TW202403537A - Apparatus and method for analyzing traffic status and computer program product implementing the method - Google Patents

Abstract

An apparatus and a method for analyzing traffic status, and a computer program product for implementing the method are provided. Firstly, a batch analysis filtering is performed on a plurality of mobile signaling data of a plurality of mobile devices received, so as to generate a plurality of mobile signaling location data. Then, the plurality of mobile signaling location data is mapped to a roadway, so as to calculate a total sample volume of the roadway, interval sample volumes and their respective corresponding hourly speed of the roadway. Afterward, based on the total sample volume of the roadway and a congestion threshold of the roadway calculated from historical data of the roadway, a sample group being selected of the total sample volume is determined. Finally, an aggregation is performed on the interval sample volumes and their respective corresponding hourly speed within the selected sample group, thereby quickly and precisely generating real-time hourly speed information of the roadway.

Description

Equipment, methods and computer program products for analyzing road section conditions

This case is about a traffic condition analysis technology. Specifically, it is about equipment, methods and computer program products that use mobile signaling data to analyze congested road conditions.

In recent years, Cellular-Based Vehicle Probe (CVP) technology based on mobile device base stations has been widely used in the detection of road traffic information due to its wide coverage and low cost. Generally speaking, CVP technology mainly uses mobile phone signaling, that is, using the time difference and location difference of CVP signaling data to calculate the average vehicle speed and travel time on the road section.

However, since mobile signaling traffic analysis technology analyzes traffic conditions through the time and location of mobile signaling points, it will be affected by signaling drift and differences in vehicles. For example, signaling drift is often limited by base station positioning technology and is unavoidable. However, this problem will affect the accuracy of traffic analysis. Especially during congestion, the signaling position will be more likely to drift and become unstable, resulting in inaccurate traffic analysis. Accurate. On the other hand, transport vehicles usually need to use heterogeneous data for verification, such as mass transport vehicle schedules, ticket information, image recognition information, etc., which only increases system or computational complexity.

Therefore, how to use mobile signaling data to quickly and accurately analyze traffic jams is an issue that needs to be solved.

In order to solve the above problems and other problems, this case discloses a device, method, computer program product and computer-readable recording medium for analyzing road section conditions.

The equipment disclosed in this case for analyzing road section conditions includes: a data receiving module, which is used to receive a plurality of mobile signaling data from a plurality of mobile devices; a batch filtering module, which is used to receive the plurality of mobile signaling data from a plurality of mobile devices. The data is subjected to batch analysis and filtering to generate a plurality of mobile signaling location data; the road segment corresponding module maps the plurality of mobile signaling location data to a segment to calculate the total sample size and multiple interval samples of the road segment. and the speed corresponding to the sample size of each section; and the dynamic congestion threshold calculation module calculates the congestion threshold of the road section based on the historical data of the road section; the dynamic congestion detection module calculates the congestion threshold of the road section based on the The congestion threshold and the total sample size of the road section determine the sample group selected from the total sample size; and the speed aggregation module performs speed aggregation of the sample size and corresponding speed of each section in the selected sample group. , in order to generate real-time speed information for this road section.

In one embodiment, the batch filtering module of this case cuts the plurality of mobile signaling data in batches in chronological order to perform principal component analysis, direction filtering and distance filtering on each batch of mobile signaling data. After each batch of the mobile signaling data is subjected to principal component analysis, direction filtering and distance filtering, mobile signaling position data of each batch is generated to form the plurality of mobile signaling position data.

In one embodiment, the dynamic congestion detection module of this case compares the total sample size with the congestion threshold, so as to select a sample group with a faster speed when the total sample size is not greater than the congestion threshold, and when the total sample size is not greater than the congestion threshold, When the total sample size is greater than the congestion threshold, it is determined whether the low-speed proportion of the road section is greater than the low-speed proportion threshold of the road section calculated based on the historical data of the road section, so that when the low-speed proportion is not greater than the low-speed proportion When the low-speed ratio is greater than the threshold, a sample group with a faster speed is selected, and when the low-speed ratio is greater than the low-speed ratio threshold, a larger sample group is selected.

In one embodiment, the speed aggregation module of this case performs a weighted average of the low speed values and speed probability values of each section in the selected sample group to generate real-time speed information for the road section.

The method disclosed in this case for analyzing road segment conditions includes: performing batch analysis and filtering on a plurality of mobile signaling data received from a plurality of mobile devices to generate a plurality of mobile signaling location data; The location data is mapped to a road segment to calculate the total sample size of the road segment, the sample sizes of multiple intervals, and the speed corresponding to the sample size of each section; calculated based on the total sample size of the road segment and the historical data of the road segment Based on the congestion threshold of the road section, the sample group selected from the total sample size is determined; and the sample size and corresponding speed of each section in the selected sample group are aggregated to generate real-time speed information of the road section. .

In one embodiment, the step described in this case of batch analyzing and filtering a plurality of mobile signaling data received from a plurality of mobile devices to generate a plurality of mobile signaling location data includes: cutting the mobile signaling data in batches in chronological order. A plurality of mobile signaling data; performing principal component analysis, direction filtering and distance filtering on each batch of mobile signaling data; and performing principal component analysis, direction filtering and distance filtering on each batch of mobile signaling data Then, each batch of mobile signaling location data is generated to constitute the plurality of mobile signaling location data.

In one embodiment, the step of determining the sample group selected from the total sample size based on the total sample size of the road segment and the congestion threshold value of the road segment calculated based on the historical data of the road segment includes : Compare the total sample size with the congestion threshold, where, when the total sample size is not greater than the congestion threshold, select the faster sample group, and when the total sample size is greater than the congestion threshold, determine the Whether the low-speed ratio of the road section is greater than the low-speed ratio threshold of the road section calculated based on the historical data of the road section. When the low-speed ratio is not greater than the low-speed ratio threshold, samples with faster speeds are selected. group, and when the low-speed proportion is greater than the low-speed proportion threshold, a larger sample group is selected.

In one embodiment, the step of selecting a sample group with a faster speed described in this case includes: calculating the sample size of the first N intervals/the total sample size and N/the total number of intervals in order from fast to slow; and in the first N The sample size of intervals/the total sample size is greater than or equal to N/the total number of intervals, and the group with the sample size of the first N intervals is selected as the faster sample group.

In one embodiment, the steps of selecting a large number of sample groups described in this case include: dividing M adjacent intervals into one group to calculate the total sample size of each group; and selecting the sample group with the largest total number. The quantitative group is the sample group with a larger number.

In one embodiment, the step described in this case of aggregating the sample size of each section in the selected sample group and its corresponding speed to generate the speed information of the road section includes: The low speed value and the speed probability value are weighted and averaged to generate real-time speed information for the road section.

The computer program product disclosed in this case for analyzing road section conditions can be downloaded from a computer to execute the method disclosed above for analyzing road section conditions.

The computer for analyzing the condition of the road section disclosed in this case can read the recording medium and download it to the computer to execute the method of analyzing the condition of the road section disclosed above.

Through the equipment, methods, computer program products and computer-readable recording media disclosed in this case for analyzing road section conditions, batch analysis and filtering is performed to filter out drift points, and then the mobile signaling position data is mapped to the road section, and then Implement congestion threshold calculation and dynamic congestion detection technology to track changes in speed over time to avoid situations that are inconsistent with actual road conditions. Therefore, there is no need to additionally check heterogeneous data to quickly and accurately analyze whether the road conditions are congested or smooth. Improve the accuracy of traffic analysis.

10: Base station

2: Equipment

21: Data receiving module

22:Batch filter module

23:Road section corresponding module

24:Dynamic congestion threshold calculation module

25:Dynamic congestion detection module

26: Speed aggregation module

30:Database

P1~P12: Points

S201~S206: steps

S301~S304: steps

S401~S406: steps

Figure 1 is a schematic structural diagram of an embodiment of a device for analyzing road section conditions according to this case.

Figure 2 is a schematic flow chart of an embodiment of a method for analyzing road section conditions according to this case.

Figure 3A is a schematic diagram of an embodiment of batch analysis and filtering in the method of analyzing road section conditions in this case.

Figure 3B is a schematic diagram of an embodiment of principal component analysis in the method of analyzing road section conditions in this case.

Figure 4 is a schematic diagram of an embodiment of clustering in the method of analyzing road section conditions in this case.

Figure 5 is a schematic diagram of an example of the interval sample size and the corresponding hourly speed in the method of analyzing road section conditions in this case.

Figure 6 is a schematic diagram of an embodiment of selecting a fast group in the method of analyzing road section conditions in this case.

Figure 7 is a schematic diagram of an embodiment of selecting large groups in the method of analyzing road section conditions in this case.

The following uses specific examples to illustrate the implementation of the present invention. People familiar with this art can easily understand other advantages and effects of the present invention from the content disclosed in this article. The structures, ratios, sizes, etc. shown in the drawings attached to this manual are only used to coordinate with the content disclosed in the manual for the understanding and reading of those familiar with this art. They are not used to limit the conditions for the implementation of this case. Therefore, any modification, change or adjustment, without affecting the effectiveness and purpose of this case, should still fall within the scope of the technical content disclosed in this case.

Please refer to Figure 1. The device 2 in this case includes a data receiving module 21, a batch filtering module 22, a road section corresponding module 23, a dynamic congestion threshold calculation module 24, a dynamic congestion detection module 25, and an hourly speed aggregation module. 26.

In one embodiment, the device 2 is a remote or cloud server. In another embodiment, each module in Figure 1 can be software, hardware or firmware; if it is hardware, it can be a processing unit, processor, or computer with data processing and computing capabilities; if it is Software or firmware can include processing units, processors, and computer-executable instructions.

The data receiving module 21 receives a plurality of mobile signaling data of a plurality of mobile devices from the base station 10 . Specifically, the mobile device can be, for example, a smartphone, a smart watch, a tablet computer, etc. with a SIM (Subscriber Identity Module) card, which is connected to Mobile signaling data is transmitted between the base stations 10, including data such as the location, time and speed of the mobile device. In a specific embodiment, the mobile signaling data is, for example, CVP signaling data.

The batch filtering module 22 batches, analyzes, and filters the plurality of mobile signaling data of the plurality of mobile devices received by the data receiving module 21 to generate a plurality of mobile signaling location data. Specifically, for a mobile device, the batch filtering module 22 accumulates a plurality of mobile signaling data in chronological order, accumulates to a certain amount, cuts it into a batch, and then uses principal component analysis (principal components analysis) on the batch. , PCA) method is used to analyze the principal components of this batch of points, and then perform direction filtering and distance filtering, and then filter out points that are opposite to the movement direction of the principal components or are too far perpendicular to the principal components. Then, a part of the filtered points is transferred to the road segment corresponding module 23, and the other part is reserved as the first few points of the next batch, so as to continue to use the principal component analysis method and direction filtering for the next batch. Distance filtering, and so on. Furthermore, the batch filtering module 22 performs the above-mentioned chronological accumulation, batch cutting, principal component analysis, direction filtering and distance filtering on the plurality of mobile signaling data of each mobile device, and the mobile signaling data of each batch The data processed and transmitted to the road segment corresponding module 23 are the plurality of mobile signaling location data.

The road segment corresponding module 23 corresponds the plurality of mobile signaling location data generated by the batch filtering module 22 to a road segment to calculate the total sample size of the road segment, the plurality of interval sample sizes and their corresponding hourly speeds. Specifically, the locations of multiple user devices can be calculated based on multiple mobile signaling, and the number of all user devices on the road section (ie, the total sample size) can be obtained by combining the road network information, and cut according to the speed limit of the road section. A plurality of speed intervals are generated, and each speed interval has a corresponding number of user devices (ie, a plurality of interval sample sizes and their corresponding speeds). It should be noted that since the batch filtering module 22 continuously executes time-sequential accumulation and batch cutting, Cutting, principal component analysis, direction filtering and distance filtering are processed, and then the plurality of mobile signaling position data are transmitted to the road segment corresponding module 23, so the total sample size of the road segment calculated by the road segment corresponding module 23, the plurality of The interval sample size and its corresponding hour speed also change with the time series.

The dynamic congestion threshold calculation module 24 calculates the congestion threshold of the road section based on the historical data of the road section in the database 30 . Specifically, the database 30 stores road network information, road capacity data, and historical data. The historical data is, for example, the traffic information of the road section at every other time period (such as every five minutes) during the same period of time (such as the past seven days). Based on the total historical sample size, the dynamic congestion threshold calculation module 24 selects approximately ten sample sizes with the largest total sample size, and averages approximately 60% as the congestion threshold. In addition, if there is no sample information in the past few days, the dynamic congestion threshold calculation module 24 can set the road capacity of the road section in the database 30 as the congestion threshold. In addition, the dynamic congestion threshold calculation module 24 calculates the low-speed proportion threshold of the road section based on the historical data of the road section. It can also select approximately 80% of the average speed every five minutes of the past seven days on the road section as the low-speed threshold, and then calculate The proportion of the sample size below the low-speed threshold relative to the total historical sample size is used as the low-speed proportion threshold.

The dynamic congestion detection module 25 determines the total sample volume based on the congestion threshold value of the road section and the total sample volume of the road section, or based on the low speed proportion threshold value of the road section and the low speed proportion of the road section. The selected sample group. In one embodiment, the dynamic congestion detection module 25 compares the total sample size of the road segment with the congestion threshold value of the road segment. If the total sample size of the road segment calculated by the road segment corresponding module 23 is not greater than (i.e. is less than or equal to) the congestion threshold value calculated by the dynamic congestion threshold calculation module 24, then the dynamic congestion detection module 25. determines that there are few cars or smooth traffic, so the dynamic congestion detection module 25 determines from the total sample size Select a sample group with a faster speed. In another embodiment, if the total sample size of the road segment calculated by the road segment corresponding module 23 is greater than the dynamic Based on the congestion threshold value calculated by the congestion threshold calculation module 24, the dynamic congestion detection module 25 determines that there are too many vehicles or congestion. The dynamic congestion detection module 25 then compares the low-speed ratio of the road section with the low-speed ratio of the road section. than the threshold value. If the low-speed proportion of the road section calculated by the road section corresponding module 23 is not greater (that is, less than or equal to) the low-speed proportion threshold of the road section calculated by the dynamic congestion threshold calculation module 24, there may be many cars but the speed of the road section It is fast enough, but the congestion condition has not yet been reached, so the dynamic congestion detection module 25 decides to select a sample group with a faster speed from the total sample size. If the low-speed proportion of the road section calculated by the road section corresponding module 23 is greater than the low-speed proportion threshold of the road section calculated by the dynamic congestion threshold calculation module 24, there may be many vehicles and slow speeds, and the congestion should be considered conditions, so the dynamic congestion detection module 25 decides to select a larger number of sample groups from the total sample size.

The speed aggregation module 26 performs speed aggregation on the sample size and corresponding speed of each interval in the sample group selected by the dynamic congestion detection module 25, that is, the speed low value and speed probability of each interval in the selected sample group. The values are weighted and averaged to generate real-time speed information for the road section, where the speed probability value refers to the sample size of an interval compared to the sample size of the selected sample group. In addition, the generated real-time speed information of the segment will also be transmitted to the database 30 as an update basis for subsequent calculation of the congestion threshold.

Therefore, through the equipment 2 of this case including the data receiving module 21, the batch filtering module 22, the road segment corresponding module 23, the dynamic congestion threshold calculation module 24, the dynamic congestion detection module 25 and the speed aggregation module 26 , receiving mobile signaling data from the base station 10 for batch analysis and filtering to filter out drift points, iteratively tracking changes in speed over time, and calculating dynamic thresholds based on the historical data of the corresponding road sections in the database 30. Dynamic congestion detection is performed, and in traffic conditions with low traffic flow, a sample group with a faster speed is selected to reduce the impact of sampling a small number of slow cars on the slow speed calculation result. In traffic conditions with medium to high traffic flow, further examination The vehicle speed data is taken into account to increase the judgment conditions of traffic jams, and a dynamic update mechanism for road section congestion thresholds is introduced to avoid inconsistencies with actual road conditions caused by the use of basic fixed data on road sections, thereby improving the accuracy of road condition analysis.

In one embodiment, the data generated by each of the above modules in this case can be used by various traffic services for subsequent applications and presented through APPs or web pages for early road diversion or issuing traffic measures to avoid road congestion, thereby saving money. Travel time. In another embodiment, the above-mentioned modules in this case can also be disassembled separately, such as the processing performed by the batch filtering module, in which the number and proportion of drifting points can be captured, and subsequently applied to the stop points. , accident detection and other technologies; and the processing performed by the dynamic congestion detection module, among which the congestion road condition judgment can be used to label the degree of road congestion, using the labeling data as one of the attributes, can be used in other traffic analysis services.

Please refer to Figure 2. The method of analyzing road section conditions in this case mainly includes steps S201~S206, which are mainly executed by the device 2 in Figure 1.

In step S201, a plurality of mobile signaling data of a plurality of mobile devices are received from the base station, and then proceed to step S202.

In step S202, batch analysis and filtering is performed on a plurality of mobile signaling data of a plurality of mobile devices to generate a plurality of mobile signaling location data. The details of the batch analysis and filtration steps are shown in Figure 3A, including steps S301 to S304.

In step S301, batch cutting is performed. Specifically, multiple mobile signaling data of each mobile device are accumulated in chronological order. After accumulating to a certain number, they are cut into batches, such as batch 1, and then proceed to step S302.

In step S302, principal component analysis is performed. In detail, principal component analysis is performed on multiple points of batch 1 (that is, mobile signaling data). Refer to Figure 3B. The batch in the figure contains There are 12 points P1~P12, and the point numbers represent the order of point time. The principal components are found using the principal component analysis method, as shown by the arrows, and then proceed to step S303.

In step S303, direction filtering and distance filtering are performed. As shown in Figure 3B, among the 12 points, it can be seen that the parallel movement directions of points P5 and P6 do not match the principal components, while the vertical distances of points P2, P4, P9, and P10 from the principal components are too large, so filtering After that, there are only 6 points left: P1, P3, P7, P8, P11, and P12, and then proceed to step S304.

In step S304, the filtered point group is retained, and some points are reserved for the next batch. Specifically, the last 30% of the points of batch 1, that is, points P11 and P12, will be retained as the first two of the next batch, so the points of batch 2 are P11, P12, P13, P14, ..., batch 3 is also generated by analogy. Therefore, the filtered point group 1 includes four points P1, P3, P7, and P8, which are used as mobile signaling location data. The filtered point groups 2 and 3 are also generated by analogy, so they are included. That is, as the mobile signaling location data, the process proceeds to step S203.

In step S203, a plurality of mobile signaling location data are mapped to road segments, and then step S204 is entered.

In step S204, the total sample size of the road section, the plurality of interval sample sizes and their corresponding hourly speeds are calculated, and then proceed to step S205. In a specific embodiment, please refer to Figure 5. Taking the speed sample distribution of a road section with a speed limit of 80 as an example, the speed is allowed up to the speed limit + 20, so (80+20)/5 is used to cut the speed interval. Therefore, Figure 5 shows the sample size on the vertical axis and the speed interval on the horizontal axis. It can be seen that the total sample size of the road section at the current time, the sample size of multiple intervals and their corresponding speeds.

In step S205, the sample group selected from the total sample size is determined based on the total sample size of the road section and the congestion threshold calculated based on the historical data of the road section. concrete Please refer to Figure 4. The method of analyzing road section conditions in this case includes steps S401~S406.

In step S401, the congestion threshold of the road segment is calculated. In one embodiment, the congestion threshold is calculated as follows:

Congestion threshold = AVG (peak sample size) * 0.6

In one embodiment, the peak sample size: based on the current time, push back each five-minute sample size within seven days, and select the ten sample sizes with the largest sample size. Therefore, the congestion threshold is 60% of the average sample peak value in the recent seven days. If there is no sample information within seven days, the road capacity of the road section will be used as the congestion threshold.

In step S402, it is determined whether the total sample size of the road segment is greater than the congestion threshold of the road segment. If the total sample size is not greater than the congestion threshold, it can be considered that there are few vehicles or the traffic is smooth, and the process proceeds to step S403. If the total sample size is greater than the congestion threshold, it can be considered that there are too many vehicles and the process proceeds to step S405.

In step S403, a sample group with a faster speed is selected. In one embodiment, assuming that the total sample size of the road section is 407, the fast group selection method is as follows.

(Sample size of the first N intervals from fast to slow/total sample size)>=N/number of intervals.

See Figure 6.

N=1，4/407<1/20

N=2，(4+8)/407<2/20

N=3，(4+8+7)/407<3/20

…

N=12，(4+8+7+10+10+8+16+16+41+38+57+31)/407>12/20

In other words, the interval between N=1~12 is the sample group with faster speeds. The box in Figure 6 shows the selected sample group, which serves as the basis for subsequent calculation of the real-time speed information of this road section.

In step S405, it is determined whether the low-speed ratio of the road section is greater than the low-speed ratio threshold of the road section. Prior to this, in step S405, the low speed ratio threshold of the road section is calculated.

The low-speed ratio threshold is calculated as follows:

In one embodiment, assuming that the average speed of the road section in the past seven days is 70, the low-speed threshold is 70*0.8=56. Based on the current time of the road, the low-speed proportion threshold in the past seven days = low-speed sample size in the past seven days/ Total sample size over the past seven days.

In addition, as shown in Figure 7, assuming that in this example, the total sample size is 407, there are 292 samples with speeds below 56kph, and Figure 7 shows that there are 287 samples in the 0-55 speed range, then the road section is currently The low speed ratio is 287/407 (about 0.7).

If the low-speed ratio is not greater than the low-speed ratio threshold, it can be considered that although there are many vehicles, the vehicle speed is not slow, and the process proceeds to step S403. If the low-speed ratio is greater than the low-speed ratio threshold, it can be considered that there are many and slow vehicles, and the congestion condition is met, and the process proceeds to step S406.

In step S406, a larger sample group is selected. In one embodiment, assuming that the total sample size of the road section is 407, the large group selection method is: M adjacent sections are divided into one group, and the group with the highest sum of sample sizes in speed sections is the large group. If more than one group has the highest total sample size, the group with the lower speed is selected.

As shown in Figure 7, in one embodiment, three adjacent intervals are divided into a group. It can be seen that the group composed of speed intervals 35-40, 40-45, and 45-50 has the highest total sample size. This is a large group, as shown in the box in Figure 7, which is the selected sample group, which is used as the basis for subsequent calculation of the real-time speed information of this road section.

After completing the sample group selection in step S205 (including steps S401 to S406), proceed to step S206.

In step S206, speed aggregation is performed on the sample size of each section in the selected sample group and its corresponding speed, so as to quickly and accurately generate real-time speed information of the road section. Specifically, the weighted average of the low speed value and speed probability value of each section in the selected sample group is used to generate real-time speed information of the road section, where:

In one embodiment, if the fast group is selected, refer to Figure 6. The fast group shown in the box has a total of 246 samples in 12 intervals, and the hourly aggregation is as follows:

(4/246)*95+(8/246)*90+(7/246)*85+(10/246)*80+(10/246)*75+(8/246)*70+(16 /246)*65+(16/246)*60+(41/246)*55+(38/246)*50+(57/246)*45+(31/246)*40=55.96

Therefore, in this embodiment, since the total sample size of the road section during the current period is less than the congestion threshold, or the total sample size is more than the congestion threshold, the proportion of low speeds is still less than the low speed threshold, so the fast group is selected and calculated. The real-time speed during this period was 55.96kph.

In another embodiment, if a large group is selected, refer to Figure 6. The large group shown in the box has a total sample size of 136 in 3 intervals, and the hourly aggregation is as follows:

(48/136)*35+(31/136)*40+(57/136)*45=40.33

Therefore, in this embodiment, since the total sample size of the road section during the current period is more than the congestion threshold and the low-speed ratio is also greater than the low-speed threshold, a large group is selected, and the calculated real-time speed of the period is 40.33kph.

It should be noted that the method of the present invention can be executed in, for example, a server, a computer, or other equipment with functions of data processing, computing, storage, network communication, etc., in a single or multiple sets, wherein the server, computer Or equipment includes central processing unit, hard disk, memory, etc.

In addition, the computer program product of the present invention executes the method after loading the program through the computer, and the computer-readable recording medium (such as hard disk, floppy disk, optical disk, USB flash drive) of the present invention stores the computer program product . In addition, computer program products can also be directly transmitted and provided over the Internet. Therefore, computer program products are things that contain computer-readable programs and are not limited to external forms.

In addition, the present invention also provides a computer-readable recording medium, which is used in a computing device or computer with a processor and/or memory, and the computer-readable recording medium stores instructions, and can be used by the computing device or computer The computer-readable recording medium is executed by the processor and/or memory to execute the above method and/or content when the computer-readable recording medium is executed.

In summary, this case discloses a device, method and computer program product for analyzing road section conditions, which uses mobile signaling data to analyze congested road conditions, and sequentially performs steps such as signaling reception, drift signaling filtering, road section correspondence, and speed calculation. The principal component filtering method is used to find the vector through the movement direction of the batch points. Points that do not conform to the directionality of the vector are regarded as drift points. In terms of reducing the impact of transportation vehicles, after the road segment is mapped and before the road segment speed is calculated, the number of historical data signaling samples, vehicle speed data or road capacity are used to determine whether the current traffic flow and speed meet the congestion characteristics, so there is no need to use heterogeneous data Through cross-checking, the signaling can be grouped and grouped through the mobile characteristics of mobile signaling itself, and then the sample group used for final speed calculation can be adjusted to improve the accuracy of road condition analysis.

The above-mentioned embodiments are only illustrative of the effects of this case, and are not used to limit this case. Anyone familiar with this technology can perform the above-mentioned practical actions without violating the spirit and scope of this case. Make modifications and changes to your appearance. Therefore, the scope of rights protection in this case should be as listed in the patent application scope described below.

10: Base station

2: Equipment

21:Data receiving module

22:Batch filter module

23:Road section corresponding module

24:Dynamic congestion threshold calculation module

25:Dynamic congestion detection module

26: Speed aggregation module

30:Database

Claims (10)

A device for analyzing road section conditions, including: A data receiving module is used to receive a plurality of mobile signaling data from a plurality of mobile devices; The batch filtering module performs batch analysis and filtering on the plurality of mobile signaling data of the plurality of mobile devices to generate a plurality of mobile signaling location data; The road segment corresponding module maps the plurality of mobile signaling location data to a road segment to calculate the total sample size of the road segment, the plurality of interval sample sizes, and the speed corresponding to the sample size of each interval; and The dynamic congestion threshold calculation module calculates the congestion threshold of the road section based on the historical data of the road section; The dynamic congestion detection module determines the sample group to be selected from the total sample size based on the congestion threshold of the road section and the total sample size of the road section; and The speed aggregation module performs speed aggregation on the sample size of each interval in the selected sample group and its corresponding speed, so as to generate real-time speed information on the road section. The device as described in claim 1, wherein the batch filtering module cuts the plurality of mobile signaling data in batches in chronological order to perform principal component analysis and direction filtering on each batch of mobile signaling data. and distance filtering, so that each batch of mobile signaling data is subjected to principal component analysis, direction filtering and distance filtering to generate mobile signaling position data of each batch to form the plurality of mobile signaling position data. The device as described in claim 1, wherein the dynamic congestion detection module compares the total sample volume with the congestion threshold, so as to select a sample with a faster speed when the total sample volume is not greater than the dynamic congestion threshold. group, and when the total sample size is greater than the congestion threshold, it is determined that the road section Whether the low-speed proportion of the road section is greater than the low-speed proportion threshold of the road section calculated based on the historical data of the road section, in order to select a sample group with a faster speed when the low-speed proportion is not greater than the low-speed proportion threshold, and in When the low-speed proportion is greater than the low-speed proportion threshold, a larger number of sample groups are selected. The device as described in claim 1, wherein the speed aggregation module performs a weighted average of the low speed value and speed probability value of each section in the selected sample group to generate real-time speed information of the road section. A method of analyzing road section conditions includes: Perform batch analysis and filtering on a plurality of mobile signaling data received from a plurality of mobile devices to generate a plurality of mobile signaling location data; Correspond the plurality of mobile signaling location data to a road segment to calculate the total sample size of the road segment, the plurality of interval sample sizes, and the speed corresponding to the sample size of each interval; Determine the sample group selected from the total sample size based on the total sample size of the road segment and the congestion threshold value of the road segment calculated based on the historical data of the road segment; and The sample size of each interval in the selected sample group and its corresponding speed are aggregated to generate real-time speed information of the road section. The method of claim 5, wherein the step of performing batch analysis and filtering on a plurality of received mobile signaling data of a plurality of mobile devices to generate a plurality of mobile signaling location data includes: Cut the plurality of mobile signaling data in batches in chronological order; Perform principal component analysis, direction filtering and distance filtering on each batch of mobile signaling data; and After each batch of the mobile signaling data is subjected to principal component analysis, direction filtering and distance filtering, mobile signaling position data of each batch is generated to constitute the plurality of mobile signaling position data. The method described in claim 5, wherein the sample group selected from the total sample size is determined based on the total sample size of the road segment and the congestion threshold value of the road segment calculated based on historical data of the road segment. The steps include: Compare the total sample size with the congestion threshold, where, when the total sample size is not greater than the congestion threshold, select the faster sample group, and when the total sample size is greater than the congestion threshold, determine the road section Whether the low-speed proportion of the road section is greater than the low-speed proportion threshold of the road section calculated based on the historical data of the road section, among which, when the low-speed proportion is not greater than the low-speed proportion threshold, the sample group with a faster speed is selected , and when the low-speed proportion is greater than the low-speed proportion threshold, a larger sample group is selected. A method as described in request 7, Wherein, the step of selecting a sample group with a faster speed includes: calculating the sample size of the first N intervals/the total sample size and N/the total number of intervals from fast to slow; and calculating the sample size of the first N intervals/ The total sample size is greater than or equal to the N/total number of intervals, and the group with the sample size of the first N intervals is selected as the faster sample group; Wherein, the step of selecting a large number of sample groups includes: dividing M adjacent intervals into one group to calculate the total sample size of each group; and selecting the group with the largest total sample size as the Larger sample group. According to the method described in claim 5, the step of aggregating the sample size of each interval in the selected sample group and its corresponding speed to generate the speed information of the road section includes Including: weighted average of the low speed value and speed probability value of each section in the selected sample group to generate real-time speed information of the road section. A computer program product downloaded from a computer to perform the method described in any one of claims 5-9.

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