:
[0036] The first congested road section determination submodule is used to determine the mean value corresponding to the evaluation index data of each dimension according to the multi-dimensional evaluation index data corresponding to the at least one road section to be processed; and determine, from the at least one road section to be processed, the road section to be processed whose evaluation index data of all dimensions exceeds the mean value as the target congested road section.
[0037] According to a third aspect of the present application, a traffic road congestion risk identification device is provided, the device comprising:
[0038] at least one processor; and
[0039] a memory communicatively coupled to the at least one processor;
[0040] Wherein: the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the traffic road congestion risk identification method as described above.
[0041] The traffic road congestion risk identification method, system and device provided by the present application are a scheme for first obtaining the historical traffic flow data of all sections in the urban traffic road network, and then constructing a road congestion risk identification model, so as to evaluate the congestion risk of traffic roads based on the road congestion risk identification model. Specifically, by obtaining the historical traffic flow data of all traffic road sections, it is possible to analyze the essential characteristics of road congestion in depth from multiple dimensions based on the historical traffic flow data of each point section of the traffic road in advance, and to construct a road congestion risk identification model, so that the road congestion risk identification model can analyze the congestion risk indicators of each section to be processed from multiple dimensions, and the traffic management department can use the road congestion risk identification model to identify and evaluate the congestion risk of the section to be processed on the traffic road, so as to determine the high-risk target congested sections, which is convenient for the traffic management department to carry out congestion management in a targeted manner on the one hand, and convenient for the people to avoid the routes that may be congested in advance during their daily travel. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.
[0043] Figure 1 This is a flow chart of the method for identifying traffic congestion risks in this application;
[0044] Figure 2 This is a schematic diagram of the structure of the traffic road congestion risk identification system in this application;
[0045] Figure 3 This is a schematic diagram of the structural composition of the traffic road congestion risk identification device in this application. DETAILED DESCRIPTION
[0046] The technical solution of the present application is further elaborated in detail below in conjunction with the accompanying drawings and specific embodiments of the specification.
[0047] The various specific technical features in the various embodiments described in the specific implementation methods can be combined in various ways without contradiction. For example, different implementation methods can be formed by combining different specific technical features. In order to avoid unnecessary repetition, the various possible combinations of the specific technical features in this application will not be described separately.
[0048] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that the specific order or sequence of "first\second\third" can be interchanged where permitted. It should be understood that the objects distinguished by "first\second\third" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.
[0049] To facilitate those skilled in the art to understand the technical solutions provided in the embodiments of the present application, the relevant technologies are described below:
[0050] In the prior art, the three colors of "red, yellow, and green" are usually used on maps to intuitively indicate the congestion of traffic roads. In real life and work, the people and traffic management departments can only understand the congestion conditions of various points and sections in urban traffic roads through the "red, yellow, and green" reflected on the map. However, the "red, yellow, and green" used on the map to reflect the degree of road congestion generally changes dynamically in real time according to the real-time congestion index or average vehicle speed. Although this can reflect the instantaneous congestion status of various points and sections on the road in real time and intuitively, it is difficult to reflect some deeper essential characteristics of congestion, for example, it is difficult to reflect the impact range, duration, whether it is the starting point section, whether it is acceptable congestion, etc. of the congestion at each point and section on the road. Therefore, the existing scheme has the problem of unclear characteristics of urban road traffic congestion, which makes it difficult for traffic management departments to control congestion and for the people to avoid routes that may be congested in advance during their daily travel.
[0051] To this end, the embodiment of the present application provides a technical solution for identifying the risk of traffic congestion. In this technical solution, the historical traffic flow data of all traffic road sections are obtained, and a road congestion risk identification model is constructed based on the historical traffic flow data; at least one road section to be processed is obtained; based on the road congestion risk identification model and the historical traffic flow data corresponding to each road section to be processed, the multidimensional evaluation index data corresponding to each road section to be processed is determined; based on the multidimensional evaluation index data corresponding to each road section to be processed, a target congested road section is determined from the at least one road section to be processed. Therefore, by obtaining the historical traffic flow data of all traffic road sections, it is possible to conduct an in-depth analysis of the essential characteristics of road congestion from multiple dimensions based on the historical traffic flow data of each point section of the traffic road, and construct a road congestion risk identification model. The road congestion risk identification model can analyze the congestion risk indicators of each section to be processed from multiple dimensions. The traffic management department can use the road congestion risk identification model to identify and evaluate the congestion risks of all points and sections on the traffic road, so as to determine the high-risk target congested sections. On the one hand, it is convenient for the traffic management department to carry out congestion control in a targeted manner, and on the other hand, it is convenient for the people to avoid routes that may be congested in advance when traveling on a daily basis.
[0052] The technical solutions of the present application are described below through multiple embodiments. It should be noted that these embodiments can be implemented in a variety of different forms and should not be construed as being limited to the embodiments described here.
[0053] Embodiment 1
[0054] Figure 1 This is a schematic diagram of the process implementation of the traffic road congestion risk identification method in this application, such as Figure 1 As shown, the method includes steps S101-S104:
[0055] Step S101, obtaining historical traffic flow data of all traffic road sections, and constructing a road congestion risk identification model based on the historical traffic flow data;
[0056] Specifically, the historical traffic flow data of each road section can be collected by the monitoring equipment and / or checkpoint monitoring equipment installed by the transportation department on each traffic road, and the essential characteristics of road congestion can be deeply analyzed from multiple dimensions based on the historical traffic flow data of each point and section of the traffic road, and a road congestion risk identification model can be constructed. The road congestion risk identification model can analyze the congestion risk indicators of each traffic road section from multiple dimensions.
[0057] In this embodiment, the historical traffic flow data includes: basic data of urban road network, data of vehicles passing through checkpoints, and data of congestion index. Among them, basic data of urban road network includes information such as unique number, name, longitude and latitude position, road grade, road direction, etc. of all points and segments of urban roads; data of vehicles passing through checkpoints is data of passing vehicles recorded by checkpoint monitoring equipment, including information such as recording time, license plate number, checkpoint equipment number, etc.; congestion index data refers to the ratio of the time required for a vehicle to actually travel to the time required for the vehicle to travel in a free flow state. The larger the congestion index, the more serious the road congestion.
[0058] Step S102, obtaining at least one road section to be processed;
[0059] In this embodiment, when the traffic management department needs to conduct traffic control, it can obtain at least one section to be processed in the urban traffic roads according to the area managed by the department, so as to select the congested sections with higher risks from these sections to be processed, and carry out effective congestion control in time. Specifically, it is possible to maintain a city traffic road network data in advance, which contains the unique number, name, longitude and latitude position and other information corresponding to each section to be processed, so that the matching traffic sections can be selected as the sections to be processed according to the area to be managed, wherein the sections to be processed can include one or more, and the embodiment of the present application does not impose specific restrictions on this.
[0060] Step S103, determining multi-dimensional evaluation index data corresponding to each road section to be processed according to the road congestion risk identification model and the historical traffic flow data corresponding to each road section to be processed;
[0061] In this embodiment, based on a pre-constructed road congestion risk identification model, the historical traffic data corresponding to each section to be processed within a preset period of time in the past (such as the preset period can be one month or 15 days, etc., and the embodiment of the present application does not impose specific restrictions on this) is obtained, and the congestion risk status of each section to be processed at each time point is evaluated using these historical traffic data to obtain multi-dimensional evaluation index data corresponding to each section to be processed.
[0062] In one example, the multidimensional evaluation index data may include six indicators, namely: the average congestion index data of each traffic road section at each time; the congestion time interval of each traffic road section throughout the day; the congestion impact range of each traffic road section in each congestion time interval; the congestion duration of each traffic road section in each congestion time interval; whether each traffic road section is the starting point of congestion in each congestion time interval; and whether the road traffic flow change of each traffic road section in each congestion time interval is acceptable.
[0063] Specifically, the above multi-dimensional evaluation index data is explained as follows:
[0064] 1. Average congestion index data of each traffic road section at each time: This index changes with time, reflecting the congestion status of each traffic road section at any time in 24 hours a day, and is the basis for the calculation of subsequent indicators;
[0065] 2. The congestion time interval of each traffic road section throughout the day: This indicator reflects the time intervals of each traffic road section in the 24 hours of the day when it is congested;
[0066] 3. Congestion impact range of each traffic road section in each congestion time interval: This indicator reflects the impact range of each traffic road section on the traffic flow of surrounding sections after congestion occurs, reflecting the severity of congestion;
[0067] 4. Duration of congestion for each traffic road section in each congestion time interval: This indicator reflects the average duration of congestion from the beginning to the end of congestion for each traffic road section, which can reflect the severity of congestion;
[0068] 5. Whether each traffic road section is the starting point of congestion in each congestion time interval: This indicator reflects whether a traffic road section is the first point section to be congested in the surrounding area when congestion occurs, and is used for congestion tracing;
[0069] 6. Whether the changes in road traffic flow for each traffic road section in each congestion time interval are acceptable: When this indicator determines that a certain traffic road section is in a congested state through the congestion index, its traffic flow changes are analyzed based on the traffic data at the checkpoint. If the traffic flow drops significantly during the congestion period, the congestion on the congested road section at that time point is considered unacceptable; conversely, if the traffic flow does not drop significantly, it is considered acceptable.
[0070] Step S104: determining a target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed.
[0071] In this embodiment, after obtaining the multidimensional evaluation index data corresponding to each section to be processed through the road congestion risk identification model analysis, the target congested section can be further determined from at least one section to be processed based on the multidimensional evaluation index data corresponding to each section to be processed.
[0072] Specifically, after analyzing and obtaining the multidimensional evaluation index data of all the sections to be processed, there can be a variety of methods to screen out the points with high congestion risks. As an example, the mean of the six indicators for each section to be processed can be calculated. For each section to be processed, if the values of the six indicators exceed the mean at a certain moment, the point segment is considered to be at a high congestion risk at that moment. More strictly, the quartiles of the six indicators for each section to be processed can also be calculated. If the values of the six indicators for a section to be processed exceed the 75% quantile at a certain moment, the section to be processed is considered to be at a high congestion risk at that moment. In addition, other target congested sections screening methods can be set according to actual needs. The embodiment of the present application does not impose specific restrictions on this. For example, the multidimensional evaluation index data of each section to be processed are sorted, and then N (where N can be set as needed) points are selected as target congested sections according to the sorting results.
[0073] In a preferred embodiment of the present application, step S104 may include the following steps:
[0074] According to the multidimensional evaluation index data corresponding to the at least one road section to be processed, the mean value corresponding to the evaluation index data of each dimension is determined respectively; from the at least one road section to be processed, the road section to be processed whose evaluation index data of all dimensions exceeds the mean value is determined as the target congested road section.
[0075] In this embodiment, the mean corresponding to the evaluation index data of each dimension is calculated from the multidimensional evaluation index data corresponding to at least one pending road section currently needing to be managed (that is, all pending road sections), and then from these pending road sections, the pending road sections whose evaluation index data of all dimensions exceeds the mean corresponding to the evaluation index data of the dimension are screened out as target congested road sections.
[0076] Specifically, assuming that there are evaluation index data corresponding to six dimensions, the mean is calculated for the evaluation index data of each dimension to obtain the means corresponding to the evaluation index data of the six dimensions. Then, the sections to be processed whose evaluation index data of the six dimensions all exceed the means corresponding to the evaluation index data of the dimension are selected as the target congested sections.
[0077] In another preferred embodiment of the present application, the step S104 may further include the following steps:
[0078] According to the multidimensional evaluation index data corresponding to the at least one road section to be processed, the quartiles corresponding to the evaluation index data of each dimension are determined respectively, and the numerical values corresponding to the preset quantiles are determined according to the quartiles; from the at least one road section to be processed, the road section to be processed whose evaluation index data of all dimensions exceeds the numerical values corresponding to the preset quantiles is determined as the target congested road section.
[0079] In this embodiment, the quartiles corresponding to the evaluation index data of each dimension are calculated from the multidimensional evaluation index data corresponding to at least one pending road section currently needing to be managed (that is, all pending road sections), and the numerical values corresponding to the preset quantiles are determined based on the quartiles. Then, from these pending road sections, the pending road sections whose evaluation index data of all dimensions exceed the numerical values corresponding to the preset quantiles are screened out as target congested road sections.
[0080] Among them, the preset percentile can be freely set according to the congestion control effect requirements. For example, if the preset percentile is the 75% percentile, the road section to be processed whose evaluation index data of all dimensions exceeds the value corresponding to the 75% percentile is selected as the target congested road section. Specifically, assuming that there are six dimensions of evaluation index data, the quartiles are calculated for the evaluation index data of each dimension respectively, and the quartiles corresponding to the evaluation index data of the six dimensions are obtained. The values corresponding to the preset percentiles are determined according to the quartiles of the six dimensions. Then, the road sections to be processed whose evaluation index data of the six dimensions exceed the values corresponding to the preset percentiles (such as the preset percentiles are 75% percentiles or 25% percentiles, etc.) are selected as the target congested road sections.
[0081] In another preferred embodiment of the present application, the step S104 may further include the following steps:
[0082] According to the multidimensional evaluation index data corresponding to the at least one road section to be processed, the evaluation index data of each dimension are sorted respectively to obtain a sorting result; according to the sorting result, a preset number of road sections to be processed are selected to form a candidate road section set corresponding to each dimension; and the road section to be processed in the candidate road section set of all dimensions is determined as the target congested road section.
[0083] The preset number is the number of candidate road sections that are preset, and can be set according to the actual traffic management effect. The embodiment of the present application does not specifically limit this. For example, if the preset number is 100, it is necessary to select the top 100 road sections to be processed for each dimension to form a candidate road section set; if the preset number is 80, it is necessary to select the top 80 road sections to be processed for each dimension to form a candidate road section set.
[0084] In this embodiment, the evaluation index data of each dimension are sorted respectively from the multidimensional evaluation index data corresponding to at least one pending road section that currently needs to be managed (that is, all pending road sections), and the sorting results corresponding to the evaluation index data of each dimension are obtained. According to the sorting results, a preset number of pending road sections are screened out from these pending road sections to obtain a candidate road section set corresponding to the dimension, and then, the pending road sections in the candidate road section set of all dimensions are screened out as target congested sections.
[0085] Specifically, assuming that there are evaluation index data corresponding to six dimensions, the evaluation index data of each dimension are sorted respectively to obtain the sorting results corresponding to the evaluation index data of the six dimensions, and according to the sorting results, a certain number of to-be-processed sections are screened out from these to-be-processed sections to obtain a candidate section set corresponding to the dimension, and then, the to-be-processed sections in the candidate section set of these six dimensions are screened out as target congested sections.
[0086] In another preferred embodiment of the present application, the step S104 may further include the following steps:
[0087] Determine the congestion risk assessment value of each dimension of the evaluation index data corresponding to each road section to be processed respectively; determine the total congestion risk assessment value corresponding to each road section to be processed according to the congestion risk assessment value of the evaluation index data of each dimension; determine the target congested road section from the at least one road section to be processed according to the total congestion risk assessment value corresponding to each road section to be processed.
[0088] Specifically, a rule for grading and assigning a logarithmic value is pre-set, and then the evaluation index data of each dimension in each section to be processed are graded and scored according to the grading and scoring rule, so as to obtain a congestion risk assessment value of the evaluation index data of each dimension; then, according to the congestion risk assessment value of the evaluation index data of each dimension, a weighted calculation is performed according to a preset weight (for example, a preset weight coefficient of 1 or 0.5) to obtain a total congestion risk assessment value corresponding to each section to be processed; finally, each section to be processed is sorted according to its total congestion risk assessment value, and a certain number (for example, 20 or 30, etc.) of sections to be processed are screened out as target congested sections according to the sorting result.
[0089] Specifically, assuming that there are six dimensions of evaluation index data, the evaluation index data of each dimension in each section to be processed are graded and scored respectively to obtain the congestion risk assessment values corresponding to the evaluation index data of the six dimensions. Then, the congestion risk assessment values of the six dimensions are weightedly calculated to obtain the total congestion risk assessment value corresponding to each section to be processed. Finally, each section to be processed is sorted according to its total congestion risk assessment value, and a certain number of sections to be processed are selected as target congested sections according to the sorting results.
[0090] In another preferred embodiment of the present application, the road congestion risk identification model constructed according to the historical traffic flow data may include the following steps:
[0091] According to the historical traffic flow data set, the historical multi-dimensional evaluation index data corresponding to each traffic road section is calculated; according to the historical multi-dimensional evaluation index data corresponding to all the sections to be processed, the road congestion risk identification model is constructed.
[0092] Specifically, when constructing a road congestion risk identification model, first obtain historical traffic data for congestion risk identification. In this embodiment, the historical traffic data is a multidimensional data set, which may include three types of data: basic urban road network data; checkpoint vehicle passing data; and congestion index data. Among them, the basic urban road network data includes the unique number, name, longitude and latitude position, road grade, road direction and other information of all points and segments of urban roads; the checkpoint vehicle passing data is the passing vehicle data recorded by the checkpoint monitoring equipment, including the recording time, license plate number, checkpoint equipment number and other information; the congestion index data refers to the ratio of the actual driving time of the vehicle to the time required for the vehicle to travel in a free flow state. The larger the congestion index, the more serious the road congestion.
[0093] Furthermore, a multidimensional indicator system for congestion risk identification and evaluation can be constructed based on the historical traffic flow data of all traffic road sections. The multidimensional indicator system can include the following six indicators:
[0094] 1. Average congestion index data of each traffic road section at each time: This index changes with time, reflecting the congestion status of a traffic road section at any time in 24 hours a day, and is the basis for the calculation of subsequent indicators;
[0095] 2. The congestion time interval of each traffic road section throughout the day: This indicator reflects the time intervals of a certain traffic road section in a 24-hour day when it is congested;
[0096] 3. The congestion impact range of each traffic road section in each congestion time interval: This indicator reflects the impact range of a traffic road section on the traffic flow of surrounding sections after congestion occurs, reflecting the severity of congestion;
[0097] 4. Duration of congestion for each traffic road section in each congestion time interval: This indicator reflects the average duration of congestion from the start time to the end time of congestion on a certain traffic road section, reflecting the severity of congestion;
[0098] 5. Whether each traffic road section is the starting point of congestion in each congestion time interval: This indicator reflects whether a traffic road section is the first point section to be congested in the surrounding area when congestion occurs, and is used for congestion tracing;
[0099] 6. Whether the changes in road traffic flow for each traffic road section in each congestion time interval are acceptable: When this indicator determines that a certain traffic road section is in a congested state through the congestion index, its traffic flow changes are analyzed based on the traffic data at the checkpoint. If the traffic flow drops significantly during the congestion period, the congestion on the congested road section at that time point is considered unacceptable; conversely, if the traffic flow does not drop significantly, it is considered acceptable.
[0100] Then, the historical traffic flow data corresponding to all traffic road sections are traversed, and the historical multi-dimensional evaluation index data corresponding to each traffic road section are calculated. First, the congestion index data of each point segment is associated and matched with the urban road network infrastructure data, and then all traffic road sections are traversed, and the following six indicators are calculated for each traffic road section:
[0101] 1. Average congestion index data of each traffic road section at each time: It can be directly calculated based on the historical congestion index data of the traffic road section at that time;
[0102] 2. The congestion time interval of each traffic road section throughout the day: Based on indicator 1, a congestion index threshold is set. When the congestion index is higher than the congestion index threshold, the traffic road section is considered to be in a congested state; the time when a traffic road section is in a congested state in 24 hours a day is extracted and integrated into the congestion time interval;
[0103] 3. The congestion impact range of each traffic road section in each congestion time interval: the mileage ratio of all congested sections in a certain range upstream of the point section during the duration from the beginning of congestion to the end of congestion (optionally, the range of upstream can be 1000 meters, 2000 meters, or 3000 meters);
[0104] 4. The duration of congestion for each traffic road section in each congestion time interval: the duration of the congestion index of a traffic road section from the time it exceeds the congestion threshold to the time it falls below the congestion threshold;
[0105] 5. Whether each traffic road section is a congestion starting point section in each congestion time interval: when the congestion index of a traffic road section at a certain time exceeds the congestion threshold for the first time and enters the congestion state, if there are no other congested traffic road sections within a certain range around it, then the point section is considered to be a congestion starting point section (optionally, the radius within the certain range around it can be within 1000 meters, 2000 meters, or 3000 meters);
[0106] 6. Whether the changes in road traffic flow for each road section in each congestion time interval are acceptable: When a road section is judged to be in a congested state through the congestion index, its traffic flow changes are analyzed based on the checkpoint passing data. If the traffic flow drops significantly during the congestion period, it is considered that the congestion on the congested road section at that time point is unacceptable; conversely, if the traffic flow does not drop significantly, it is considered acceptable.
[0107] Finally, the road congestion risk identification model is constructed based on the historical multi-dimensional evaluation index data corresponding to all traffic road sections. That is, the road congestion risk identification model is established by combining the six index data of all traffic road sections, and the various parameters of the model are determined. The road congestion risk identification model can be used for urban road congestion risk identification and evaluation.
[0108] The traffic road congestion risk identification method provided by the present application, through in-depth analysis of the essential characteristics of congestion and construction of a road congestion risk identification model based on the historical traffic flow data of each point section of the traffic road in advance, so that the road congestion risk identification model can analyze the congestion risk indicators of each section to be processed from multiple dimensions, and the traffic management department can use the road congestion risk identification model to identify and evaluate the congestion risks of all points and sections on the traffic road, so as to determine the high-risk target congested sections. On the one hand, it is convenient for the traffic management department to carry out congestion management in a targeted manner, and on the other hand, it is convenient for the people to avoid routes that may be congested in advance during their daily travel.
[0109] Embodiment 2
[0110] Figure 2The following is a schematic diagram of the structure of the traffic congestion risk identification system in this application. The system can be divided into one or more program modules, one or more program modules are stored in a storage medium, and are executed by one or more processors to complete the embodiment of this application. The program module referred to in the embodiment of this application refers to a series of computer program instruction segments that can complete specific functions. The following description will specifically introduce the functions of each program module in this embodiment. Figure 2 As shown, the traffic road congestion risk identification system may include: an identification model building module 201, a to-be-processed road section acquisition module 202, a congestion risk status assessment module 203 and a target congested road section determination module 204, wherein:
[0111] The recognition model building module 201 is used to obtain the historical traffic flow data of all traffic road sections, and to build a road congestion risk recognition model based on the historical traffic flow data;
[0112] A road section acquisition module 202 for acquiring at least one road section for processing;
[0113] The congestion risk status evaluation module 203 is used to determine the multi-dimensional evaluation index data corresponding to each road section to be processed according to the road congestion risk identification model and the historical traffic flow data corresponding to each road section to be processed;
[0114] The target congested road section determination module 204 is used to determine a target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed.
[0115] In a preferred embodiment of the present application, the target congested road section determination module 204 includes:
[0116] The first congested road section determination submodule is used to determine the mean value corresponding to the evaluation index data of each dimension according to the multi-dimensional evaluation index data corresponding to the at least one road section to be processed; and determine, from the at least one road section to be processed, the road section to be processed whose evaluation index data of all dimensions exceeds the mean value as the target congested road section.
[0117] In a preferred embodiment of the present application, the target congested road section determination module 204 includes:
[0118] The second congested road section determination module is used to determine the quartiles corresponding to the evaluation index data of each dimension according to the multi-dimensional evaluation index data corresponding to the at least one road section to be processed, and determine the numerical value corresponding to the preset quartile according to the quartiles; from the at least one road section to be processed, determine the road section to be processed whose evaluation index data of all dimensions exceeds the numerical value corresponding to the preset quartile as the target congested road section.
[0119] In a preferred embodiment of the present application, the target congested road section determination module 204 includes:
[0120] The third congested road section determination module is used to sort the evaluation index data of each dimension according to the multi-dimensional evaluation index data corresponding to the at least one road section to be processed to obtain a sorting result; select a preset number of road sections to be processed according to the sorting result to form a candidate road section set corresponding to each dimension; and determine the road section to be processed in the candidate road section set of all dimensions as the target congested road section.
[0121] In a preferred embodiment of the present application, the target congested road section determination module 204 includes:
[0122] The fourth congested road section determination module is used to respectively determine the congestion risk assessment value of the evaluation index data of each dimension corresponding to each road section to be processed; determine the total congestion risk assessment value corresponding to each road section to be processed according to the congestion risk assessment value of the evaluation index data of each dimension; and determine the target congested road section from the at least one road section to be processed according to the total congestion risk assessment value corresponding to each road section to be processed.
[0123] In a preferred embodiment of the present application, the recognition model building module 201 is specifically used for:
[0124] Calculating historical multi-dimensional evaluation index data corresponding to each traffic road section according to the historical traffic flow data set;
[0125] The road congestion risk identification model is constructed based on the historical multi-dimensional evaluation index data corresponding to all road sections to be processed.
[0126] In a preferred embodiment of the present application, the historical traffic flow data includes: basic data of urban road network, checkpoint vehicle passing data, and historical congestion index data.
[0127] In a preferred embodiment of the present application, the historical multi-dimensional evaluation index data includes:
[0128] The average congestion index data of each traffic road section at each time; the congestion time interval of each traffic road section throughout the day; the congestion impact range of each traffic road section in each congestion time interval; the congestion duration of each traffic road section in each congestion time interval; whether each traffic road section is the starting point of congestion in each congestion time interval; whether the road traffic flow change of each traffic road section in each congestion time interval is acceptable.
[0129] The traffic road congestion risk identification system provided by the present application, through pre-analyzing the essential characteristics of congestion based on the historical traffic flow data of each point section of the traffic road and constructing a road congestion risk identification model, the road congestion risk identification model can analyze the congestion risk indicators of each section to be processed from multiple dimensions, and the traffic management department can use the road congestion risk identification model to identify and evaluate the congestion risks of all points and sections on the traffic road, so as to determine the high-risk target congested sections. On the one hand, it is convenient for the traffic management department to carry out congestion control in a targeted manner, and on the other hand, it is convenient for the people to avoid routes that may be congested in advance during their daily travel.
[0130] It should be noted that the traffic road congestion risk identification system provided in the above embodiment is different from the above Figure 1 The provided traffic road congestion risk identification method belongs to the same concept, and the specific implementation process can refer to the above system embodiment, which will not be repeated here.
[0131] Embodiment 3
[0132] Figure 3 For the traffic congestion risk identification device in this application, such as Figure 3 As shown, the traffic road congestion risk identification device 300 includes at least one processor 301 and a memory 302 for storing a computer program that can be run on the processor 301. The processor 301 is used to execute the traffic road congestion risk identification method suggested by the above embodiments of the present application when running the computer program. The traffic road congestion risk identification device 300 also includes at least one network interface 304 and a user interface 303. The various components in the traffic road congestion risk identification device 300 are coupled together through a bus system 305. It can be understood that the bus system 305 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 305 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, in Figure 3 Various buses are labeled as bus system 305 .
[0133] The user interface 303 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.
[0134] It can be understood that the memory 302 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 302 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memories.
[0135] The memory 302 in the embodiment of the present application is used to store various types of data to support the operation of the traffic road congestion risk identification device 300. Examples of these data include: any computer program used to operate on the traffic road congestion risk identification device 300, such as an operating system 3021, an application 3022, and a traffic road congestion risk identification system 3023; wherein the operating system 3021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 3022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The traffic road congestion risk identification system 3023 is in this application Figure 1 The system shown is used to implement traffic congestion simulation. The program for implementing the method of the embodiment of the present application can be included in the application program 3022, and can also be included in the traffic road congestion risk identification system 3023.
[0136] The processor 301 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 301 or an instruction in the form of software. The above-mentioned processor 301 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 301 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. In conjunction with the steps of the method disclosed in the embodiments of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in the memory 302, and the processor 301 reads the information in the memory 302, and completes the steps of the above method in conjunction with its hardware.
[0137] In an exemplary embodiment, the traffic road congestion risk identification device 300 can be implemented by one or more application-specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), field programmable gate array (FPGA), general-purpose processor, controller, microcontroller (MCU), microprocessor, or other electronic components to execute the aforementioned method.
[0138] In an exemplary embodiment, the present application also provides a computer-readable storage medium, such as a memory 302 including a computer program, which can be executed by a processor 301 of a traffic road congestion risk identification device 300 to complete the steps of the aforementioned method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disk, or CD-ROM; or it can be various devices including one or any combination of the above memories, such as a computer, a tablet device, a personal digital assistant, etc.
[0139] A computer-readable storage medium stores a computer program, which, when executed by a processor, executes the compressor surge warning method suggested by the above embodiment of the present application.
[0140] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. In addition, the features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.
[0141] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.
Claims
1. A method for identifying traffic congestion risk, characterized in that: The method comprises: Obtaining historical traffic flow data of all traffic road sections, and constructing a road congestion risk identification model based on the historical traffic flow data; Obtain at least one road segment to be processed; Determine multidimensional evaluation index data corresponding to each road section to be processed according to the road congestion risk identification model and the historical traffic flow data corresponding to each road section to be processed; According to the multi-dimensional evaluation index data corresponding to each of the to-be-processed road sections, a target congested road section is determined from the at least one to-be-processed road section.
2. The method for identifying traffic congestion risk according to claim 1, characterized in that: The step of determining a target congested road section from at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed comprises: According to the multidimensional evaluation index data corresponding to the at least one road section to be processed, respectively determine the mean value corresponding to the evaluation index data of each dimension; From the at least one road section to be processed, a road section to be processed whose evaluation index data in all dimensions exceeds the average is determined as a target congested road section.
3. The method for identifying traffic congestion risk according to claim 1, characterized in that: The step of determining a target congested road section from at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed comprises: According to the multidimensional evaluation index data corresponding to the at least one to-be-processed road section, respectively determine the quartiles corresponding to the evaluation index data of each dimension, and determine the value corresponding to the preset quartile according to the quartiles; From the at least one road section to be processed, a road section to be processed whose evaluation index data in all dimensions exceeds the numerical value corresponding to the preset percentile is determined as a target congested road section.
4. The method for identifying traffic congestion risk according to claim 1, characterized in that: The step of determining a target congested road section from at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed comprises: According to the multi-dimensional evaluation index data corresponding to the at least one road section to be processed, the evaluation index data of each dimension is sorted to obtain a sorting result; According to the sorting result, a preset number of to-be-processed sections are selected to form a set of candidate sections corresponding to each dimension; A road section to be processed in a set of candidate road sections in all dimensions is determined as a target congested road section.
5. The method for identifying traffic congestion risk according to claim 1, characterized in that: The step of determining a target congested road section from at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed comprises: Determine the congestion risk assessment value of each dimension of the evaluation index data corresponding to each road section to be processed; Determine the total congestion risk assessment value corresponding to each road section to be processed according to the congestion risk assessment value of the evaluation index data of each dimension; According to the total congestion risk assessment value corresponding to each of the to-be-processed road sections, a target congested road section is determined from the at least one to-be-processed road section.
6. The method for identifying traffic congestion risk according to claim 1, characterized in that: The road congestion risk identification model constructed according to the historical traffic flow data includes: Calculate the historical multi-dimensional evaluation index data corresponding to each traffic road section according to the historical traffic flow data; The road congestion risk identification model is constructed based on the historical multi-dimensional evaluation index data corresponding to all traffic road sections.
7. The method for identifying traffic congestion risk according to claim 6, characterized in that: The historical multi-dimensional evaluation index data includes: The average congestion index data of each traffic road section at each time; the congestion time interval of each traffic road section throughout the day; the congestion impact range of each traffic road section in each congestion time interval; the congestion duration of each traffic road section in each congestion time interval; whether each traffic road section is the starting point of congestion in each congestion time interval; whether the road traffic flow change of each traffic road section in each congestion time interval is acceptable.
8. A traffic congestion risk identification system, characterized in that: The system comprises: An identification model building module is used to obtain historical traffic flow data of all traffic road sections and build a road congestion risk identification model based on the historical traffic flow data; A road section acquisition module to be processed, used to acquire at least one road section to be processed; A congestion risk status assessment module, used to determine multi-dimensional evaluation index data corresponding to each road section to be processed based on the road congestion risk identification model and the historical traffic flow data corresponding to each road section to be processed; The target congested road section determination module is used to determine the target congested road section from the at least one road section to be processed according to the multi-dimensional evaluation index data corresponding to each road section to be processed.
9. The traffic road congestion risk identification system according to claim 1, characterized in that: The target congested road section determination module includes: The first congested road section determination submodule is used to determine the mean value corresponding to the evaluation index data of each dimension according to the multi-dimensional evaluation index data corresponding to the at least one road section to be processed; and determine, from the at least one road section to be processed, the road section to be processed whose evaluation index data of all dimensions exceeds the mean value as the target congested road section.
10. A traffic congestion risk identification device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein: The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the traffic road congestion risk identification method according to any one of claims 1 to 7.