A method, device, computer equipment, computer-readable storage medium and computer program product for calculating the probability of acceptance of a parking space induction scheme adjusted by driver psychological factors

Abstract

The present application relates to the field of intelligent traffic control technology, and discloses a method, device, computer equipment, computer-readable storage medium and computer program product for calculating the probability of acceptance of a parking space induction scheme regulated by driver psychological factors. The method includes constructing a driver state sequence and a parking lot state sequence; constructing a parking space acceptance model; calculating the urgency of the driver to obtain a parking space during the parking search process according to the driver's current state sequence; inputting the driver's current state sequence and the parking lot's current state sequence into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot; using the initial acceptance, combined with the urgency, to calculate the driver's acceptance of the target parking lot after being affected by the urgency of obtaining a parking space, which is used to assist in generating a parking induction scheme. The present application has the effect of accurately calculating the driver's acceptance of the parking lot and assisting in generating a more effective parking space induction scheme.

Description

Technical Field

[0001] The present application relates to the field of intelligent traffic control technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for calculating the acceptance probability of a parking space induction scheme regulated by driver psychological factors. Background Art

[0002] The rapid development of the domestic economy has led to a surge in the number of motor vehicles. The configuration of existing parking facilities can no longer meet the surge in parking demand, especially in core commercial areas with a high degree of development. The mismatch between parking supply and demand and the uneven distribution have led to a large number of invalid parking-seeking traffic flows in the urban transportation network, which has not only aggravated the traffic congestion in the core commercial areas, but also increased the parking costs of drivers. Studies have shown that effective parking guidance can alleviate the phenomenon of "difficult parking" and reduce invalid traffic volume.

[0003] With the development of urban economy, people's living standards have also been greatly improved. Drivers' sensitivity to objective factors such as parking prices has decreased. In different parking scenarios, the driver's psychological perception factors may play different roles. In some scenarios, the driver's psychological perception factors will become the decision-making factors of the driver's parking choice. Studies have shown that there is a nonlinear effect between individual traffic behavior and its influencing factors. However, the existing parking space induction schemes are mostly based on static parking demand survey data to model individual behavior, and a large number of studies believe that objective factors such as parking prices and parking time are the determining factors of drivers' parking selection behavior. They simply substitute one of the many objective factors directly into existing theories or algorithms, such as prospect theory, GBDT (Gradient Boosting Decision Tree) model in deep learning, etc., calculate the utility value as the decision value of the driver's parking selection behavior, and obtain the basis for determining the parking induction scheme. The relationship between psychological perception factors and driver's parking selection behavior is rarely considered, and the consideration of driver's psychological perception factors is lacking.

[0004] With respect to the above-mentioned related technologies, the inventors have found that the existing method for determining parking space induction schemes has the problem of low accuracy of the generated parking space induction schemes due to the lack of consideration of the driver's psychological perception factor, which is a dynamic parking demand. Summary of the invention

[0005] In order to improve the accuracy of parking space induction schemes, the present application provides a method, apparatus, computer device, computer-readable storage medium and computer program product for calculating the acceptance probability of parking space induction schemes adjusted by driver psychological factors.

[0006] In a first aspect, the present application provides a method for calculating the acceptance probability of a parking space induction scheme adjusted by the driver's psychological factors.

[0007] This application is achieved through the following technical solutions:

[0008] A method for calculating the acceptance probability of a parking space induction scheme regulated by driver psychological factors comprises the following steps:

[0009] Based on the pre-constructed driver historical state sequence and the pre-constructed parking lot historical state sequence, determine the parking lot's charging price at the historical moment, the time required for the driver to park at the parking lot and reach the destination, the driver's familiarity with the parking lot at the historical moment, and the convenience of the parking facilities at the parking lot at the historical moment, so as to construct a parking space acceptance model;

[0010] Obtain the driver's current state and preference information and the current parking lot information, and construct the driver's current state sequence and the parking lot's current state sequence respectively;

[0011] Determine, according to the driver's current state sequence, a conversion function of the deviation between the driver's travel destination and the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination, so as to calculate the urgency of the driver to obtain a parking space during the parking search process;

[0012] Inputting the driver's current state sequence and the parking lot's current state sequence into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot;

[0013] The initial acceptance is combined with the urgency to calculate the driver's acceptance of the target parking lot after being influenced by the urgency of obtaining a parking space for assisting in generating a parking guidance plan.

[0014] In a preferred example, the present application can be further configured as follows: it also includes the following steps:

[0015] Based on the driver's acceptance of the target parking lot influenced by the urgency of obtaining a parking space, the probability of the driver selecting the target parking lot is calculated.

[0016] In a preferred example, the present application can be further configured as follows: it also includes the following steps:

[0017] Based on the probability that the driver selects the target parking lot, a cumulative probability that the driver selects the target parking lot is calculated.

[0018] In a preferred example, the present application can be further configured as follows: when determining the deviation value conversion function between the driver's travel destination and the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination according to the driver's current state sequence, so as to calculate the urgency of the driver to obtain a parking space during the parking search process, the following formula is used:

[0019]

[0020] Where U i is the urgency of the driver to obtain a parking space during the parking search process, T is the current time, and t min is the fastest actual arrival time at the destination, Tp is the expected arrival time at the destination, g is the destination, is a deviation value conversion function, wherein the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination are obtained from the driver's current state sequence.

[0021] In a preferred example, the present application can be further configured as follows: the expression of the parking space acceptance model includes:

[0022]

[0023] c pj max =max{c p1 ,c p2 ,...,c pn};

[0024] c pj min =min{c p1 ,c p2 ,...,c pn};

[0025] t a(i,j)max =max{t a(i,1) ,t a(i,2) ,...,t a(i,n)};

[0026] t a(i,j)min =min{t a(i,1) ,t a(i,2) ,...,t a(i,n)};

[0027] f (i,j)max =N ij max =max{N i1 ,N i2 ,...,N in};

[0028] f (i,j)min =N ij min =min{Ni1 ,N i2 ,...,N in};

[0029] S j max =max{S1,S2,...,S n};

[0030] S j min =min{S1,S2,...,S n};

[0031] In the formula, A (i,j) is the driver i’s acceptance of parking lot j, c pj is the charge price of parking lot j, is the time required for driver i to reach parking lot j and destination from the time the driver issues the request, and f (i,j) is the driver i’s familiarity with parking lot j, s j is the convenience of parking facilities in parking lot j, θ1 is the weight coefficient of the charging price of parking lot j, θ2 is the weight coefficient of the time required for driver i to reach the destination when parking in parking lot j, θ3 is the weight coefficient of driver i's familiarity with parking lot j, θ4 is the weight coefficient of the convenience of parking facilities in parking lot j, n is the total number of parking lots in the preset divided area within the induction area, N ij is the cumulative number of times driver i goes to parking lot j.

[0032] In a preferred example, the present application may be further configured as follows: using the initial acceptance and the urgency to measure the driver's acceptance of the target parking lot after being affected by the urgency of obtaining a parking space for assisting in generating a parking guidance plan, the following formula is used:

[0033] A u (i,j) =(1+f(u i ))A (i,j) ;

[0034]

[0035] In the formula, A u (i,j) is the driver i’s acceptance of parking lot j after being affected by the urgency of obtaining a parking space; u i is the urgency of driver i to obtain a parking space during the parking search process, A (i,j) is the driver i’s acceptance of parking lot j.

[0036] In a second aspect, the present application provides a parking space induction scheme acceptance probability calculation device adjusted by the driver's psychological factors.

[0037] This application is achieved through the following technical solutions:

[0038] A parking space induction scheme acceptance probability calculation device adjusted by driver psychological factors, comprising:

[0039] A modeling module is used to determine the charging price of the parking lot at a historical moment, the time required for the driver to park at the parking lot and the destination, the driver's familiarity with the parking lot at the historical moment, and the convenience of the parking facilities of the parking lot at the historical moment based on the pre-constructed driver historical state sequence and the pre-constructed parking lot historical state sequence, so as to construct a parking space acceptance model;

[0040] The driving data module is used to obtain the driver's current state and preference information and the current parking lot information, and to construct the driver's current state sequence and the parking lot's current state sequence respectively;

[0041] A psychological factor module is used to determine, according to the current state sequence of the driver, a conversion function of the deviation between the driver's travel destination and the actual fastest time to reach the travel destination and the expected time to reach the travel destination, so as to calculate the urgency of the driver to obtain a parking space during the parking search process;

[0042] An initial acceptance module, used for inputting the driver's current state sequence and the parking lot's current state sequence into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot;

[0043] The acceptance adjustment module is used to use the initial acceptance and the urgency to measure the driver's acceptance of the target parking lot after being influenced by the urgency of obtaining a parking space for assisting in generating a parking guidance plan.

[0044] In a third aspect, the present application provides a computer device.

[0045] This application is achieved through the following technical solutions:

[0046] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of calculating the acceptance probability of any one of the above-mentioned parking space induction schemes adjusted by driver psychological factors are implemented.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium.

[0048] This application is achieved through the following technical solutions:

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for calculating the acceptance probability of a parking space induction scheme adjusted by driver psychological factors.

[0050] In a fifth aspect, the present application provides a computer program product.

[0051] This application is achieved through the following technical solutions:

[0052] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of any one of the above-mentioned methods for calculating the acceptance probability of a parking space induction scheme adjusted by driver psychological factors.

[0053] In summary, compared with the prior art, the technical solution provided by this application has at least the following beneficial effects:

[0054] The charging price of the parking lot at historical moments, the time required for the driver from making a request to parking at the parking lot and reaching the destination, the driver's familiarity with the parking lot at historical moments, and the convenience of the parking facilities of the parking lot at historical moments are selected to construct a parking space acceptance model from the driver's state information level and the parking lot state information level. The driver's current state sequence and the parking lot's current state sequence are input into the parking space acceptance model. The driver's initial acceptance of the parking lot is predicted by combining objective factors and the subjective factor of the driver's familiarity with the parking lot, and the prediction accuracy is higher. According to the driver's current state sequence, the deviation value conversion function between the driver's travel destination and the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination is determined to calculate The urgency of the driver to obtain a parking space during the parking search process is calculated to consider the impact of the driver's subjective factor of the urgency of obtaining a parking space on the parking space induction plan; through the initial acceptance, combined with the urgency, the driver's acceptance of the target parking lot after being affected by the urgency of obtaining a parking space, which is used to assist in generating a parking induction plan, is calculated; then the mechanistic relationship between the driver's subjective and objective psychological perception factors and the driver's parking selection behavior is clarified, and the driver's acceptance of the parking lot under the combined influence of objective costs and driver psychological perception factors is solved, which provides a basis for the next step of determining the parking induction plan, assists in generating a more effective parking space induction plan, makes the generated parking space induction plan more accurate, and improves the induction success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of the main flow of a method for calculating the probability of acceptance of a parking space induction scheme adjusted by driver psychological factors is provided for an exemplary embodiment of the present application.

[0056] Figure 2 A schematic diagram of the execution flow of a method for calculating the acceptance probability of a parking space induction scheme adjusted by the driver's psychological factors is provided as yet another exemplary embodiment of the present application.

[0057] Figure 3 A perception physical system architecture diagram of a method for calculating the probability of acceptance of a parking space induction scheme adjusted by driver psychological factors is provided for another exemplary embodiment of the present application.

[0058] Figure 4 A structural block diagram of a parking space induction scheme acceptance probability calculation device adjusted by driver psychological factors provided for an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0059] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0062] The present application utilizes the traffic information-physical system based on information and communication technology in the intelligent traffic guidance system to perceive the information of traffic elements such as traffic vehicles, traffic participants, and infrastructure, collects the driver's current status and preference information and current parking lot information as model input through the front-end perception layer, solves the driver's acceptance probability of different parking lots, and further calculates the driver's acceptance of the parking lot after being affected by the current parking urgency, so as to calculate the driver's acceptance probability of different parking lots based on the driver's dynamic parking needs, comprehensive objective factors and driver psychological perception factors when modeling, and can accurately estimate the driver's acceptance of different parking space guidance schemes, provide strong technical support for urban intelligent parking guidance systems, generate more effective parking guidance schemes, improve the driver's compliance rate with the guidance system scheme, help reduce the traffic flow caused by repeated parking searches in urban traffic systems, and help achieve a more reasonable allocation of limited parking spaces in urban parking systems.

[0063] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0064] Reference Figure 1 , an embodiment of the present application provides a method for calculating the acceptance probability of a parking space induction scheme adjusted by the driver's psychological factors, and the main steps of the method are described as follows.

[0065] S1: Based on the pre-constructed driver historical state sequence and the pre-constructed parking lot historical state sequence, determine the parking lot's charging price at the historical moment, the time required for the driver to park at the parking lot and the destination, the driver's familiarity with the parking lot at the historical moment, and the convenience of the parking facilities at the parking lot at the historical moment, so as to construct a parking space acceptance model;

[0066] S2: Obtain the driver's current state and preference information and the current parking lot information, and construct the driver's current state sequence and the parking lot's current state sequence respectively;

[0067] S3: determining a conversion function of the deviation between the driver's travel destination and the actual fastest time to reach the travel destination and the expected time to reach the travel destination according to the driver's current state sequence, so as to calculate the urgency of the driver to obtain a parking space during the parking search process;

[0068] S4: inputting the driver's current state sequence and the parking lot's current state sequence into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot;

[0069] S5: Using the initial acceptance and the urgency, the driver's acceptance of the target parking lot after being influenced by the urgency of obtaining a parking space for assisting in generating a parking guidance plan is calculated.

[0070] Specifically, with the help of the existing traffic information-physical system, the information of traffic elements such as traffic vehicles, traffic participants, and infrastructure is perceived, and the driver's status and preference information and parking lot information are obtained, such as the driver's historical status and historical preference information, parking lot historical information, the driver's current status and current preference information, and the parking lot current information, etc. The efficient and reliable information transmission and computing processing capabilities of the traffic information-physical system are utilized to construct effective and complete driver status sequences and parking lot status sequences as control information, thereby realizing real-time and efficient optimization control of the transportation system.

[0071] In this embodiment, data information of transportation vehicles, traffic participants and traffic facilities is perceived by the front-end equipment of the perception layer, combined with the traffic data of the Internet platform, and provided to the control center through communication transmission; the control center processes and analyzes the data information transmitted by the perception layer; and serves as the basis for calculating the probability of the driver accepting the parking lot induction plan and the control data of the transportation system.

[0072] The data information perceived by the front-end devices of the perception layer includes:

[0073] (1) Driver state sequence: S i = {g i ,T pi ,t d(i,j) ,t w(i,j) ,f (i,j)};

[0074] Among them, S i is the driver state sequence, g i For travel purposes, T pi is the planned arrival time, t d(i,j) is the driving time for driver i to reach parking lot j from the current location, t w(i,j) is the walking time from driver i to the destination after arriving at parking lot j, f (i,j) is the familiarity of driver i with parking lot j;

[0075] (2) Parking lot state sequence: P j ={c pj ,s j ,n j};

[0076] Among them, P j is the parking lot state sequence, c pj is the parking fee, s j is the parking facility situation, n j is the parking space utilization rate of the parking lot at the current moment.

[0077] Next, a parking space acceptance model is constructed to calculate the driver's initial acceptance of the parking lot. In this embodiment, the initial acceptance of the parking lot j by the driver i may depend on the price of the parking lot j, the time required for the driver i to reach the parking lot j and the destination from the time the request is issued, the driver's familiarity with the parking lot, and the convenience of the parking facilities. Includes the driving time from driver i to parking lot j and the walking time of driver i from parking lot j to destination Two parts.

[0078] In one embodiment, the expression of the parking space acceptance model includes:

[0079]

[0080] c pj max =max{c p1 ,c p2 ,...,c pn};

[0081] c pj min =min{c p1 ,c p2 ,...,c pn};

[0082] t a(i,j)max =max{t a(i,1) ,t a(i,2) ,...,t a(i,n)};

[0083] t a(i,j)min =min{t a(i,1) ,ta (i,2) ,...,t a(i,n)};

[0084] f (i,j)max =N ij max =max{N i1 ,N i2 ,...,N in};

[0085] f (i,j)min =N ij min =min{N i1 ,N i2 ,...,N in};

[0086] S j max =max{S1,S2,...,S n};

[0087] S j min =min{S1,S2,...,S n};

[0088] In the formula, A (i,j) is the driver i’s acceptance of parking lot j, c pj is the charge price of parking lot j, is the time required for driver i to reach parking lot j and destination from the time the driver issues the request, and f (i,j) is the driver i’s familiarity with parking lot j, s j is the convenience of parking facilities in parking lot j, θ1 is the weight coefficient of the charging price of parking lot j, θ2 is the weight coefficient of the time required for driver i to reach the destination when parking in parking lot j, θ3 is the weight coefficient of driver i's familiarity with parking lot j, θ4 is the weight coefficient of the convenience of parking facilities in parking lot j, n is the total number of parking lots in the preset divided area within the induction area, N ij is the cumulative number of times driver i goes to parking lot j.

[0089] In one embodiment, θ1, θ2, θ3 and θ4 can be determined by the entropy weight method. The weights are determined by analyzing the degree of discreteness of the evaluation indicators. The greater the degree of discreteness, the more information is provided and the corresponding weight is greater. This can objectively reflect the importance of each indicator in the evaluation system. It is suitable for indicators that are difficult to quantify directly, so as to reduce the interference of human factors and objectively assign weights, which is conducive to more accurate prediction of the driver's initial acceptance of the target parking lot.

[0090] Parking fee c pj It can be directly obtained. The charging prices of different parking lots vary from region to region, and different people are also sensitive to different parking lot charging prices.

[0091] In one embodiment, the time required for driver i to reach parking lot j and destination from the time the driver i issues the request is Including the driving time from the request to the parking lot and the walking time from the parking lot to the destination, the time required for driver i from the request to the parking lot j and the destination (travel time) is It can be expressed as:

[0092]

[0093] In the formula, t d(i,j) represents the driving time from the request to the parking lot; t w(i,j)represents the walking time from the parking lot to the destination; β1 and β2 represent the weight coefficients of the corresponding time. Under normal conditions, drivers are more sensitive to walking time; when traffic is congested, drivers' sensitivity to driving time will increase.

[0094] Driver i’s familiarity with parking lot j (i,j) The frequency N of the driver's visit to the parking lot is determined by the number of times the driver has been to the parking lot. ij , and set it as the driver's familiarity with the parking lot, that is, the familiarity of driver i with parking lot j is: f (i,j) =N ij .

[0095] Parking facilities in the parking lot j It is mainly used to reflect the convenience of parking. According to the type of parking lot and its ancillary facilities, the following formula is used to quantify the parking facility indicators of the parking lot:

[0096]

[0097] The parking space acceptance model is obtained by normalizing the parking lot's charging price, the time required for the driver to park in the parking lot and reach the destination, the driver's familiarity with the parking lot, and the convenience of parking facilities. The driver's current state sequence and the parking lot's current state sequence are input into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot.

[0098] Furthermore, in order to consider the impact of the driver's subjective factor of the urgency of obtaining a parking space on the parking space induction scheme, a conversion function of the deviation value between the driver's travel destination and the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination is determined according to the driver's current state sequence, so as to calculate the driver's urgency to obtain a parking space during the parking search process.

[0099] Specifically, the degree of urgency felt by the driver during the parking search process is related to the parking destination, the time to the destination and the difference between the expected arrival time. According to Vroom's expectancy theory, motivation is related to valence and expectancy, that is, the sense of urgency is related to the individual's emphasis on finding a parking space and the probability of not arriving on time. The specific formula is as follows:

[0100] M=V*E

[0101] Among them, M is equivalent to the driver's urgency to obtain a parking space; V is valence, which refers to the value of the behavioral goal in satisfying individual needs, that is, the individual's value assessment of the behavioral results, V is equivalent to the driver's travel purpose; E is the expected value, which is an individual's confidence in getting the ideal result after the action, and refers to a person's estimate of the possibility (probability) of achieving a certain goal. E is equivalent to the probability function converted from the deviation between the actual fastest time to reach the destination and the expected arrival time.

[0102] In one embodiment, when determining the deviation conversion function between the driver's travel destination and the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination according to the driver's current state sequence to calculate the driver's urgency to obtain a parking space during the parking search process, the following formula is used:

[0103]

[0104] Where U i is the urgency of the driver to obtain a parking space during the parking search process, T is the current time, and t min is the fastest actual arrival time at the destination, Tp is the expected arrival time at the destination, g is the destination, 1+e -(T+tmin-Tp) is a deviation value conversion function, wherein the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination are obtained from the driver's current state sequence.

[0105] In the process of finding a parking space, the driver's sense of urgency for parking spaces is related to the importance the individual attaches to finding a parking space and the probability of not arriving on time. The importance the driver attaches to parking lots is reflected in the type of the driver's travel destination. The probability that the driver believes he or she will not arrive on time can be converted into the driver's time sufficiency, and the deviation between the actual fastest time to reach the destination and the expected time to reach the destination is converted into a probability function.

[0106] Because the driver's acceptance of the parking lot is not only affected by objective factors, but also has a great relationship with the driver's psychological state at the time. The driver's psychological factors are mainly reflected in the changes with the urgency of the driver's parking needs. Therefore, the initial acceptance is combined with the urgency to calculate the driver's acceptance of the target parking lot after being affected by the urgency of obtaining a parking space for assisting in generating a parking guidance plan. The specific formula is as follows:

[0107] A u (i,j) =(1+f(u i ))A (i,j) ;

[0108]

[0109] In the formula, A u (i,j) is the driver i’s acceptance of parking lot j after being affected by the urgency of obtaining a parking space; u i is the urgency of driver i to obtain a parking space during the parking search process, A (i,j) is the driver i’s acceptance of parking lot j.

[0110] The sigmoid function is used to describe the driver's acceptance of the parking lot after being affected by the urgency of obtaining a parking space. When the driver's urgency to obtain a parking space increases, the driver's acceptance of the parking lot will also increase to varying degrees.

[0111] Reference Figure 2 , when calculating the acceptance probability of the parking space induction scheme adjusted by the driver's psychological factors, the relevant data information perceived by the traffic physical information system is used to construct the driver state sequence and the parking lot state sequence; based on the driver state sequence and the parking lot state sequence, the driver's initial acceptance of the parking lot and the driver's urgency to obtain a parking space during the parking search process are calculated; then according to the driver's initial acceptance of the parking lot and the driver's urgency to obtain a parking space during the parking search process.

[0112] In one embodiment, a method for calculating the acceptance probability of a parking space induction scheme adjusted by the driver's psychological factors further includes the following steps:

[0113] Based on the driver's acceptance of the target parking lot influenced by the urgency of obtaining a parking space, the probability of the driver selecting the target parking lot is calculated.

[0114] For example, an exponential function is used to calculate the probability that the driver selects the target parking lot. The calculation formula is as follows:

[0115]

[0116] Where P (i,j) is the probability that driver i chooses parking lot j, A u (i,j) is the driver i’s acceptance of parking lot j after being influenced by the urgency of obtaining a parking space; N is the total number of parking lots in the induction area.

[0117] In one embodiment, a method for calculating the acceptance probability of a parking space induction scheme adjusted by the driver's psychological factors further includes the following steps:

[0118] Based on the probability that the driver selects the target parking lot, the cumulative probability that the driver selects the target parking lot is calculated using the following formula:

[0119]

[0120] In the formula, q (i,j) P is the cumulative probability of driver i choosing parking lot j, that is, the sum of all acceptance probabilities of driver i choosing parking lot 1 to parking lot j, (i,m) The probability that driver i chooses parking lot m.

[0121] According to the driver's acceptance of the target parking lot after being affected by the urgency of parking needs, the probability and cumulative probability of the driver choosing a parking lot are calculated, and the probability and cumulative probability of driver i choosing parking lot j are statistically analyzed to provide more data basis for the next step of determining the parking guidance plan.

[0122] In summary, a parking space induction scheme acceptance probability calculation method regulated by driver psychological factors considers the mechanistic relationship between psychological perception factors and driver parking selection behavior, clarifies the decision between driver's psychological perception subjective factors (dynamic parking demand) and objective factors and driver parking selection behavior, solves the driver's acceptance of parking lots under the combined influence of objective factors and driver psychological perception factors, provides a basis for the next step of determining the parking induction scheme, assists in generating a more effective parking space induction scheme, makes the generated parking space induction scheme more accurate, and improves the induction success rate.

[0123] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0124] Reference Figure 3 The perception-physical system architecture applied to the above-mentioned parking space induction scheme acceptance probability calculation method adjusted by driver psychological factors includes a perception layer, a communication layer, a computing layer, a control layer, and a service layer. The perception layer and the service layer belong to the physical domain, and the computing layer belongs to the information domain. The physical domain and the information domain communicate with each other through actuators and sensors.

[0125] The perception layer uses perception devices to send the positioning and navigation information input by the perceived mobile phone user (driver) to the computing layer through the access device communication base station of the communication layer, sends the perceived line map and electronic tags of the parking lot to the computing layer through the access device network node of the communication layer, and sends the perceived video information of the parking lot to the computing layer through the access device shared gateway of the communication layer.

[0126] The communication layer builds the next generation communication network, wired broadband, DSRC, 2G / 3G / 4G, WiFi, etc. based on communication base stations, network nodes and shared gateways. The communication layer also includes firewalls and community service networks.

[0127] The computing layer processes and stores information through the information processing database, and provides distributed storage and backup services when storing information.

[0128] The data processing results of the computing layer are uploaded to the control center of the control layer, which predicts the driver's urgency to obtain a parking space, the acceptance of obtaining the target parking space, and the parking space allocation plan.

[0129] Finally, the prediction results are uploaded to the service layer, and the parking space guidance plan push, parking space locking, parking guidance and navigation prompts and other functions are realized through application services.

[0130] Reference Figure 4 The present application also provides a parking space induction scheme acceptance probability calculation device adjusted by the driver's psychological factors, and the parking space induction scheme acceptance probability calculation device adjusted by the driver's psychological factors corresponds to the parking space induction scheme acceptance probability calculation method adjusted by the driver's psychological factors in the above embodiment. The parking space induction scheme acceptance probability calculation device adjusted by the driver's psychological factors includes:

[0131] A modeling module is used to determine the charging price of the parking lot at a historical moment, the time required for the driver to park at the parking lot and the destination, the driver's familiarity with the parking lot at the historical moment, and the convenience of the parking facilities of the parking lot at the historical moment based on the pre-constructed driver historical state sequence and the pre-constructed parking lot historical state sequence, so as to construct a parking space acceptance model;

[0132] The driving data module is used to obtain the driver's current state and preference information and the current parking lot information, and to construct the driver's current state sequence and the parking lot's current state sequence respectively;

[0133] A psychological factor module is used to determine, according to the current state sequence of the driver, a conversion function of the deviation between the driver's travel destination and the actual fastest time to reach the travel destination and the expected time to reach the travel destination, so as to calculate the urgency of the driver to obtain a parking space during the parking search process;

[0134] An initial acceptance module, used for inputting the driver's current state sequence and the parking lot's current state sequence into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot;

[0135] The acceptance adjustment module is used to use the initial acceptance and the urgency to measure the driver's acceptance of the target parking lot after being influenced by the urgency of obtaining a parking space for assisting in generating a parking guidance plan.

[0136] Regarding the specific limitations of a device for calculating the probability of acceptance of a parking space induction plan adjusted by the driver's psychological factors, please refer to the limitations of the method for calculating the probability of acceptance of a parking space induction plan adjusted by the driver's psychological factors in the above text, which will not be repeated here.

[0137] Each module in the above-mentioned parking space induction scheme acceptance probability calculation device adjusted by the driver's psychological factors can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0138] In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, any one of the above-mentioned parking space induction scheme acceptance probability calculation methods adjusted by driver psychological factors is implemented.

[0139] In one embodiment, a computer-readable storage medium is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any one of the above-mentioned methods for calculating the probability of acceptance of parking space induction schemes adjusted by driver psychological factors is implemented.

[0140] In one embodiment, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, any one of the above-mentioned methods for calculating the acceptance probability of parking space induction schemes adjusted by driver psychological factors is implemented.

[0141] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. When the computer program is executed, it may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0142] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for calculating the probability of acceptance of a parking space induction scheme adjusted by the driver's psychological factors, characterized in that: The following steps are included: Based on the pre-constructed driver historical state sequence and the pre-constructed parking lot historical state sequence, determine the parking lot's charging price at the historical moment, the time required for the driver to park at the parking lot and reach the destination, the driver's familiarity with the parking lot at the historical moment, and the convenience of the parking facilities at the parking lot at the historical moment, so as to construct a parking space acceptance model; Obtain the driver's current state and preference information and the current parking lot information, and construct the driver's current state sequence and the parking lot's current state sequence respectively; Determine, according to the driver's current state sequence, a conversion function of the deviation between the driver's travel destination and the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination, so as to calculate the urgency of the driver to obtain a parking space during the parking search process; Inputting the driver's current state sequence and the parking lot's current state sequence into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot; Using the initial acceptance and the urgency, the driver's acceptance of the target parking lot after being influenced by the urgency of obtaining a parking space for assisting in generating a parking guidance plan is calculated; The expression of the parking space acceptance model includes: c pjmax =max{c p1 ,c p2 ,...,c pn }; c pjmin =min{c p1 ,c p2 ,...,c pn }; t a(i,j)max =max{t a(i,1) ,t a(i,2) ,...,t a(i,n) }; t a(i,j)min =min{t a(i,1) ,t a(i,2) ,...,t a(i,n) }; f (i,j)max =N ijmax =max{N i1 ,N i2 ,...,N in }; f (i,j)min =N ijmin =min{N i1 ,N i2 ,…,N in }; S jmax =max{S1,S2,...,S n }; S jmin =min{S1,S2,...,S n }; In the formula, A (i,j) is the driver i’s acceptance of parking lot j, c pj is the charging price of parking lot j, t a (i,j) is the time required for driver i to reach parking lot j and destination from the time the driver issues the request, and f (i,j) is the familiarity of driver i with parking lot j, s j is the convenience of parking facilities in parking lot j, θ1 is the weight coefficient of the charging price of parking lot j, θ2 is the weight coefficient of the time required for driver i to reach the destination when parking in parking lot j, θ3 is the weight coefficient of driver i's familiarity with parking lot j, θ4 is the weight coefficient of the convenience of parking facilities in parking lot j, n is the total number of parking lots in the preset divided area within the induction area, N ij is the cumulative number of times driver i goes to parking lot j; The following formula is used to determine the deviation conversion function between the driver's travel destination and the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination according to the driver's current state sequence, so as to calculate the urgency of the driver to obtain a parking space during the parking search process: Where U i is the urgency of the driver to obtain a parking space during the parking search process, T is the current time, and t min is the fastest actual arrival time at the destination, Tp is the expected arrival time at the destination, g is the destination, is a deviation value conversion function, wherein the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination are obtained from the current state sequence of the driver; The initial acceptance is combined with the urgency to calculate the driver's acceptance of the target parking lot after being influenced by the urgency of obtaining a parking space for assisting in generating a parking guidance plan, using the following formula: A u (i,j) =(1+f(u i ))A (i,j) ; In the formula, A u (i,j) is the driver i’s acceptance of parking lot j after being affected by the urgency of obtaining a parking space; u i is the urgency of driver i to obtain a parking space during the parking search process, A (i,j) is the driver i’s acceptance of parking lot j.

2. The method for calculating the probability of acceptance of a parking space induction scheme adjusted by the driver's psychological factors according to claim 1, characterized in that: The following steps are also included: Based on the driver's acceptance of the target parking lot influenced by the urgency of obtaining a parking space, the probability of the driver selecting the target parking lot is calculated.

3. The method for calculating the probability of acceptance of parking space induction schemes adjusted by driver psychological factors according to claim 2, characterized in that: The following steps are also included: Based on the probability that the driver selects the target parking lot, a cumulative probability that the driver selects the target parking lot is calculated.

4. A parking space induction scheme acceptance probability calculation device adjusted by the driver's psychological factors, characterized in that: include, The modeling module is used to determine the parking lot's charging price at a historical moment, the time required for the driver to park at the parking lot and the destination from issuing a request to parking at the parking lot, the driver's familiarity with the parking lot at a historical moment, and the convenience of the parking facilities of the parking lot at a historical moment based on a pre-constructed driver's historical state sequence and a pre-constructed parking lot's historical state sequence, so as to construct a parking space acceptance model, wherein the expression of the parking space acceptance model includes: c pjmax =max{c p1 ,c p2 ,...,c pn }; c pjmin =min{c p1 ,c p2 ,...,c pn }; t a(i,j)max =max{t a(i,1) ,t a(i,2) ,...,t a(i,n) }; t a(i,j)min =min{t a(i,1) ,t a(i,2) ,...,t a(i,n) }; f (i,j)max =N ijmax =max{N i1 ,N i2 ,...,N in }; f (i,j)min =N ijmin =min{N i1 ,N i2 ,…,N in }; S jmax =max{S1,S2,...,S n }; S jmin =min{S1,S2,...,S n }; In the formula, A (i,j) is the driver i’s acceptance of parking lot j, c pj is the charging price of parking lot j, t a (i,j) is the time required for driver i to reach parking lot j and destination from the time the driver issues the request, and f (i,j) is the familiarity of driver i with parking lot j, s j is the convenience of parking facilities in parking lot j, θ1 is the weight coefficient of the charging price of parking lot j, θ2 is the weight coefficient of the time required for driver i to reach the destination when parking in parking lot j, θ3 is the weight coefficient of driver i's familiarity with parking lot j, θ4 is the weight coefficient of the convenience of parking facilities in parking lot j, n is the total number of parking lots in the preset divided area within the induction area, N ij is the cumulative number of times driver i goes to parking lot j; The driving data module is used to obtain the driver's current state and preference information and the current parking lot information, and to construct the driver's current state sequence and the parking lot's current state sequence respectively; The psychological factor module is used to determine the deviation value conversion function between the driver's travel destination and the actual fastest time to reach the travel destination and the expected time to reach the travel destination according to the driver's current state sequence, so as to calculate the driver's urgency to obtain a parking space during the parking search process. The urgency of the parking space is calculated using the following formula: Where U i is the urgency of the driver to obtain a parking space during the parking search process, T is the current time, and t min is the fastest actual arrival time at the destination, Tp is the expected arrival time at the destination, g is the destination, is a deviation value conversion function, wherein the actual fastest time to arrive at the travel destination and the expected time to arrive at the travel destination are obtained from the current state sequence of the driver; An initial acceptance module, used for inputting the driver's current state sequence and the parking lot's current state sequence into the parking space acceptance model to obtain the driver's initial acceptance of the parking lot; The acceptance adjustment module is used to use the initial acceptance and the urgency to measure the driver's acceptance of the target parking lot after being affected by the urgency of obtaining a parking space for assisting in generating a parking guidance plan. The acceptance of the target parking lot after being affected by the urgency of obtaining a parking space is calculated using the following formula: A u (i,j) =(1+f(u i ))A (i,j) ; In the formula, A u (i,j) is the driver i’s acceptance of parking lot j after being affected by the urgency of obtaining a parking space; u i is the urgency of driver i to obtain a parking space during the parking search process, A (i,j) is the driver i’s acceptance of parking lot j.

5. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 3 when being executed by a processor.

Images