Method for estimating traffic flow of service area and data processing equipment

Abstract

The invention discloses a method for estimating traffic flow of a service area and data processing equipment. The method comprises the following steps: acquiring driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by a checkpoint device closest to the target service area; determining the probability that the target vehicle enters the target service area according to the driving data of the target vehicle; if the probability is greater than a probability threshold, determining that the target vehicle will drive into the target service area; determining an estimated residence time interval of the target vehicle in the target service area according to the estimated driving time of the target vehicle driving from the position of the gate device to the target service area and the estimated residence time of the target vehicle in the target service area; and determining the traffic flow of the target service area in the first set time period according to the estimated staying time interval of the at least one target vehicle. According to the design, the traffic flow of the target service area in the first set time period can indicate whether the vehicle enters the service area or not, and congestion caused by too many vehicles in the service area is avoided.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a method and data processing device for estimating vehicle flow in a service area. Background Art

[0002] When driving at high speeds, the service area can provide drivers with services such as rest and refueling. In related technologies, drivers can determine whether to enter the service area based on the traffic volume of the downstream service area displayed on the traffic guidance screen, thereby avoiding entering a congested service area. In actual application, the traffic guidance screen displays the real-time number of vehicles in the downstream service area, that is, the number of vehicles in the downstream service area when the driver passes the traffic guidance screen. At that time, when the real-time number of vehicles displayed on the traffic guidance screen is small, the driver plans to enter the service area, but when the driver arrives at the service area, the service area may have no parking spaces.

[0003] Therefore, it is important to more accurately estimate the traffic flow inside the service area during the period from the vehicle's arrival at the service area. Summary of the invention

[0004] In an exemplary embodiment of the present application, a method and data processing device for estimating the traffic flow in a service area are provided to accurately estimate the traffic flow in a period of time before the vehicle enters the service area. The traffic flow can indicate whether the vehicle enters the service area to avoid congestion caused by too many vehicles in the service area.

[0005] According to a first aspect of an exemplary embodiment, a method for estimating vehicle flow in a service area is provided, the method being applied to a data processing device; the method comprising at least the following steps:

[0006] Obtain driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by the checkpoint equipment closest to the target service area;

[0007] According to the driving data of the target vehicle, the probability of the target vehicle entering the target service area is determined;

[0008] If the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area;

[0009] Determine an estimated stay time interval of the target vehicle in the target service area based on an estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and an estimated stay time of the target vehicle in the target service area;

[0010] The vehicle flow rate of the target service area within a first set time period is determined according to the estimated stay time interval of at least one target vehicle.

[0011] According to a second aspect of the exemplary implementation, there is provided a data processing device, comprising a data transmission unit and a processor;

[0012] The data transfer unit is configured to perform:

[0013] Obtain driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by the checkpoint equipment closest to the target service area;

[0014] The processor is configured to execute:

[0015] According to the driving data of the target vehicle, the probability of the target vehicle entering the target service area is determined;

[0016] If the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area;

[0017] Determine an estimated stay time interval of the target vehicle in the target service area based on an estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and an estimated stay time of the target vehicle in the target service area;

[0018] The vehicle flow rate of the target service area within a first set time period is determined according to the estimated stay time interval of at least one target vehicle.

[0019] According to a third aspect of an exemplary embodiment, there is provided a device for estimating vehicle flow in a service area, the device comprising:

[0020] The data acquisition unit is used to: acquire driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by the checkpoint device closest to the target service area;

[0021] A processing unit, used to: determine the probability of the target vehicle entering the target service area according to the driving data of the target vehicle;

[0022] The processing unit is further used to: if the probability is greater than a probability threshold, determine that the target vehicle will enter the target service area;

[0023] The processing unit is further used to: determine an estimated stay time interval of the target vehicle in the target service area according to an estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and an estimated stay time of the target vehicle in the target service area;

[0024] The estimation unit is used to determine the vehicle flow of the target service area within a first set time period according to the estimated stay time interval of at least one target vehicle.

[0025] According to a fourth aspect of an exemplary implementation, a computer storage medium is provided, in which computer program instructions are stored. When the instructions are executed on a computer, the computer executes the method for estimating the traffic flow of a service area as described in the first aspect.

[0026] The embodiment of the present application can estimate the traffic volume of the target service area within a period of time. In this process, the driving data of the target vehicle monitored by the bayonet device closest to the target service area is used, and the probability of the target vehicle entering the target service area is determined based on the driving data. When the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area. Then, based on the estimated driving time of the target vehicle from the location of the bayonet device to the target service area, and the estimated stay time of the target vehicle in the target service area, the estimated stay time interval of the target vehicle in the target service area is determined, and then based on the estimated stay time interval of at least one target vehicle, the traffic volume of the target service area within the first set time period is determined. Such a design takes into account the entry and stay conditions of the vehicle. The traffic volume can indicate whether the vehicle has entered the service area, avoiding congestion caused by too many vehicles in the service area. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0028] Figure 1 The following is a schematic diagram showing a display of a traffic guidance screen provided in an embodiment of the present application;

[0029] Figure 2 A flowchart of a method for estimating vehicle flow in a service area provided by an embodiment of the present application is exemplarily shown;

[0030] Figure 3 A schematic diagram of a highway provided by an embodiment of the present application is exemplarily shown;

[0031] Figure 4 A schematic diagram of applying a network model to determine whether a target vehicle enters a service area is exemplarily shown in an embodiment of the present application;

[0032] Figure 5 A schematic diagram showing exemplary time nodes of a vehicle entering and exiting provided by an embodiment of the present application;

[0033] Figure 6 The following is a schematic diagram showing another display of a traffic guidance screen provided in an embodiment of the present application;

[0034] Figure 7 A flowchart of a complete method for estimating vehicle flow in a service area provided by an embodiment of the present application is exemplarily shown;

[0035] Figure 8An interactive flow chart of a method for estimating vehicle flow in a service area provided by an embodiment of the present application is exemplarily shown;

[0036] Fig. 9 A schematic diagram of the structure of a device for estimating the traffic flow of a service area provided in an embodiment of the present application is exemplarily shown;

[0037] Fig.10 A structural diagram of a data processing device provided in an embodiment of the present application is exemplified. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions 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.

[0039] For ease of understanding, the terms involved in the embodiments of the present application are explained below:

[0040] (1) Traffic guidance screen is a multifunctional information display screen made of LED light-emitting diodes and used for traffic guidance. It is generally used on highways. Traffic guidance screens contain graphic guidance signs, which are composed of ordinary guidance signs and variable information signs.

[0041] (2) Target vehicle: the vehicle that can be monitored by the checkpoint equipment closest to the target service area.

[0042] (3) The vehicle itself determines whether it is a vehicle entering the target service area based on the traffic volume of the target service area displayed on the traffic guidance screen.

[0043] (4) Data processing equipment, which may be a traffic guidance screen with data processing function, or other electronic equipment or server with data processing function. If it is other electronic equipment or server with data processing function, the electronic equipment or server may send the obtained traffic flow during the period between the vehicle arriving at the service area to the traffic guidance screen for display.

[0044] When driving at high speeds, the service area can provide drivers with services such as rest and refueling. In related technologies, drivers can determine whether to enter the service area based on the traffic volume of the downstream service area displayed on the traffic guidance screen, thereby avoiding entering a congested service area. In actual application, the traffic guidance screen displays the real-time number of vehicles in the downstream service area, that is, the number of vehicles in the downstream service area when the driver passes the traffic guidance screen. At that time, when the real-time number of vehicles displayed on the traffic guidance screen is small, the driver plans to enter the service area, but when the driver arrives at the service area, the service area may have no parking spaces.

[0045] Therefore, it is important to more accurately estimate the traffic flow inside the service area during the period from the vehicle's arrival at the service area.

[0046] To this end, an embodiment of the present application provides a method for estimating the traffic flow of a service area, in which the traffic flow of a target service area within a period of time can be estimated. In this process, the driving data of the target vehicle monitored by the bayonet device closest to the target service area is used to determine the probability of the target vehicle entering the target service area based on the driving data, and when the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area. Then, based on the estimated driving time of the target vehicle from the location of the bayonet device to the target service area, and the estimated stay time of the target vehicle in the target service area, the estimated stay time interval of the target vehicle in the target service area is determined, and then based on the estimated stay time interval of at least one target vehicle, the traffic flow of the target service area within the first set time period is determined. Such a design takes into account the entry and exit of vehicles and realizes the estimation of the traffic flow in the target service area within a period of time.

[0047] After introducing the design ideas of the embodiments of the present application, the following briefly introduces the application scenarios to which the technical solutions of the embodiments of the present application can be applied. It should be noted that the application scenarios introduced below are only used to illustrate the embodiments of the present application and are not limited. In specific implementation, the technical solutions provided by the embodiments of the present application can be flexibly applied according to actual needs.

[0048] refer to Figure 1 , shows a display schematic diagram of a traffic guidance screen, wherein the traffic guidance screen can be used to display the traffic flow of a target service area.

[0049] To further illustrate the technical solution provided by the embodiment of the present application, this is described in detail below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiment of the present application provides the method operation steps shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided in the embodiment of the present application.

[0050] Combine the following Figure 1 The application scenario shown is shown in Figure 2 A flowchart of a method for estimating the traffic flow in a service area is shown to illustrate the technical solution provided in an embodiment of the present application.

[0051] S201: The data processing device obtains driving data of the target vehicle.

[0052] Among them, the target vehicle is the vehicle monitored by the checkpoint equipment closest to the target service area.

[0053] S202: The data processing device determines the probability of the target vehicle entering the target service area according to the driving data of the target vehicle.

[0054] S203: If the probability is greater than the probability threshold, the data processing device determines that the target vehicle will enter the target service area.

[0055] S204: The data processing device determines an estimated stay time interval of the target vehicle in the target service area according to the estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and the estimated stay time of the target vehicle in the target service area.

[0056] S205: The data processing device determines the vehicle flow rate of the target service area within a first set time period according to the estimated stay time interval of at least one target vehicle.

[0057] The embodiment of the present application can estimate the traffic volume of the target service area within a period of time. In this process, the driving data of the target vehicle monitored by the bayonet device closest to the target service area is used, and the probability of the target vehicle entering the target service area is determined based on the driving data. When the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area. Then, based on the estimated driving time of the target vehicle from the location of the bayonet device to the target service area, and the estimated stay time of the target vehicle in the target service area, the estimated stay time interval of the target vehicle in the target service area is determined, and then based on the estimated stay time interval of at least one target vehicle, the traffic volume of the target service area within the first set time period is determined. Such a design takes into account the entry and stay conditions of the vehicle. The traffic volume can indicate whether the vehicle has entered the service area, avoiding congestion caused by too many vehicles in the service area.

[0058] In order to obtain the traffic flow of the target service area within a certain time period, the estimated residence time interval of the target vehicle in the target service area can be determined for each target vehicle, so that the traffic flow of the target service area within a certain time period can be determined. Next, taking a target vehicle as an example, the estimated residence time of the target vehicle in the target service area is explained.

[0059] In S201, a certain number of checkpoints are set up on the urban traffic road network or highway to take photos of vehicles to achieve illegal capture, etc. In the embodiment of the present application, the checkpoint closest to the target service area is used to take photos of each target vehicle passing through the checkpoint, obtain the driving data of each target vehicle, and determine whether the target vehicle is about to enter the target service area based on the driving data. Take any target vehicle as an example for explanation.

[0060] Figure 3A schematic diagram of a highway provided in an embodiment of the present application, wherein a signboard, such as "Su Maoshan Service Area", is provided at the entrance of the service area; a checkpoint device and a traffic guidance screen are provided upstream of the service area.

[0061] In actual application, if the target vehicle has been on the road for a long time on the day, or the target vehicle is a large truck, or the target vehicle has a license plate from another place, or the target vehicle has stayed in the target service area and the length of time, these can all be used as conditions for determining whether the target vehicle has entered the service area. Therefore, in an embodiment of the present application, the driving data of the target vehicle may include the target vehicle's on-the-road time, type, and place of affiliation on the day, as well as historical stay information at the target service area, where the historical stay information includes whether to stay, the length of stay, and the stay period.

[0062] Therefore, the data processing device acquires the data from the camera device in real time or periodically, and identifies the driving data of the target vehicle from the data.

[0063] In S202, in order to judge whether the target vehicle will enter the target service area, the data processing device determines the probability of the target vehicle entering the target service area according to the driving data of the target vehicle.

[0064] Optionally, the probability of the target vehicle entering the target service area may be determined through steps A1-A2.

[0065] A1: The data processing device inputs the driving data of the target vehicle into the pre-trained first model, second model and third model to obtain first label data, second label data and third label data respectively for indicating whether the target vehicle enters the target service area.

[0066] The first model is a back propagation (BP) neural network model, the second model is a decision tree model, and the third model is a logistic regression model. The pre-built BP neural network model is trained using the training samples to obtain the first model; the pre-built decision tree model is trained to obtain the second model; and the pre-built logistic regression model is trained to obtain the third model.

[0067] For the BP neural network model, the activation function of the output layer can be a softmax function, so that the probability of the target vehicle entering the target service area and the probability of not entering the service area can be obtained. Here, x is the characteristic data of the training sample, such as the historical vehicle's travel time, type, location, and stop ratio on the day of travel.

[0068] For the decision tree model, the C4.5 algorithm can be used. In this algorithm, the selection criterion of the decision tree feature is the information gain ratio, and its calculation formula is:

[0069]

[0070] Among them, g R (T,X (j) ) is feature X (j) For the information gain of T, g(T,X (j) )=H(T)-H(T|X (j) ), T is the training sample, T={(x1,y1),(x2,y2),...,(x n ,y n )};X (j) =(x j ,y j ) is the jth feature of T. In the embodiment of the present application, for the jth historical vehicle, then x j are three-dimensional features, namely the travel time, type, and location of historical vehicle j on the day of travel, y j is the stop ratio of vehicle j in history.

[0071] For the logistic regression model, the Sigmoid function can be introduced, and it can also be used to determine whether the vehicle enters the service area.

[0072] Among them, the model structures of the BP neural network model, the decision tree model and the logistic regression model can be implemented by the structures in the related technologies and are not limited here.

[0073] Exemplarily, the training samples are N groups of historical driving data of N historical vehicles. N is a positive integer, and each group of historical driving data includes the transit time, type, location and stop ratio of the historical vehicle on the day of travel. The stop ratio here is the ratio of the number of stops of the historical vehicle passing through the target service area to the total number of passes. For example, the historical vehicle V1 passed through the target service area a total of 10 times, of which 6 times it stopped in the target service area, then the stop ratio is 0.6. In the actual application process, the historical driving data can be data from the past six months, which is not limited here. In addition, as time goes by, the specific behavior of the vehicle may change. Therefore, the data of the training sample is usually not too much. Every other month, the data of the furthest month is discarded, the data of the most recent month is supplemented, and the model is retrained.

[0074] The driving data of the target vehicle is input into the pre-trained first model, second model and third model, and the outputs of the three models are first label data, second label data and third label data, respectively. The first label data, second label data and third label data can be 0 or 1, respectively, 0 means that the target vehicle does not enter the target service area, and 1 means that the target vehicle enters the service area.

[0075] They are first label data, second label data and third label data respectively, and these three probabilities are all probabilities of whether the target vehicle enters the target service area.

[0076] A2: Obtain the probability of the target vehicle entering the target service area based on the first label data, the second label data and the third label data.

[0077] Among them, the three models have different architectures, and the use of the three models for identification can avoid errors caused by inaccurate identification of a single model. Therefore, the data processing device can perform weighted processing on the first label data, the second label data, and the third label data to obtain the probability of the target vehicle entering the service area. The weights of the three probabilities can be set according to the network types of the corresponding three models, which are not limited here. The probability obtained after weighted processing is used as the probability of the target vehicle entering the target service area.

[0078] Optionally, the first label data is represented by p1, the second label data is represented by p2, the third label data is represented by p3, and the probability of the target vehicle entering the target service area is represented by P, then P = (p1+p2+p3) / 3.

[0079] Figure 4 A schematic diagram of using a network model to determine whether a target vehicle enters a service area is provided in an embodiment of the present application. The number and category of the three selected models are different, and the probability of the target service area obtained is different, and thus the estimation accuracy of whether the target vehicle enters the target service area is different.

[0080] In step S203, if the obtained probability of the target vehicle entering the target service area is greater than the probability threshold, it is determined that the target vehicle will enter the target service area. Optionally, the probability threshold may be 1 / 3.

[0081] In order to improve the estimation of the traffic volume of the target service area in a certain time period, it is necessary to estimate not only the target vehicles entering, but also the target vehicles leaving. Therefore, for each target vehicle, it is also necessary to estimate the exit of the target vehicle, wherein the estimation of the exit can also be converted into the residence time of the target vehicle in the target service area, and the exit time can be obtained by the entry time and the estimated residence time.

[0082] In S204, the data processing device determines an estimated stay time interval of the target vehicle in the target service area based on the estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and the estimated stay time of the target vehicle in the target service area.

[0083] Wherein, the estimated travel time of the target vehicle from the location of the checkpoint device to the target service area is, for example, T1, the estimated stay time of the target vehicle in the target service area is T2, the current time is T0, and the estimated stay time interval of the target vehicle in the target service area is (T0+T1, T0+T1+T2). For example, the current time T0 is 16:00, T1 is 10 minutes, and T2 is 30 minutes, and the estimated stay time interval is (16:10, 16:40).

[0084] Optionally, the process of determining the estimated travel time T1 of the target vehicle from the location of the checkpoint device to the target service area and the estimated stay time T2 of the target vehicle in the target service area can be divided into the following two cases:

[0085] The first case Q1: within the second set time period, there is historical driving data of the target vehicle.

[0086] In this case, the estimated travel time T1 of the target vehicle from the location of the checkpoint device to the target service area is determined by way B1, and the estimated stay time T2 of the target vehicle in the target service area is determined by way B2.

[0087] B1: According to the historical driving time of the target vehicle from the location of the checkpoint device to the target service area, the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area is obtained.

[0088] The second set time period may be one year before the current moment. During this time period, if there is driving data of the target vehicle, the historical driving time of the target vehicle from the location of the checkpoint device to the target service area can be extracted from the driving data, and the average value is calculated to obtain the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area. For example, within the second set time period, if the target vehicle has driven from the location of the checkpoint device to the target service area 10 times, the average value of the 10 historical driving times is calculated as the estimated driving time of the target device from the checkpoint device to the target service area.

[0089] B2: According to the historical stay time of the target vehicle in the target service area, the estimated stay time of the target vehicle in the target service area is obtained.

[0090] The second set time period may be one year before the current moment. During this time period, if there is driving data of the target vehicle, the historical stay time of the target vehicle in the target service area may be extracted from the driving data and averaged to obtain the estimated stay time of the target vehicle in the target service area. For example, during the second set time period, if the target vehicle stays in the target service area 10 times, the average of the 10 historical stay times is calculated as the estimated stay time of the target device in the target service area.

[0091] The first case Q2: within the second set time period, there is no historical driving data of the target vehicle.

[0092] In this case, the estimated travel time T1 of the target vehicle from the location of the checkpoint device to the target service area is determined by way C1, and the estimated stay time T2 of the target vehicle in the target service area is determined by way C2.

[0093] C1: Based on the historical travel time of all vehicles passing through the checkpoint device from the location of the checkpoint device to the target service area, the estimated travel time of the target vehicle from the location of the checkpoint device to the target service area is obtained.

[0094] Among them, when there is no historical driving data of the target vehicle within the second set time period, it indicates that the target vehicle did not drive from the location of the checkpoint device to the target service area within the second set time period. Therefore, the historical driving time of all vehicles passing through the checkpoint device from the location of the checkpoint device to the target service area is averaged as the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area.

[0095] C2: According to the historical stay time of all vehicles passing through the checkpoint equipment in the target service area, the estimated stay time of the target vehicle in the target service area is obtained.

[0096] Among them, when there is no historical driving data of the target vehicle within the second set time period, it indicates that the target vehicle did not stay in the target service area within the second set time period. Therefore, the historical stay time of all vehicles passing through the checkpoint device at the target server is averaged as the estimated stay time of the target vehicle in the target service area.

[0097] In S205, the above embodiment is a process for determining the estimated stay time interval of a target vehicle in the target service area, and other target vehicles are processed in the same manner. With such a design, the data processing device can determine the vehicle flow of the target service area within the first set time period based on the estimated stay time interval of at least one target vehicle.

[0098] Among them, the first time period can be 10 minutes. In the embodiment of the present application, M groups of traffic flow data of M 10 minutes after the current time can be determined. Optionally, the current time can be the time when the vehicle passes the traffic guidance screen. Taking 16:00 as an example, the traffic flow in the three time periods of 16:00-16:10, 16:10-16:20 and 16:200-16:30 can be determined. Usually, according to the location of the traffic guidance screen and the speed limit of the highway, it can be determined that from the current time, the vehicle can reach the entrance of the service area within 20 minutes.

[0099] Optional, Figure 5 A schematic diagram of a vehicle's entry and exit time nodes provided in an embodiment of the present application, through Figure 5 It can be seen that at time t+3, one car passes through the service area, and two cars enter the service area, and these two cars are in the intervals of (t+10, t+20) and (t+20, t+30) respectively. In addition, in the intervals of (t+10, t+20) and (t+20, t+30), one car leaves the service area respectively.

[0100] For example, Figure 6 A display schematic diagram of a traffic guidance screen provided in an embodiment of the present application, wherein the traffic flow of three 10-minute time intervals with the current time as the starting point is displayed, that is, the current time is 16:00, and the three time intervals are 16:00-16:10, 16:10-16:20, and 16:20-16:30.

[0101] Figure 7 A flowchart of a complete method for estimating the traffic flow of a service area provided in an embodiment of the present application, Figure 7 At least the following steps are included:

[0102] S701: The data processing device obtains driving data of the target vehicle.

[0103] The implementation method of this step can refer to step S201 and will not be repeated here.

[0104] S702: The data processing device inputs the driving data of the target vehicle into the pre-trained BP neural network model, decision tree model and logistic regression model to obtain first label data, second label data and third label data respectively for indicating whether the target vehicle enters the target service area.

[0105] The implementation method of this step can refer to step A1, which will not be repeated here.

[0106] S703: The data processing device obtains the probability of the target vehicle entering the target service area according to the first label data, the second label data and the third label data.

[0107] The implementation method of this step can refer to step A2, which will not be repeated here.

[0108] S704: If the probability is greater than the probability threshold, the data processing device determines that the target vehicle will enter the target service area.

[0109] The implementation method of this step can refer to step S203, which will not be repeated here.

[0110] S705-1: If there is historical driving data of the target vehicle within the second set time period, the data processing device obtains the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area based on the historical driving time of the target vehicle from the location of the checkpoint device to the target service area.

[0111] The implementation method of this step can refer to method B1, which will not be repeated here.

[0112] S705-2: If there is no historical driving data of the target vehicle within the second set time period, the data processing device obtains the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area based on the historical driving time of all vehicles passing through the checkpoint device from the location of the checkpoint device to the target service area.

[0113] The implementation method of this step can refer to method B2, which is not repeated here.

[0114] S706-1: If there is historical driving data of the target vehicle within the second set time period, the data processing device obtains an estimated stay time of the target vehicle in the target service area based on the historical stay time of the target vehicle in the target service area.

[0115] The implementation method of this step can refer to method C1, which is not repeated here.

[0116] S706-2: If there is no historical driving data of the target vehicle within the second set time period, the estimated stay time of the target vehicle in the target service area is obtained based on the historical stay time of all vehicles passing through the checkpoint device in the target service area.

[0117] The implementation method of this step can refer to method C2, which is not repeated here.

[0118] S707: The data processing device determines an estimated stay time interval of the target vehicle in the target service area according to the estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and the estimated stay time of the target vehicle in the target service area.

[0119] The implementation method of this step can refer to step S204, which will not be repeated here.

[0120] S708: The data processing device determines the vehicle flow rate of the target service area within a first set time period according to the estimated stay time interval of at least one target vehicle.

[0121] The implementation method of this step can refer to step S205, which will not be repeated here.

[0122] Among them, S705-1 to S705-2 is the process of determining the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area, and S706-1 to S706-2 is the process of determining the estimated stay time of the target vehicle in the target service area. There is no obvious order relationship between the two processes. Figure 7 This is just an example and does not constitute a specific limitation.

[0123] The embodiment of the present application can help the driver of the vehicle to better plan his or her itinerary and make a decision to stop at a suitable service area by estimating the entry and exit conditions of the target vehicle respectively. This can avoid congestion caused by too many vehicles in some service areas and waste of resources caused by too few vehicles in some service areas, save a certain amount of time, and improve driver satisfaction.

[0124] Figure 8 This is an interactive flow chart of a method for estimating the traffic flow of a service area provided in an embodiment of the present application. In this diagram, the data processing device is other electronic devices or servers with data processing functions. In actual application, the data processing device can also be a traffic guidance screen with data processing functions, which is not shown here. Figure 8 At least the following steps are included:

[0125] S800: The checkpoint device monitors the checkpoint data of at least one target vehicle.

[0126] Among them, the checkpoint equipment can take pictures of passing vehicles, and for the target vehicle, its checkpoint data can be monitored.

[0127] S801: The checkpoint device sends the checkpoint data of at least one target vehicle to the data processing device.

[0128] The checkpoint device can establish communication transmission with the data processing device to transmit data to each other. The checkpoint data includes the time, type and location of each target vehicle on the day and is sent to the data processing device.

[0129] S802: The data processing device determines an estimated stay interval of each target vehicle in a target service area according to driving data of at least one target vehicle.

[0130] The implementation method of this step can refer to step S201 to step S204, which will not be repeated here.

[0131] The driving data includes checkpoint data and historical stay information of the target vehicle in the target service area.

[0132] S803: The data processing device determines the traffic volume of the target service area within a first set time period according to the driving data of at least one target vehicle.

[0133] The implementation method of this step may refer to step S205 and will not be described in detail here.

[0134] S804: The data processing device sends the traffic flow of the target service area within the first set time period to the traffic guidance screen.

[0135] Among them, the traffic guidance screen can establish communication transmission with the data processing equipment to transmit data to each other.

[0136] S805: The traffic guidance screen displays the traffic volume of the target service area.

[0137] Among them, the traffic guidance screen can display the traffic flow within a certain period of time after the current time for the driver's reference to evaluate whether to enter the service area.

[0138] In the embodiment of the present application, after the traffic flow of the target service area is obtained, it is displayed on the traffic guidance screen. Such a design makes the information of the service area displayed on the traffic guidance screen more comprehensive and more reasonable.

[0139] like Fig. 9 As shown, based on the same inventive concept, an embodiment of the present application provides a device for estimating the traffic flow in a service area, including a data acquisition unit 91 and a processing unit 92.

[0140] The data acquisition unit 91 is used to: acquire driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by the checkpoint device closest to the target service area;

[0141] The processing unit 92 is used to determine the probability of the target vehicle entering the target service area according to the driving data of the target vehicle;

[0142] The processing unit 92 is further configured to: if the probability is greater than a probability threshold, determine that the target vehicle will enter the target service area;

[0143] The processing unit 92 is further used to: determine an estimated stay time interval of the target vehicle in the target service area according to an estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and an estimated stay time of the target vehicle in the target service area;

[0144] The processing unit 92 is used to determine the vehicle flow of the target service area within a first set time period according to the estimated stay time interval of at least one target vehicle.

[0145] In an optional implementation, the processing unit 92 is specifically configured to:

[0146] Input the driving data of the target vehicle into the pre-trained first model, the second model and the third model to obtain first label data, second label data and third label data respectively for indicating whether the target vehicle enters the target service area;

[0147] The probability of the target vehicle entering the target service area is obtained according to the first label data, the second label data and the third label data.

[0148] In an optional implementation, the first model is a BP neural network model, the second model is a decision tree model, and the third model is a logistic regression model.

[0149] In an optional implementation, the training samples of the first model, the second model and the third model are the same, and the training samples include:

[0150] N groups of historical driving data of N historical vehicles;

[0151] Wherein, N is a positive integer, and each set of historical driving data includes the travel time, type, location and stop ratio of the historical vehicle on the day of driving; wherein, the stop ratio is the ratio of the number of stops of the historical vehicle passing through the target service area to the total number of passes.

[0152] In an optional implementation, the driving data of the target vehicle in the first set period includes:

[0153] The target vehicle’s time in transit, type, location, and historical stop information at the target service area on the day; the historical stop information includes whether the vehicle stopped, the length of the stop, and the time period of the stop.

[0154] In an optional implementation, the processing unit 92 is further configured to:

[0155] If there is historical driving data of the target vehicle within the second set time period, then the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area is obtained according to the historical driving time of the target vehicle from the location of the checkpoint device to the target service area;

[0156] If there is no historical driving data of the target vehicle within the second set time period, the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area is obtained based on the historical driving time of all vehicles passing through the checkpoint device within the third set time period from the location of the checkpoint device to the target service area.

[0157] In an optional implementation, the processing unit 92 is further configured to:

[0158] If there is historical driving data of the target vehicle within the second set time period, then the estimated stay time of the target vehicle in the target service area is obtained according to the historical stay time of the target vehicle in the target service area;

[0159] If there is no historical driving data of the target vehicle within the second set time period, the estimated stay time of the target vehicle in the target service area is obtained based on the historical stay time of all vehicles passing through the checkpoint device in the target service area.

[0160] Since the device is the device in the method in the embodiment of the present application, and the principle of solving the problem by the device is similar to that of the method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0161] like Fig.10 As shown, based on the same inventive concept, an embodiment of the present application provides a data processing device, including a data transmission unit 1002 and a processor 1001;

[0162] The data transmission unit 1002 is configured to perform:

[0163] Obtain driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by the checkpoint equipment closest to the target service area;

[0164] The processor 1001 is configured to execute:

[0165] According to the driving data of the target vehicle, the probability of the target vehicle entering the target service area is determined;

[0166] If the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area;

[0167] Determine an estimated stay time interval of the target vehicle in the target service area based on an estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and an estimated stay time of the target vehicle in the target service area;

[0168] The vehicle flow rate of the target service area within a first set time period is determined according to the estimated stay time interval of at least one target vehicle.

[0169] In an optional implementation, the processor 1001 is specifically configured to execute:

[0170] Input the driving data of the target vehicle into the pre-trained first model, the second model and the third model to obtain first label data, second label data and third label data respectively for indicating whether the target vehicle enters the target service area;

[0171] The probability of the target vehicle entering the target service area is obtained according to the first label data, the second label data and the third label data.

[0172] In an optional implementation, the first model is a BP neural network model, the second model is a decision tree model, and the third model is a logistic regression model.

[0173] In an optional implementation, the training samples of the first model, the second model and the third model are the same, and the training samples include:

[0174] N groups of historical driving data of N historical vehicles;

[0175] Wherein, N is a positive integer, and each set of historical driving data includes the travel time, type, location and stop ratio of the historical vehicle on the day of driving; wherein, the stop ratio is the ratio of the number of stops of the historical vehicle passing through the target service area to the total number of passes.

[0176] In an optional implementation, the driving data of the target vehicle in the first set period includes:

[0177] The target vehicle’s time in transit, type, location, and historical stop information at the target service area on the day; the historical stop information includes whether the vehicle stopped, the length of the stop, and the time period of the stop.

[0178] In an optional implementation, the processor 1001 is further configured to execute:

[0179] If there is historical driving data of the target vehicle within the second set time period, then the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area is obtained according to the historical driving time of the target vehicle from the location of the checkpoint device to the target service area;

[0180] If there is no historical driving data of the target vehicle within the second set time period, the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area is obtained based on the historical driving time of all vehicles passing through the checkpoint device from the location of the checkpoint device to the target service area.

[0181] In an optional implementation, the processor 1001 is further configured to execute:

[0182] If there is historical driving data of the target vehicle within the second set time period, then the estimated stay time of the target vehicle in the target service area is obtained according to the historical stay time of the target vehicle in the target service area;

[0183] If there is no historical driving data of the target vehicle within the second set time period, the estimated stay time of the target vehicle in the target service area is obtained based on the historical stay time of all vehicles passing through the checkpoint device in the target service area.

[0184] An embodiment of the present application further provides a computer storage medium, in which computer program instructions are stored. When the instructions are executed on a computer, the computer executes the steps of the above-mentioned method for estimating the traffic flow of a service area.

[0185] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0186] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0187] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0189] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for estimating the traffic flow in a service area, characterized in that: include: Acquire driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by a checkpoint device closest to a target service area; Determining the probability of the target vehicle entering the target service area according to the driving data of the target vehicle; If the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area; Determine an estimated stay time interval of the target vehicle in the target service area according to an estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and an estimated stay time of the target vehicle in the target service area; The vehicle flow rate of the target service area within a first set time period is determined according to the estimated stay time interval of at least one target vehicle.

2. The method according to claim 1, characterized in that The determining, based on the driving data of the target vehicle, the probability that the target vehicle enters the target service area includes: Inputting the driving data of the target vehicle into a pre-trained first model, a second model, and a third model to obtain first label data, second label data, and third label data, respectively, for indicating whether the target vehicle enters the target service area; The probability of the target vehicle entering the target service area is obtained according to the first label data, the second label data and the third label data.

3. The method according to claim 2, characterized in that The first model is a BP neural network model, the second model is a decision tree model, and the third model is a logistic regression model.

4. The method according to claim 2 or 3, characterized in that: The training samples of the first model, the second model and the third model are the same, and the training samples include: N groups of historical driving data of N historical vehicles; Wherein, N is a positive integer, and each set of historical driving data includes the transit time, type, location and stop ratio of the historical vehicle on the day of travel; wherein, the stop ratio is the ratio of the number of stops of the historical vehicle passing through the target service area to the total number of passes.

5. The method according to claim 1, characterized in that The driving data of the target vehicle includes: The target vehicle's on-the-go time, type, and location on that day, as well as historical stop information at the target service area; wherein the historical stop information includes whether to stop, the length of stay, and the time period of stay.

6. The method according to any one of claims 1 to 3 and 5, characterized in that: The method further comprises: If there is historical driving data of the target vehicle within the second set time period, then the estimated driving time of the target vehicle from the location of the bayonet device to the target service area is obtained based on the historical driving time of the target vehicle from the location of the bayonet device to the target service area; or If there is no historical driving data of the target vehicle within the second set time period, the estimated driving time of the target vehicle from the location of the checkpoint device to the target service area is obtained based on the historical driving time of all vehicles passing through the checkpoint device from the location of the checkpoint device to the target service area.

7. The method according to any one of claims 1 to 3 and 5, characterized in that: The method further comprises: If there is historical driving data of the target vehicle within the second set time period, then the estimated stay time of the target vehicle in the target service area is obtained according to the historical stay time of the target vehicle in the target service area; or If there is no historical driving data of the target vehicle within the second set time period, the estimated stay time of the target vehicle in the target service area is obtained according to the historical stay time of all vehicles passing through the checkpoint device in the target service area.

8. A data processing device, characterized in that: including a data transmission unit and a processor; The data transmission unit is configured to perform: Acquire driving data of a target vehicle; wherein the target vehicle is a vehicle monitored by a checkpoint device closest to a target service area; The processor is configured to perform: Determining the probability of the target vehicle entering the target service area according to the driving data of the target vehicle; If the probability is greater than the probability threshold, it is determined that the target vehicle will enter the target service area; Determine an estimated stay time interval of the target vehicle in the target service area according to an estimated travel time of the target vehicle from the location of the checkpoint device to the target service area and an estimated stay time of the target vehicle in the target service area; The vehicle flow rate of the target service area within a first set time period is determined according to the estimated stay time interval of at least one target vehicle.

9. The data processing device according to claim 8, characterized in that The processor is specifically configured to execute: Inputting the driving data of the target vehicle into a pre-trained first model, a second model, and a third model to obtain first label data, second label data, and third label data, respectively, for indicating whether the target vehicle enters the target service area; The probability of the target vehicle entering the target service area is obtained according to the first label data, the second label data and the third label data.

10. The data processing device according to claim 9, characterized in that The first model is a BP neural network model, the second model is a decision tree model, and the third model is a logistic regression model.

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