CN117315944B - Traffic jam state prediction method - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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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
- G08G1/0133—Traffic data processing for classifying traffic situation
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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
Abstract
The invention provides a prediction method of traffic jam state, and belongs to the technical field of intelligent traffic prediction. The method specifically comprises the following steps: s1, acquiring traffic flow data; s2, preprocessing the data acquired in the step S1; s3, obtaining estimated traffic flow density based on the LWR model, the flow density, the time, the road space, the vehicle flow speed and the congestion degree correction function; s4, carrying out traffic simulation and predicting traffic states based on the CA model according to the estimated traffic density and the vehicle state transition rule; the method solves the technical problems of insufficient accuracy and detail degree of the traffic jam prediction result in the prior art. The method realizes traffic jam prediction, provides an important reference for traffic management, and has more accurate result of jam prediction.
Description
Technical Field
The invention relates to a traffic state prediction method, in particular to a traffic jam state prediction method, and belongs to the technical field of intelligent traffic prediction.
Background
With the acceleration of the urban process and the increase of the vehicle possession, urban traffic congestion becomes a great challenge. Knowing the travel state of a vehicle is critical to urban traffic planning and traffic management.
In the prior art, the traffic state prediction method generally adopts devices such as a traffic sensor, a traffic camera and the like to acquire real-time traffic data. The GPS trajectory data of the vehicle is utilized for analysis and evaluation. By collecting GPS data of the vehicle, information such as the running track, speed, running time and the like of the vehicle is known, so that traffic jam conditions are deduced. And then, by establishing a mathematical model, the running and traffic flow of the vehicle are simulated, so that the traffic state is estimated and the congestion condition is predicted.
The traffic state prediction method in the prior art has some defects:
conventional traffic system assessment and congestion prediction often use simplified models, such as conventional LWR models, which have certain limitations in considering the overall movement of traffic flows and individual behaviors of traffic participants, and are difficult to accurately simulate actual traffic conditions, especially with limited effectiveness in complex traffic scenarios. The prior art lacks the capability of refinement in traffic system assessment and congestion prediction, and cannot fully consider individual behaviors and interactions of traffic participants. This results in insufficient accuracy and detail of the assessment and prediction results, failing to provide accurate traffic state simulation and congestion prediction.
Disclosure of Invention
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. Its purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
In view of the above, the invention provides a traffic congestion state prediction method for solving the technical problems of insufficient accuracy and detail of traffic congestion prediction results in the prior art.
The scheme I is a method for predicting traffic jam state, comprising the following steps:
s1, acquiring traffic flow data;
s2, preprocessing the data acquired in the step S1;
s3, obtaining estimated traffic flow density based on the LWR model, the flow density, the time, the road space, the vehicle flow speed and the congestion degree correction function;
s4, carrying out traffic simulation and predicting traffic states based on the CA model according to the estimated traffic density and the vehicle state transition rule;
the vehicle state transition rules are:
if State (t, x) =normal running and State (t, x+1) =normal running, state (t+1, x) =normal running;
if State (t, x) =normal travel and State (t, x+1) =stop, state (t+1, x) =stop;
if State (t, x) =stop and State (t, x-1) =normal running, state (t+1, x) =normal running;
in other cases, state (t+1, x) =state (t, x), then the vehicle State remains unchanged;
when the state of the vehicle is stopped, predicting traffic state congestion; when the state of the vehicle is normal running, the traffic state is predicted to be not congested.
Preferably, the LWR model formula is:
；
wherein: ρ represents the flow density; t represents time; x represents road space; v represents the vehicle flow rate; f (ρ, v) represents a congestion degree correction function;
the congestion degree correction function is:
；
wherein: ρmax represents the maximum flow density of the road; vmax represents the maximum flow rate of the road;
the partial derivatives for t and x are partially separated and the variables are integrated:
；
finally, the method comprises the following steps:
；
where C (t) is an integration constant, depending on the time t.
Preferably, the CA model formula is:
；
wherein State (t+1, x) represents the State of the vehicle at position x at time t+1; state (t, x) represents the State of the vehicle at position x at time t; state (t, x+1) represents the State of the vehicle at position x+1 at time t; state (t, x-1) represents the State of the vehicle at position x-1 at time t; updatrule represents a state transition rule, and the state of the vehicle at the next moment is determined according to the states of the adjacent vehicles.
Preferably, the method for acquiring traffic flow data comprises the following steps: one or more combinations of traffic sensors, GPS track data, traffic cameras, or mobile communication data.
Preferably, the method for preprocessing the data acquired in the step S1 is as follows: data deduplication and missing value processing.
The second scheme is an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the step of the traffic congestion state prediction method in the first scheme.
A third aspect is a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for predicting a traffic congestion state according to the first aspect.
The beneficial effects of the invention are as follows:
1. the invention provides improvement on the basis of the LWR model, introduces congestion degree correction parameters, better considers the nonlinear characteristics of traffic flow, can describe the overall movement of the traffic flow more accurately through the improved model, and provides more accurate traffic prediction results;
2. the invention provides a Cellular Automaton (CA) model which is used for simulating individual behaviors of traffic participants, defining state transition rules and simulating the running process of vehicles.
3. The invention carries out congestion prediction, and can judge whether traffic is in a congestion state or not by inputting new traffic flow data. The method has important significance for traffic management and decision makers, and can timely take measures to relieve traffic jams and improve traffic efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
fig. 1 is a flow chart of a method of predicting traffic congestion status.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present invention more apparent, the following detailed description of exemplary embodiments of the present invention is provided in conjunction with the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention and not exhaustive of all embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
Example 1: referring to fig. 1, a method for predicting a traffic congestion state according to the present embodiment includes the steps of:
s1, acquiring traffic data;
the traffic data includes: road segment ID, vehicle speed, vehicle location, and time stamp information; referring to the traffic sensor data table of table 1 and the GPS track data table of table 2;
the method for acquiring traffic flow data comprises the following steps: one or more combinations of traffic sensors, GPS track data, traffic cameras, or mobile communication data.
Table 1 traffic sensor data sheet
| Time stamp | Road segment ID | Speed (km/h) |
| 2023-06-08 09:00 | A | 60 |
| 2023-06-08 09:01 | B | 70 |
| 2023-06-08 09:02 | C | NaN |
| 2023-06-08 09:03 | A | 50 |
| 2023-06-08 09:04 | B | 65 |
Table 2 GPS track data table
| Time stamp | Road segment ID | Coordinates of |
| 2023-06-08 09:00 | A | (40.7128, -74.0060) |
| 2023-06-08 09:01 | B | (34.0522, -118.2437) |
| 2023-06-08 09:02 | C | NaN |
| 2023-06-08 09:03 | A | (40.7128, -74.0060) |
| 2023-06-08 09:04 | B | (34.0522, -118.2437) |
S2, preprocessing the data acquired in the step S1; referring to the traffic sensor data table cleaned in table 3 and the GPS track data table cleaned in table 4;
the pretreatment comprises the following steps: data deduplication and missing value processing;
when the data is subjected to the de-duplication treatment, if the data set has no repeated record, the de-duplication operation is not needed;
and processing the missing values according to the requirement, and deleting records containing the missing values or supplementing the missing values.
When the data is subjected to missing value processing, if a speed value (NaN) is missing in the traffic sensor data set, and a coordinate value (NaN) is missing in the GPS track data set, the two records can be deleted;
table 3 traffic sensor data sheet after cleaning
| Time stamp | Road segment ID | Speed (km/h) |
| 2023-06-08 09:00 | A | 60 |
| 2023-06-08 09:01 | B | 70 |
| 2023-06-08 09:03 | A | 50 |
| 2023-06-08 09:04 | B | 65 |
Table 4 GPS track data table after cleaning
| Time stamp | Road segment ID | Coordinates of |
| 2023-06-08 09:00 | A | (40.7128, -74.0060) |
| 2023-06-08 09:01 | B | (34.0522, -118.2437) |
| 2023-06-08 09:03 | A | (40.7128, -74.0060) |
| 2023-06-08 09:04 | B | (34.0522, -118.2437) |
Through the data cleaning process, records containing missing values are deleted, and the data are aligned and matched, so that the data set becomes more regular and consistent.
According to the invention, the LWR model and the CA model are combined, and model parameters are trained and optimized by using training data, so that the prediction of traffic state is realized;
s3, estimating traffic flow density according to actual traffic flow data based on an LWR model;
and the LWR model is used as a macroscopic traffic flow model to describe the overall motion of traffic flow, and congestion degree correction parameters are increased based on the relationship between flow density and flow velocity so as to improve the accuracy of traffic prediction results. Wherein the flow density represents the number of vehicles per unit road length; the flow rate represents the speed of the vehicle through a unit road length;
the formula of the improved LWR model is:
；
wherein: ρ represents the flow density; t represents time; x represents road space; v represents the vehicle flow rate; f (ρ, v) represents a congestion degree correction function;
the time t is a time period for evenly dividing an observation time period for observing the LWR model into time periods; for example, with each minute being a time period, t is 1;
the road space refers to a position on a road;
by adding the congestion degree correction function, the nonlinear traffic flow effect is considered, and the congestion phenomenon is increased along with the increase of the flow density and the decrease of the flow velocity; the congestion degree correction function is:
；
wherein: ρmax represents the maximum flow density of the road; vmax represents the maximum flow rate of the road;
obtaining flow density from an improved LWR modelProcessing partial differential equation of LWR model by using a method of separating variables;
the partial derivatives for t and x are partially separated and the variables are integrated:
；
finally, the method comprises the following steps:
；
wherein C (t) is an integration constant, dependent on time t;
from the data in the data sets of tables 3 and 4, the flow Density (Density) was calculated using LWR model calculations:
in time step 0, the flow density is:
in position 0: number of vehicles 2;
in position 1: number of vehicles 3;
in position 2: number of vehicles 4;
similarly, the flow density for each location is calculated.
Calculating a congestion correction parameter using a congestion correction function:
from the vehicle speed and maximum speed information in the data sets of tables 3 and 4, a congestion degree correction parameter (f (ρ, v)) is calculated.
In time step 0, assuming that the maximum flow density (ρmax) is 10 and the maximum flow rate (v_max) is 30, the formula of the congestion degree correction function is based on:
in position 0: f (ρ, v) =2 (1-2/10) (20/30) =0.8
In position 1: f (ρ, v) =3 (1-3/10) (25/30) =1.35
In position 2: f (ρ, v) =4 (1-4/10) (20/30) =1.07
Similarly, the congestion degree correction parameter is calculated for each position.
S4, carrying out traffic simulation and predicting traffic states based on the CA model according to the estimated traffic flow density and the vehicle state transition rule;
the C model is used as a microscopic traffic flow model for simulating individual behaviors of traffic participants, the CA model divides the road into a series of discrete cells, and each cell represents one traffic participant;
taking the estimated traffic flow density as the input of a CA model, combining with a vehicle state transition rule, carrying out traffic simulation through the CA model, and adjusting parameters of the model according to actual vehicle flow data and a simulation result through continuous iteration to enable the model to achieve a best prediction state, so that the prediction accuracy and the fitting degree of the CA model are improved;
the formula of the CA model is:
；
wherein State (t+1, x) represents the State of the vehicle at position x at time t+1; state (t, x) represents the State of the vehicle at position x at time t; state (t, x+1) represents the State of the vehicle at position x+1 at time t; state (t, x-1) represents the State of the vehicle at position x-1 at time t; updatrule represents a state transition rule, and the state of the vehicle at the next moment is determined according to the states of the adjacent vehicles.
The vehicle state transition rules are:
if State (t, x) =normal running and State (t, x+1) =normal running, state (t+1, x) =normal running;
if State (t, x) =normal travel and State (t, x+1) =stop, state (t+1, x) =stop;
if State (t, x) =stop and State (t, x-1) =normal running, state (t+1, x) =normal running;
in other cases, state (t+1, x) =state (t, x), the vehicle State remains unchanged;
updating the vehicle state according to the state transition rule of the CA model:
vehicle State is updated according to defined State transition rules and the formula State (t+1, x) =update rule (State (t, x), state (t, x+1), state (t, x-1)).
In time step 0, assume that the state transition rule is:
if State (t, x) = "normal running" and State (t, x+1) = "normal running", state (t+1, x) = "normal running";
if State (t, x) = "normal running" and State (t, x+1) = "stop", state (t+1, x) = "stop";
if State (t, x) = "stop" and State (t, x-1) = "normal running", state (t+1, x) = "normal running";
in other cases, state (t+1, x) =state (t, x).
According to these rules, the state of the vehicle at each location is updated according to the state at each location and the state of the adjacent locations.
The mode of combining the LWR model and the CA model to predict whether traffic is congested is as follows:
setting the length of a road as L, distributing vehicles on the road according to the flow density rho, dividing the road into discrete cells for converting the flow density into an initial state, representing one vehicle by each cell, and setting the state of each cell into the initial state;
calculating the total number of vehicles according to the flow density rho and the road length L；
Dividing the road into M cells, wherein each cell represents a vehicle, and setting an initial State (t, x) for each cell; the flow density ρ is defined as the number of vehicles per unit distance, i.e.:
；
wherein N is the total number of vehicles, and L is the road length;
the vehicles are uniformly distributed on the road, each cell represents one vehicle, and given the width of each cell as w, the number of cells M is expressed as:
；
；
due to hope thatSo solve for w:
；
after dividing the road into M discrete cells, each cell having a width w, the initial position of the vehicle i is expressed asThe method comprises the steps of carrying out a first treatment on the surface of the Setting the vehicle state to an initial state: />=initial state. According toAnd (5) performing iteration.
Based on the trained model, new traffic flow data, road section ID and coordinates are input, congestion prediction is carried out, and whether traffic is in a congestion state is judged according to the output of the model.
Example 2: the computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit. And the processor is used for realizing the steps of the traffic jam state prediction method when executing the computer program stored in the memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Example 3: computer-readable storage medium embodiments.
The computer readable storage medium of the present invention may be any form of storage medium that is read by a processor of a computer device, including but not limited to a nonvolatile memory, a volatile memory, a ferroelectric memory, etc., on which a computer program is stored, and when the processor of the computer device reads and executes the computer program stored in the memory, the steps of a traffic congestion state prediction method described above may be implemented.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
Claims (6)
1. A method for predicting traffic congestion status, comprising the steps of:
s1, acquiring traffic flow data;
s2, preprocessing the data acquired in the step S1;
s3, obtaining estimated traffic flow density based on the LWR model, the flow density, the time, the road space, the vehicle flow speed and the congestion degree correction function;
the LWR model formula is:
wherein: ρ represents the flow density; t represents time; x represents road space; v represents the vehicle flow rate; f (ρ, v) represents a congestion degree correction function;
the congestion degree correction function is:
wherein: ρmax represents the maximum flow density of the road; vmax represents the maximum flow rate of the road;
the partial derivatives of t and x are separated and the variables are integrated:
finally, the method comprises the following steps:
wherein C (t) is an integration constant, dependent on time t;
s4, carrying out traffic simulation and predicting traffic states based on the CA model according to the estimated traffic flow density and the vehicle state transition rule;
the vehicle state transition rules are:
if State (t, x) =normal running and State (t, x+1) =normal running, state (t+1, x) =normal running;
if State (t, x) =normal travel and State (t, x+1) =stop, state (t+1, x) =stop;
if State (t, x) =stop and State (t, x-1) =normal running, state (t+1, x) =normal running;
in other cases, state (t+1, x) =state (t, x), then the vehicle State remains unchanged;
when the state of the vehicle is stopped, predicting traffic state congestion; when the State of the vehicle is normal running, predicting that the traffic State is not congested, wherein State (t+1, x) represents the State of the vehicle at the position x at the time t+1; state (t, x) represents the State of the vehicle at position x at time t; state (t, x+1) represents the State of the vehicle at position x+1 at time t; state (t, x-1) represents the State of the vehicle at position x-1 at time t.
2. The method for predicting traffic congestion status according to claim 1, wherein the CA model formula is: state (t+1, x) =update rule (State (t, x), state (t, x+1), state (t, x-1), wherein update rule represents a State transition rule, and the State of the vehicle at the next moment is determined according to the states of the adjacent vehicles.
3. The method for predicting traffic congestion status according to claim 2, wherein the method for acquiring traffic flow data comprises: one or more combinations of traffic sensors, GPS track data, traffic cameras, or mobile communication data.
4. A method for predicting a traffic congestion state according to claim 3, wherein the method for preprocessing the data acquired in S1 is as follows: data deduplication and missing value processing.
5. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a traffic congestion state prediction method according to any one of claims 1-4 when executing the computer program.
6. A computer readable storage medium, having stored thereon a computer program which, when executed by a processor, implements a method of predicting a traffic congestion status according to any one of claims 1 to 4.
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| JP2007179430A (en) * | 2005-12-28 | 2007-07-12 | Matsushita Electric Ind Co Ltd | Traffic flow control system |
| US9633560B1 (en) * | 2016-03-30 | 2017-04-25 | Jason Hao Gao | Traffic prediction and control system for vehicle traffic flows at traffic intersections |
| CN110009257A (en) * | 2019-04-17 | 2019-07-12 | 青岛大学 | Multiple dimensioned variable window cellular Automation Model based on urban traffic blocking sprawling analysis |
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|---|---|---|---|---|
| JP2007179430A (en) * | 2005-12-28 | 2007-07-12 | Matsushita Electric Ind Co Ltd | Traffic flow control system |
| US9633560B1 (en) * | 2016-03-30 | 2017-04-25 | Jason Hao Gao | Traffic prediction and control system for vehicle traffic flows at traffic intersections |
| CN110009257A (en) * | 2019-04-17 | 2019-07-12 | 青岛大学 | Multiple dimensioned variable window cellular Automation Model based on urban traffic blocking sprawling analysis |
Non-Patent Citations (1)
| Title |
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| NS模型的随机特性及其在LWR模型中的应用;邹杰;刘国林;朱凤春;柳林;;微计算机信息(01);第92-93页 * |
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