TWI825354B - Route vehicle trajectory analysis system and method thereof and computer readable medium - Google Patents

Abstract

A route vehicle trajectory analysis system and a method thereof are provided. The method includes: receiving dynamic data of a vehicle driven along a route, wherein the dynamic data includes a plurality of coordinates of the vehicle along the route; and constructing a standard grid model of the route according to the dynamic data, wherein the standard grid model includes a plurality of grids obtained by dividing a distribution range of the coordinates. The standard grid model is used to determine whether a vehicle deviates from the route and can also be used for merging and analyzing vehicle trajectory data. The present invention further provides a computer-readable medium for performing the route vehicle trajectory analysis method.

Description

Route vehicle trajectory analysis system, method and computer-readable medium

The present invention relates to vehicle trajectory analysis technology, and in particular, to a route vehicle trajectory analysis system and method.

Vehicle trajectory tracking and its driving route correlation and subsequent extended analysis have always been issues that public transportation management units attach great importance to. Vehicle trajectory tracking technology using the Global Positioning System (GPS) has become increasingly mature. Managers can use electronic maps to monitor the location and status of vehicles in real time. However, when the number of fleets is huge, managers cannot rely on manual tracking of all vehicle operations. situation, when a vehicle deviates from the established route due to an unexpected situation, the opportunity to deal with it immediately is often lost. In addition, for vehicle trajectory data, only GPS coordinates are currently used for analysis, but GPS coordinates have errors. If used for precise analysis such as route deviation judgment, it will take a long time due to the large amount of data, and misjudgments may occur. Therefore, an effective, fast, and accurate vehicle trajectory analysis technology is needed.

In order to solve the above problems, the present invention provides a route vehicle trajectory analysis system, including: a data collection module for receiving dynamic data of vehicles traveling on the route, wherein the dynamic data The data includes the plurality of coordinates when the vehicle travels on the route; and a grid construction module is used to construct a standard grid model of the route based on the dynamic data, where the standard grid model includes the distribution range of the cut coordinates The resulting complex grid.

The present invention also provides a route vehicle trajectory analysis method, which includes: receiving dynamic data of vehicles traveling on the route, wherein the dynamic data includes plural coordinates of the vehicle when traveling on the route; and constructing standards for the route based on the dynamic data. A grid model, wherein the standard grid model includes a complex grid obtained by cutting the distribution range of the coordinates.

The present invention further provides a computer-readable medium, which is used in a computing device or a computer and stores instructions to execute the above-mentioned route vehicle trajectory analysis method.

The above standard grid model is used for fusion analysis of vehicle trajectory data. In this way, the present invention can reduce the amount of data on the vehicle trajectory, and quickly determine whether the vehicle deviates from the route and whether an abnormal situation occurs, so as to immediately notify the management personnel as a basis for management personnel's processing.

1~10,6’~10’: trajectory data

100:Route vehicle trajectory analysis system

110:Data collection module

120:Grid construction module

130:Data fusion module

140:Data storage module

150:Data analysis module

170:Vehicle

180:Mobile device

300:Route

301~303: Site

500: Standard mesh model

550: base point

800:Trajectory

S11~S13, S21~S23, S31~S33: Grid

S210~S270: Method steps

FIG. 1 is a block diagram of a route vehicle trajectory analysis system according to an embodiment of the present invention.

Figure 2 is a flow chart of a route vehicle trajectory analysis method according to an embodiment of the present invention.

Figure 3 is a schematic diagram of static data received by a route vehicle trajectory analysis system according to an embodiment of the present invention.

Figure 4 is a schematic diagram of dynamic data received by a route vehicle trajectory analysis system according to an embodiment of the present invention.

5 and 6 are schematic diagrams of a standard grid model constructed by a route vehicle trajectory analysis system according to an embodiment of the present invention.

7 and 8 are schematic diagrams of a route vehicle trajectory analysis system for determining whether a vehicle deviates from the route according to an embodiment of the present invention.

Figure 9 is a schematic diagram of fusion statistical data calculated by a route vehicle trajectory analysis system according to an embodiment of the present invention.

The following describes the implementation of the present invention through specific embodiments. Those with ordinary knowledge in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

FIG. 1 is a block diagram of a route vehicle trajectory analysis system 100 according to an embodiment of the present invention. The route vehicle trajectory analysis system 100 includes a data collection module 110, a grid construction module 120, a data fusion module 130, a data storage module 140, and a data analysis module 150.

Each of the above modules can be software, hardware or firmware; if it is hardware, it can be a processing unit, processor, computer or server with data processing and computing capabilities; if it is software or firmware, then It may include instructions executable by a processing unit, processor, computer or server, and may be installed on the same hardware device or distributed across multiple different hardware devices.

The route vehicle trajectory analysis system 100 receives dynamic data generated by the mobile device 180 installed on the vehicle 170 through a wireless network or a mobile communication network. The dynamic data is related to the driving trajectory of the vehicle 170, and the route vehicle trajectory analysis system 100 is Calculate, store and analyze this dynamic data.

In one embodiment, the vehicle 170 is a vehicle that follows a fixed route, such as a bus, a school bus or a shuttle bus. The route has multiple stops, and the vehicle 170 will stop at each stop on the route while traveling.

Figure 2 is a flow chart of a route vehicle trajectory analysis method according to an embodiment of the present invention. The method is executed by the route vehicle trajectory analysis system 100.

In one embodiment, the method can be divided into two stages, in which the first stage is to construct a standard grid model of the route in steps S210~S220, and the second stage is to use the standard grid model in steps S230~S270. Fusion, storage and analysis of trajectory data of each vehicle on the route. Details of each stage and step of the method are described below.

In step S210 , the data collection module 110 receives the static data of the route traveled by the vehicle 170 and the first dynamic data generated by the mobile device 180 of the vehicle 170 . As shown in FIG. 3 , in this embodiment, the route 300 is the route traveled by the vehicle 170 , and the route 300 is provided with three stations 301 to 303 . In addition, as shown in Table 1 below, the static data includes the route number, serial number, station name and coordinates of each station 301 to 303 of the route 300, where the coordinates include longitude and latitude. In this embodiment, the route 300 has three websites, but the invention is not limited thereto. In other embodiments, the route traveled by the vehicle can have any number of stations.

The first dynamic data is shown in Figure 4 and Table 2 below. The first dynamic data includes multiple trajectory data when the vehicle 170 travels on the route 300. Each piece of trajectory data includes the serial number of the trajectory data, the license plate number of the vehicle 170 and the route number traveled by the vehicle 170. The mobile device 180 generates the trajectory. The time of the data, the longitude and latitude of the coordinates of the vehicle 170 at that time, the driving status of the vehicle 170 at that time, and the real-time speed of the vehicle 170 at that time.

The mobile device 180 on the vehicle 170 can periodically acquire the coordinates, longitude and latitude of the vehicle 170 through a positioning system such as GPS, and calculate the real-time speed of the vehicle 170 accordingly. In addition, the mobile device 180 can pre-store the license plate number of the vehicle 170 and the static data of the route 300, and compare the coordinates of the vehicle 170 with the coordinates of each station in the static data to generate the driving status of the vehicle 170, and then combine the above trajectories The data is transmitted to the route vehicle trajectory analysis system 100. The mobile device 180 can generate and transmit a piece of trajectory data every preset time. For example, the preset time can be 30 seconds, but the invention is not limited thereto.

The trajectory data 1 to 10 in Figure 4 are respectively located at the positions corresponding to the coordinates of the trajectory data 1 to 10. In this embodiment, the first dynamic data includes 10 items of trajectory data, but the invention is not limited thereto. In other embodiments, the first dynamic data may include any number of items of trajectory data.

Next, in step S220, the grid construction module 120 constructs the standard grid model 500 of the route 300 based on the static data and the first dynamic data, as shown in FIGS. 5 and 6 . The standard grid model 500 includes a reference point 550 and complex grids S11~S13, S21~S23 and S31~S33 obtained by cutting the distribution range of the coordinates of the vehicle 170 in the first dynamic data, wherein the grids are arranged in a matrix , and each grid has the same width and height, that is, it has the same size and area. datum point 550 is located in the upper left corner of the matrix, but the invention is not limited thereto. In other embodiments, the reference point 550 may be located in other corners of the matrix or in the center of the matrix.

In addition, the standard grid model 500 includes complex parameters, such as the coordinates of the reference point 550 shown in Table 3 below (such as GPS coordinates or coordinates of other positioning systems), the number of horizontal grids and the number of vertical grids of the matrix. number, as well as the width and height of each grid. In this embodiment, the number of horizontal grids m and the number of vertical grids n of the matrix are both 3. In other embodiments, m and n can be other integers, and m and n can be equal or unequal. .

Furthermore, the standard grid model 500 also includes the grid numbers and the order of the grids that the route 300 travels through. For example, as shown in Table 4 below, the grids that the route 300 travels through are in sequence S11, S12, S22, S23 and S33.

Next, in step S230, the data collection module 110 receives the second dynamic data. In this embodiment, the second dynamic data is the first dynamic data. In other embodiments, the second dynamic data may be another dynamic data generated by the mobile device 180 of the vehicle 170 in another shift. , or another dynamic data generated by the mobile device of another vehicle when it is traveling on route 300. If the second dynamic data is another dynamic data, it can also include plural trajectory data, and its format is also shown in Table 2.

Next, in step S240, the data analysis module 150 determines whether the vehicle (vehicle 170 or another vehicle) that generated the second dynamic data deviates from the route 300 based on the vehicle coordinates in the trajectory data of the second dynamic data, that is, determines whether the vehicle 170 or another vehicle deviates from the route 300. Whether the vehicle is about to enter or has entered the untraveled grid of the route 300 in the grid of the standard grid model 500. For example, as shown in Figure 7, the grids traveled by the vehicle are S11, S12, S22, S23 and S33 in sequence, which are the same as the grids traveled by route 300 (marked with dots in Figure 7). Therefore, the data analysis model Group 150 determines that the vehicle has not deviated from route 300.

In another embodiment, the driving trajectory of the vehicle is trajectory 800 in Figure 8, and the second dynamic data transmitted by the vehicle includes trajectory data 1~5 and 6'~10', wherein trajectory data 1~5 and The trajectory data 1 to 5 of the first dynamic data are the same, and the locations of the trajectory data 6' to 10' in Figure 8 are the coordinate positions of the trajectory data 6' to 10' respectively. The first half of trajectory 800 matches the first half of route 300. Therefore, when the vehicle has transmitted trajectory data 1 to 5, the data analysis module 150 determines that the vehicle has not deviated from route 300. When the vehicle transmits the trajectory data 6', the data analysis module 150 determines that the vehicle has not deviated from the route 300 because the coordinates in the trajectory data 6' are still located in the grid S22 along which the route 300 travels.

Later, when the vehicle transmits the trajectory data 7', the data analysis module 150 determines that the vehicle is about to enter the untraveled grid S32 of the route 300. In other words, the vehicle may deviate from the route 300. Therefore, the data analysis module 150 issues an alarm on the monitoring screen of the management center of the route 300 to remind the management personnel to monitor the vehicle or take other contingency measures. When the vehicle transmits the trajectory data 8', the vehicle has entered the untraveled grid S32 of the route 300. The data analysis module 150 determines that the vehicle has deviated from the route 300. Therefore, the data analysis module 150 can continue to issue alarms and record This abnormal situation and continue to track the vehicle.

Next, in step S250, the data fusion module 130 determines each grid that the vehicle travels in the grid of the standard grid model 500 based on the coordinates of the trajectory data in the second dynamic data of the vehicle, and calculates the The fusion statistics of the trajectory data in each grid that the vehicle travels through.

For example, if the vehicle travels through grids S11, S12, S22, S23 and S33, each of the above grids corresponds to a fused statistical data, and each fused statistical data is generated by the vehicle in the corresponding grid. The transmitted trajectory data is generated by integrating statistics. As shown in Figure 9 and Table 5 below, each fusion statistical data includes the time when the vehicle entered the corresponding grid, the time when the vehicle left the corresponding grid, the station where the vehicle stayed in the corresponding grid, and the time it stayed. The length of time, and the average speed of the vehicle in the corresponding grid.

Next, in step S260, the data storage module 140 stores the above-mentioned fusion statistical data. For example, the data storage module 140 can use the corresponding grid number as an index to store the fused statistical data in the database. The database may be part of the data storage module 140 , or may be an external database independent of the route vehicle trajectory analysis system 100 .

Vehicles traveling on the route 300 can generate the above-mentioned second dynamic data. These second dynamic data can be calculated according to the same standard grid model 500 using the above method to generate the fusion statistical data shown in Figure 9 and Table 5. and stored in the database.

Next, in step S270, the data analysis module 150 can instantly determine whether an abnormality occurs in the vehicle currently traveling on the route 300 based on the fused statistical data of each grid. If an abnormal situation occurs, the data analysis module 150 issues an alarm to remind the management personnel to monitor the vehicle or take other contingency measures.

For example, the data analysis module 150 can compare the fused statistical data of each grid traveled by the vehicle with the average value of the fused statistical data of previous shifts in the same grid to determine whether an abnormality has occurred.

For example, if the difference between the entry time of the vehicle in a certain grid and the average entry time of previous shifts in the same grid is greater than a preset value, the data analysis module 150 determines that an abnormal condition has occurred.

Similarly, if the difference between the departure time of the vehicle in a certain grid and the average departure time of previous shifts in the same grid is greater than the preset value, the data analysis module 150 determines that an abnormality has occurred.

Alternatively, if the difference between the length of stay of the vehicle at a certain station and the average length of stay of previous flights at the same station is greater than a preset value, the data analysis module 150 determines that an abnormal condition has occurred.

Alternatively, if the difference between the vehicle's average speed in a certain grid and the average speed of the previous shift in the same grid is greater than a preset value, the data analysis module 150 determines that an abnormal condition has occurred.

For another example, the data analysis module 150 may not be based on the fused statistical data of the previous shift, but may compare the fused statistical data of each grid traveled by the vehicle with a preset value or a preset range to determine whether an abnormality has occurred. condition.

For example, if the entry time or exit time of the vehicle in a certain grid exceeds a preset range, the data analysis module 150 determines that an abnormal situation occurs.

Or, if the length of stay of the vehicle at a certain station is greater than the preset value, the data analysis module 150 determines that an abnormal situation occurs.

Or, if the average speed of the vehicle in a certain grid exceeds the preset range, the data analysis module 150 determines that an abnormal situation occurs.

In addition, the present invention also discloses a computer-readable medium, which is applied to a computing device or computer having a processor (eg, CPU, GPU, etc.) and/or a memory, and stores instructions, and can utilize the computing device or computer. The computer executes the computer-readable medium through the processor and/or memory to perform the above methods and steps when executing the computer-readable medium.

To sum up, the present invention has the following advantages:

First, to establish a standard grid model of the route, the numerous trajectory data in each grid are integrated into a few statistical data based on the grid range, thereby significantly reducing the amount of data and simplifying the analysis operations, so as to improve subsequent data analysis and Application efficiency.

Second, the integrated statistical data within the grid can avoid single-point GPS coordinate errors from affecting subsequent system judgments, help improve the accuracy of information predictions for transportation services, and improve the competitiveness of smart transportation service technology products.

Third, unlike the past analysis using GPS coordinates of driving trajectories between stations, data such as distance between stations, average speed between stations, and driving time may be ignored and cannot reflect the actual situation. Through the standard grid of the present invention, The model can efficiently segment route driving trajectories, and can simultaneously record detailed data such as entry and exit time, average speed, and stop time within the grid. It can record and store trajectory data in more detail and more efficiently.

Fourth, after the construction of the standard grid model is completed, the data analysis module is used to analyze previous historical data and current real-time data based on the grid to quickly determine whether the vehicle deviates from the scheduled route, and can use the average speed and stop time and other parameters to determine whether there are any abnormal conditions in the driving trajectory of the day's shift, which can be used as a basis for management personnel to handle.

The above embodiments are only illustrative to illustrate the principles and effects of the present invention, but are not intended to limit the present invention. Anyone with ordinary knowledge in this technical field can modify and change the above embodiments without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be as listed in the patent application scope described below.

100:Route vehicle trajectory analysis system

110:Data collection module

120:Grid construction module

130:Data fusion module

140:Data storage module

150:Data analysis module

170:Vehicle

180:Mobile device

Claims (7)

A route vehicle trajectory analysis system, including: a data collection module for receiving first dynamic data of a first vehicle traveling on the route, and receiving second dynamic data of a second vehicle, wherein the first dynamic data includes the The plurality of coordinates when the first vehicle is traveling on the route, and the second dynamic data includes the plurality of trajectory data when the second vehicle is traveling; a grid construction module is used to construct a standard network of the route based on the first dynamic data A grid model, wherein the standard grid model includes complex grids obtained by cutting the distribution range of the coordinates; the data fusion module is used to determine what the second vehicle traveled in the grids based on the trajectory data. , based on which the plurality of fused statistical data of the trajectory data in each of the grids traveled by the second vehicle is calculated, wherein, for each of the grids traveled by the second vehicle, the fused statistical data include The time when the second vehicle enters the grid, the time when the second vehicle leaves the grid, the station and length of stay of the second vehicle in the grid, and the time when the second vehicle stays in the grid the average speed; and a data analysis module for comparing at least one of the fused statistical data in each grid traveled by the second vehicle with a preset value or a preset range to determine Whether an abnormal condition occurs when the second vehicle is driving, so that the data analysis module can issue an alarm when the abnormal condition occurs. The route vehicle trajectory analysis system as described in claim 1, wherein the data analysis module is multiplexed to determine whether the second vehicle is about to enter or has entered a network in the grid that the route has not traveled based on the trajectory data. grid, so that when the second vehicle is about to enter or has entered a grid in the grid that the route has not traveled, the data analysis module will issue an alarm. A route vehicle trajectory analysis system, including: The data collection module is used to receive the first dynamic data of the first vehicle traveling on the fixed route, and to receive the second dynamic data of the second vehicle, wherein the first dynamic data includes the first dynamic data of the first vehicle traveling on the fixed route. plural coordinates at that time, and the second dynamic data includes plural trajectory data when the second vehicle is traveling; a grid construction module is used to construct a standard grid model of the fixed route based on the first dynamic data, wherein the The standard grid model includes a reference point and a complex grid obtained by cutting the distribution range of the vehicle's coordinates in the first dynamic data, and the complex grid is arranged in a matrix, and each grid has the same width and height; The data fusion module is used to determine who the second vehicle travels in the grids based on the trajectory data, and to calculate the trajectory data in each grid that the second vehicle travels based on it. Plural fusion statistics, wherein, for each grid traveled by the second vehicle, the fusion statistics include the time when the second vehicle enters the grid, the time when the second vehicle leaves the grid, the time when the second vehicle leaves the grid, and the time when the second vehicle leaves the grid. The stops and length of stay of the second vehicle in the grid, and the average speed of the second vehicle in the grid; and a data analysis module used to determine whether the second vehicle is about to arrive based on the trajectory data. Enter or have entered a grid in the grid that the fixed route has not traveled, so that when the second vehicle is about to enter or has entered a grid in the grid that the fixed route has not traveled, it can judge that it has deviated from the route and caused the second vehicle to deviate from the route. The data analysis module issues an alarm on the monitoring screen of the management center of the fixed route to monitor and track the vehicle, and the second vehicle is not about to enter and has not entered any of the grids that the fixed route has not traveled. When, it is judged that it has not deviated from the route; among them, the data analysis module compares the fused statistical data of each grid traveled by the vehicle with the average value of the fused statistical data of the previous shifts in the same grid, or if the vehicle is in The difference between the entry time of a grid and the average entry time of previous shifts in the same grid is greater than the preset value, or, if The difference between the departure time of the vehicle in a certain grid and the average departure time of previous shifts in the same grid is greater than the preset value, or if the length of stay of the vehicle at a certain station is different from the average departure time of previous shifts at the same station. The difference between the average length of stay is greater than the preset value, or, if the difference between the average speed of the vehicle in a certain grid and the average speed of the previous shift in the same grid is greater than the preset value, or, the data analysis module Instead of using the fusion statistics of previous shifts, the fusion statistics of each grid that the vehicle travels through can be compared with the preset value or preset range, or if the vehicle enters at the time of a certain grid, Or the departure time exceeds the preset range, or if the vehicle's stay time at a certain station is greater than the preset value, or if the average speed of the vehicle in a certain grid exceeds the preset range, in order to determine whether an abnormality has occurred . A route vehicle trajectory analysis method, including: receiving first dynamic data of a first vehicle traveling on the route, wherein the first dynamic data includes plural coordinates of the first vehicle when traveling on the route; according to the first dynamic data Construct a standard grid model of the route, wherein the standard grid model includes a plurality of grids obtained by cutting the distribution range of the coordinates; receive second dynamic data of the second vehicle, wherein the second dynamic data includes the third 2. The plurality of trajectory data of the vehicle when it is traveling; determine what the second vehicle has traveled in the grids based on the trajectory data; calculate the plurality of trajectory data in each of the grids that the second vehicle has traveled through. Fusion statistics, wherein, for each grid traveled by the second vehicle, the fusion statistics include the time when the second vehicle enters the grid, the time when the second vehicle leaves the grid, the time when the second vehicle leaves the grid, The stops and length of stay of the vehicle in the grid, and the average speed of the second vehicle in the grid; and Compare at least one of the fused statistical data in each grid traveled by the second vehicle with a preset value or a preset range to determine whether an abnormal condition occurs when the second vehicle is driving. Issue an alarm when this abnormal condition occurs. The route vehicle trajectory analysis method described in request item 4 further includes: determining whether the second vehicle is about to enter or has entered a grid in the grid that the route has not traveled based on the trajectory data; and if the second vehicle is about to enter or has entered a grid in the grid that the route has not traveled; If the vehicle is about to enter or has entered a grid in these grids that the route has not traveled, an alarm will be issued. A route vehicle trajectory analysis method, including the first stage and the second stage: the first stage is to construct a standard grid model of the route, in which the data collection module is allowed to receive static data of the route traveled by the vehicle and the mobile device of the vehicle The first dynamic data generated, wherein the static data includes the route number, serial number, station name and coordinates of each station on the route, and the first dynamic data includes multiple trajectory data of the vehicle when traveling on the route, Among them, each piece of trajectory data includes the serial number of the trajectory data, the license plate number of the vehicle and the route number traveled, the time when the mobile device generated the trajectory data, the longitude and latitude of the coordinates of the vehicle at that time, the The driving status of the vehicle at that time and the real-time speed of the vehicle at that time are used to transmit a plurality of the above-mentioned trajectory data to the route vehicle trajectory analysis system through the mobile device at a preset time; the grid construction module is configured according to the static The data and the first dynamic data construct a standard grid model of the route, wherein the standard grid model includes a reference point and a complex grid obtained by cutting the distribution range of the vehicle's coordinates in the first dynamic data, and the complex grid The grids are arranged in a matrix, and each grid has the same width and height. The standard grid model includes the coordinates of the datum point, the horizontal network of the matrix The number of grids and the number of vertical grids, as well as the plural parameters of the width and height of each grid, as well as the grid numbers and grid sequences traveled by the route; and the second stage uses the standard network The lattice model performs fusion storage and analysis of the trajectory data of each vehicle shift on the route, wherein the data collection module is made to receive the second dynamic data, where the second dynamic data is the first dynamic data, or the second dynamic data is the first dynamic data. Another dynamic data including multiple trajectory data generated by the vehicle's mobile device on another shift, or another dynamic data generated by the vehicle's mobile device when another vehicle is traveling on the route; order data The analysis module determines whether the vehicle that generated the second dynamic data has deviated from the route based on the vehicle coordinates in the trajectory data of the second dynamic data. If it has not deviated from the route, it means that the vehicle is not about to enter and has not entered the standard grid model. Grids in the grid that the route does not travel through. If it deviates from the route, it means that the vehicle is about to enter or has entered the grid data in the grid of the standard grid model that the route does not travel through, so that the data analysis module can manage the route. The monitoring screen of the center issues an alarm and reminds the management personnel to monitor the vehicle or record the abnormal situation and continue to track the vehicle; the data fusion module determines the vehicle based on the coordinates of the trajectory data in the second dynamic data of the vehicle. For each grid traveled in the grid of the standard grid model, the fusion statistics of the trajectory data in each grid traveled by the vehicle are calculated, where each grid corresponds to one fusion statistics , and each fusion statistical data is generated by the fusion statistics of the trajectory data transmitted by the vehicle in the corresponding grid, and each fusion statistical data includes the time when the vehicle enters the corresponding grid, the time when the vehicle leaves the corresponding network The time of the grid, the station and length of stay of the vehicle in the corresponding grid, and the average speed of the vehicle in the corresponding grid; let the data storage module store the above fused statistical data with the corresponding grid number for suo To introduce, store the fused statistical data in an internal or external database, in which the vehicles traveling on the route generate the above-mentioned second dynamic data to calculate the fused statistical data based on the same standard grid model and store it in in the database; and allow the data analysis module to instantly determine whether an abnormality occurs in the vehicle currently driving on the route based on the fused statistical data of each grid. If an abnormality occurs, the data analysis module issues an alarm to remind management. Personnel monitor the vehicle; wherein, the data analysis module compares the fused statistical data of each grid traveled by the vehicle with the average of the fused statistical data of previous shifts in the same grid, or if the vehicle is in a certain The difference between the entry time of the grid and the average entry time of previous shifts in the same grid is greater than the preset value, or if the difference between the departure time of the vehicle in a certain grid and the average departure time of previous shifts in the same grid The value is greater than the preset value, or if the difference between the length of stay of the vehicle at a certain station and the average length of stay of previous shifts at the same station is greater than the preset value, or if the difference of the length of stay of the vehicle at a certain grid The difference between the average speed and the average speed of the previous shift in the same grid is greater than the preset value. Alternatively, the data analysis module can not be based on the fusion statistics of the previous shift, but the fusion of each grid that the vehicle travels through. Compare the statistical data with the preset value or preset range, or if the vehicle's entry time or departure time in a certain grid exceeds the preset range, or if the vehicle's stay time at a certain station is greater than the preset range. Set a value, or if the average speed of the vehicle in a certain grid exceeds the preset range, it can be determined whether an abnormal situation has occurred. A computer-readable medium, used in a computing device or a computer, stores instructions to execute the route vehicle trajectory analysis method described in any one of claims 4 to 6.

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