CN111583631A - Method and device for predicting difficulty of passing through signal control intersection and storage medium - Google Patents
Method and device for predicting difficulty of passing through signal control intersection and storage medium Download PDFInfo
- Publication number
- CN111583631A CN111583631A CN202010297213.0A CN202010297213A CN111583631A CN 111583631 A CN111583631 A CN 111583631A CN 202010297213 A CN202010297213 A CN 202010297213A CN 111583631 A CN111583631 A CN 111583631A
- Authority
- CN
- China
- Prior art keywords
- signal control
- vehicle
- time
- passing
- control intersection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Game Theory and Decision Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method for predicting the difficulty of passing through a signal control intersection, which comprises the following steps: acquiring a driving track of a vehicle on a signal control road section, and acquiring a travel distance of the vehicle in a complete green light time according to the driving track; acquiring the queuing length of the signal control intersection in a certain passing direction, and acquiring a queuing length distribution diagram of the signal control intersection in the certain passing direction according to a time sequence; obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the queuing length distribution diagram; and obtaining the passing difficulty in the certain passing direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time. By the method, the difficulty of passing at the signal control intersection can be accurately predicted by using the mobile position data.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a method and a device for predicting the difficulty of passing at a signal control intersection and a storage medium.
Background
With the rapid increase of the number of motor vehicles in cities, the problems of traffic jam and the like become more serious, and a large amount of travel time can be saved for travelers by reasonably planning a route. The signal control intersections exist in a large number in cities, for example, in Beijing, a plurality of 30000 signal lamps are arranged on roads in the Beijing city, and each travel track relates to 12 signal lamps on average. The congestion and the overlong queuing length of the signal control intersection occupy a large amount of travel time, so the difficulty of passing of the signal control intersection is an important factor to be referred to for path planning.
In the prior art, most of the signal control intersections are monitored through hardware devices such as cameras and coils, but when a vehicle passes through an urban signal control intersection, due to the influence of signal lamp phase switching, the vehicle is switched between two running states of parking waiting and driving advancing, the time and the position of the vehicle reaching the intersection are different, and the difficulty of passing of different vehicles at the signal lamp intersection is obviously different. The calculation and description of the difficulty of passing at the signal control intersection have great difficulty.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for predicting the difficulty of passing through a signal control intersection and a storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In some optional embodiments, a method for predicting difficulty of passing at a signal control intersection comprises:
acquiring a driving track of a vehicle on a signal control road section, and acquiring a travel distance of the vehicle in a complete green light time according to the driving track;
acquiring the queuing length of the signal control intersection in a certain passing direction, and acquiring a queuing length distribution diagram of the signal control intersection in the certain passing direction according to a time sequence;
obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the queuing length distribution diagram;
and obtaining the passing difficulty in the certain passing direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time.
Further, obtaining the travel distance of the complete green time of the vehicle according to the driving track comprises the following steps:
obtaining a speed and time distribution map of the vehicle according to the running track;
obtaining a set of travelling distances of a plurality of complete green light time of the vehicle according to the distribution diagram of the speed and the time;
and clustering the set of the travelling distances of the vehicles in the complete green light time through a clustering algorithm to obtain the travelling distance of the complete green light time of the vehicles.
Further, according to the queuing length distribution diagram, a difference method and a fitting method are adopted to obtain a maximum value and a minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection, and the method comprises the following steps:
obtaining a maximum value and a minimum value of the queuing length of the signal control intersection in a certain passing direction by adopting an envelope extraction method according to the queuing length distribution diagram;
and obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the maximum value and the minimum value of the queuing length in the certain passing direction of the signal control intersection.
Further, according to the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time, the passing difficulty in the certain passing direction at the next moment of the signal control intersection is obtained, and the method comprises the following steps:
obtaining a maximum value and a minimum value of the passing difficulty degree in a certain passing direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time;
and obtaining the passing difficulty degree in a certain passing direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the passing difficulty degree in the certain passing direction at the next moment of the signal control intersection.
Further, still include:
and obtaining the time of the vehicle passing through the signal control intersection according to the traveling distance of the complete green light time of the vehicle, the distance from the vehicle parking position to the signal control intersection, the first parking time of the vehicle, the red light approximate time and the street lamp approximate time.
Further, still include:
and obtaining the queuing length of the signal control intersection in a certain passing direction at the next moment according to the maximum value and the minimum value of the queuing length of the signal control intersection in the certain passing direction at the next moment.
Further, still include:
acquiring the passing states of vehicles turning in different directions in each passing direction of the signal control intersection, wherein the passing states in each passing direction comprise the maximum value and the minimum value of the queuing length in each passing direction and the maximum value and the minimum value of the passing difficulty in each passing direction;
and when the passing state of the vehicle does not accord with the passing state in the current passing direction and accords with the passing states in other directions, determining that the vehicle has violation.
In some optional embodiments, a device for predicting difficulty of passing through a signal-controlled intersection comprises:
the first acquisition module is used for acquiring a driving track of a vehicle on a signal control road section and acquiring a travel distance of the vehicle in the complete green light time according to the driving track;
the second acquisition module is used for acquiring the queuing length of the signal control intersection in a certain passing direction and acquiring a queuing length distribution diagram of the signal control intersection in the certain passing direction according to the time sequence;
the queuing length prediction module is used for obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the queuing length distribution diagram;
and the traffic difficulty degree prediction module is used for obtaining the traffic difficulty degree in a certain traffic direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain traffic direction at the next moment of the signal control intersection, the travelling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time.
In some optional embodiments, a system for predicting ease of traffic at a signal-controlled intersection comprises:
one or more processors, storage devices storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement a method for predicting difficulty of passing at a signal control intersection provided by the above embodiments.
In some optional embodiments, a computer readable storage medium, on which a computer program is stored, is provided, where the computer program, when executed by a processor, implements a method for predicting difficulty of passing at a signal control intersection provided in the above embodiments.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the invention provides a method for predicting the traffic difficulty of a signal control intersection, which can predict the queuing length of the signal control intersection based on mobile position data, further accurately predict the difficulty of passing the intersection, does not need hardware facilities such as video monitoring and the like, and has simple and convenient calculation and wide coverage range. According to the driving track of the vehicle, the illegal driving behavior of the vehicle can be detected, the intersection order of the current signal control intersection can be described according to the number of illegal vehicles, and technical support is provided for traffic supervision and maintenance of related personnel.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart illustrating a method for predicting difficulty in passing at a traffic-controlled intersection according to an exemplary embodiment;
FIG. 2 is a plot of speed versus time for a vehicle, according to an exemplary embodiment;
FIG. 3 is a diagram illustrating a queue length distribution of a vehicle in a traffic direction at a signal-controlled intersection according to an exemplary embodiment;
FIG. 4 is a schematic diagram illustrating an arrangement of a traffic difficulty prediction device for a traffic-controlled intersection according to an exemplary embodiment;
fig. 5 is a schematic structural diagram illustrating a system for predicting difficulty in traffic at a traffic-controlled intersection according to an exemplary embodiment.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
FIG. 1 is a flow chart illustrating a method for predicting difficulty in passing at a traffic-controlled intersection according to an exemplary embodiment;
in some optional embodiments, a method for predicting difficulty of passing at a signal control intersection comprises:
s101, acquiring a driving track of a vehicle on a signal control road section, and acquiring a traveling distance of the vehicle in a complete green light time according to the driving track;
in the embodiment of the disclosure, the difficulty of the vehicle passing through the signal control intersection is predicted based on the running track of the vehicle, and hardware facilities such as video monitoring and the like are not needed. Specifically, firstly, the running track data of the sample vehicle on the information control road section is obtained, wherein the track data of the vehicle comprises speed information, time information, direction information, geographical position information and the like of the vehicle, and the running track of the vehicle on the information control road section can be obtained through a Beidou satellite navigation system, a GPS positioning system and the like installed on the vehicle.
According to the driving track of the vehicle on the signal control road section, a speed and time distribution diagram of the vehicle can be drawn, fig. 2 is a speed and time distribution diagram of the vehicle according to an exemplary embodiment, as shown in fig. 2, the abscissa represents the time of the vehicle, the ordinate represents the speed of the vehicle, and the time of recording the vehicle driving away from the signal control intersection is tleaveWherein t is0To t1、t2To t3、t4To t5Is a red light stopped state, t1To t2、t3To t4、t5To tleaveIs the green light travel state.
From the velocity versus time profile, a set of travel distances for several complete green time of the vehicle can be derived. In particular, L is definedadvanceIndicating the distance a vehicle travels during the next green time at the signal controlled intersection, ltIndicating the location of a vehicle at time t. Referring to FIG. 2, then
The last distance traveled was discarded, since the last drive was not necessarily a complete green time. Definition of phiadvanceSet representing travel distances of a plurality of samples, then
And clustering the set of the travelling distances of the vehicles in the complete green light time through a clustering algorithm to obtain the travelling distance of the complete green light time of the vehicles. In particular, for the set phiadvanceGaussian mixture clustering is carried out, but the Gaussian mixture clustering is not limited, other clustering algorithms such as a K-means clustering algorithm and a DBSCAN clustering algorithm can be used, and the travel distance L of the complete green time of the vehicle can be obtainedgreen。
By the method, the travel distance L of the complete green light time of the vehicle is obtained based on the track data of the vehiclegreen。
S102, obtaining the queuing length of the signal control intersection in a certain passing direction, and obtaining a queuing length distribution diagram of the signal control intersection in the certain passing direction according to a time sequence;
in particular, define
Represents tiThe queuing length of the intersection in a certain passing direction is controlled by the time signal, the data of different times are arranged according to the time, and the time sequence phi of the queuing length can be obtainedL_orderWherein, in the step (A),
the queue length will also exhibit a certain degree of fluctuation due to the phase change of the signal lights, and fig. 3 is a diagram showing a queue length distribution of a vehicle in a traffic direction at a traffic-controlled intersection according to an exemplary embodiment, wherein the middle solid line represents the queue length phi at each timeL_order。
S103, obtaining a maximum value and a minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the queuing length distribution diagram;
specifically, according to a queuing length distribution diagram, a maximum value and a minimum value of the queuing length of the signal control intersection in a certain passing direction are obtained by adopting an envelope extraction method;
and obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the maximum value and the minimum value of the queuing length in the certain passing direction of the signal control intersection.
Firstly, according to the queuing length distribution diagram, after abnormal data is removed, the maximum value L of the queuing length can be obtained by utilizing an envelope extraction methodupMinimum value of queuing length LdownAs shown in FIG. 3, the dotted line above the solid line is the maximum L of the queue lengthupThe dotted line below the solid line is the minimum value L of the queue lengthdown。
By combining the curve data in fig. 3, the maximum value of the queuing length in a certain passing direction at the next moment can be obtained by adopting a difference method and a fitting method
Minimum value
By the method, the maximum value of the queuing length in the direction at the next moment of the vehicle can be predicted
Minimum value
And S104, obtaining the passing difficulty degree of the signal control intersection in a certain passing direction at the next moment according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment, the traveling distance of the complete green light time of the vehicle, the first-time parking time of the vehicle, the red light approximate time and the green light approximate time.
Specifically, the maximum value and the minimum value of the difficulty of passing in a certain passing direction at the next moment of the signal control intersection are obtained according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time;
wherein, define
A maximum value representing the difficulty of passing at the next time in a certain passing direction,
a minimum value indicating the difficulty of passing at the next time in a certain passing direction.
Wherein, t0Representing the time of first stop, with a certain randomness, tredIndicating the red light approximate time, tgreenRepresenting the approximate time of the green light, which can be calculated according to the prior art, LgreenA travel distance representing a complete green time of the vehicle,
a maximum value representing the queue length in a traffic direction at the next moment,
a minimum value representing the length of the queue for a certain traffic direction at the next moment.
And obtaining the passing difficulty degree in a certain passing direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the passing difficulty degree in the certain passing direction at the next moment of the signal control intersection.
Specifically, t is definednextThe difficulty of passing in a certain passing direction at the next moment of the signal control intersection is represented by:
by the method, the queuing length of the signal control intersection can be predicted based on the mobile position data, so that the difficulty of passing the intersection can be accurately predicted, hardware facilities such as video monitoring and the like are not needed, the calculation is simple and convenient, and the coverage range is wide.
Further, obtaining the travel distance of the complete green time of the vehicle according to the driving track comprises the following steps:
obtaining a speed and time distribution map of the vehicle according to the running track;
obtaining a set of travelling distances of a plurality of complete green light time of the vehicle according to the distribution diagram of the speed and the time;
and clustering the set of the travelling distances of the vehicles in the complete green light time through a clustering algorithm to obtain the travelling distance of the complete green light time of the vehicles.
Further, according to the queuing length distribution diagram, a difference method and a fitting method are adopted to obtain a maximum value and a minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection, and the method comprises the following steps:
obtaining a maximum value and a minimum value of the queuing length of the signal control intersection in a certain passing direction by adopting an envelope extraction method according to the queuing length distribution diagram;
and obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the maximum value and the minimum value of the queuing length in the certain passing direction of the signal control intersection.
Further, according to the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time, the passing difficulty in the certain passing direction at the next moment of the signal control intersection is obtained, and the method comprises the following steps:
obtaining a maximum value and a minimum value of the passing difficulty degree in a certain passing direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time;
and obtaining the passing difficulty degree in a certain passing direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the passing difficulty degree in the certain passing direction at the next moment of the signal control intersection.
Further, still include:
and obtaining the time of the vehicle passing through the signal control intersection according to the traveling distance of the complete green light time of the vehicle, the distance from the vehicle parking position to the signal control intersection, the first parking time of the vehicle, the red light approximate time and the street lamp approximate time.
Specifically, L is defined to represent the distance from a parking location of a vehicle to a signal-controlled intersection, and t is defined to represent the time when the vehicle is expected to pass through the signal-controlled intersection.
t＝t0+int(L/Lgreen)*(tred+tgreen)+(L-int(L/Lgreen)*Lgreen)/Lgreen*tgreenWherein, t0Representing the time of first stop, with a certain randomness, tredIndicating the red light approximate time, tgreenRepresenting the approximate time of the green light, which can be calculated according to the prior art, LgreenA travel distance representing a complete green time of the vehicle.
By the method, the time of the vehicle passing through the signal control intersection can be calculated according to the distance from the vehicle parking position to the signal control intersection, and the user experience is improved.
Further, still include:
and obtaining the queuing length of the signal control intersection in a certain passing direction at the next moment according to the maximum value and the minimum value of the queuing length of the signal control intersection in the certain passing direction at the next moment.
In particular, L is definednextIndicating a next moment in time a particular passThe queuing length in the row direction is as follows:
by the method, the queuing length in the certain passing direction at the next moment can be calculated according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment.
Further, still include:
acquiring the passing states of vehicles turning in different directions in each passing direction of the signal control intersection, wherein the passing states in each passing direction comprise the maximum value and the minimum value of the queuing length in each passing direction and the maximum value and the minimum value of the passing difficulty in each passing direction;
and when the passing state of the vehicle does not accord with the passing state in the current passing direction and accords with the passing states in other directions, determining that the vehicle has violation.
Generally, a plurality of traffic directions exist at a signal control intersection, when congestion occurs in a certain direction or the traffic time is long, a violation behavior similar to left-turn in a straight-going direction occurs, the violation behavior not only brings influence and harm to traffic, but also influences the accuracy of calculating the difficulty of traffic, and therefore, the method is significant in monitoring abnormal data.
In particular, define
The traffic states in different directions are represented, and the traffic states in the respective directions can be respectively obtained by using sample data of different turning directions according to the method provided by the embodiment by taking the left turning direction and the straight moving direction as an example.
Detecting samples which pass through the communication control intersection, taking detection of left-turning vehicles as an example: definition of L0，t0Respectively indicating the queuing distance of the vehicle and the passing from the parking position to the signal control intersectionIn the meantime, the following two conditions should be satisfied by the illegal driving:
(1) the difference of the queuing distance and the passing time is larger than that of the current steering traffic state:
wherein the content of the first and second substances,1、2the threshold value representing the data difference is large, and can be set by a user.
(2) Close to other diverted traffic states, the queuing distance difference is small or the transit time difference is small:
wherein the content of the first and second substances,3、4the threshold value indicating that the data difference is small can be set by the user.
By the method, the abnormal data are detected, and the abnormal data are provided when the traffic difficulty is calculated, so that the accuracy of the calculation result can be improved.
Meanwhile, the number of vehicles which are illegally driven can be accumulated, so that the crossing order of the signal control crossing is judged. Defining count to represent the total number of vehicles in the current road illegal driving within a certain time, and recording when the detected vehicle simultaneously meets the two conditions: count + 1;
defining order to express the crossing order of the signal control crossing, and dividing the crossing order into three conditions of no violation, slight disorder and serious disorder according to the size of the count value.
When the intersection condition is that no violation exists, the count is 0; when the intersection situation is slightly chaotic, 0< count < &; when the intersection situation is severely chaotic, count > &. Wherein, & represents the threshold of slight confusion and severe confusion, which can be set by the user.
By the method, the illegal driving behaviors of the vehicles can be detected according to the driving tracks of the vehicles, the intersection order of the current signal control intersection can be described according to the number of the illegal vehicles, and technical support is provided for traffic supervision and maintenance of related personnel.
FIG. 4 is a schematic diagram illustrating an arrangement of a traffic difficulty prediction device for a traffic-controlled intersection according to an exemplary embodiment;
in some optional embodiments, a device for predicting difficulty of passing through a signal-controlled intersection comprises:
s401, a first obtaining module, configured to obtain a driving track of a vehicle on a traffic control road section, and obtain a travel distance of the vehicle in a complete green time according to the driving track;
s402, a second obtaining module, configured to obtain a queuing length of the signal-controlled intersection in a certain passing direction, and obtain a queuing length distribution map of the signal-controlled intersection in the certain passing direction according to a time sequence;
s403, a queuing length prediction module used for obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the queuing length distribution diagram;
s404, a traffic difficulty prediction module used for obtaining the traffic difficulty in a certain traffic direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain traffic direction at the next moment of the signal control intersection, the advancing distance of the complete green light time of the vehicle, the first stop time of the vehicle, the red light approximate time and the green light approximate time.
Fig. 5 is a schematic structural diagram illustrating a system for predicting difficulty in traffic at a traffic-controlled intersection according to an exemplary embodiment.
In some embodiments, a system for predicting ease of traffic at a traffic-controlled intersection includes a processor 51 and a memory 52 storing program instructions, and may further include a communication interface 53 and a bus 54. The processor 51, the communication interface 53 and the memory 52 may communicate with each other through the bus 54. The communication interface 53 may be used for information transfer. The processor 51 can call the logic instructions in the memory 52 to execute the method for predicting the difficulty of traffic at the traffic intersection provided by the above-mentioned embodiment.
Furthermore, the logic instructions in the memory 52 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product.
The memory 52 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 51 executes the functional application and data processing by executing the software program, instructions and modules stored in the memory 52, that is, implements the method in the above-described method embodiments.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-volatile memory.
The embodiment of the disclosure provides a computer readable medium, on which computer readable instructions are stored, and the computer readable instructions can be executed by a processor to implement the method for predicting the difficulty of passing at a signal control intersection provided by the above embodiment.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for predicting the difficulty of passing at a signal control intersection is characterized by comprising the following steps:
acquiring a running track of a vehicle on a signal control road section, and acquiring a travel distance of the vehicle in a complete green light time according to the running track;
acquiring the queuing length of the signal control intersection in a certain passing direction, and acquiring a queuing length distribution diagram of the signal control intersection in the certain passing direction according to a time sequence;
obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the queuing length distribution diagram;
and obtaining the passing difficulty degree of the signal control intersection in a certain passing direction at the next moment according to the maximum value and the minimum value of the queuing length in the certain passing direction at the next moment, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time.
2. The method of claim 1, wherein said deriving a travel distance for a full green time of a vehicle from said travel trajectory comprises:
obtaining a speed and time distribution map of the vehicle according to the running track;
obtaining a set of travelling distances of a plurality of complete green light time of the vehicle according to the distribution diagram of the speed and the time;
and clustering the set of the travelling distances of the vehicle in the plurality of complete green light times through a clustering algorithm to obtain the travelling distance of the complete green light time of the vehicle.
3. The method of claim 1, wherein obtaining the maximum value and the minimum value of the queuing length in a traffic direction at a next moment of the signal-controlled intersection according to the queuing length distribution map by using a difference method and a fitting method comprises:
obtaining a maximum value and a minimum value of the queuing length of the signal control intersection in a certain passing direction by adopting an envelope extraction method according to the queuing length distribution diagram;
and obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the maximum value and the minimum value of the queuing length in the certain passing direction of the signal control intersection.
4. The method of claim 1, wherein obtaining the difficulty of passing in a traffic direction at a next time of the signal control intersection according to the maximum value and the minimum value of the queuing length in the traffic direction at the next time of the signal control intersection, the travel distance of the complete green light time of the vehicle, the first stop time of the vehicle, the approximate red light time and the approximate green light time comprises:
obtaining the maximum value and the minimum value of the traffic difficulty degree in a certain traffic direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain traffic direction at the next moment of the signal control intersection, the traveling distance of the complete green light time of the vehicle, the first parking time of the vehicle, the red light approximate time and the green light approximate time;
and obtaining the passing difficulty degree of the signal control intersection in a certain passing direction at the next moment according to the maximum value and the minimum value of the passing difficulty degree of the signal control intersection in a certain passing direction at the next moment.
5. The method of claim 1, further comprising:
and obtaining the time of the vehicle passing through the signal control intersection according to the travelling distance of the complete green light time of the vehicle, the distance from the vehicle parking position to the signal control intersection, the first parking time of the vehicle, the red light approximate time and the street lamp approximate time.
6. The method of claim 1, further comprising:
and obtaining the queuing length of the signal control intersection in a certain passing direction at the next moment according to the maximum value and the minimum value of the queuing length of the signal control intersection in the certain passing direction at the next moment.
7. The method of claim 1, further comprising:
acquiring the passing states of vehicles turning in different directions in each passing direction of the signal control intersection, wherein the passing states in each passing direction comprise the maximum value and the minimum value of the queuing length in each passing direction and the maximum value and the minimum value of the passing difficulty in each passing direction;
when the passing state of the vehicle does not accord with the passing state in the current passing direction and accords with the passing states in other directions, determining that the vehicle has violation behaviors.
8. A device for predicting difficulty of traffic at a signal control intersection is characterized by comprising:
the first acquisition module is used for acquiring a running track of a vehicle on a signal control road section and acquiring the traveling distance of the vehicle in the complete green light time according to the running track;
the second acquisition module is used for acquiring the queuing length of the signal control intersection in a certain passing direction and acquiring a queuing length distribution diagram of the signal control intersection in the certain passing direction according to a time sequence;
the queuing length prediction module is used for obtaining the maximum value and the minimum value of the queuing length in a certain passing direction at the next moment of the signal control intersection by adopting a difference method and a fitting method according to the queuing length distribution diagram;
and the traffic difficulty degree prediction module is used for obtaining the traffic difficulty degree in a certain traffic direction at the next moment of the signal control intersection according to the maximum value and the minimum value of the queuing length in the certain traffic direction at the next moment of the signal control intersection, the travelling distance of the complete green light time of the vehicle, the first stop time of the vehicle, the red light approximate time and the green light approximate time.
9. A system for predicting difficulty of traffic at a signal control intersection comprises:
one or more processors, storage devices storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement a method for predicting difficulty of passing at a controlled intersection according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements a method for predicting difficulty of passing at a signal-controlled intersection according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010297213.0A CN111583631B (en) | 2020-04-15 | 2020-04-15 | Method and device for predicting difficulty of passing through signal control intersection and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010297213.0A CN111583631B (en) | 2020-04-15 | 2020-04-15 | Method and device for predicting difficulty of passing through signal control intersection and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111583631A true CN111583631A (en) | 2020-08-25 |
| CN111583631B CN111583631B (en) | 2021-06-15 |
Family
ID=72111538
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010297213.0A Active CN111583631B (en) | 2020-04-15 | 2020-04-15 | Method and device for predicting difficulty of passing through signal control intersection and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111583631B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102376162A (en) * | 2011-09-28 | 2012-03-14 | 东南大学 | Method for setting optimal distance between curb parking lot of intersection exit lane and intersection |
| KR101356660B1 (en) * | 2012-10-30 | 2014-02-04 | 대보정보통신 주식회사 | Interval speed measurement method |
| US9076332B2 (en) * | 2006-10-19 | 2015-07-07 | Makor Issues And Rights Ltd. | Multi-objective optimization for real time traffic light control and navigation systems for urban saturated networks |
| CN107248297A (en) * | 2017-08-01 | 2017-10-13 | 东南大学 | A kind of rasterizing signal phase duration calculation method in intersection under bus or train route cooperative surroundings |
| CN109637127A (en) * | 2018-12-06 | 2019-04-16 | 北京掌行通信息技术有限公司 | A kind of determination method and system of signal lamp approximation duration |
-
2020
- 2020-04-15 CN CN202010297213.0A patent/CN111583631B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9076332B2 (en) * | 2006-10-19 | 2015-07-07 | Makor Issues And Rights Ltd. | Multi-objective optimization for real time traffic light control and navigation systems for urban saturated networks |
| CN102376162A (en) * | 2011-09-28 | 2012-03-14 | 东南大学 | Method for setting optimal distance between curb parking lot of intersection exit lane and intersection |
| KR101356660B1 (en) * | 2012-10-30 | 2014-02-04 | 대보정보통신 주식회사 | Interval speed measurement method |
| CN107248297A (en) * | 2017-08-01 | 2017-10-13 | 东南大学 | A kind of rasterizing signal phase duration calculation method in intersection under bus or train route cooperative surroundings |
| CN109637127A (en) * | 2018-12-06 | 2019-04-16 | 北京掌行通信息技术有限公司 | A kind of determination method and system of signal lamp approximation duration |
Non-Patent Citations (1)
| Title |
|---|
| 张诗姣: "基于轨迹数据的城市道路旅行时间的检测与研究", 《工业控制计算机》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111583631B (en) | 2021-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11747827B2 (en) | Vehicle platoon system control for intersections | |
| JP4809321B2 (en) | Traffic information calculation device, traffic information calculation program, and traffic information calculation method | |
| US7809503B2 (en) | Navigation apparatus | |
| CN1975801B (en) | City traffic information service system | |
| CN110276973B (en) | Automatic intersection traffic rule identification method | |
| CN105023428A (en) | Road condition information assessment method and apparatus | |
| CN103727946B (en) | A kind of Floating Car map match data preprocessing method and system | |
| CN113347254A (en) | Intelligent traffic control car networking system based on V2X and control method thereof | |
| CN108603763A (en) | Traveling plan generating means, traveling scheduling method and traveling plan generate program | |
| CN109637127B (en) | Method and system for determining approximate duration of signal lamp | |
| CN113327419B (en) | Green wave speed determination method and device, electronic equipment and storage medium | |
| CN112712729B (en) | Method and system for predicting motion trajectory | |
| JP2014241090A (en) | Road state determination system | |
| CN103854504A (en) | Square matrix type road traffic inducing method and system | |
| CN103177585A (en) | Road turning average travel speed calculating method based on floating car data | |
| Saremi et al. | Combining map-based inference and crowd-sensing for detecting traffic regulators | |
| CN113724497A (en) | Method and device for predicting real-time traffic flow of target road | |
| JP5563495B2 (en) | Travel direction prediction apparatus, travel direction prediction method and program at intersection | |
| CN114155734A (en) | Equal lamp information determining method and vehicle information displaying method | |
| CN113570868A (en) | Intersection green light passing rate calculation method, device, equipment and storage medium | |
| CN100472579C (en) | Traffic information calculation device, traffic information calculation method, traffic information display method, and traffic information display device | |
| CN111583631B (en) | Method and device for predicting difficulty of passing through signal control intersection and storage medium | |
| CN112633812A (en) | Track segmentation method, device, equipment and storage medium for freight vehicle | |
| JP2018021887A (en) | Route search device and computer program | |
| CN113380046B (en) | Method and device for identifying vehicle driving state based on fixed line and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |