CN117079457A - Traffic jam prediction method based on GPS data - Google Patents
Traffic jam prediction method based on GPS data Download PDFInfo
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- CN117079457A CN117079457A CN202310986170.0A CN202310986170A CN117079457A CN 117079457 A CN117079457 A CN 117079457A CN 202310986170 A CN202310986170 A CN 202310986170A CN 117079457 A CN117079457 A CN 117079457A
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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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- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/0969—Systems involving transmission of navigation instructions to the vehicle having a display in the form of a map
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
The application discloses a traffic jam prediction method based on GPS data in the technical field of traffic prediction, which comprises the following steps: s1, building a basic reference model; s2, importing historical congestion information, and marking a pre-blocking area on a basic reference model based on the historical congestion information; s3, correcting the pre-blocking area, and updating the pre-blocking area into a blocking area, a unblocked area, a first deceleration creep area and a second deceleration creep area based on the distribution of the pre-blocking area on a basic reference model and the traffic condition of a downstream location area corresponding to the location area after the interval time period to obtain a prediction model of the interval time period; s4, uploading the vehicle GPS data to a prediction model, and obtaining an average running speed corresponding to a place block based on the place block where the vehicle GPS data is located in the prediction model; the prediction model is more accurate through the correction of the pre-blocking area, so that traffic road congestion can be predicted conveniently through GPS data of the current time, and the estimated arrival time of the vehicle can be obtained.
Description
Technical Field
The application belongs to the technical field of traffic prediction, and particularly relates to a traffic jam prediction method based on GPS data.
Background
Traffic congestion refers to a traffic phenomenon that when the total traffic flow through a certain section or intersection in a road is greater than the traffic capacity of the road (the traffic capacity of the section or intersection) due to an increase in traffic demand within a certain period of time, the traffic flow on the road cannot be smooth, and more traffic flows stay on the road (the section or intersection).
The method is an important means for relieving traffic jams by identifying and predicting traffic states, for example, the patent with the Chinese patent publication number of CN104778834B discloses a method for judging the traffic jams of urban roads based on vehicle GPS data, and the problem that the application range of the method for judging the traffic jams based on the data of the detection equipment is greatly limited due to the adoption of traditional traffic information detection equipment in the conventional method for judging the traffic jams of the urban roads is solved. Constructing an urban road section travel time prediction model based on the artificial neural network model; calculating to obtain the road section travel time data of the current moment according to the position vector, the road section number vector, the timestamp vector and the speed vector of the current moment obtained by the GPS of the vehicle by utilizing the urban road section travel time prediction model; further calculating the road section traffic flow speed and the road section traffic flow density based on the road section travel time data; and judging the road traffic jam state by taking the road traffic flow speed and the density data as input conditions. The GPS data at the current moment can be used for rapidly and accurately judging the traffic jam state.
However, the traffic congestion state is judged through the GPS data at the current moment, and because the traffic congestion has an association relationship with the whole traffic, the influence of traffic conditions outside the current road section on the current road section is ignored, the accuracy of traffic congestion prediction can be influenced, and the prediction of the time for reaching a destination by a driver or a passenger is inconvenient.
Disclosure of Invention
In order to solve the problem that the driver or the passenger cannot conveniently estimate the time to reach the destination, the application aims to provide a traffic jam prediction method based on GPS data, which predicts traffic road jam conditions through the GPS data at the current moment and facilitates the pre-judgment of the time of the current vehicle to reach the destination.
In order to achieve the above object, the technical scheme of the present application is as follows:
a traffic jam prediction method based on GPS data comprises the following steps: s1, a basic reference model is established, a current urban road map is used as a basic reference model, and vehicle history running data in different time periods are imported into the corresponding basic reference model;
s2, importing historical congestion information, dividing a basic reference model into a plurality of place blocks in a certain interval period, calculating to obtain the traffic volume of the vehicles corresponding to the place blocks and the running time of the vehicles based on the historical running data of the vehicles corresponding to the place blocks, and obtaining the average running speed corresponding to the place blocks according to the running time of the vehicles;
the historical blocking information is associated with a corresponding place block in the basic reference model, the historical blocking information comprises blocking and unblocking, the average running speed corresponding to different place blocks when blocking occurs is obtained, and the place block is marked as a pre-blocking area in the basic reference model when blocking occurs;
s3, correcting the pre-blocking area, namely acquiring the probability A of marking the place block as the pre-blocking area in all basic reference models based on all basic reference models of the interval time period, and updating the pre-blocking area as the blocking area if the probability A is more than 60%;
if the probability A is smaller than 60%, after the interval period, acquiring the traffic condition of a downstream place block corresponding to the place block, wherein the traffic condition of the downstream place block takes the historical blocking information corresponding to the same place block in all the basic reference models as a basic value, if the historical blocking information is that the occupancy rate of the blocking information in the basic value is larger than the occupancy rate of the historical blocking information in the basic value, the traffic condition of the downstream place block is blocked, and if the occupancy rate of the historical blocking information in the basic value is smaller than the occupancy rate of the historical blocking information in the basic value, the traffic condition of the downstream place block is unblocked;
if the traffic condition of the downstream site block is smooth, updating the pre-blocking area into a smooth area;
if the traffic condition of the downstream location block is a jam, repeating the traffic condition acquisition process of the downstream location block, and acquiring the traffic conditions of all the upstream location blocks associated with the downstream location block in the interval time period; if the traffic condition of the upstream location block is that the probability of blocking the total upstream location block is greater than 50%, updating the pre-blocking area into a first deceleration creep area, and if the traffic condition of the upstream location block is that the probability of blocking the total upstream location block is less than 50%, updating the pre-blocking area into a second deceleration creep area;
obtaining a prediction model of the interval time period based on the distribution of the blocking area, the unblocked area, the first deceleration creep area and the second deceleration creep area in the basic reference model;
and S4, uploading the vehicle GPS data to a prediction model, obtaining a prediction model of a corresponding interval time end according to the vehicle GPS data of the current time, obtaining an average running speed corresponding to a place block based on the place block where the vehicle GPS data is located in the prediction model, and obtaining the estimated time based on the average running speed corresponding to each place block.
After the scheme is adopted, the following beneficial effects are realized: by importing the vehicle history running data with different time periods into the corresponding basic reference model, the vehicle history running data and the basic reference model are conveniently connected, and therefore the distribution condition of the pre-blockage area on the basic reference model is obtained.
In an interval period, the probability that the same place block in all the basic reference models is a pre-blocking area is calculated, so that whether the pre-blocking area is a blocking area or not is conveniently confirmed,
when the probability A of the place block marked as a pre-blocking area in all basic reference models is smaller than 60%, after the interval time period, acquiring the traffic condition of a downstream place block corresponding to the place block, and conveniently confirming whether the pre-blocking area is a smooth area or a deceleration creep area by acquiring the traffic condition of the downstream place block and the traffic condition of all upstream place blocks related to the downstream place block; and the obtained prediction model is used for linking each place block of the current interval time with a downstream place block of the next interval time, so that the accuracy of traffic jam prediction is improved.
And acquiring the average running speed corresponding to each corresponding place block through the GPS data at the current moment, so as to obtain the estimated time, and meanwhile, the method is convenient for a driver to know the estimated traffic road congestion condition in advance, is convenient for pre-judging the time of the current vehicle reaching the destination, and reduces the uncertainty of waiting for the traffic to recover smoothly.
Further, in S4, geographic location information corresponding to the location block where the current vehicle is located is also obtained, where the geographic location information includes an intersection crossing section, a construction section and a speed-limiting section, and the estimated time is adjusted in an extended manner based on the geographic location information.
The beneficial effects are that: by identifying the geographic position information, more reserved space is provided for prediction of the blocking time, so that the prediction time is more accurate, and the traffic jam judgment is facilitated.
Further, in S1, the basic reference model includes a special prediction model and a conventional prediction model, where the special prediction model is built according to vehicle running data corresponding to different special time periods of one year, and a time period other than the special time period in one year is a conventional time period; and building a conventional prediction model according to the vehicle driving data corresponding to different time periods of one day in the conventional time period.
The beneficial effects are that: the special time period is distinguished from the conventional time period, different basic reference models are established, and the special prediction model or the conventional prediction model can be selected according to different time periods in one year, so that the accuracy of traffic jam prediction is improved.
Further, in S3, the specific prediction model is established with reference to only the vehicle history running data and the history blocking information corresponding to the specific time period, and the conventional prediction model is established with reference to only the vehicle history running data and the history blocking information corresponding to the conventional time period.
The beneficial effects are that: different basic reference models select corresponding vehicle historical data to establish, so that a special prediction model or a conventional prediction model can be more matched with actual traffic conditions, and the accuracy of traffic jam prediction is improved.
Further, in S3, the average running speed of the first deceleration creep zone is smaller than the average running speed of the second deceleration creep zone.
The beneficial effects are that: the average running speed reflects the traffic jam condition, and the accuracy of the subsequent traffic jam detection is facilitated by setting the first decelerating creep zone or the second creep zone with different average running speeds.
Further, in S4, when the vehicle GPS data is located in the first deceleration creep zone or the second deceleration creep zone, the average running speed corresponding to the first deceleration creep zone or the second deceleration creep zone is displayed.
The beneficial effects are that: the method is convenient for a driver to timely adjust the running speed of the vehicle and reduces the occurrence of ghost traffic jam, thereby reducing the possibility of congestion of the vehicle.
Further, the special time period takes holidays as reference time.
The beneficial effects are that: the vehicle running condition of the holiday is different from the vehicle running condition of the conventional time, and the basic reference model during the holiday is used as a special prediction model, so that the holiday is conveniently distinguished from the conventional time, and the accuracy of traffic jam prediction is conveniently improved.
Further, the regular time period includes a normal time period and a weekend time period, wherein the time period of the regular time period includes a daily time period, a shift-in time period, a shift-out time period, and a special time period.
The beneficial effects are that: the vehicle running condition in the normal time period is different from the vehicle running condition in the weekend time period, and the vehicle running condition in the conventional time period has obvious time period characteristics, and the accuracy of traffic jam prediction is improved conveniently by establishing a prediction model for different time periods.
Drawings
Fig. 1 is a schematic diagram of a traffic congestion prediction method according to an embodiment of the present application.
Detailed Description
The following is a further detailed description of the embodiments:
an example is substantially as shown in figure 1: a traffic jam prediction method based on GPS data comprises the following steps: s1, a basic reference model is established, a current urban road map is used as a basic reference model, and vehicle history running data in different time periods are imported into the corresponding basic reference model;
the basic reference model comprises a special prediction model and a conventional prediction model, wherein the special prediction model is built according to vehicle running data corresponding to different special time periods of one year, and the time periods except the special time periods in one year are conventional time periods; establishing a conventional prediction model according to vehicle running data corresponding to different time periods of one day in a conventional time period;
the special time period takes holidays as reference time; the regular time period comprises a normal time period and a weekend time period, wherein the time period of the regular time period comprises a daily time period, a working time period and a special time period.
For example, taking a common city as an example, on holidays such as mid-autumn, the traffic flow obviously tends to increase on the day before the holiday or at the end of the holiday, and traffic jam is easy to occur; the traffic flow obviously increases in the rush hour of working or working and tends to be in the traffic jam in the conventional time, and the traffic flow tends to decrease in the rush hour of working or working, so that the special prediction model and the conventional prediction model have certain difference, and the accuracy of the prediction of the follow-up traffic jam is convenient to improve by distinguishing the special prediction model from the conventional prediction model.
S2, importing historical congestion information, dividing a basic reference model into a plurality of place blocks in a certain interval period, calculating to obtain the traffic volume of the vehicles corresponding to the place blocks and the running time of the vehicles based on the historical running data of the vehicles corresponding to the place blocks, and obtaining the average running speed corresponding to the place blocks according to the running time of the vehicles;
the historical blocking information is associated with a corresponding place block in the basic reference model, the historical blocking information comprises blocking and unblocking, the average running speed corresponding to different place blocks when blocking occurs is obtained, and the place block is marked as a pre-blocking area in the basic reference model when blocking occurs;
for example, in an urban road, roads of different location blocks have differences, one location block is easy to generate a ghost traffic jam phenomenon due to delay time of a plurality of vehicles due to the reason that the roads are connected at the periphery, and the roads are in a smooth state, so that accuracy of prediction of subsequent traffic jams is improved by comparing the phenomenon.
S3, correcting the pre-blocking area, and based on all basic reference models of the interval time period, wherein the building of the special prediction model only takes vehicle historical running data and historical blocking information corresponding to the special time period as references, and the building of the conventional prediction model only takes vehicle historical running data and historical blocking information corresponding to the conventional time period as references;
acquiring the probability A of the place block marked as a pre-blocking area in all basic reference models, and if the probability A is more than 60%, updating the pre-blocking area as a blocking area;
if the probability A is smaller than 60%, after the interval period, acquiring the traffic condition of a downstream place block corresponding to the place block, wherein the traffic condition of the downstream place block takes the historical blocking information corresponding to the same place block in all the basic reference models as a basic value, if the historical blocking information is that the occupancy rate of the blocking information in the basic value is larger than the occupancy rate of the historical blocking information in the basic value, the traffic condition of the downstream place block is blocked, and if the occupancy rate of the historical blocking information in the basic value is smaller than the occupancy rate of the historical blocking information in the basic value, the traffic condition of the downstream place block is unblocked;
if the traffic condition of the downstream site block is smooth, updating the pre-blocking area into a smooth area;
for example, a certain road among urban roads may temporarily have a reduced vehicle speed due to a confluence, a speed limitation, or the like, but after the vehicle passes through the road section, the vehicle flow rate of the upstream spot block around the downstream spot block is small, and the vehicle runs smoothly.
If the traffic condition of the downstream location block is a jam, repeating the traffic condition acquisition process of the downstream location block, and acquiring the traffic conditions of all the upstream location blocks associated with the downstream location block in the interval time period; if the traffic condition of the upstream location block is that the probability of blocking the total upstream location block is greater than 50%, updating the pre-blocking area into a first deceleration creep area, and if the traffic condition of the upstream location block is that the probability of blocking the total upstream location block is less than 50%, updating the pre-blocking area into a second deceleration creep area;
for example, a road in an urban road may temporarily have a reduced vehicle speed due to a traffic flow, a speed limit, or the like, but after passing through the road section, the traffic flow of an upstream spot block around the downstream spot block is large, and a jam occurs when the vehicle travels to the downstream spot block due to the traffic flow.
Wherein the average running speed of the first deceleration creep zone is smaller than the average running speed of the second deceleration creep zone.
Obtaining a prediction model of the interval time period based on the distribution of the blocking area, the unblocked area, the first deceleration creep area and the second deceleration creep area in the basic reference model;
and S4, uploading the vehicle GPS data to a prediction model, obtaining a prediction model of a corresponding interval time end according to the vehicle GPS data of the current time, obtaining an average running speed corresponding to a place block based on the place block where the vehicle GPS data is located in the prediction model, and obtaining the estimated time based on the average running speed corresponding to each place block.
And (3) the prediction model is obtained through the S4, and each place block of the current interval time is related to the downstream place block of the next interval time, so that the accuracy of traffic jam prediction is improved, the traffic road jam condition is predicted conveniently through GPS data of the current moment, the time that the current vehicle reaches the destination is predicted, the uncertainty of waiting for smooth traffic restoration is reduced, the driving tension emotion of a driver is relieved, the driver can conveniently make rational judgment, and the occurrence of ghost traffic jam is reduced.
Example 2
The difference from the above embodiment is that in S4, the geographic location information corresponding to the location block where the current vehicle is located is also obtained, where the geographic location information includes an intersection crossing section, a construction section and a speed limit section, and the estimated time is adjusted based on the geographic location information in an extended manner, and the adjusted time is determined according to the road condition of the corresponding location block.
The specific implementation process is as follows: the road traffic is complex or the road occupation condition occurs in the place block based on the geographic position information, traffic accidents or blocking are easy to occur, uncertainty exists, more reserved space is provided for prediction of blocking time by identifying the geographic position information, the prediction time is more accurate, and the traffic jam judgment is convenient.
Example 3
Unlike the above embodiment, in S4, when the vehicle GPS data is located in the first deceleration creep zone or the second deceleration creep zone, the average running speed corresponding to the first deceleration creep zone or the second deceleration creep zone is also displayed to the vehicle driver or the passenger.
The specific implementation process is as follows: the average driving speed is used for reminding the driver, so that the restarting time after the vehicle is stopped is reduced, the driver can adjust the driving speed of the vehicle in time, the phenomenon of ghost traffic jam is reduced, and the possibility of congestion of the vehicle is reduced.
The foregoing is merely exemplary of the present application and the specific structures and/or characteristics of the present application that are well known in the art have not been described in detail herein. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (8)
1. The traffic jam prediction method based on the GPS data is characterized by comprising the following steps of: s1, a basic reference model is established, a current urban road map is used as a basic reference model, and vehicle history running data in different time periods are imported into the corresponding basic reference model;
s2, importing historical congestion information, dividing a basic reference model into a plurality of place blocks in a certain interval period, calculating to obtain the traffic volume of the vehicles and the running time of the vehicles corresponding to the place blocks based on the historical running data of the vehicles corresponding to the place blocks, and obtaining the average running speed corresponding to the place blocks according to the traffic volume of the vehicles and the running time of the vehicles;
the historical blocking information is associated with a corresponding place block in the basic reference model, the historical blocking information comprises blocking and unblocking, the average running speed corresponding to different place blocks when blocking occurs is obtained, and the place block is marked as a pre-blocking area in the basic reference model when blocking occurs;
s3, correcting the pre-blocking area, namely acquiring the probability A of marking the place block as the pre-blocking area in all basic reference models based on all basic reference models of the interval time period, and updating the pre-blocking area as the blocking area if the probability A is more than 60%;
if the probability A is smaller than 60%, after the interval period, acquiring the traffic condition of a downstream place block corresponding to the place block, wherein the traffic condition of the downstream place block takes the historical blocking information corresponding to the same place block in all the basic reference models as a basic value, if the historical blocking information is that the occupancy rate of the blocking information in the basic value is larger than the occupancy rate of the historical blocking information in the basic value, the traffic condition of the downstream place block is blocked, and if the occupancy rate of the historical blocking information in the basic value is smaller than the occupancy rate of the historical blocking information in the basic value, the traffic condition of the downstream place block is unblocked;
if the traffic condition of the downstream site block is smooth, updating the pre-blocking area into a smooth area;
if the traffic condition of the downstream location block is a jam, repeating the traffic condition acquisition process of the downstream location block, and acquiring the traffic conditions of all the upstream location blocks associated with the downstream location block in the interval time period; if the traffic condition of the upstream location block is that the probability of blocking the total upstream location block is greater than 50%, updating the pre-blocking area into a first deceleration creep area, and if the traffic condition of the upstream location block is that the probability of blocking the total upstream location block is less than 50%, updating the pre-blocking area into a second deceleration creep area;
obtaining a prediction model of the interval time period based on the distribution of the blocking area, the unblocked area, the first deceleration creep area and the second deceleration creep area in the basic reference model;
and S4, uploading the vehicle GPS data to a prediction model, obtaining a prediction model of a corresponding interval time end according to the vehicle GPS data of the current time, obtaining an average running speed corresponding to a place block based on the place block where the vehicle GPS data is located in the prediction model, and obtaining the estimated time based on the average running speed corresponding to each place block.
2. The traffic congestion prediction method based on GPS data according to claim 1, wherein: in S4, geographic position information corresponding to the place block where the current vehicle is located is also obtained, wherein the geographic position information comprises an intersection crossing section, a construction section and a speed limiting section, and the estimated time is prolonged and adjusted based on the geographic position information.
3. The traffic congestion prediction method based on GPS data according to claim 1, wherein: in S1, a basic reference model comprises a special prediction model and a conventional prediction model, wherein the special prediction model is built according to vehicle running data corresponding to different special time periods of one year, and the time periods except the special time periods in one year are conventional time periods; and building a conventional prediction model according to the vehicle driving data corresponding to different time periods of one day in the conventional time period.
4. A traffic congestion prediction method based on GPS data according to any one of claims 1 or 3, wherein: in S3, the specific prediction model is built with reference to only the vehicle history running data and the history blocking information corresponding to the specific time period, and the conventional prediction model is built with reference to only the vehicle history running data and the history blocking information corresponding to the conventional time period.
5. The traffic congestion prediction method based on GPS data according to claim 1, wherein: in S3, the average running speed of the first deceleration creep zone is smaller than the average running speed of the second deceleration creep zone.
6. The traffic congestion prediction method based on GPS data according to claim 1, wherein: in S4, when the vehicle GPS data is located in the first deceleration creep zone or the second deceleration creep zone, the average running speed corresponding to the first deceleration creep zone or the second deceleration creep zone is displayed.
7. A traffic congestion prediction method based on GPS data according to claim 3, wherein: the special time period takes holidays as reference time.
8. A traffic congestion prediction method based on GPS data according to claim 3, wherein: the regular time period comprises a normal time period and a weekend time period, wherein the time period of the regular time period comprises a daily time period, a working time period and a special time period.
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| CN117689298A (en) * | 2024-02-02 | 2024-03-12 | 瑞熙(苏州)智能科技有限公司 | Logistics transportation informatization management method and system based on Beidou navigation |
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| CN117689298A (en) * | 2024-02-02 | 2024-03-12 | 瑞熙(苏州)智能科技有限公司 | Logistics transportation informatization management method and system based on Beidou navigation |
| CN117689298B (en) * | 2024-02-02 | 2024-05-03 | 瑞熙(苏州)智能科技有限公司 | Logistics transportation informatization management method and system based on Beidou navigation |
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