Road traffic flow monitoring method based on big data analysis

Abstract

The invention discloses a road traffic flow monitoring method based on big data analysis, and the method can more accurately determine the target traffic flow monitoring value of each lane through the integration of the vehicle type data of each lane. The number of small automobiles and the number of large automobiles are distinguished and compared with different threshold values, different flow indexes are adopted to adjust the initial traffic flow monitoring value, the difference of influences of different automobile types on traffic flow is fully considered, and the traffic flow monitoring data better fit the actual traffic condition. When the full-lane flow monitoring value is determined, the lane position information is further combined, different lane indexes and the target monitoring value of each lane are multiplied and then summed, so that the traffic flow information of each lane is integrated, and the influence of the lane position factor on the whole traffic flow is reflected.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle network traffic information processing, and in particular to a road traffic flow monitoring method based on big data analysis. Background Art

[0002] Traffic flow monitoring aims to improve traffic operation efficiency and management level in many aspects with the help of big data analysis. First of all, it can provide accurate basis for the intelligent control of traffic lights, reasonably allocate green light duration according to the dynamic changes of traffic flow in different time periods and sections, reduce vehicle waiting time, and alleviate congestion nodes. Secondly, it is helpful for road planning and construction. Analysis of long-term traffic flow data can determine traffic bottlenecks and high-traffic areas, so as to widen roads and build new infrastructure in a targeted manner. Furthermore, it is of great significance to the traffic induction system. It can feed back real-time traffic flow information to drivers to help them plan the best travel route, avoid congested sections, and improve travel efficiency. At the same time, it is also conducive to the scheduling optimization of public transportation such as buses, so that resource allocation is more in line with actual needs and improve the smoothness and convenience of the overall urban traffic.

[0003] Traffic flow monitoring based on big data analysis has some shortcomings in terms of lanes and vehicle types. In lane monitoring, some monitoring systems find it difficult to accurately distinguish the details of traffic flow in different lanes. For example, in the case of multiple lanes with complex lane functions (such as tidal lanes, bus lanes, etc.), the data acquisition equipment may be unable to accurately identify the lane to which the vehicle belongs due to angle, resolution or algorithm limitations, resulting in confusion of traffic flow data in each lane, making management strategies such as traffic signal timing less targeted. For some special lanes, such as curves and ramps, the accuracy and consistency of monitoring are also poor, and data loss or misjudgment is prone to occur.

[0004] In terms of vehicle types, existing big data analysis technologies are not sophisticated and comprehensive enough in identifying vehicle types. On the one hand, some small vehicles are fuzzy in classification. For example, ordinary cars, mini vans and other vehicles with similar appearances may be misclassified, which affects the formulation of traffic management strategies for specific models, such as the control of load-restricted areas. On the other hand, for emerging vehicle types, such as special models of new energy vehicles and various modified vehicles, the recognition ability is limited, and their traffic data cannot be accurately counted, which is not conducive to the layout considerations of vehicles of different energy types in traffic planning and the construction planning of related supporting facilities. In addition, there are also deficiencies in the analysis of the association between vehicle types and lane use rules. It is difficult to deeply explore the driving rules and potential conflicts of different types of vehicles on different lanes, making it difficult to formulate more scientific lane use specifications and traffic diversion plans. Summary of the invention

[0005] In an exemplary embodiment of the present application, a road traffic flow monitoring method based on big data analysis is provided. This road traffic flow monitoring method integrates lane vehicle type data, adjusts the initial value according to the number of small and large vehicles and thresholds, combines the lane position and index to determine the full-lane traffic flow value, accurately presents the road conditions, helps traffic management departments make scientific decisions, and improves traffic operation efficiency and safety.

[0006] The present application provides a road traffic flow monitoring method based on big data analysis, which includes:

[0007] S1: Determine the number of lanes on the currently monitored road section, and obtain the number and vehicle types of vehicles within the preset monitoring area range on each lane. The vehicle types include small cars and large cars;

[0008] S2: Determine the target traffic flow monitoring value for each lane, specifically including: if the number of small cars is greater than the first threshold, increase the initial traffic flow monitoring value by the first traffic flow index to determine the target traffic flow monitoring value; if the number of large cars is greater than the second threshold, increase the initial traffic flow monitoring value by the second traffic flow index to determine the target traffic flow monitoring value. Among them, the first threshold is greater than the second threshold, and the first traffic flow index is less than the second traffic flow index;

[0009] S3: Determine the full-lane traffic flow monitoring value. Multiply the target traffic flow monitoring value determined in S2 by the lane index to determine the target monitoring value, and determine the full-lane traffic flow monitoring value based on the sum of the target monitoring values of each lane;

[0010] The lane index includes a first lane index and a second lane index. If the lane is located on the outermost side of all lanes, multiply the traffic flow monitoring value of this lane by the first lane index to determine the target monitoring value; if the lane is not located on the outermost side of all lanes, multiply the traffic flow monitoring value of this lane by the second lane index to determine the target monitoring value. The first lane index is greater than the second lane index.

[0011] S4: Determine whether the full-lane traffic flow monitoring value is greater than or equal to the prompt threshold. If the full-lane traffic flow monitoring value is greater than the prompt threshold, output a traffic flow warning prompt message; otherwise, do not output the traffic flow warning prompt message.

[0012] Further, the ratio range of the first threshold to the second threshold is 3.

[0013] Further, the ratio range of the second traffic flow index to the first traffic flow index is 2, and the initial traffic flow monitoring value is 0.

[0014] Further, the ratio range of the first lane index to the second lane index is 4.

[0015] Further, the lane located on the outermost side of all lanes refers to any one of the two outermost lanes among all the lanes.

[0016] Further, within a preset time period, the target traffic flow monitoring values of each lane are determined and the total lane traffic flow monitoring value is determined, and the length of the preset distance is 200 meters.

[0017] Further, the preset monitoring range is determined by a preset distance in the length direction along the lane.

[0018] The embodiments of the present application have the following beneficial effects: The traffic flow monitoring method for road traffic based on big data analysis can more accurately determine the target traffic flow monitoring values of each lane by integrating the vehicle type data of each lane. By comparing the numbers of small cars and large cars with different thresholds and adjusting the initial traffic flow monitoring values with different flow indices, the differences in the impacts of different vehicle types on traffic flow are fully considered, making the traffic flow monitoring data more in line with the actual traffic conditions. When determining the total lane traffic flow monitoring value, further combining the lane position information, and summing after multiplying the different lane indices by the target monitoring values of each lane, not only integrates the traffic flow information of each lane, but also reflects the influence of the lane position factor on the overall traffic flow. This integration of the vehicle type data of each lane and the lane position information can comprehensively, meticulously and accurately reflect the traffic flow conditions of the entire monitored section, providing more valuable decision-making basis for traffic management departments, such as predicting the traffic congestion trend in advance, reasonably arranging traffic guidance measures, effectively improving the scientificity and efficiency of traffic management, reducing traffic management mistakes caused by inaccurate traffic flow monitoring, and enhancing the operation efficiency and safety of road traffic. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 Exemplarily shows a flowchart of a traffic flow monitoring method for road traffic based on big data analysis provided by an embodiment of the present application;

[0021] Figure 2 Exemplarily shows a lane schematic diagram in an embodiment of the present application. Detailed Embodiments

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application.

[0023] To further illustrate the technical solutions provided by the embodiments of this application, the following will provide a detailed description thereof with reference to the accompanying drawings and specific implementation manners. Although the embodiments of this application provide method operation steps as shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or non-creative labor. In steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of this application.

[0024] Referring Figure 1 As shown, this application provides a road traffic vehicle flow monitoring method based on big data analysis, which includes:

[0025] S1: Determine the number of lanes on the currently monitored road section, and obtain the number and vehicle types of vehicles within the preset monitoring area range on each lane. The vehicle types include small cars and large cars. The preset monitoring range is determined by a preset distance in the length direction of the lane, and the length of the preset distance is 200 meters.

[0026] Determining the number of lanes, obtaining the number and types of vehicles within a specific 200-meter preset monitoring area provides basic data for subsequent accurate analysis, clarifies the monitoring range and objects, and makes data collection more targeted and effective.

[0027] By determining the number of lanes, the basic framework of the overall traffic capacity can be clarified; obtaining the number and types of vehicles within the 200-meter preset monitoring area can accurately locate the actual vehicle distribution status of the current road section. Whether it is small cars or large cars, their quantity information helps in the subsequent analysis of the traffic flow pressure and passing capacity of each lane, making the data collection have a clear range and pertinence, laying a solid data foundation for the subsequent steps, ensuring that the entire monitoring process can be carried out based on accurate and detailed information, and thus improving the accuracy and reliability of vehicle flow monitoring.

[0028] By setting different thresholds to distinguish the numbers of small and large cars, adjust the initial value according to the corresponding flow index (set to 0 for convenient calculation of the starting point). The first threshold is greater than the second threshold (ratio 3), because small cars are flexible and a larger number has a greater impact on the flow; the second flow index is greater than the first flow index (ratio 2), because large cars are large in size and a small increase will significantly affect the flow. Such settings can accurately quantify the contribution of different vehicle types to the vehicle flow.

[0029] Differentially evaluate and quantify the vehicle flow according to vehicle types to more accurately reflect the impact of different vehicle types on the traffic flow.

[0030] S2: Determine the target traffic flow monitoring values for each lane, specifically including: if the number of small cars is greater than the first threshold, increase the initial traffic flow monitoring value by the first flow index; if the number of large cars is greater than the second threshold, increase the initial traffic flow monitoring value by the second flow index.

[0031] The first threshold is greater than the second threshold, and the ratio range of the first threshold to the second threshold is 3

[0032] The first flow index is less than the second flow index, and the ratio range of the second flow index to the first flow index is 2. The initial traffic flow monitoring value is 0.

[0033] Exemplarily, the first threshold is set to 60 and the second threshold is set to 20. On a certain lane, if the number of small cars is 70, which is greater than the first threshold of 60, at this time, the impact of small cars on the traffic flow is calculated by the first flow index. Assume the first flow index is 0.3. Since the initial traffic flow monitoring value is 0, the traffic flow monitoring value increased by small cars on this lane is 0 + 0.3×(70 - 60) = 3.

[0034] If the number of large cars on this lane is 25, which is greater than the second threshold of 20, and the second flow index is assumed to be 0.6, then the traffic flow monitoring value increased by large cars is 0.6×(25 - 20) = 3. Adding the two together, the total target traffic flow monitoring value for this lane is 6.

[0035] The first threshold is greater than the second threshold because small cars are relatively flexible and their impact on the traffic flow is more significant only when the quantity is large. Therefore, a higher threshold needs to be set to trigger the adjustment of the traffic flow monitoring value. While large cars are large in size and not very flexible in driving, a small change in quantity will have a greater impact on the traffic flow, so the second threshold is lower.

[0036] The first flow index is less than the second flow index because large cars interfere more with road space occupation and traffic fluency. Therefore, the flow index corresponding to the change in their quantity is larger, which can more accurately reflect their influence in the traffic flow. Through such a design, it is possible to accurately determine the traffic flow monitoring values for each lane based on the quantity changes of different vehicle types, making the monitoring results more in line with the actual traffic operation rules and providing a reliable single-lane data basis for the subsequent calculation of the total lane traffic flow monitoring value.

[0037] Set different thresholds and flow indices. The number of small cars needs to exceed a relatively high first threshold to increase the initial traffic flow monitoring value with a relatively small first flow index. Because small cars are highly flexible and occupy little space, a relatively large number is required to significantly change the traffic flow. For large cars, due to their large size and poor driving flexibility, when the number exceeds a relatively low second threshold, the monitoring value is increased with a relatively large second flow index. This can carefully distinguish the influence of different vehicle types on the traffic flow, avoid errors caused by unified calculation, make the traffic flow monitoring values of each lane more in line with the actual traffic conditions, and provide a more accurate single-lane data basis for subsequent full-lane traffic flow calculation.

[0038] S3: Determine the full-lane traffic flow monitoring value. Multiply the target traffic flow monitoring value determined in S2 by the lane index to determine the target monitoring value, and determine the full-lane traffic flow monitoring value based on the sum of the target monitoring values of each lane.

[0039] The lane index includes a first lane index and a second lane index. If the lane is located on the outermost side of all lanes, the lane multiplies the first lane index by the traffic flow monitoring value to determine the target monitoring value. If the lane is not located on the outermost side of all lanes, referring to Figure 2 the L2 lane in

[0040] Referring to Figure 2 as shown, the lane L1 located on the outermost side of all lanes refers to any one of the two outermost lanes L1 among all lanes.

[0041] The first lane index is greater than the second lane index, and the ratio range of the first lane index to the second lane index is 4.

[0042] In the monitoring section in the previous example, there are three lanes in total, and the two outermost lanes are edge lanes. The first lane index is set to 4, and the second lane index is set to 1.

[0043] For the edge lane whose target traffic flow monitoring value was calculated to be 6 previously, its target monitoring value is 6×4 = 24. And if the target traffic flow monitoring value of the middle lane is 4 (hypothetical data), then the target monitoring value of the middle lane is 4×1 = 4.

[0044] The edge lanes are greatly affected by factors such as roadside parking, pedestrian crossing, and vehicle entry and exit at entrances and exits, and the uncertainty of their traffic flow is higher. Therefore, a larger first lane index is adopted. In this way, when calculating the target monitoring value, the traffic flow monitoring value of the edge lanes will be amplified, highlighting their potential impact on the full-lane traffic flow more. The inner lanes are relatively stable and less affected by the outside world, so a smaller second lane index is adopted, and the weight of their traffic flow monitoring value is relatively lower when calculating the target monitoring value.

[0045] Add the target monitoring values of each lane. In the above example, the traffic flow monitoring value of the entire lane is 24 + 4 = 28. In this way, based on considering the vehicle types, step S3 further combines the lane position factor, enabling the traffic flow monitoring value of the entire lane to more comprehensively and accurately reflect the true traffic flow conditions of the monitored section, providing more valuable data for traffic management decisions, such as determining which areas or lanes are more prone to congestion, so as to plan diversion schemes in advance or reasonably allocate traffic resources, etc.

[0046] Introduce lane indices. For the outermost lane (the edge lane is subject to many interference factors), a larger first lane index (the ratio to the second lane index is 4) is used, and for the inner lane, the second lane index is used. Multiply them separately with the lane traffic flow monitoring values and then sum to obtain the traffic flow monitoring value of the entire lane, which can more reasonably reflect the weight differences of lanes at different positions in the overall traffic flow and make the traffic flow monitoring of the entire lane more accurate.

[0047] Assign different lane indices according to whether the lane is the outermost one. The outermost lane is greatly affected by the roadside environment and entrances and exits, and is calculated with a larger first lane index. The inner lane has relatively less interference and thus uses a smaller second lane index. In this way, based on the target traffic flow monitoring values of each lane, reasonably amplify or reduce their weights in the traffic flow of the entire lane, enabling the traffic flow monitoring value of the entire lane to not only reflect the vehicle quantity and type, but also reflect the lane position differences, making the final result more scientifically and comprehensively display the traffic flow conditions of the monitored section.

[0048] Specifically, by making the ratio range of the first threshold to the second threshold greater than the ratio range of the second traffic index to the first traffic index, it can make large vehicles have a greater impact on the traffic flow monitoring value when their quantity increases slightly, and at the same time ensure that small vehicles can also have a reasonable impact when reaching a relatively high quantity, so that the traffic flow monitoring system can more accurately reflect the actual traffic impacts of different vehicle types.

[0049] Large vehicles are large in size and poor in mobility. When driving on the road, they occupy more space, and their driving speed is usually slow, which has a more obvious obstructive effect on traffic fluency. Therefore, in terms of quantity change, even if the quantity of large vehicles increases slightly, it should have a relatively large increase in the traffic flow monitoring value. For example, when the quantity of large vehicles just exceeds the second threshold, due to the relatively large second traffic index, it can quickly increase the traffic flow monitoring value of this lane, reflecting its greater interference to traffic.

[0050] Although small cars are relatively flexible and have relatively little interference with traffic flow within a certain quantity range, when the quantity reaches a certain level (exceeding the first threshold), they will also have a non-negligible impact on traffic flow. However, due to the characteristics of small cars themselves, the growth rate of their impact on traffic flow does not need to be as fast as that of large cars. Therefore, the ratio range of the first threshold to the second threshold is relatively large, and the ratio range of the first flow index to the second flow index is relatively small, making it necessary for small cars to have a relatively large increase in quantity to have a certain impact on the traffic flow monitoring value, which is in line with the actual situation of small cars in traffic.

[0051] Specifically, the different settings of the ratio range of the first threshold to the second threshold and the ratio range of the first lane index to the second lane index are to accurately measure the impact degree of vehicle types and lane positions on traffic flow from different dimensions.

[0052] For the setting of the threshold, it is to distinguish the different starting points of the impact of small cars and large cars on traffic flow; while the setting of the lane index is to reflect the different weights of lanes in different positions in the overall traffic flow.

[0053] Although the number of small cars may be large, they are relatively flexible, and operations such as driving and lane changing within the lane have relatively little interference with traffic fluency. Therefore, even if the number of small cars increases to a certain extent, the impact on traffic flow will not be as significant as that of large cars. Therefore, the ratio range of the first threshold (for small cars) to the second threshold (for large cars) is relatively small. For example, small cars may need to reach a certain quantity scale (such as the first threshold) to have a relatively obvious impact on traffic flow, while large cars, due to their large volume and inflexible driving characteristics, a relatively small number (the second threshold) will have a greater impact on traffic flow.

[0054] The outermost lane (edge lane) is subject to many interference factors, such as frequent roadside parking, pedestrian access, vehicle merging and exiting, etc. These factors make the traffic conditions of the edge lane more complex, and its potential impact on the traffic flow of the entire lane is much greater than that of the inner lane. Therefore, the ratio range of the first lane index (for the edge lane) to the second lane index (for the inner lane) is relatively large to highlight the importance of the edge lane in the overall traffic flow assessment.

[0055] S4: Determine whether the traffic flow monitoring value of the entire lane is greater than or equal to the prompt threshold. If the traffic flow monitoring value of the entire lane is greater than the prompt threshold, output the traffic flow warning prompt information; otherwise, do not output the traffic flow warning prompt information.

[0056] Determine the target traffic flow monitoring value of each lane and the traffic flow monitoring value of the entire lane within the preset time period.

[0057] After completing the above calculations within a preset time period and comparing with the prompt threshold, it is possible to promptly determine whether the traffic flow exceeds the standard, so as to output a warning prompt message, effectively assisting the traffic management department to take countermeasures in advance and ensuring road smoothness and safety.

[0058] After obtaining the full-lane traffic flow monitoring value by completing the previous steps within a preset time period and comparing with the prompt threshold, when the prompt threshold is exceeded, a warning prompt message is output, enabling the traffic management department to quickly be aware of the traffic congestion risk, plan a diversion plan in advance, adjust the signal light strategy or release traffic condition information, etc., effectively preventing the formation or deterioration of traffic jams, ensuring the efficient passage of roads, reducing time waste, energy consumption and potential traffic accident risks caused by traffic congestion, and improving the operation efficiency and safety of the entire road traffic system.

[0059] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0061] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 in one process or a plurality of processes and / or boxes Figure 1 or steps of functions specified in a plurality of boxes.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to cover these modifications and variations.

Claims

1. A road traffic flow monitoring method based on big data analysis, characterized in that: It includes: S1: Determine the number of lanes on the current monitoring section, and obtain the number and type of vehicles on each lane within the preset monitoring area, wherein the vehicle types include small cars and large cars; S2: Determine the target vehicle flow monitoring value of each lane, specifically comprising: if the number of the small cars is greater than a first threshold, then increase the initial vehicle flow monitoring value by a first flow index to determine the target vehicle flow monitoring value; if the number of the large cars is greater than a second threshold, then increase the initial vehicle flow monitoring value by a second flow index to determine the target vehicle flow monitoring value, wherein the first threshold is greater than the second threshold, and the first flow index is less than the second flow index; S3: Determine the traffic flow monitoring value of the entire lane, multiply the target traffic flow monitoring value determined in S2 by the lane index to determine the target monitoring value, and determine the traffic flow monitoring value of the entire lane by the sum of the target monitoring values ​​of each lane; The lane index includes a first lane index and a second lane index. If the lane is located at the outermost side of all lanes, the target monitoring value is determined by multiplying the first lane index by the vehicle flow monitoring value for the lane. If the lane is not located at the outermost side of all lanes, the target monitoring value is determined by multiplying the second lane index by the vehicle flow monitoring value for the lane. The first lane index is greater than the second lane index. S4: Determine whether the full lane traffic monitoring value is greater than or equal to the prompt threshold. If the full lane traffic monitoring value is greater than the prompt threshold, output the vehicle traffic warning prompt information; otherwise, do not output the vehicle traffic warning prompt information.

2. The road traffic flow monitoring method based on big data analysis according to claim 1 is characterized in that: The ratio range of the first threshold value to the second threshold value is 3.

3. The road traffic flow monitoring method based on big data analysis according to claim 2 is characterized in that: The ratio range of the second flow index to the first flow index is 2, and the initial vehicle flow monitoring value is 0.

4. The road traffic flow monitoring method based on big data analysis according to claim 3 is characterized in that: The ratio of the first lane index to the second lane index is in the range of 4.

5. The road traffic flow monitoring method based on big data analysis according to claim 4 is characterized in that: The outermost lane of all lanes refers to any one of the two outermost lanes among all the lanes.

6. The road traffic flow monitoring method based on big data analysis according to claim 5 is characterized in that: The target vehicle flow monitoring value of each lane and the vehicle flow monitoring value of all lanes are determined within a preset time period.

7. The road traffic flow monitoring method based on big data analysis according to claim 6 is characterized in that: The preset monitoring range is determined by a preset distance along the length direction of the lane, and the length of the preset distance is 200 meters.

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