Big data-based tram driver driving behavior analysis method

Abstract

The present application relates to a kind of tram driver driving behavior analysis method based on big data, the method is analyzed by big data analysis means to section statistical analysis driver's driving behavior, wherein analysis direction includes early and late point analysis, driving safety analysis and passenger comfort analysis, analysis index includes driving time, travel speed, parking operation time, parking accuracy, brake acceleration, overspeed warning and advance warning.Compared with prior art, the present application has the advantages of comprehensive analysis of driver's driving behavior from driving safety, on-time rate, passenger comfort and other dimensions.

Description

Technical Field

[0001] This invention relates to train signal control systems, and more particularly to a method for analyzing the driving behavior of tram drivers based on big data. Background Technology

[0002] Modern trams, as a medium-capacity mode of transportation, are characterized by high efficiency and environmental friendliness. Compared to traditional urban rail transit, the construction cost of modern tram lines is far lower than that of subway projects. Current modern trams all feature semi-independent right-of-way, meaning they share right-of-way with other traffic at level crossings. Therefore, trams are controlled by a combination of driver visual observation and manual operation. This operating mode requires drivers to rely entirely on experience and visual observation, demanding a high level of driving skill.

[0003] A search revealed Chinese Publication No. CN113548091A, which discloses a driving assistance method for tram drivers based on vehicle-to-vehicle communication. Specifically, it discloses a vehicle-to-vehicle communication scheme to obtain the operating status information of the tram ahead, and combines this information with relevant line information such as station information, signal information, and speed limits for analysis. The method calculates and plots the corresponding driver assistance curve on the DMI (Driver Assistance Management System). Its main focus is on assisting drivers in safe driving. However, it does not address the analysis of tram driver behavior.

[0004] To improve driver skills and thus ensure tram safety, punctuality, and passenger comfort, accurate analysis and evaluation of tram drivers' behavior are essential. However, due to the complexity of tram operations and the vast amount of driver data, manual analysis of driver behavior is extremely difficult and inefficient. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for analyzing the driving behavior of tram drivers based on big data.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] According to a first aspect of the present invention, a method for analyzing the driving behavior of tram drivers based on big data is provided. This method uses big data analysis to statistically analyze the driving behavior of drivers in different segments. The analysis directions include morning / evening analysis, driving safety analysis, and passenger comfort analysis. The analysis indicators include driving time, travel speed, parking operation time, parking accuracy, start-stop acceleration, overspeed warning, and reckless driving warning.

[0008] As a preferred technical solution, the method specifically includes the following steps:

[0009] Step S101: Divide the area into sections using the station parking point and the intersection as boundaries;

[0010] Step S102: Transfer the raw log information to a large database for categorized storage;

[0011] Step S103: Analyze the cleaned train status information at a set period.

[0012] Step S104: Aggregate and statistically analyze the results to present driver behavior from multiple dimensions.

[0013] As a preferred technical solution, the section in step S101 is divided into the main line section, station stopping point, intersection stopping point and intersection section.

[0014] As a preferred technical solution, in step S101, speed limit information is added to each section according to the route design data, and the location of the section is calculated.

[0015] As a preferred technical solution, in step S102, the original logs are cleaned and parsed before being transmitted to a large database.

[0016] As a preferred technical solution, the original logs in step S102 include communication front-end logs and vehicle logs.

[0017] As a preferred technical solution, step S103 specifically includes the following steps:

[0018] Step S103a: Collect train data and driver driving data at various times.

[0019] Step S103b: According to the timetable, the train data is grouped and statistically analyzed according to the train number, the running time of each train is calculated, and compared with the timetable plan information to determine whether the train status is on time, early or late.

[0020] Step S103c: Based on the train location information, the train data is mapped to each section to obtain the instantaneous speed information and travel time of the train in each section.

[0021] Step S103d: Calculate the train's travel speed in each section based on the travel time and section length;

[0022] Step S103e: Plot the standard driving curve and calculate the standard travel speed for each segment;

[0023] Step S103f: Based on the section speed limit, determine whether the instantaneous speed and travel speed exceed the speed limit. If the speed limit is exceeded, generate an alarm.

[0024] Step S103g: Statistically aggregate the parking data of intersection parking spots and station parking spots and their adjacent interval data, calculate the distance between the actual parking location and the parking spot, and record it as the parking accuracy;

[0025] Step S103h: Collect information on vehicles overshooting; if any are found, generate an alarm.

[0026] Step S103i: Store the analysis results and alarms from steps S103a to S103h into the large database.

[0027] As a preferred technical solution, the train data in step S103a includes the train's speed, location, and running time information.

[0028] As a preferred technical solution, the step S103e of drawing the standard driving curve specifically involves: calculating the travel time and stopping time of all punctual trains in each section, and fitting the travel time curve of the standard section, which is denoted as the standard driving curve.

[0029] As a preferred technical solution, in step S103g, the train speed information before and after the stopping position is also statistically analyzed, and the starting and braking acceleration of the train is calculated.

[0030] As a preferred technical solution, step S104 specifically includes the following steps:

[0031] Step S104a: Analyze the reasons why drivers are arriving early or late;

[0032] Step S104b: Analyze whether each driver has engaged in unsafe driving behavior;

[0033] Step S104c: Analyze passenger comfort during each driver's driving.

[0034] As a preferred technical solution, step S104a specifically includes:

[0035] The analysis results of train punctuality status, travel speed, travel time, standard driving curve, and parking accuracy are aggregated and statistically analyzed. The punctuality rate and average travel speed of each driver are analyzed, and the average segment travel-time curve of each driver is fitted and recorded as the driver driving curve. The curve is then compared with the standard driving curve to analyze the reasons for the drivers' punctuality.

[0036] As a preferred technical solution, step S104b specifically includes:

[0037] The analysis results of train overshoot warnings and speeding warnings are aggregated and statistically analyzed to determine whether each driver has engaged in unsafe driving behavior.

[0038] As a preferred technical solution, step S104c specifically includes:

[0039] The analysis results of train starting and braking acceleration and stopping accuracy are aggregated and statistically analyzed to assess passenger comfort during each driver's operation.

[0040] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0041] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] 1) This invention can comprehensively analyze the driver's driving behavior from dimensions such as driving safety, punctuality rate, and passenger comfort;

[0044] 2) This invention uses big data analytics to analyze driver behavior, resulting in high efficiency and accurate analysis results;

[0045] 3) This invention analyzes driver behavior to help improve driver skills. Attached Figure Description

[0046] Figure 1 This is a diagram of the driver behavior analysis architecture of the present invention;

[0047] Figure 2 This is a flowchart of the driver behavior analysis process of the present invention;

[0048] Figure 3 This is a flowchart illustrating the specific data analysis process of this invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0050] like Figure 1 As shown, this tram driver behavior analysis method utilizes big data analytics to statistically analyze driver behavior across different sections. The analysis focuses on morning / event departure times, driving safety, and passenger comfort. Analytical indicators include driving time, travel speed, parking operation time, parking accuracy, acceleration during start-stop, overspeed warnings, and reckless driving warnings. Figure 2As shown, the method of the present invention consists of the following steps:

[0051] Step S101: Divide the track into sections using station stopping points and intersections as boundaries. Add speed limit information to each section according to the line design data and calculate the section's location. Sections are divided into four categories: mainline sections, station stopping points, intersection stopping points, and intersection sections. The starting point of a mainline section is a station stopping point or intersection departure beacon, and the ending point is an intersection stopping point or station stopping point. The starting point of an intersection section is an intersection departure beacon, intersection stopping point, or station stopping point, and the ending point is an intersection departure beacon or station stopping point. Station stopping points and intersection stopping points are the locations where trains stop for operation at stations and intersections.

[0052] Step S102: Clean the driver's driving data, train driving data, timetable and other information in the communication front-end log and vehicle log, and parse them into key-value format. Then, transfer the parsed logs into the large database for classified storage.

[0053] Step S103: After the daily operation ends, analyze the status information of the cleaned trains, such as... Figure 3 As shown, the specific steps are as follows:

[0054] a. Acquire train travel information and driver information such as speed, position, and time at various times, with a period of 500ms.

[0055] b. Based on the timetable information, train data is grouped and statistically analyzed according to train number. Simultaneously, train travel information is correlated and merged with driver information from both time and train number perspectives. After data grouping, the travel time for each train is calculated and compared with the timetable plan. If the actual travel time deviates from the plan by less than 10%, it is recorded as an on-time train; otherwise, it is recorded as early or late.

[0056] c. Based on the real-time location information of the trains, map the merged train travel data to each section:

[0057] I. For station stopping points, if the train stops within 5 meters before or after the stopping point, the corresponding data will be recorded as station stopping point data.

[0058] II. For intersection stopping points, if the train stops within 5 meters before or after the stopping point, the corresponding data will be recorded as intersection stopping point data. If the train does not stop, it will be recorded as normal priority passage, and the stopping time will be recorded as 0.

[0059] III. For the mainline section, the actual location of the station stop or the location of the intersection away from the beacon is taken as the starting point, and the actual location of the intersection stop or station stop is taken as the ending point. The data within the corresponding interval is recorded as the data of the intersection section.

[0060] d. For intersection sections, the actual location of the intersection parking point or station parking point, or the location of the intersection away from the beacon, is taken as the starting point, and the actual location of the intersection away from the beacon or station parking point is taken as the ending point. The data within the corresponding interval is recorded as the data of the intersection section.

[0061] e. Based on the train's travel time and section length in each section, calculate the train's travel speed in each section by grouping the train's travel data into sections.

[0062] f. Calculate the travel time and stopping time of all punctual trains at each section and stop, and then calculate the weighted average based on the deviation from the planned timetable; the larger the deviation, the lower the weight. Using the average operating time and sections of punctual trains as a basis, fit a standard section-travel time curve, denoted as the standard driving curve, and calculate the standard travel speed for each section.

[0063] g. Based on the section speed limit, determine whether the instantaneous speed and travel speed exceed the limit. If speeding is exceeded, generate an alarm.

[0064] h. Statistically aggregate the parking data of intersection parking points and station parking points and their adjacent interval data, calculate the distance between the actual parking position and the parking point, and record it as the parking accuracy. Statistically collect the train speed information before and after the parking position, and calculate the train's starting and braking acceleration.

[0065] i. Collect information on vehicles that overshoot their designated lanes; if any overshoot occurs, generate an alarm.

[0066] The analysis results and alerts from ja to i are stored in a large database for subsequent aggregation and display.

[0067] Step S104: Aggregate and statistically analyze the results to present driver behavior from multiple dimensions:

[0068] a. Aggregate and statistically analyze the train's on-time performance, travel speed, travel time, standard driving curve, and stopping accuracy. Analyze the on-time rate and average travel speed of each driver, and fit the average segment travel-time curve for each driver (referred to as the driver's driving curve). Compare this curve with the standard driving curve to analyze the reasons for drivers' on-time performance. If the travel speed differs significantly from the standard curve, it is mainly due to driver driving factors; if the stopping time at intersections differs significantly from the standard curve, it is mainly due to social traffic factors; if the stopping time at stations differs significantly from the standard curve, it is mainly due to factors such as passenger flow.

[0069] b. Aggregate and statistically analyze the results of train overshoot warnings, speeding warnings, etc., to analyze whether each driver has engaged in unsafe driving behavior.

[0070] c. Aggregate and statistically analyze the results of train starting and braking acceleration, stopping accuracy, etc., to analyze passenger comfort during each driver's operation.

[0071] The above is an introduction to the method embodiments. The following embodiments using electronic devices and storage media will further illustrate the solution of the present invention.

[0072] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0073] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0074] The processing unit executes the various methods and processes described above, such as methods S101 to S104. For example, in some embodiments, methods S101 to S104 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S101 to S104 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S101 to S104 by any other suitable means (e.g., by means of firmware).

[0075] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0076] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0077] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0078] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for analyzing the driving behavior of tram drivers based on big data, characterized in that, This method uses big data analytics to statistically analyze drivers' driving behavior in different segments. The analysis includes morning / evening analysis, driving safety analysis, and passenger comfort analysis. The analysis indicators include driving time, travel speed, parking operation time, parking accuracy, start-stop acceleration, overspeed warning, and reckless driving warning. The method specifically includes the following steps: Step S101: Divide the area into sections using the station parking point and the intersection as boundaries; Step S102: Transfer the raw log information to a large database for categorized storage; Step S103: Analyze the cleaned train status information at a set period. Step S104: Aggregate and statistically analyze the results to present driver behavior from multiple dimensions; Step S103 specifically includes the following steps: Step S103a: Collect train data and driver driving data at various times. Step S103b: According to the timetable, the train data is grouped and statistically analyzed according to the train number, the running time of each train is calculated, and compared with the timetable plan information to determine whether the train status is on time, early or late. Step S103c: Based on the train location information, the train data is mapped to each section to obtain the instantaneous speed information and travel time of the train in each section. Step S103d: Calculate the train's travel speed in each section based on the travel time and section length; Step S103e: Plot the standard driving curve and calculate the standard travel speed for each segment; Step S103f: Based on the section speed limit, determine whether the instantaneous speed and travel speed exceed the speed limit. If the speed limit is exceeded, generate an alarm. Step S103g: Statistically aggregate the parking data of intersection parking spots and station parking spots and their adjacent interval data, calculate the distance between the actual parking location and the parking spot, and record it as the parking accuracy; Step S103h: Collect information on vehicles overshooting; if any are found, generate an alarm. Step S103i: Store the analysis results and alarms from steps S103a to S103h into the large database.

2. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, The section in step S101 is divided into the main line section, station stopping point, intersection stopping point and intersection section.

3. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, In step S101, speed limit information is added to each section according to the route design data, and the location of the section is calculated.

4. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, In step S102, the original logs are cleaned and parsed before being transmitted to a large database.

5. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, The original logs in step S102 include the communication front-end logs and the vehicle logs.

6. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, The train data in step S103a includes the train's speed, location, and running time information.

7. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, The step S103e of drawing the standard driving curve specifically involves: calculating the travel time and stopping time of all punctual trains in each section, and fitting the travel time curve of the standard section, which is denoted as the standard driving curve.

8. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, In step S103 g, the train speed information before and after the stopping position is also collected, and the starting and braking acceleration of the train is calculated.

9. The method for analyzing the driving behavior of tram drivers based on big data according to claim 1, characterized in that, Step S104 specifically includes the following steps: Step S104a: Analyze the reasons why drivers are arriving early or late; Step S104b: Analyze whether each driver has engaged in unsafe driving behavior; Step S104c: Analyze passenger comfort during each driver's driving.

10. A method for analyzing the driving behavior of tram drivers based on big data, as described in claim 9, is characterized in that... The specific steps of S104a are as follows: The analysis results of train punctuality status, travel speed, travel time, standard driving curve, and parking accuracy are aggregated and statistically analyzed. The punctuality rate and average travel speed of each driver are analyzed, and the average segment travel-time curve of each driver is fitted and recorded as the driver driving curve. The curve is then compared with the standard driving curve to analyze the reasons for the drivers' punctuality.

11. The method for analyzing the driving behavior of tram drivers based on big data according to claim 9, characterized in that, The specific steps of S104b are as follows: The analysis results of train overshoot warnings and speeding warnings are aggregated and statistically analyzed to determine whether each driver has engaged in unsafe driving behavior.

12. The method for analyzing the driving behavior of tram drivers based on big data according to claim 9, characterized in that, The specific steps of S104c are as follows: The analysis results of train starting and braking acceleration and stopping accuracy are aggregated and statistically analyzed to assess passenger comfort during each driver's operation.

13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 12.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 12.

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