CN115346359A - Traffic jam prediction method and system based on job and live tracks - Google Patents
Traffic jam prediction method and system based on job and live tracks Download PDFInfo
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- CN115346359A CN115346359A CN202110518462.2A CN202110518462A CN115346359A CN 115346359 A CN115346359 A CN 115346359A CN 202110518462 A CN202110518462 A CN 202110518462A CN 115346359 A CN115346359 A CN 115346359A
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- 230000011664 signaling Effects 0.000 claims abstract description 49
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/20—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
- H04W8/183—Processing at user equipment or user record carrier
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Abstract
The invention provides a traffic jam prediction method and system based on a job and live track, wherein the method comprises the following steps: acquiring historical signaling data of a vehicle-mounted Internet of things card and a user terminal in a target area; binding each user terminal with a vehicle provided with the vehicle-mounted Internet of things card according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the position track of each user terminal according to the binding result; and acquiring historical traffic flow data of each road section at each time period every day according to the working and dwelling track of each user terminal, and predicting the occurrence probability of traffic jam according to the historical traffic flow data. The method realizes the identification of one vehicle and multiple people by combining the vehicle networking card and the mobile phone card signaling, predicts the traffic jam based on the working track, can be used for predicting multiple days in the future, and has larger predicted granularity and higher prediction accuracy.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a traffic jam prediction method and system based on a occupation track.
Background
The existing traffic jam prediction methods mainly comprise 2 methods: firstly, based On-Board Diagnostics (OBD) data of a vehicle-mounted system, obtaining information such as a position track and a speed of a vehicle, and calculating the traffic flow at the next moment; secondly, monitoring is carried out through mobile phone signaling, traffic flow and pedestrian flow monitored by the base station are correspondingly analyzed, vehicle speed is calculated, and prediction analysis is carried out by combining road condition information.
However, the prior art has the following disadvantages: 1. the on-board system OBD data is limited by the popularization of vehicles, and in addition, a unified platform is lacked to access all vehicle data in real time; 2. through mobile phone signaling monitoring, the binding of a vehicle and people is not accurate, a method for effectively distinguishing one vehicle from multiple people is lacked, and meanwhile, the coverage range of a base station signal is large, especially in an open area, and the signal cannot be specifically applied to a certain road; 3. most of the existing schemes can only predict the condition of the future of the current time within tens of minutes or hours, the prediction time is short, the prediction granularity is small (generally, minute granularity), the day granularity cannot be predicted, and the requirement of planning a route in advance in some scenes cannot be met. Therefore, the existing traffic jam prediction method has the defects, and the prediction accuracy and the prediction range of the existing traffic jam prediction method are further improved.
Therefore, a method and a system for predicting traffic congestion are needed to solve the above problems.
Disclosure of Invention
The invention provides a traffic jam prediction method and system based on working and living tracks, which are used for solving the defects that the granularity of traffic jam prediction is smaller, day granularity prediction cannot be carried out, and the requirement of planning a route in advance in some scenes cannot be met in the prior art, and realizing accurate prediction of traffic jam.
In a first aspect, the present invention provides a traffic congestion prediction method based on a job trail, including:
acquiring historical signaling data of a vehicle-mounted Internet of things card and a user terminal in a target area;
binding each user terminal with a vehicle provided with the vehicle-mounted Internet of things card according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the occupation track of each user terminal according to the binding result, wherein the occupation track is the travel track of a worker between a residence place and a working place;
and acquiring historical traffic flow data of each road section at each time period every day according to the working and dwelling track of each user terminal, and predicting the occurrence probability of traffic jam according to the historical traffic flow data.
In one embodiment, the acquiring historical signaling data of the vehicle-mounted internet of things card and the user terminal in the target area includes:
determining an area of the traffic jam condition to be predicted as a target area;
and in the target area, obtaining historical signaling data of the vehicle-mounted Internet of things card and the user terminal through an operator.
In one embodiment, the historical signaling data of the vehicle-mounted internet of things card and the user terminal at least includes:
position information data, internet of things card registration information data, user terminal data and base station information data.
In one embodiment, the binding each user terminal with a vehicle on which the vehicle-mounted internet of things card is installed according to the registration information of each vehicle-mounted internet of things card and the historical signaling data includes:
acquiring all-day reported track information of a mobile phone card of a user terminal and all-day reported track information of a vehicle-mounted Internet of things card, wherein the track information is obtained by acquiring a location area code and a Cell-ID when a base station is switched through the mobile phone card or the vehicle-mounted Internet of things card;
and binding the user terminal and the vehicle within the track information complete coincidence time period.
In one embodiment, the determining the occupation trajectory of each user terminal includes:
acquiring target track information corresponding to the bound user terminal and vehicle;
determining the base station position with the longest stay time in the daytime and daytime of the working day and the longest stay time in night time of the working day in the target track information as a working place, and determining the base station position with the longest stay time in the weekend and the frequency higher than a preset threshold value in the target track information as a living place;
and taking the round trip track of the workplace and the residence as a job track.
In one embodiment, the obtaining traffic data of each road segment in each time period every day according to the working track of each user terminal to predict the occurrence probability of traffic congestion according to the traffic data includes:
acquiring historical traffic flow data of each road section in each time period every day based on a preset time sequence model and the working and stopping track;
and predicting the occurrence probability of traffic jam according to the historical traffic flow data and a preset traffic flow threshold.
In a second aspect, the present invention provides a traffic congestion prediction system based on a job trail, including:
the signaling acquisition module is used for acquiring historical signaling data of the vehicle-mounted Internet of things card and the user terminal in the target area;
the system comprises a position track determining module, a position track determining module and a position track determining module, wherein the position track determining module is used for binding each user terminal with a vehicle provided with the vehicle-mounted internet of things according to registration information of each vehicle-mounted internet of things and the historical signaling data, and determining the position track of each user terminal according to a binding result, and the position track is a travel path track of a worker between a residence and a work place;
and the prediction module is used for acquiring historical traffic flow data of each road section in each time period every day according to the working track of each user terminal so as to predict the occurrence probability of traffic jam according to the historical traffic flow data.
In one embodiment, the signaling acquisition module includes:
the traffic jam area determination unit is used for determining an area of the traffic jam condition to be predicted as a target area;
and the data acquisition unit is used for acquiring historical signaling data of the vehicle-mounted Internet of things card and the user terminal in the target area through an operator.
In a third aspect, the present invention provides an electronic device, comprising a memory and a memory storing a computer program, wherein when the processor executes the program, the steps of the method for predicting traffic congestion based on a occupancy trajectory according to the first aspect are implemented.
In a fourth aspect, the present invention provides a processor-readable storage medium storing a computer program for causing a processor to execute the steps of the method for predicting traffic congestion based on a job trail of the first aspect.
According to the traffic jam prediction method and system based on the working and dwelling track, the vehicle networking card and the mobile phone card are combined to realize the recognition of one vehicle and multiple people, and the traffic jam is predicted based on the working and dwelling track, so that the method and system can be used for predicting multiple days in the future, and the prediction granularity is larger, and the prediction accuracy is higher.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a traffic congestion prediction method based on a job track according to the present invention;
fig. 2 is a schematic structural diagram of a traffic congestion prediction system based on a job-stop trajectory according to the present invention;
fig. 3 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a traffic congestion prediction method based on a job track provided by the present invention, and as shown in fig. 1, the present invention provides a traffic congestion prediction method based on a job track, which includes:
step 101, obtaining historical signaling data of a vehicle-mounted internet of things card and a user terminal in a target area.
In the invention, a traffic jam prediction detection area is selected firstly, and then historical signaling data of a vehicle-mounted internet of things card and a user terminal (such as a mobile phone, an iPad and the like) in the internet of vehicles in the area are obtained.
And 102, binding each user terminal with a vehicle provided with the vehicle-mounted Internet of things card according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the occupation track of each user terminal according to the binding result, wherein the occupation track is the traveling track of the employee between the residence and the working place.
According to the invention, the vehicle-mounted Internet of things card and the mobile phone card are continuously matched according to the historical signaling data and the registration information of the vehicle-mounted Internet of things card, the vehicle and the user terminal are further bound, and then the residence place and the working place where the user terminal is located can be judged based on the historical track information of the user terminal, so that the working track of the user can be obtained.
According to the invention, the superposed tracks are analyzed according to the working and stopping tracks of each user to obtain the vehicle condition of each road section in each time period every day, so that the traffic condition of each road section in each time period in the next several days is predicted through a preset time sequence model. It should be noted that the preset time series model is to divide the target area based on each road segment at each time interval, count the vehicle data of each road segment at each time interval, and compare the vehicle data with a preset threshold value, so as to predict whether traffic congestion occurs according to the comparison result.
According to the traffic jam prediction method based on the working track, the vehicle networking card and the mobile phone card are combined to realize the recognition of one vehicle and multiple people, and the traffic jam is predicted based on the working track, so that the method can be used for predicting multiple days in the future, and the prediction granularity is larger, and the prediction accuracy is higher.
On the basis of the above embodiment, the acquiring historical signaling data of the vehicle-mounted internet of things card and the user terminal in the target area includes:
determining an area of the traffic jam condition to be predicted as a target area;
and in the target area, obtaining historical signaling data of the vehicle-mounted Internet of things card and the user terminal through an operator.
In the invention, the area needing to be predicted for traffic jam is firstly determined to be predicted in a targeted manner, and the prediction is performed by taking the city level as a unit, such as predicting Beijing city, baoding city and the like, and the invention is not limited specifically; and then, historical signaling data of the vehicle-mounted internet of things card and the user mobile phone are obtained through an operator, and it can be understood that interactive data are managed and controlled through a mobile operator, so that a user group using the data can be inquired through the operator.
On the basis of the above embodiment, the historical signaling data of the vehicle-mounted internet of things card and the user terminal at least includes:
position information data, internet of things card registration information data, user terminal data and base station information data.
In the invention, the information to be acquired comprises position information data, internet of things card registration information data, user terminal data, base station information data and the like. The position information data is used for analyzing the occupation track of the user, the user terminal data and the Internet of things card registration information data are used for checking the identity of the user and binding the identity, and the base station information data is used for positioning the specific position of the user.
On the basis of the above embodiment, the binding each user terminal with the vehicle on which the vehicle-mounted internet of things card is installed according to the registration information of each vehicle-mounted internet of things card and the historical signaling data includes:
acquiring track information reported all day by a mobile phone card of a user terminal and track information reported all day by a vehicle-mounted internet of things card, wherein the track information is acquired by a location area code and a Cell-ID when a base station is switched through the mobile phone card or the vehicle-mounted internet of things card;
and binding the user terminal and the vehicle within the track information complete coincidence time period.
In the invention, the registration information of the vehicle-mounted Internet of things card is bound with the user terminal, and the vehicle-mounted Internet of things card without the registration information is bound with the user terminal through track information verification. The specific situation is that the track information reported all day by the user mobile phone card and the vehicle-mounted internet of things card can completely coincide, and the situation can often occur, so that the vehicle and the user terminal are preliminarily bound. In the present invention, the time interval when the track information is completely overlapped is confirmed by switching the Location Area Code (LAC) and the Cell-ID of the base station.
On the basis of the above embodiment, the determining the occupation trajectory of each user terminal includes:
acquiring target track information corresponding to the bound user terminal and vehicle;
determining the base station position with the longest stay time in the day-night period of the working day and the frequency higher than a preset threshold value in the target track information as a working place, and determining the base station position with the longest stay time in the night period of the working day in the target track information as a residence place;
and taking the round trip track of the workplace and the residence as a job track.
According to the invention, the working place and the residence place of the user are judged according to the human-vehicle binding condition through the human-vehicle historical track. Specifically, when the day of the working day generally refers to 09-00, the same situation that the user terminal stays for the longest time in the track information at the base station and occurs more than 15 times in a natural month is considered as the working place, and it can be understood that 15 times are the threshold set according to the general working situation of the present invention, and the present invention is not limited to this specifically. In the evening of a working day, generally, 00. In the case of a weekend non-working situation, the destination to which the weekend is frequently moved may be set as a working place by analyzing alone.
On the basis of the above embodiment, the acquiring traffic data of each road segment in each time period every day according to the working track of each user terminal to predict the occurrence probability of traffic congestion according to the traffic data includes:
acquiring historical traffic flow data of each road section in each time period every day based on a preset time sequence model and the working and dwelling track;
and predicting the occurrence probability of traffic jam according to the historical traffic flow data and a preset traffic flow threshold.
In the invention, the working place and the residence are judged through the embodiment, the working track is determined, then the track with the starting point as the working place or the residence and the ending point as the corresponding residence or the working place and the track in the period are taken out through the working track, and the traffic flow data of each section in each period is obtained by distinguishing the track according to the time and the road based on the preset time series model. And predicting the acquired historical data of each road section through a preset time sequence model, analyzing the time sections and road sections possibly suffering from congestion, and providing basis for travel planning of part of crowds. For example, the preset time series model sets a traffic flow threshold of a certain road section at a peak time of a working day, historical data of the road section is compared with a vehicle threshold, and if the historical data of the road section is larger than the vehicle threshold, the traffic jam occurrence probability is calculated according to a ratio of the historical data of the road section to the vehicle threshold.
The traffic congestion prediction system based on the working and stopping tracks provided by the invention is described below, and the traffic congestion prediction system based on the working and stopping tracks described below and the traffic congestion prediction method based on the working and stopping tracks described above can be referred to correspondingly.
Fig. 2 is a schematic structural diagram of a traffic congestion prediction system based on a job track according to the present invention, and as shown in fig. 2, the present invention provides a traffic congestion prediction system based on a job track, including: the system comprises a signaling acquisition module 201, a occupation track determination module 202 and a prediction module 203, wherein the signaling acquisition module 201 is used for acquiring historical signaling data of a vehicle-mounted internet of things card and a user terminal in a target area; the job position track determining module 202 is configured to bind each user terminal with a vehicle on which the vehicle-mounted internet of things card is installed according to the registration information of each vehicle-mounted internet of things card and the historical signaling data, and determine a job position track of each user terminal according to a binding result, where the job position track is a travel path track of a person between a residence place and a work place; the prediction module 203 is configured to obtain historical traffic flow data of each road segment at each time period every day according to the working track of each user terminal, so as to predict the occurrence probability of traffic congestion according to the historical traffic flow data.
The traffic jam prediction system based on the working track realizes the recognition of one car and multiple people by combining the car networking card and the mobile phone card signaling, predicts the traffic jam based on the working track, can be used for predicting multiple days in the future, and has larger prediction granularity and higher prediction accuracy.
On the basis of the above embodiment, the signaling acquisition module includes a congestion area determination unit and a data acquisition unit, wherein the congestion area determination unit is configured to determine an area of a traffic congestion situation to be predicted as a target area; and the data acquisition unit is used for acquiring historical signaling data of the vehicle-mounted Internet of things card and the user terminal in the target area through an operator.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor) 310, a Communication Interface (Communication Interface) 320, a memory (memory) 330 and a Communication bus 340, wherein the processor 310, the Communication Interface 320 and the memory 330 complete the Communication with each other through the Communication bus 340. The processor 310 may invoke computer programs in the memory 330 to perform the steps of a method for traffic congestion prediction based on occupancy tracks, including, for example: acquiring historical signaling data of a vehicle-mounted Internet of things card and a user terminal in a target area; binding each user terminal with a vehicle provided with the vehicle-mounted Internet of things card according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the occupation track of each user terminal according to the binding result, wherein the occupation track is the travel track of a worker between a residence place and a working place; and acquiring historical traffic flow data of each road section at each time period every day according to the working and dwelling track of each user terminal, and predicting the occurrence probability of traffic jam according to the historical traffic flow data.
In addition, the logic instructions in the memory 330 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer being capable of executing the method for predicting traffic congestion based on a job trail provided by the above methods, the method including: acquiring historical signaling data of a vehicle-mounted Internet of things card and a user terminal in a target area; binding each user terminal with a vehicle provided with the vehicle-mounted Internet of things card according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the occupation track of each user terminal according to the binding result, wherein the occupation track is the travel track of a worker between a residence place and a working place; and acquiring historical traffic flow data of each road section at each time interval every day according to the working track of each user terminal, so as to predict the occurrence probability of traffic jam according to the historical traffic flow data.
In another aspect, the present application further provides a processor-readable storage medium, where the processor-readable storage medium stores a computer program, where the computer program is configured to cause the processor to execute the method provided in the foregoing embodiments, for example, including: acquiring historical signaling data of a vehicle-mounted Internet of things card and a user terminal in a target area; binding each user terminal with a vehicle provided with the vehicle-mounted Internet of things according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the occupational trajectory of each user terminal according to the binding result, wherein the occupational trajectory is the travel trajectory of a worker between a residence and a work place; and acquiring historical traffic flow data of each road section at each time period every day according to the working and dwelling track of each user terminal, and predicting the occurrence probability of traffic jam according to the historical traffic flow data.
The processor-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid State Disks (SSDs)), etc.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A traffic jam prediction method based on a job-stop track is characterized by comprising the following steps:
acquiring historical signaling data of a vehicle-mounted Internet of things card and a user terminal in a target area;
binding each user terminal with a vehicle provided with the vehicle-mounted Internet of things card according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the occupation track of each user terminal according to the binding result, wherein the occupation track is the travel track of a worker between a residence place and a working place;
and acquiring historical traffic flow data of each road section at each time period every day according to the working and dwelling track of each user terminal, and predicting the occurrence probability of traffic jam according to the historical traffic flow data.
2. The method for predicting traffic congestion based on the occupation trajectory according to claim 1, wherein the obtaining historical signaling data of the vehicle-mounted internet of things card and the user terminal in the target area comprises:
determining an area with traffic jam to be predicted as a target area;
and in the target area, obtaining historical signaling data of the vehicle-mounted Internet of things card and the user terminal through an operator.
3. The method according to claim 2, wherein the historical signaling data of the vehicle internet of things card and the user terminal at least comprises:
position information data, internet of things card registration information data, user terminal data and base station information data.
4. The method according to claim 1, wherein the binding of each user terminal with a vehicle on which the vehicle internet of things card is installed according to the registration information of each vehicle internet of things card and the historical signaling data comprises:
acquiring all-day reported track information of a mobile phone card of a user terminal and all-day reported track information of a vehicle-mounted Internet of things card, wherein the track information is obtained by acquiring a location area code and a Cell-ID when a base station is switched through the mobile phone card or the vehicle-mounted Internet of things card;
and binding the user terminal and the vehicle within the track information complete coincidence time period.
5. The method of claim 4, wherein the determining the job trail for each user terminal comprises:
acquiring target track information corresponding to the bound user terminal and vehicle;
determining the base station position with the longest stay time in the day-night period of the working day and the frequency higher than a preset threshold value in the target track information as a working place, and determining the base station position with the longest stay time in the night period of the working day in the target track information as a residence place;
and taking the round trip track of the workplace and the residence as a job track.
6. The method according to claim 1, wherein the obtaining traffic data of each road segment in each time period every day according to the occupation trajectory of each user terminal to predict the traffic congestion occurrence probability according to the traffic data comprises:
acquiring historical traffic flow data of each road section in each time period every day based on a preset time sequence model and the working and dwelling track;
and predicting the occurrence probability of traffic jam according to the historical traffic flow data and a preset traffic flow threshold.
7. A system for predicting traffic congestion based on a job-stop trajectory, comprising:
the signaling acquisition module is used for acquiring historical signaling data of the vehicle-mounted Internet of things card and the user terminal in the target area;
the job track determining module is used for binding each user terminal with a vehicle corresponding to each vehicle-mounted Internet of things card according to the registration information of each vehicle-mounted Internet of things card and the historical signaling data, and determining the job track of each user terminal according to the binding result, wherein the job track is a traveling track of a resident between a residence and a workplace;
and the prediction module is used for acquiring historical traffic flow data of each road section in each time period every day according to the working track of each user terminal so as to predict the occurrence probability of traffic jam according to the historical traffic flow data.
8. The system of claim 7, wherein the signaling acquisition module comprises:
the congestion area determining unit is used for determining an area with traffic congestion conditions to be predicted as a target area;
and the data acquisition unit is used for acquiring historical signaling data of the vehicle-mounted Internet of things card and the user terminal in the target area through an operator.
9. An electronic device comprising a processor and a memory storing a computer program, wherein the processor when executing the computer program implements the steps of the method for predicting traffic congestion based on occupancy tracks of any one of claims 1 to 6.
10. A processor-readable storage medium storing a computer program for causing a processor to execute the steps of the method for predicting traffic congestion based on a stopping trajectory according to any one of claims 1 to 6.
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| CN202110518462.2A CN115346359A (en) | 2021-05-12 | 2021-05-12 | Traffic jam prediction method and system based on job and live tracks |
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| CN202110518462.2A CN115346359A (en) | 2021-05-12 | 2021-05-12 | Traffic jam prediction method and system based on job and live tracks |
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