CN111951557B - Regional short-term traffic flow prediction method and system based on Internet of vehicles big data - Google Patents

Regional short-term traffic flow prediction method and system based on Internet of vehicles big data Download PDF

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CN111951557B
CN111951557B CN202010848663.4A CN202010848663A CN111951557B CN 111951557 B CN111951557 B CN 111951557B CN 202010848663 A CN202010848663 A CN 202010848663A CN 111951557 B CN111951557 B CN 111951557B
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刘剑
秦飞龙
黄兆飞
谢三山
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Yami Technology Guangzhou Co ltd
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Abstract

The invention discloses a regional short-term traffic flow prediction method and a regional short-term traffic flow prediction system based on Internet of vehicles big data, which can be used for predicting the types and the number of various vehicles flowing through a target region, improving the traffic control capability of a park, the resource utilization efficiency of commercial vehicles and providing guidance for production arrangement of merchants, and meanwhile, the predicted amount of unit time is divided into a plurality of time zones, and a time zone method is adopted for prediction, so that the time zone with lower prediction accuracy is diluted, and the prediction accuracy of the unit time is obviously improved.

Description

Regional short-term traffic flow prediction method and system based on Internet of vehicles big data
Technical Field
The invention relates to the technical field of vehicle flow prediction, in particular to a regional short-term traffic flow prediction method and system based on Internet of vehicles big data.
Background
The commercial vehicle networking is through big dipper/GPS, RFID, the sensor, devices such as camera image processing, accomplish the collection of self environment and state information, assemble the various information transmission of vehicle self to server (high in the clouds) through the internet, these have the information of a large amount of vehicle positions and running state, commercial vehicle networking big data has been constituted, utilize big data analysis technique, go to analyze and handle these information through the computer, can extend numerous, valuable application scene, for example can do commercial vehicle's best route planning in the ITS field, timely road conditions information reports, the traffic signal lamp dynamic adjustment on the dangerous goods transport vechicle route and so on.
ITS is an abbreviation of Intelligent Transportation System (Intelligent Transportation System), and traffic flow refers to the number of traffic entities passing through a certain area, a certain road section or a certain lane in a selected time zone. The prediction information of the traffic flow is the key for intelligent traffic control, dynamic traffic state identification and prediction and real-time traffic flow dynamic induction in the ITS. The current traffic flow prediction technical methods mainly comprise two types: the method comprises the following steps of firstly, modeling based on artificial intelligence, namely a machine learning algorithm; the second is a statistical prediction algorithm model, such as moving average, autoregressive moving average, kalman filter, linear regression, and the like.
Because the existing traffic flow prediction technical method mainly focuses on the traffic flow prediction of a certain road section or a certain lane, few technical methods are used for the traffic flow prediction of specific areas, such as large logistics park, mines and other areas. In the traffic environment of such areas, the movement change of the vehicles is strong periodically, and at the same period, some areas have higher traffic flow, while some areas have lower traffic flow, and the imbalance of the traffic flow in these areas can bring serious influence (such as long loading and unloading time) to the traffic control of the park and the resource utilization efficiency of the commercial vehicles. Particularly, in an industrial park, commercial freight vehicles are of multiple types, and the set parking spaces are respectively used for parking, unloading and loading different types of vehicles, (the parking spaces in the park are basically unloaded fully automatically, but the types of trucks are multiple, the trucks of different types are different, and the configured unloading manipulators are inconsistent, so that the trucks of corresponding types need to be parked in corresponding parking spaces for unloading), and the types and the corresponding quantity of the vehicles are difficult to predict in the conventional prediction technology.
Disclosure of Invention
The invention aims to: aiming at the problem that the prediction cannot be carried out on various types of vehicles in a fixed area in the prior art, a regional short-term traffic flow prediction method and a regional short-term traffic flow prediction system based on vehicle networking big data are provided.
In order to achieve the purpose, the invention adopts the technical scheme that:
a regional short-term traffic flow prediction method based on Internet of vehicles big data comprises the following steps:
s100, acquiring continuous X periods of vehicle conditions in a target area based on the Internet of vehicles big data, wherein X is larger than 1;
s200, dividing the vehicle conditions into m types of vehicles to obtain the number of each type of vehicle, wherein m is greater than or equal to 1;
s300, predicting by adopting a statistical matrix method, calculating the average condition of the vehicle types and the corresponding quantity in X periods, and considering the influence of uncontrollable factors on the vehicle to obtain the preliminary prediction result of the vehicle types and the corresponding quantity in the next period; wherein the uncontrollable factors include weather, vehicle failure, loading and unloading equipment failure;
s400, aiming at one or more types of vehicles, forecasting by adopting a time division method according to the preliminary forecasting result of the next period, dividing each period of X periods into a plurality of time zones, calculating the average value of X periods of each time zone, multiplying the ratio of the average value of X periods of each time zone to the average value of X periods of the next period by the preliminary forecasting result of the next period to obtain the preliminary forecasting value of each time zone, multiplying the preliminary forecasting value of each time zone by the corresponding forecasting accuracy, and then summing to obtain the final forecasting result of the number of vehicles in the next period.
A regional short-term traffic flow prediction method and system based on Internet of vehicles big data are provided, the types and the number of multi-type vehicles flowing through a target region are predicted, the campus traffic control capacity and the resource utilization efficiency of commercial vehicles are improved, guidance is provided for production arrangement of merchants, the predicted quantity per unit time is divided into a plurality of time zones, a time zone division method is adopted for prediction, the time zone with low prediction accuracy is diluted, and therefore the prediction accuracy per unit time is obviously improved.
Preferably, the step S200 includes:
the vehicle condition is divided into m types of vehicles, and is represented by a vehicle flow set V:
V＝{V11，V12,…,V1i；V21，V22,…,V2j；…；Vm1，Vm2,…,Vmk}
dividing a period into n time zones, and using time zone combination to represent:
T＝{T1，T2,…,Tn}
type of vehicle and corresponding number of kth cycleQuantity Pk：
Figure BDA0002643962350000031
Wherein the content of the first and second substances,
Figure BDA0002643962350000032
indicated as the number of occurrences of the type of vehicle in section Vi during the k-th cycle Tj.
Preferably, the step S300 includes:
s310, unbiased estimation is carried out to obtain unbiased prediction results of the types and the corresponding number of the vehicles in the (k + 1) th cycle
Figure BDA0002643962350000033
S320, introducing a deviation factor to obtain the vehicle types of the (k + 1) th cycle and the corresponding number of preliminary prediction results; wherein the deviation factor represents the degree of influence of the vehicle by the uncontrollable factor.
Preferably, the step S310 includes:
when unbiased estimation is performed, the unbiased prediction result of the k +1 th cycle is considered
Figure BDA0002643962350000034
Vehicle type equal to the k-th cycle and corresponding number PkAverage situation on X days
Figure BDA0002643962350000035
Namely:
Figure BDA0002643962350000041
preferably, the step S320 includes:
introducing deviation factors
Figure BDA0002643962350000042
Selecting the deviation factor of the (k + 1) th period by a roulette selection method
Figure BDA0002643962350000043
The types and corresponding quantities of the vehicles in the X +1 th cycle are predicted, namely:
Figure BDA0002643962350000044
obtaining the types and the corresponding number of the vehicles in the X +1 th cycle:
Figure BDA0002643962350000045
predicted deviation η of k-th cyclek：
Figure BDA0002643962350000051
Preferably, the step S400 includes:
final prediction of the number of vehicles in the next cycle for one or more types of vehicles, Nk+1：
Figure BDA0002643962350000052
Wherein (N)k+1) ' is the number of vehicles in the preliminary prediction result;
Figure BDA0002643962350000053
is an average value of the number of real vehicles in the Ti time zones of 1 to K cycles,
Figure BDA0002643962350000054
Figure BDA0002643962350000055
the number of vehicles actually collected for the ith time zone of the jth period;
Figure BDA0002643962350000056
is the average value of the number of real vehicles per period from 1 to K periods,
Figure BDA0002643962350000057
Njthe number of vehicles actually acquired in the j period;
Figure BDA0002643962350000058
the average accuracy of the prediction accuracy for the number of vehicles in the Ti time zones of 1 to K cycles,
Figure BDA0002643962350000059
Pi jthe prediction accuracy for the ith time zone of the jth cycle,
Figure BDA00026439623500000510
if Pi j<0, then P is ordered i j＝0。
Preferably, the period is one day, one week, or one month.
A regional short-term traffic flow prediction system based on Internet of vehicles big data comprises at least one processor, a memory and a display, wherein the memory and the display are in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above; the processor is provided with a data input/output interface, is connected with the display, the keyboard, the mouse and the electronic equipment with the USB interface through the data input/output interface and is used for inputting and outputting data; the results of the instructions being executed by the at least one processor are displayed via a display while the results are transmitted to a vehicle network system.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the regional short-term traffic flow prediction method and system based on the Internet of vehicles big data, disclosed by the invention, can be used for predicting the types and the number of various vehicles flowing through a target region, improving the traffic control capability of a park, improving the resource utilization efficiency of commercial vehicles and providing guidance for production arrangement of merchants, and meanwhile, the predicted quantity per unit time is divided into a plurality of time zones, and a time zone with low prediction accuracy is diluted by adopting a time zone division method, so that the prediction accuracy per unit time is obviously improved.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
Fig. 2 is a schematic structural diagram of a system provided by the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Aiming at the problems that commercial freight vehicles are multiple in types and the arranged parking spaces are respectively used for parking, unloading and loading different types of vehicles in an industrial park, (the parking spaces in the park basically adopt full-automatic unloading, but the types of the trucks are multiple, the trucks in different types are different in the configured unloading mechanical arms, so that the trucks in the corresponding types need to be parked in the corresponding parking spaces for unloading) the existing prediction technology is difficult to predict the types of the vehicles and the corresponding quantity of the vehicles, if a certain type of commercial freight vehicle in a certain time section in the park is already parked at all the parking spaces for parking the type of the vehicles, if a merchant still commissions the type of the freight vehicles to distribute goods in the time section, the merchant obviously needs to wait due to the fact that the empty parking spaces for parking and unloading the type of the vehicles are not already available, and the allocation of the goods cannot be completed in time.
Therefore, the regional short-term traffic flow prediction method based on the big data of the internet of vehicles in the embodiment 1, as shown in fig. 1, includes the following steps:
s100, continuously acquiring X days for vehicles entering and leaving the park by a front-section data acquisition system of a big data platform of the Internet of vehicles to obtain vehicle condition data of the X days.
S200, obtaining the vehicle condition in one day in a park, processing the vehicle condition, classifying according to types to obtain m types of vehicles, and obtaining the quantity (i, j, k are equal or unequal) of each type of vehicles, so that the park vehicle flow set can be expressed as:
V＝{V11，V12,…,V1i；V21，V22,…,V2j；…；Vm1，Vm2,…,Vmk}；
dividing a day into n time segments, the set of time segments being represented as: t ═ T { [ T1，T2,…,Tn}；
Statistics were started on the occurrence of the campus vehicles on the first day, as shown in the table below,
Figure BDA0002643962350000071
in the table, the middle number 1 is represented as: at time T1, 1 vehicle of the V1 type was present on the campus. 0 indicates that the time zone is absent for that type of vehicle.
With PkIndicating the situation of the park vehicle on day k,
Figure BDA0002643962350000072
expressed as the number of occurrences of a vehicle of the type section Vi on day k Tj, in a matrix representation of:
Figure BDA0002643962350000081
average for S300X days, calculated as follows:
Figure BDA0002643962350000082
by following the principles of statistics, it is possible to determine,
Figure BDA0002643962350000083
the above is an unbiased estimation, but in practical situations, there is often a bias factor, and therefore, a bias factor is introduced
Figure BDA0002643962350000084
The deviation factor represents the influence degree of uncontrollable factors (weather, vehicle fault, equipment unloading (loading) fault and the like) on a certain type of vehicle, and the deviation factor of the (k + 1) th day is selected and valued by adopting a roulette selection method, including
Figure BDA0002643962350000085
To predict the vehicle condition on day X +1, there are:
Figure BDA0002643962350000086
and (3) combining (2) and (3) to obtain the situation of the vehicles in the park in the X +1 th day:
Figure BDA0002643962350000091
predicted deviation on day k: etakEqual to the vehicle predicted condition minus the square root of the captured vehicle true condition, as follows:
Figure BDA0002643962350000092
the smaller the prediction deviation value is, the higher the prediction accuracy is.
S400, a time division method is further adopted to predict the traffic flow in unit time, specifically, one unit time is divided into a plurality of time zones, the prediction number in the unit time is equal to the prediction number in each time zone multiplied by the accuracy of the time zone prediction, and then superposition and summation are carried out. Because the vehicle flow prediction accuracy rate is greatly different in different sections within a unit time, such as in the morning, noon, evening and the like, the time division method is adopted, so that the prediction accuracy in the whole unit time is improved after the time section with lower prediction accuracy rate is effectively calculated, and the specific method is as follows:
The traffic flow situation of the whole day of the next day is predicted by taking the day as a unit and obtaining the traffic flow situation of the past day,
a day is divided into T time segments,
the predicted number of the next day is summed up { the predicted number obtained by a statistical method × [ (the average value of the actual traffic flow in each time zone in the past/the average value of the actual traffic flow in each time zone in the past) × (the predicted accuracy in each time zone in accordance with the past) ] }
It is formulated as:
Figure BDA0002643962350000101
in the formula, Nk+1For the final predicted number obtained by the time-division method,
(Nk+1) ' obtaining a predicted number by using an algorithm such as a statistical matrix method;
Figure BDA0002643962350000102
is the average value of the number of Ti time zones of 1 to K days,
Figure BDA0002643962350000103
Figure BDA0002643962350000104
the number of vehicles actually collected for the ith time zone on the jth day;
Figure BDA0002643962350000105
in 1 to K days, the average of the actual daily periodThe average value of the number of vehicles,
Figure BDA0002643962350000106
Njthe number of vehicles actually collected on the jth day;
Figure BDA0002643962350000107
the average accuracy of the prediction accuracy for the number of vehicles in the Ti time zones of 1 to K days,
Figure BDA0002643962350000108
Pi jthe prediction accuracy for the ith time segment on day j,
Figure BDA0002643962350000109
Figure BDA00026439623500001010
if Pi j<0, then P is orderedi j＝0。
As shown in FIG. 2, a system (e.g., a computer server with program execution functionality) according to an exemplary embodiment of the present invention includes at least one processor, a power source, and a memory and display in communication with the at least one processor, the at least one processor being in communication with an Internet of vehicles system to obtain vehicle traffic data on a campus; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method disclosed in any one of the preceding embodiments; the processor is provided with a data input/output interface, is connected with the display, the keyboard, the mouse and the electronic equipment with the USB interface through the data input/output interface and is used for inputting and outputting data; the results of the instructions being executed by the at least one processor are displayed via a display. And meanwhile, the result is sent to a vehicle network system, and each user can obtain the traffic flow condition of the park through the vehicle networking system so as to carry out respective arrangement. The power supply is used for supplying electric energy to the system.
Those skilled in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
When the integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as a separate product, it may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
Example 2
The present embodiment is different from embodiment 1 in that, if the traffic flow rate of the next week is predicted in a unit time of one week, the time zone of one week may be divided into 7 days, and the predicted number of the next week is 7 days in total { the predicted number obtained by the statistical method × [ (the average value of the actual traffic flow rates of the previous days per week/the average value of the actual traffic flow rates of the previous days per week) × the accuracy of the traffic flow rate prediction for each day ] }.
Example 3
The present embodiment is different from embodiment 1 in that if the traffic flow in the next month is predicted in units of time of months, the time zone of one month can be divided into four weeks or 30 days.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (3)

1. A regional short-term traffic flow prediction method based on Internet of vehicles big data is characterized by comprising the following steps:
s100, acquiring continuous X periods of vehicle conditions in a target area based on the Internet of vehicles big data, wherein X is larger than 1;
s200, dividing the vehicle conditions into m types of vehicles to obtain the number of each type of vehicle, wherein m is greater than or equal to 1; the vehicle situation is divided into m types of vehicles, which are represented by a vehicle flow set V: v ═ V 11，V12,…,V1i；V21，V22,…,V2j；…；Vm1，Vm2,…,Vmk}
Dividing a period into n time sections, wherein n is greater than or equal to 1 and is represented by the combination of the time sections: t ═ T { [ T1，T2,…,Tn}
The type of vehicle and corresponding number P of the k-th cyclek：
Figure DEST_PATH_DEST_PATH_1
Wherein the content of the first and second substances,
Figure DEST_PATH_DEST_PATH_FDA0003383943560000012
expressed as the number of occurrences of vehicles of type Vi during the kth cycle Tj;
s300, predicting by adopting a statistical matrix method, calculating the average condition of the vehicle types and the corresponding quantity in X periods, and considering the influence of uncontrollable factors on the vehicle to obtain the preliminary prediction result of the vehicle types and the corresponding quantity in the next period; wherein the uncontrollable factors include weather, vehicle failure, loading and unloading equipment failure; the step S300 includes:
s310, unbiased estimation is carried out to obtain unbiased prediction results of the types and the corresponding number of the vehicles in the (k + 1) th cycle
Figure DEST_PATH_DEST_PATH_FDA0003383943560000013
Unbiased prediction result of the k +1 th cycle when unbiased estimation is performed
Figure DEST_PATH_DEST_PATH_FDA0003383943560000014
Vehicle types and corresponding number P equal to the k-th cyclekAverage condition in X days
Figure DEST_PATH_DEST_PATH_FDA0003383943560000015
Namely:
Figure DEST_PATH_DEST_PATH_2
s320, introducing a deviation factor to obtain the vehicle type of the (k + 1) th cycle and a corresponding number of preliminary prediction results; wherein the deviation factor represents the degree of influence of the vehicle by the uncontrollable factors; introducing deviation factors
Figure DEST_PATH_DEST_PATH_FDA0003383943560000022
Selecting the deviation factor of the (k + 1) th period by a roulette selection method
Figure DEST_PATH_DEST_PATH_FDA0003383943560000023
The vehicle types and corresponding quantities of the X +1 th cycle are predicted, namely:
Figure DEST_PATH_DEST_PATH_FDA0003383943560000024
obtaining the vehicle types and corresponding numbers of the X +1 th cycle:
Figure DEST_PATH_DEST_PATH_FDA0003383943560000025
predicted deviation η of k-th cyclek：
Figure DEST_PATH_DEST_PATH_FDA0003383943560000031
S400, aiming at one or more types of vehicles, forecasting by adopting a time division method according to the preliminary forecasting result of the next period, dividing each period of X periods into a plurality of time zones, calculating the average value of X periods of each time zone, multiplying the ratio of the average value of X periods of each time zone to the average value of X periods of the next period by the preliminary forecasting result of the next period to obtain the preliminary forecasting value of each time zone, multiplying the preliminary forecasting value of each time zone by the forecasting accuracy corresponding to each time zone, and then summing to obtain the final forecasting result of the number of vehicles in the next period;
final prediction of the number of vehicles in the next cycle for one or more types of vehicles, Nk+1：
Figure DEST_PATH_DEST_PATH_FDA0003383943560000032
Wherein (N)k+1) ' is the number of vehicles in the preliminary prediction result;
Figure DEST_PATH_DEST_PATH_FDA0003383943560000033
is an average value of the number of real vehicles in the Ti time zones of 1 to K cycles,
Figure DEST_PATH_DEST_PATH_FDA0003383943560000034
Figure DEST_PATH_DEST_PATH_FDA0003383943560000035
the number of vehicles actually collected for the ith time zone of the jth period;
Figure DEST_PATH_DEST_PATH_FDA0003383943560000036
is the average value of the number of real vehicles per period from 1 to K periods,
Figure DEST_PATH_DEST_PATH_FDA0003383943560000037
NjThe number of vehicles actually acquired in the j-th period;
Figure DEST_PATH_DEST_PATH_FDA0003383943560000038
the average accuracy of the prediction accuracy for the number of vehicles in the Ti time zones of 1 to K cycles,
Figure DEST_PATH_DEST_PATH_FDA0003383943560000039
Pi jthe prediction accuracy of the ith time zone of the jth period,
Figure DEST_PATH_DEST_PATH_FDA00033839435600000310
if Pi j<0, then let Pi j＝0。
2. The regional short-term traffic flow prediction method based on Internet of vehicles big data according to claim 1, characterized in that the period is one day, one week or one month.
3. A regional short-term traffic flow prediction system based on internet of vehicles big data, characterized in that the system comprises at least one processor, and a memory and a display which are in communication connection with the at least one processor; the processor is provided with a data input/output interface and is in communication connection with the Internet of vehicles system to acquire vehicle flow data in the park; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 2; the processor is connected with the display, the keyboard, the mouse and the electronic equipment with the USB interface through the data input and output interface and is used for inputting and outputting data; the result of the instruction executed by the at least one processor is displayed through a display, and meanwhile, the result is transmitted to the Internet of vehicles system.
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