CN114566041A - Multi-source data fusion urban congestion road section multi-mode travel induction method and equipment - Google Patents
Multi-source data fusion urban congestion road section multi-mode travel induction method and equipment Download PDFInfo
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Abstract
The invention relates to a multi-mode travel induction method for a multi-source data fusion urban congested road section, which comprises the steps of determining a to-be-induced shunting road section and a demand traffic volume based on multi-source data; determining inducible shunting traffic based on the determined quasi-inducible shunting road section and the required traffic; determining a multi-mode induced shunting scheme according to the inducible shunting traffic volume; the reward value and the issuing condition of each mode induced distribution scheme are determined. By combining the multi-source data fusion method with the high-grade road condition data, the automatic license plate identification data, the bus card swiping data and the parking lot data, the congested road sections of the urban expressway are identified and clustered, the standard of a differentiated induced shunting scheme is provided for vehicles to be induced, the dynamic balance of supply and demand of the urban expressway and the urban road can be promoted, the personal trip time cost is shortened, and meanwhile the problem of congestion of the urban road in the peak period is remarkably relieved.
Description
Technical Field
The invention relates to the technical field of intelligent traffic systems, in particular to a multi-mode travel induction method and device for a congested road section of a multi-source data fusion city.
Background
Urban roads bear a large number of urban motorized trips. However, under the background of the rapid development of urban social economy and motorized processes, the number of private cars is increasing day by day, the road blockage is serious, especially in the early and late peak periods, the traffic pressure is getting higher and higher, and the traffic problem is bringing about a great obstacle to the development of urban economy. Based on the data of passing vehicles through the card port and the data of high grade road conditions, the traffic jam of the road in the early and late peak periods can be found to be serious, the traffic distribution in time and space is extremely unbalanced, and effective traffic control measures are urgently needed to be taken to relieve the traffic pressure of the urban road in the peak periods, so that the traffic capacity of the built road is utilized to the maximum extent.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the problems in the prior art, and provide a multi-source data fusion urban road congestion section multi-mode travel induction method and equipment, which can promote the dynamic balance of supply and demand of an urban expressway and an urban road, shorten the personal travel time cost and simultaneously remarkably relieve the problem of urban road peak congestion.
In order to solve the technical problem, the invention provides a multi-source data fusion urban congested road section multi-mode travel induction method, which comprises the following steps:
S10: determining a to-be-induced shunting road section and a required traffic volume based on multi-source data;
s20: determining inducible shunting traffic volume based on the determined quasi-inducible shunting road section and the required traffic volume;
s30: determining a multi-mode induced traffic diversion scheme according to the inducible traffic diversion volume;
s40: the reward value and the issuing condition of each mode induced distribution scheme are determined.
In an embodiment of the present invention, in S10, the multi-source data includes the data of highway condition and automatic license plate recognition data, bus card swiping data and parking lot data provided by a certain area.
In one embodiment of the present invention, in S10, determining the planned diversion road section and the demanded traffic volume based on the multi-source data includes:
s11: calculating time-varying vehicle speeds of all road sections of the express way by using the God road condition data, and identifying congested road sections in all road sections of the express way according to the time-varying vehicle speeds;
s12: dividing the types of the congested road sections to obtain peak congested road sections;
s13: identifying a congestion source road section on the peak congestion road section;
s14: obtaining time-varying flow of the congestion source road section through automatic license plate recognition data, obtaining time-varying speed of the congestion source road section through high-grade road condition data, calculating time-varying density of the congestion source road section, and fitting traffic flow characteristics of the congestion source road section by adopting a Greenshirds model to obtain the required traffic flow Q of the congestion source road section to be induced and shunted need＝(Kpeak-Kv =)40 × 40, where QneedRequired traffic volume for the intended induced diversion, KpeakFor peak road density, KV40 is the density corresponding to the speed of the road section of 40km/h, VpeakThe average speed of the road section in the peak period.
In one embodiment of the present invention, in S20, determining an inducible diversion traffic volume based on the determined pseudo-inducible diversion road segment and the demanded traffic volume includes:
s21: calculating inducible shunting traffic volume, and determining the smaller value of the demand traffic volume and the inducible shunting traffic volume to be induced and shunted as final inducible shunting traffic volume, wherein the inducible shunting traffic volume comprises staggered inducible traffic volume and bypassing inducible traffic volume;
s22: and after the final inducible shunting traffic volume is obtained, analyzing the vehicle travel characteristics by using automatic license plate recognition data, and clustering the vehicle travel characteristics by using a k-means + + algorithm to obtain the vehicle to be induced.
In one embodiment of the present invention, in S22, the vehicle travel characteristics include a vehicle travel intensity and a travel dispersion during a peak period, the vehicle travel intensity during the peak period is calculated by the formula,
wherein q isiRepresents the travel intensity, x, of the vehicle i i,jIndicating whether vehicle i was detected by the notch during rush hour on day j; the travel dispersion is calculated by the formula,
wherein s isiRepresenting travel dispersion, t, of vehicle ii,jA time unit representing that vehicle i was detected by the gate during the peak period of day j,
represents the average time unit of vehicle i.
In an embodiment of the present invention, in S30, the multi-mode induced diversion scheme includes a staggered-time trip scheme, a detour trip scheme and a bus trip scheme.
In one embodiment of the present invention, the prize value and the distribution condition for each pattern inducement-diversion scheme are determined at S40 to include a requirement for prize distribution and a method for determining whether the user accepts inducement.
In one embodiment of the present invention, a method for determining whether a user accepts an inducement comprises:
the user needs to punch a card once before starting and after arriving at a destination, and simultaneously obtains the positions and time of the two times of punching the card, if the user selects a wrong trip scheme, whether the user follows an induction scheme is judged according to whether the express way card port has a vehicle data record of the user in a card punching time interval or not; if the user selects the bypassing trip scheme, judging to follow the guidance scheme by judging whether the ground access has the vehicle data record of the user in the card punching time interval; and if the user selects a bus trip scheme, judging whether the user follows an induction scheme or not through bus and subway card swiping data records.
Furthermore, the present invention also provides a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method when executing the program.
Furthermore, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method as described above.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the method, the congested road sections of the urban expressway are identified and clustered by combining the multi-source data fusion method with the high-grade road condition data, the automatic license plate identification data, the bus card swiping data and the parking lot data, the standard of a differentiated induced shunting scheme is provided for vehicles to be induced, the dynamic balance of supply and demand of the urban expressway and the urban road can be promoted, the personal travel time cost is shortened, and meanwhile the problem of congestion of the urban road in the rush hour is remarkably relieved.
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In order that the present disclosure may be more readily and clearly understood, reference will now be made in detail to the present disclosure, examples of which are illustrated in the accompanying drawings.
Fig. 1 is a flow schematic diagram of a multi-source data fusion urban congested road section multi-mode travel guidance method.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Example one
Referring to fig. 1, an embodiment of the present invention provides a multi-mode travel guidance method for a congested road section in a multi-source data fusion city, including the following steps:
s10: determining a to-be-induced shunting road section and a required traffic volume based on multi-source data;
s20: determining inducible shunting traffic volume based on the determined quasi-inducible shunting road section and the required traffic volume;
s30: determining a multi-mode induced traffic diversion scheme according to the inducible traffic diversion volume;
s40: the reward value and the issuing condition of each mode induced distribution scheme are determined.
In the multi-source data fusion urban congested road section multi-mode travel guidance method disclosed by the invention, the multi-source data comprises high-grade road condition data, automatic license plate identification data provided in a certain area, bus card swiping data and parking lot data.
The specific implementation process of the present invention will be described below by taking the highway condition data, the automatic license plate recognition data, the bus card swiping data and the parking lot data as examples.
In the multi-source data fusion urban congestion road section multi-mode travel guidance method disclosed by the invention, for the S10 of the above embodiment, the method includes:
s11: and calculating the time-varying speed of each road section of the express way by using the Gaode road condition data (preferably continuous for one month), and identifying the congested road section in each road section of the express way according to the time-varying speed.
S12: and dividing the types of the congested road sections to obtain peak congested road sections, and obtaining the average speed of each type of express way in the morning and evening at the peak hour, such as the early peak congested road sections and the late peak congested road sections.
S13: and identifying a congestion source road section on the peak congestion road section, wherein the congestion source road section is a congestion starting point and is obviously characterized in that the speed difference with a downstream adjacent road section is large (the speed of the congestion source road section is small at the same time, and the speed of the downstream road section is large).
S14: obtaining the time-varying flow of the congestion source road section through automatic license plate recognition data, obtaining the time-varying speed of the congestion source road section through the high-grade road condition data, calculating the time-varying density of the congestion source road section, and fitting the congestion source road section by adopting a Greenshirds model Obtaining the required traffic volume Q of the simulated induced diversion of the congestion source road sectionneed＝(Kpeak-Kv =40) × 40, wherein QneedRequired traffic volume for the intended induced diversion, KpeakRoad density at peak hours, KV40 is the density corresponding to the speed of the road section of 40km/h, VpeakThe average speed of the road section in the peak period.
In the multi-source data fusion urban congestion road section multi-mode travel guidance method disclosed by the invention, for the S20 of the above embodiment, the method includes:
s21: calculating inducible shunted traffic, wherein the inducible shunted traffic comprises staggered inducible traffic and detour inducible traffic, the detour inducible traffic is redundant traffic capacity of detour roads in a peak time period, the earliest inducible departure time of an early peak (6:00am) is given, and the redundant traffic capacity from 6:00am to the beginning time of a congestion time period (off-peak time period) is calculated, namely the staggered inducible traffic, and the calculation method is CR，P＝C-QR，PWherein, CR,PRedundant capacity for section R at time period p; c is the design traffic capacity of the road section, QR,PThe actual flow rate for the section R during the period p. And determining the smaller value of the demand traffic volume and the inducible shunting traffic volume of the quasi-inducible shunting as the final inducible shunting traffic volume.
S22: after the final inducible shunting traffic volume is obtained, the vehicle travel characteristics are analyzed by using the automatic license plate recognition data, and the vehicle travel characteristics are clustered by using a k-means + + algorithm to obtain the vehicle to be induced.
In the above S22, the vehicle travel characteristics include a vehicle travel intensity and a travel dispersion during a peak period, where the vehicle travel intensity refers to a number of days that the vehicle uses a road within a service range during an analysis period, and the calculation formula of the vehicle travel intensity during the peak period is qi＝∑jxi，j，
Wherein q isiRepresents the travel intensity, x, of the vehicle ii,jIndicating whether vehicle i was detected by the gate during peak hours on day j, and if so, it is 1, otherwise it is 0. And the travel dispersion refers to the standard deviation of the time when the vehicle passes through the road in the service range every day. If the early peak on the same day passes through a plurality of bayonets, the first passing time is the standard. The time recorded at the gate was divided into time units (7:00am-7:10am 1, 7:10-7:20am 2.. 9:20-9:30am 15) in 10 minutes and then the standard deviation of the time units tested was calculated. The travel dispersion is calculated by the formula,
wherein s isiRepresenting travel dispersion, t, of vehicle i i,jA time unit representing that vehicle i was detected by the gate during the peak period of day j,
representing the average time unit of vehicle i.
In the multi-source data fusion urban congestion road section multi-mode travel guidance method disclosed by the invention, for the S30 of the above embodiment, the method includes:
the multi-mode induced shunting scheme determination method comprises the steps of utilizing a Baidu API to achieve travel time estimation of different departure time periods/paths, calculating the ratio of self-driving travel time to travel time achieved by using public transportation, judging parking difficulty, avoiding time, bypassing, generating various travel schemes such as public transportation modes and the like:
1) judging parking difficulty of parking lots around the destination: the calculation of the number of the time-varying parked vehicles mainly aims at obtaining the initial parking amount, and the time-varying parking amount can be obtained only by increasing or decreasing according to the entering and exiting data after the initial parking amount is obtained. Let t1And for the selected initial moment, forward and backward data of a period of time are respectively taken for obtaining the initial parking amount. It is reasonable to assume here that the time interval is sufficiently large that the vehicle is considered to be parked for a period of time less than t1-t0And t2-t1Thus t1The number of vehicles at the time is [ t ]0,t1]Drive in at intervals while [ t0,t1]The number of vehicles not driven out in the time period.
2) Conditions for recommending a public transportation scheme: and utilizing the Baidu API to realize travel time estimation of different departure time periods/routes, and recommending bus travel to the user if the vacant space of a parking lot around the selected destination of the traveler is short or the ratio of the travel time for completing travel by using the public transport to the self-driving travel time is less than or equal to 1.5, wherein the specific bus route and the travel time can be obtained through the Baidu API.
3) Conditions for recommending a wrong time scheme: if the departure time dispersion of the travelers is large and the traffic volume can be induced when there is a mistake in the planned departure time period, a mistake-time scheme is recommended.
4) Conditions for recommended detour schemes: if the departure intensity of the traveler is high and the travel time of the ground road detour path is within an acceptable range (not greater than the travel time of the express way), a detour scheme is recommended, and the specific detour path and the travel time are obtained according to the Baidu API.
5) And (3) recommending different inducing schemes for different users: one traveler can meet the requirements of various recommendation schemes at the same time, at the moment, the user can freely select one of the guidance travel schemes, and as long as the user meets the requirements of bus travel recommendation, the system can default to put the bus travel scheme at the head. And (3) providing a suggested 'optimal scheme' in a plurality of recommendation schemes by combining travel time of different schemes, user vehicle travel characteristics, travel preference survey results and historical scheme selection results.
In the multi-mode travel guidance method for the multi-source data fusion urban congestion road section, for S40 in the above embodiment, the method for determining the reward value and the issuing condition of each mode guidance and distribution scheme including the necessary condition of reward issuing and judging whether the user receives guidance includes:
1) the necessary conditions for the award distribution are as follows: and (4) giving point reward to the users who go out according to the induction scheme so as to achieve the purpose of cultivating reasonable going habits of the users. The essential condition for the reward distribution is that the travelers are identified as having trips in the research scope, and no reward value is distributed for the participants who do not travel the day. The time-staggered scheme reward is determined according to the redundant traffic capacity of different departure time periods and the change degree of the departure time of the user is considered. The larger the redundant capacity and the higher the degree of change, the higher the reward value. The bypass scheme rewards are in a single reward mode, and each reward value is the same.
2) The user needs to punch a card once before starting and after arriving at a destination, and simultaneously obtains the positions and time of the two times of punching the card, if the user selects a wrong trip scheme, whether the user follows an induction scheme is judged according to whether the express way card port has a vehicle data record of the user in a card punching time interval or not; if the user selects the bypassing trip scheme, judging to follow the guidance scheme by judging whether the ground access has the vehicle data record of the user in the card punching time interval; and if the user selects a bus trip scheme, judging whether the user follows an induction scheme or not through bus and subway card swiping data records.
According to the method, the congested road sections of the urban expressway are identified and clustered by combining the multi-source data fusion method with the high-grade road condition data, the automatic license plate identification data, the bus card swiping data and the parking lot data, the standard of a differentiated induced shunting scheme is provided for vehicles to be induced, the dynamic balance of supply and demand of the urban expressway and the urban road can be promoted, the personal travel time cost is shortened, and meanwhile the problem of congestion of the urban road in the rush hour is remarkably relieved.
In the multi-mode travel induction method for the congested road section in the multi-source data fusion city disclosed by the invention, not all roads have inducible value, for express roads, the congested roads are firstly identified and classified by using the high-grade data, the congested source road section is identified and used as a key induced shunting road section, then the time-varying traffic of the road section is obtained by using the truck passing data, and the traffic volume needing to be induced can be calculated by using a Greenshiels model by combining the time-varying speed of the road section. For the ground road, average time-varying speed of each road section of a target road is calculated by using the historical highway condition data of the high-grade road, congested road sections in different directions at the early peak time are identified, car travel OD distribution of a people road and a trunk road is analyzed by combining the data of passing the bus at a bayonet, finally, the running state of the bus on the ground road is analyzed by using the bus swiping data, the speed and time of the bus on the ground are displayed to be not necessarily inferior to the car travel, and the feasibility of ground induction is displayed. After acquiring the traffic volume to be induced and the traffic volume to be induced, the difference between the required traffic volume and the inducible traffic volume needs to be determined, wherein the required traffic volume is the traffic volume with the road density higher than the congestion speed (40km/h) in the peak time period, and the inducible traffic volume is the traffic volume which can be borne by the roads around the peak time period and is induced from the congestion road section or the induced traffic volume which can be borne by the same road section in the off-peak time period. After the inducible traffic volume is determined, induced vehicles need to be identified, the vehicle travel intensity and travel time dispersion degree of the vehicles in the peak time period are analyzed by using the bayonet vehicle passing data to cluster the vehicles passing through the congested road section, the vehicles with high departure time elasticity and high travel ratio are preferably considered as potential induced vehicles, and the induced quantity meets the inducible quantity of the road in time and space. After the guidance road and the guidance vehicle are determined, guidance schemes need to be given, the invention provides the forms of detour, peak staggering and bus recommendation, provides the number of time-varying on-board vehicles in the destination parking lot for car users, and provides various personalized travel schemes for the car users according to the information provided by the users. After the user finishes the trip, the invention sets the necessary conditions for reward distribution and the method for judging whether the user accepts the induction, and the user can develop the habit of inducing the trip by giving a positive incentive mechanism, thereby having certain significance in both personal and social aspects.
The method can be used for some APP programs after the back-end development is completed, for example, the APP programs from Suzhou to Suzhou are used, and when the method is actually used, the user needs to input the origin-destination point and the departure time (the time is 7:00am) of the next day trip in the induction software when participating in the reserved trip. After the user inputs the information, the back end automatically identifies the peak time (6:44am-10:12am) when the travel will pass through the congested section of the expressway, and simultaneously judges whether the number of people participating in incentive induction travel at present reaches the upper limit of induction demand. If the upper limit has been reached, induction is not required. When the upper induction demand limit has not been reached, the back-end queries whether there is a miscompare supply. If the time-staggered supply quantity exists, automatically estimating the travel time between the origin and destination in the time-staggered peak period by utilizing a hectometer API (application program interface), and returning the time period of travel at the time of time-staggered travel, the current remaining number of bookable people and the travel time saving quantity of travel at each time period by the front end; meanwhile, the back end inquires whether a detour path exists through a hundred-degree API, and if the detour path exists, the front end returns the detour path and the travel time.
According to the back-end processing result, the front-end return information comprises: the induction scheme of time staggering and detour, the travel time under each scheme, the remaining number of the reserved persons and the reward points. The back end inquires that the user belongs to type 3 (the departure time dispersion is high), and the travel time of the detour (20 min estimated by Baidu API) is longer than that of the express way, so that the scheme (6:20am-6:40 am) with the shortest interval time with the early peak starting time is returned to the front end as a recommended scheme. The user selects one of the recommended schemes according to the self condition, and the back end of the system automatically updates the induction demand and the induction supply after the user finishes the appointment.
Assuming the user selects a time-staggered scheme with departure times of 6:20am to 6:40am, the Baidu API estimate time is 11 minutes. The user needs to punch a card when starting and arriving, so that the back end can conveniently verify whether the user goes out according to the selection scheme. And the back end judges whether the user goes out according to the selected scheme or not by combining the reserved starting time period and the card punching time. If the conditions of the reward method are met, the small program returns that the induced trip behavior is effective, and meanwhile, the reward points of the trip are added in the personal point database at the rear end.
Corresponding to the above method embodiment, an embodiment of the present invention further provides a computer device, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the multi-source data fusion urban congestion road section multi-mode travel guidance method when executing a computer program.
In the embodiment of the present invention, the processor may be a Central Processing Unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic device, etc.
The processor may call a program stored in the memory, and specifically, the processor may perform operations in an embodiment of the multi-source data fusion urban congestion section multi-mode travel guidance method.
The memory is used for storing one or more programs, which may include program code including computer operating instructions.
Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid state storage device.
Corresponding to the above method embodiment, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for multi-mode travel guidance on a congested section in a multi-source data fusion city is implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. This need not be, nor should it be exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A multi-source data fusion urban congestion road section multi-mode travel guidance method is characterized by comprising the following steps:
s10: determining a to-be-induced shunting road section and a required traffic volume based on multi-source data;
s20: determining inducible shunting traffic volume based on the determined quasi-inducible shunting road section and the required traffic volume;
S30: determining a multi-mode induced shunting scheme according to the inducible shunting traffic volume;
s40: the reward value and the issuing condition of each mode induced distribution scheme are determined.
2. The multi-source data fusion urban congestion road section multi-mode travel induction method according to claim 1, characterized in that: in S10, the multi-source data includes the data of highway condition, the automatic license plate recognition data provided in a certain area, the bus card swiping data, and the parking lot data.
3. The multi-source data fusion urban congestion road section multi-mode travel induction method according to claim 2, wherein in S10, determining a planned-for-induction diversion road section and a demand traffic volume based on multi-source data comprises:
s11: calculating time-varying vehicle speeds of all road sections of the express way by using the God road condition data, and identifying congested road sections in all road sections of the express way according to the time-varying vehicle speeds;
s12: dividing the types of the congested road sections to obtain peak congested road sections;
s13: identifying a congestion source road section on the peak congestion road section;
s14: obtaining time-varying flow of the congestion source road section through automatic license plate recognition data, obtaining time-varying speed of the congestion source road section through high-grade road condition data, calculating time-varying density of the congestion source road section, and fitting traffic flow characteristics of the congestion source road section by adopting a Greenshirds model to obtain the required traffic flow Q of the congestion source road section to be induced and shunted need＝(Kpeak-Kv 40) × 40, wherein QneedRequired traffic volume for the intended induced diversion, KpeakFor peak road density, KV40 is the density corresponding to the speed of the road section of 40km/h, VpeakThe average speed of the road section in the peak period.
4. The multi-source data fusion urban congestion road section multi-mode travel induction method according to claim 1, wherein in S20, determining inducible diversion traffic volume based on the determined planned diversion road section and demand traffic volume comprises:
s21: calculating inducible shunting traffic volume, and determining the smaller value of the demand traffic volume and the inducible shunting traffic volume to be induced and shunted as final inducible shunting traffic volume, wherein the inducible shunting traffic volume comprises staggered inducible traffic volume and bypassing inducible traffic volume;
s22: and after the final inducible shunting traffic volume is obtained, analyzing the vehicle travel characteristics by using automatic license plate recognition data, and clustering the vehicle travel characteristics by using a k-means + + algorithm to obtain the vehicle to be induced.
5. The multi-source data fusion urban congestion road section multi-mode travel induction method according to claim 4, wherein in S22, the vehicle travel characteristics comprise vehicle travel intensity and travel dispersion in peak time period, and the calculation formula of the vehicle travel intensity in the peak time period is q i＝∑jxi，j，
Wherein q isiRepresents the travel intensity, x, of the vehicle ii,jIndicating whether vehicle i was detected by the notch during peak hours on day j; the travel dispersion is calculated according to the formula
Wherein s isiRepresenting travel dispersion, t, of vehicle ii,jA time unit representing that vehicle i was detected by the gate during the peak period of day j,
represents the average time unit of vehicle i.
6. The multi-source data fusion urban congestion road section multi-mode travel induction method according to claim 1, wherein in S30, the multi-mode induced diversion scheme comprises a time-staggered travel scheme, a detour travel scheme and a bus travel scheme.
7. The multi-source data fusion urban congestion road section multi-mode travel induction method according to claim 6, wherein in S40, the reward value and the issuing condition of each mode induction and diversion scheme are determined to include a necessary condition for reward issuing and a method for judging whether the user receives the induction.
8. The multi-source data fusion urban congestion road section multi-mode travel induction method according to claim 7, wherein the method for judging whether the user receives the induction comprises the following steps:
the user needs to punch a card once before starting and after arriving at a destination, and simultaneously obtains the positions and time of the two times of punching the card, if the user selects a wrong trip scheme, whether the user follows an induction scheme is judged according to whether the express way card port has a vehicle data record of the user in a card punching time interval or not; if the user selects the bypassing trip scheme, judging to follow the guidance scheme by judging whether the ground access has the vehicle data record of the user in the card punching time interval; and if the user selects a bus trip scheme, judging whether the user follows an induction scheme or not through bus and subway card swiping data records.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 8 are implemented when the program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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