CN118280144A - Method, device, equipment and storage medium for predicting arrival time of vehicle - Google Patents

Abstract

The application provides a method, a device, equipment and a storage medium for predicting the arrival time of a vehicle, which are applied to the fields of traffic, maps and vehicles and relate to the computer technology; in the application, at least one historical brush code data and at least one historical vehicle data are acquired; each history code brushing data comprises a code brushing machine identifier, history code brushing time and history code brushing position information; each historical vehicle data includes a vehicle identification, a historical arrival time, and historical station location information; the time period of the at least one historical brushing code data and the at least one historical vehicle data are consistent; determining a first mapping relationship of the barcode brusher and the vehicle based on the at least one historical barcode brusher data and the at least one historical vehicle data; and determining a target code brushing machine corresponding to the vehicle to be predicted based on the first mapping relation, and predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine. In case of failure of the urban traffic server, the prediction of the arrival time of the vehicle is realized.

Description

Method, device, equipment and storage medium for predicting arrival time of vehicle

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for predicting arrival time of a vehicle.

Background

With the development of technology, various applications that can provide a prediction of the arrival time of a vehicle have been developed. The application program can be used for predicting the arrival time of the vehicle, so that the target object can travel conveniently, and waiting time is reduced.

Currently, when the vehicle arrival time prediction is performed by an application program for providing the vehicle arrival time prediction, the prediction is mainly performed by an application server based on real-time data of the vehicle transmitted by an urban traffic server. However, the urban traffic server is affected by importance of real-time bus service, link construction, disaster tolerance of service architecture, etc., and the urban traffic server failure, or network failure of the urban traffic server, etc. usually occurs. When the urban traffic server fails or the network fails, the real-time data of the vehicle cannot be synchronized to the application server of the application program in time, or the interruption time of the real-time data of the vehicle is long due to untimely maintenance of the failure or time and labor waste in maintenance, and the application server cannot acquire the real-time data of the vehicle, so that the application server cannot predict the arrival time of the vehicle.

Therefore, how to implement the prediction of the arrival time of vehicles in the case of failure of an urban traffic server is a technical problem that needs to be solved at present.

Disclosure of Invention

The embodiment of the application provides a method, a device, equipment and a storage medium for predicting vehicle arrival time, which are used for realizing vehicle arrival time prediction under the condition of failure of an urban traffic server.

In a first aspect, an embodiment of the present application provides a method for predicting a vehicle arrival time, including:

Acquiring at least one historical brush code data and at least one historical vehicle data; wherein each history brush code data includes: the code brushing machine identification, the historical code brushing time and the historical code brushing position information; each historical vehicle data includes: vehicle identification, historical arrival time and historical station location information; the time period of the at least one historical brushing code data and the at least one historical vehicle data are consistent;

determining a first mapping relationship of the barcode brusher and the vehicle based on the at least one historical barcode brusher data and the at least one historical vehicle data;

And determining a target code brushing machine corresponding to the vehicle to be predicted based on a first mapping relation between the code brushing machine and the vehicle, and predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine.

In a second aspect, an embodiment of the present application provides a vehicle arrival time prediction apparatus, including:

An acquisition unit for acquiring at least one history barcode data and at least one history vehicle data; wherein each history brush code data includes: the code brushing machine identification, the historical code brushing time and the historical code brushing position information; each historical vehicle data includes: vehicle identification, historical arrival time and historical station location information; the time period of the at least one historical brushing code data and the at least one historical vehicle data are consistent;

A determining unit for determining a first mapping relationship of the barcode brusher and the vehicle based on the at least one historical barcode brushing data and the at least one historical vehicle data;

The prediction unit is used for determining a target code brushing machine corresponding to the vehicle to be predicted based on a first mapping relation between the code brushing machine and the vehicle, and predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine.

In a possible implementation, the determining unit is specifically configured to:

Determining at least one vehicle integrated area based on historical site location information included in each of the at least one historical vehicle data, and dividing the at least one vehicle integrated area into at least one grid area respectively;

Based on the history code brushing position information respectively included in the history code brushing data, mapping the history code brushing data to corresponding grid areas respectively to obtain a second mapping relation between the code brushing machine and the grid areas;

mapping at least one historical vehicle data to a corresponding grid area respectively based on the historical site position information respectively included in the at least one historical vehicle data to obtain a third mapping relation between the vehicle and the grid area;

and determining a first mapping relation between the code brushing machine and the vehicle based on the second mapping relation and the third mapping relation.

In one possible implementation, the historical site location information includes city information and site latitude and longitude information; the determining unit is specifically configured to:

classifying at least one historical vehicle data based on the city information to obtain at least one historical vehicle data set corresponding to the city information;

for at least one of the affiliated city information, the following operations are performed: selecting the longitude and latitude information of the maximum station and the longitude and latitude information of the minimum station from a historical vehicle data set corresponding to the city information; and determining a vehicle comprehensive area corresponding to the city information based on the maximum station longitude and latitude information and the minimum station longitude and latitude information.

In a possible implementation, the determining unit is further configured to:

After dividing at least one vehicle comprehensive area into at least one grid area respectively, before mapping at least one history brush code data to the corresponding grid area respectively, determining a first grid coordinate value based on a first difference value between maximum site longitude information and minimum site longitude information and a configured first grid size;

Determining a second grid coordinate value based on a second difference between the maximum site latitude information and the minimum site latitude information in a configured second grid size;

based on the first grid coordinate value and the second grid coordinate value, respective grid coordinates of at least one grid region are determined.

In a possible implementation, the determining unit is specifically configured to:

determining at least one target grid area corresponding to one code brushing machine based on the second mapping relation;

Determining at least one candidate vehicle corresponding to each of the at least one target grid region based on the third mapping relation;

Determining all candidate vehicles corresponding to one code brushing machine based on at least one target grid area corresponding to one code brushing machine and at least one candidate vehicle corresponding to each of the at least one target grid area;

And selecting a target vehicle meeting the target condition from all the candidate vehicles, and determining a first mapping relation between the code brushing machine and the target vehicle based on the selected target vehicle.

In a possible implementation, the determining unit is specifically configured to:

For one of all the candidate vehicles, the following operations are performed, respectively:

Determining a first historical vehicle data composite value for a candidate vehicle in at least one target grid region; and a second historical vehicle data composite value for all candidate vehicles in the at least one target grid region;

determining a target score for a candidate vehicle based on a ratio between the first historical vehicle data synthesis value and the second historical vehicle data synthesis value; the target score is used for representing the matching degree of the candidate vehicle and one code brushing machine;

and selecting the candidate vehicle with the largest target score from all the candidate vehicles as the target vehicle.

In a possible implementation, the determining unit is specifically configured to:

For at least one target grid region, the following operations are performed:

determining all historical code brushing times and corresponding historical code brushing position information of a code brushing machine in a target grid area, and determining historical arrival times and corresponding historical site position information of a candidate vehicle in the target grid area;

matching the historical brushing time with the historical arrival time to obtain a first matching result, and matching the historical brushing position information with the historical site position information to obtain a second matching result;

Determining a target number of successful first matching results and successful second matching results, and taking the target number as a first historical vehicle data value;

and adding the first historical vehicle data values corresponding to the at least one target grid region respectively to obtain a first historical vehicle data comprehensive value.

In one possible implementation, the prediction unit is specifically configured to:

Matching the target brushing code position information in the target brushing code data with the station position information of the route to which the vehicle to be predicted belongs to obtain target station position information successfully matched;

and predicting the arrival time of the vehicle to be predicted based on the successfully matched target station position information.

In a third aspect, embodiments of the present application provide a computing device comprising: a memory and a processor, wherein the memory is for storing computer instructions; and the processor is used for executing computer instructions to realize the steps of the vehicle arrival time prediction method provided by the embodiment of the application.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the vehicle arrival time prediction method provided by the embodiments of the present application.

In a fifth aspect, embodiments of the present application provide a computer program product comprising computer instructions stored in a computer readable storage medium; when the processor of the computing device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the computing device to perform the steps of the vehicle arrival time prediction method provided by the embodiments of the present application.

The application has the following beneficial effects:

The embodiment of the application provides a method, a device, equipment and a storage medium for predicting vehicle arrival time, relates to the technical field of computers and relates to the technical field of maps; in order to solve the problem that the vehicle arrival time prediction cannot be performed based on the vehicle data provided by the urban traffic server under the condition of the urban traffic server fault or the network fault, the application provides a mode for performing the vehicle arrival time prediction based on the code-brushing data as the supplementary data. Therefore, in order to accurately predict the arrival time of the vehicle based on the code brushing data, a first mapping relation between the vehicle and the code brushing machine is firstly determined based on the historical code brushing data and the historical vehicle data so as to ensure that the code brushing data corresponding to the code brushing machine can be smoothly used as the supplementary data of the corresponding vehicle; then, determining a target code brushing machine corresponding to the vehicle to be predicted based on the first mapping relation, and acquiring target code brushing data of the target code brushing machine; and finally, predicting the arrival time of the vehicle to be predicted based on the target brushing data. Under the condition that the data source of the urban traffic server is interrupted, the prediction of the arrival time of the vehicle is carried out, the use experience is improved, and the availability of the real-time vehicle query server is further improved.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic illustration of a page in which vehicle arrival time cannot be predicted;

FIG. 2 is a schematic diagram of a page for successfully predicting the arrival time of a vehicle according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an application scenario in an embodiment of the present application;

FIG. 4 is a flowchart of a method for predicting vehicle arrival time according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a grid matrix according to an embodiment of the present application;

FIG. 6 is a schematic diagram of another grid matrix in accordance with an embodiment of the present application;

FIG. 7 is a schematic diagram of a historical vehicle information mapping to grid areas according to an embodiment of the present application;

FIG. 8 is a schematic diagram illustrating a mapping of historical brush code information to grid areas according to an embodiment of the present application;

FIG. 9 is a flowchart of a specific implementation of a vehicle arrival time prediction in an embodiment of the present application;

FIG. 10 is a block diagram of a vehicle arrival time prediction apparatus according to an embodiment of the present application;

FIG. 11 is a block diagram of a computing device in accordance with an embodiment of the present application;

FIG. 12 is a block diagram of another computing device in accordance with an embodiment of the application.

Detailed Description

In order to make the objects, technical solutions and advantageous effects of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Some terms in the embodiments of the present application are explained below to facilitate understanding by those skilled in the art.

The vehicle to be predicted is a vehicle of which the target object inputs the arrival time to be queried in an application program for providing the prediction of the arrival time of the vehicle; the vehicle to be predicted can be determined by a line bus number directly input by the target object, such as '32 paths'; or the vehicle to be predicted is "32 ways" if the buses corresponding to the points a and B are "32 ways" based on the start point and the end point of the input of the target object.

The code brushing machine is equipment installed on a bus and used for acquiring the riding condition of a target object. In the embodiment of the application, the target object is ridden by a car by a code brushing machine, and the code brushing machine can acquire information such as the riding amount, the code brushing time, the terminal equipment identification, the code brushing place and the like. The target object is a passenger taking a bus.

The following briefly describes the design concept of the embodiment of the present application.

With the development of technology, various applications that can provide a vehicle arrival time prediction function have been developed. For example, a map application for route inquiry, a payment application for payment of a ride amount, an instant messaging application, etc. may provide a vehicle arrival time prediction function. The application program can be used for predicting the arrival time of the vehicle, so that the target object can travel conveniently, and waiting time is reduced.

At present, in the field of real-time buses, bus original data of a bus prediction arrival time inquiry service provided by an application program for predicting the arrival time of a vehicle originate from urban traffic servers of various cities, namely, the application server predicts the arrival of the vehicle based on real-time data of the vehicle transmitted by the urban traffic servers. However, the urban traffic server is affected by importance of real-time bus service, link construction, disaster tolerance of service architecture, etc., so that the phenomena of urban traffic server failure, network failure of the urban traffic server, etc. occur frequently.

When the urban traffic server fails or the network fails, the real-time data of the vehicle cannot be synchronized to the application server of the application program in time, so that the real-time data of the vehicle is interrupted. And after the data is interrupted, the real-time data of the vehicle is interrupted for a long time due to untimely maintenance or time and labor waste in maintenance, and the like, so that the application server cannot acquire the real-time data of the vehicle. The real-time data of the vehicle is interrupted, so that the application server cannot stably predict the arrival time of the vehicle.

In the related art, after the real-time data of the vehicle is interrupted, the vehicle arrival display is not provided externally, and the data interruption of the data source is directly displayed, as shown in fig. 1, the method is a page schematic diagram which cannot predict the arrival time of the vehicle; or in a short period of interruption, the application server generally adopts a vehicle position prediction technology to perform position estimation and externally gives the calculated vehicle arrival time, but in general, the available estimation time is limited, and the prediction accuracy can be rapidly reduced along with the migration of time, so that the general prediction has strict time length limitation, and the display page shown in fig. 1 is displayed after the limitation exceeds the limit.

Therefore, how to realize stable prediction of the arrival time of vehicles under the condition of urban traffic server faults or network faults is a technical problem to be solved at present.

According to the embodiment of the application, in consideration of the development of the riding code technology, as the number of target objects riding by the terminal equipment is increased increasingly, riding information of the target objects can be mined by the data, and further, the arrival time prediction of the vehicle can be carried out based on the riding information of the target objects. In view of this, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for predicting a vehicle arrival time, which are used for implementing stable prediction of the vehicle arrival time based on the brush code data as a supplementary data source in the case that real-time vehicle data cannot be acquired, so as to improve the use experience of an application program for providing the prediction of the vehicle arrival time, and further improve the availability of real-time bus inquiry service.

In one possible implementation, at least one historical barcode data is obtained, and at least one historical vehicle data; each history brush code data includes: the code brushing machine identification, the historical code brushing time and the historical code brushing position information; each historical vehicle data includes: vehicle identification, historical arrival time and historical station location information; the time period of the at least one historical brushing code data and the at least one historical vehicle data are consistent; determining a first mapping relationship of the barcode brusher and the vehicle based on the at least one historical barcode brusher data and the at least one historical vehicle data; and determining a target code brushing machine corresponding to the vehicle to be predicted based on the first mapping relation, predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine, and displaying the predicted arrival time to remind the target object of the arrival time of the vehicle. FIG. 2 is a schematic diagram of a page showing a successful prediction of vehicle arrival time.

According to the application, the code brushing data is used as a supplementary data source, so long as a target object is in a vehicle, the service for predicting the arrival time can be continuously provided, and the more the number of passengers in the vehicle is, the more the code brushing stations are covered, the more accurate the arrival time is predicted, so that the stable prediction of the arrival time of the vehicle is realized under the condition that the data source of the urban traffic server is interrupted, the use experience is improved, and the availability of the real-time vehicle inquiry server is further improved.

After the design concept of the embodiment of the present application is introduced, the application scenario set by the present application is briefly described below. It should be noted that the following scenario is only for illustrating the embodiments of the present application, and is not limiting. In the specific implementation, the technical scheme provided by the embodiment of the application can be flexibly applied according to actual needs.

Referring to fig. 3, fig. 3 is a schematic view of an application scenario provided in an embodiment of the present application, where the application scenario includes: the terminal device 310, the vehicle 320 with the code brushing machine, the urban traffic server 330 and the application server 340 can communicate with each other through a communication network, the vehicle 320 with the code brushing machine and the application server 340 can communicate with each other through a communication network, and the terminal device 310 and the application server 340 can communicate with each other through a communication network.

In an alternative embodiment, the communication network may be a wired network or a wireless network. Thus, the devices may be directly or indirectly connected by wired or wireless communication. For example, the terminal device 310 may be indirectly connected to the application server 340 through a wireless access point, or the terminal device 310 may be directly connected to the application server 340 through the internet, which is not limited herein.

In the embodiment of the present application, the terminal device 310 includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a desktop computer, an electronic book reader, an intelligent voice interaction device, an intelligent home appliance, a vehicle-mounted terminal, and the like; various clients can be installed on the terminal equipment, and the clients can be application programs (such as a browser, game software and the like) for realizing the prediction of the arrival time of the vehicle, and can also be web pages, applets and the like;

The application server 340 is a backend server corresponding to a client installed in the terminal apparatus 310. The application server 340 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, a content distribution network (Content Delivery Network, CDN), basic cloud computing services such as big data and an artificial intelligence platform.

It should be noted that, the method for predicting the arrival time of the vehicle in the embodiment of the present application may be deployed in a computing device, where the computing device may be a server or a terminal device, where the server may be the application server 340 shown in fig. 3, and the terminal device may be the terminal device 310 shown in fig. 3.

The illustration in fig. 3 is merely exemplary, and in fact, the number of terminal devices 310 and application servers 340 is not limited, and is not specifically limited in the embodiment of the present application.

In the embodiment of the present application, when the number of application servers 340 is plural, the plural application servers 340 may be formed into a blockchain, and the application servers 340 are nodes on the blockchain.

Based on the above application scenario, the vehicle arrival time prediction method provided by the exemplary embodiment of the present application is described below with reference to the above application scenario described above, and it should be noted that the above application scenario is only shown for the convenience of understanding the spirit and principle of the present application, and the embodiment of the present application is not limited in any way in this respect.

Referring to fig. 4, fig. 4 is a flowchart of a method for predicting arrival time of a vehicle according to an embodiment of the present application, including the following steps:

Step S401, acquiring at least one historical barcode brushing data and at least one historical vehicle data.

Wherein each history brush code data includes: a brusher identification (device_id), a historical brusher time (timestamp), and historical brusher location information; each historical vehicle data includes: vehicle identification (bus_id), historical arrival time, and historical site location information.

In order to continuously predict the arrival time of a vehicle under the condition of urban traffic server faults or network faults, the embodiment of the application provides an implementation mode for predicting the arrival time of the vehicle based on code brushing data. In order to predict the arrival time of the vehicle based on the data of the code brushing machine and ensure the accuracy of the prediction, the mapping relationship between the code brushing machine and the vehicle should be determined, i.e. which vehicle a certain code brushing machine is installed on.

Illustratively, a first mapping relationship between the brusher and the vehicle is determined based on the historical brusher data and the historical vehicle data. In order to ensure the accuracy of the first mapping relationship, the historical code brushing data and the historical vehicle data in the same time period should be obtained; for example, historical brush code data and historical vehicle data generated on the same day are acquired.

In one possible implementation, historical vehicle data is obtained from an urban traffic server, historical swipe code data is obtained from a swipe code machine, or historical swipe code data is obtained from a terminal device for swipe codes.

After the historical code brushing data and the historical vehicle data in the same time period are obtained, offline processing is performed on the historical code brushing data and the historical vehicle data, and a first mapping relation between the digital machine and the vehicle is mined, and in particular, step S402 is shown in detail.

Step S402, a first mapping relationship between the brusher and the vehicle is determined based on at least one historical brusher data and at least one historical vehicle data.

In the embodiment of the application, when the first mapping relation between the code brushing machine and the vehicle is determined based on the historical code brushing data and the historical vehicle data, the code brushing machine and the vehicle with the same driving route can be determined in consideration of the fact that the code brushing machine is installed on the vehicle and the target object can brush the code to take a vehicle when the vehicle arrives at a station, namely the code brushing position information of the code brushing taking the vehicle is matched with the station position information of the vehicle arriving at the station, and the code brushing time is matched with the time of arriving at the station. Therefore, when the brushing position information is matched with the station position information and the brushing time is matched with the arrival time of the vehicle, the first mapping relation between the brushing machine and the vehicle is indicated.

Therefore, in the embodiment of the application, the first mapping relation between the code brushing machine and the vehicle is determined based on analysis processing of the historical code brushing time and the historical code brushing position information corresponding to the same code brushing machine, and the historical arrival time and the historical station position information corresponding to the same vehicle.

Next, a detailed description will be given of determining a first mapping relationship of the barcode brusher and the vehicle based on the history barcode brusher data and the history vehicle data.

Firstly, considering that a large amount of historical code brushing data and historical vehicle data are generated in the same time period, if all the historical code brushing data and the historical vehicle data are analyzed together, the calculated amount is large, and time is wasted, the historical digital data and the historical vehicle data are divided in the embodiment of the application;

The historical site location information comprises city information and site longitude and latitude information of a site, and historical vehicle data belonging to the same city are divided into the same historical vehicle data set based on the city information of the site; as shown in table 1:

TABLE 1

City	Historical vehicle data set

The historical brushing code position information comprises city information of brushing codes and longitude and latitude information of brushing codes, and the historical brushing code data belonging to the same city are divided into the same historical brushing code data set based on the city information of the brushing codes; as shown in table 2:

TABLE 2

City	Historical brush code data set

After being divided according to cities, a plurality of historical vehicle data sets and historical digital data sets corresponding to the cities can be obtained; for example, city a corresponds to historical vehicle data set a and historical brush code data set a. And then, the first mapping relation between the code brushing machine and the vehicle in each city is determined in parallel by taking the city as a unit, so that the calculation efficiency is improved.

It should be noted that, the dividing process of the obtained historical code brushing data and the obtained historical vehicle data in the same time period is only illustrated by taking the city as a unit, and the historical code brushing data and the historical vehicle data may also be performed by taking the area as a unit in the embodiment of the present application.

Next, when a first mapping relation between a code brushing machine and a vehicle in a city is determined by taking the city as a unit, station longitude and latitude information contained in each piece of historical vehicle data in the historical vehicle data set is determined for the historical vehicle data set corresponding to the city, the maximum station longitude and latitude information and the minimum station longitude and latitude information are selected from all pieces of station longitude and latitude information, then a vehicle comprehensive area corresponding to the city is determined based on the maximum station longitude and latitude information and the minimum station longitude and latitude information, and the vehicle comprehensive area is divided into a plurality of grid areas to obtain a corresponding grid matrix, as shown in fig. 5, the grid matrix schematic diagram is provided. As can be seen from fig. 5, the maximum value (lng_max, lat_max) and the minimum value (lng_min ) are found in the longitude and latitude information of all sites in the same city, the vehicle comprehensive area is determined based on the maximum value and the minimum value, and then the vehicle comprehensive area is segmented to generate a corresponding grid matrix.

In another possible implementation manner, in order to ensure the accuracy of calculation, the vehicle comprehensive area and the grids in the vehicle comprehensive area are converted into grid coordinates by longitude and latitude information; illustratively, the grid coordinates of the minimum value (lng_min ) are set to coordinates (0, 0), and the grid coordinates of the maximum value (lng_max, lat_max) are set to (m, n), where m= (lng_max-lng_min)/block_size, n= (lat_max-lat_min)/block_size; the block_size is a mesh distance used when dividing the vehicle integrated area into a plurality of meshes; fig. 6 is a schematic diagram of another grid matrix according to an embodiment of the present application.

After the grid matrix is generated, based on the site longitude and latitude information contained in the historical vehicle data, mapping each historical vehicle data in the historical vehicle data set to a corresponding grid area respectively, and obtaining a third mapping relation between the vehicle and the grid area.

In the embodiment of the application, when the historical vehicle data is mapped to the corresponding grid area based on the site longitude and latitude information, if the grid matrix shown in fig. 5 is adopted, the mapping is directly performed based on the site longitude and latitude information, if the grid matrix shown in fig. 6 is adopted, the site longitude and latitude information is converted into grid coordinates (x, y), and the mapping is performed based on the grid coordinates (x, y), wherein x= (lng-lng_min)/block_size, y= (lat-lat_min)/block_size.

For the convenience of calculation, when each historical vehicle data in the historical vehicle data set is mapped into a corresponding grid area respectively, the historical vehicle data of the same vehicle is mapped into different grid areas by taking the vehicle as a unit; therefore, before mapping each historical vehicle data in the historical vehicle data set to a corresponding grid area, classifying the historical vehicle data in the historical vehicle data set according to the vehicle identification to obtain a historical vehicle data subset corresponding to each vehicle; for example, the historical vehicle data of one vehicle day is combined in a sequencing way according to the historical site position information and the historical arrival time;

In addition, when the same vehicle runs on a route, the position of the station on the route is not changed, and only the arrival time of the vehicle is different, so that the historical vehicle data subset can be further subjected to data processing to determine a historical arrival time set corresponding to each station position; as shown in table 3:

TABLE 3 Table 3

Therefore, when the history vehicle data of the same vehicle is mapped to different mesh areas, a plurality of pieces of history vehicle information of the same vehicle can be mapped to different mesh areas based on only the station position information. Fig. 7 is a schematic diagram of mapping historical vehicle information to a grid area according to an embodiment of the present application. The route of the vehicle A comprises a station A1 and a station A2 … … and a station A6; site A1 maps into grid area 0, with a corresponding arrival time of 6: 00. 8: 30. 9: 00. 11: 30. 12:00, etc.; site A2 maps into grid area 7 with a corresponding arrival time of 6: 10. 8: 20. 9: 10. 11: 20. 12:10, etc.; site A3 maps into grid area 8 with a corresponding arrival time of 6: 20. 8: 10. 9: 20. 11: 10. 12:20, etc.; site A4 maps into grid area 15 with a corresponding arrival time of 6: 30. 8: 00. 9: 30. 11: 00. 12:30, etc.; site A5 maps into grid area 16 with a corresponding arrival time of 6: 40. 7: 50. 9: 40. 10: 50. 12:40, etc.; site A6 maps into grid area 23 with a corresponding arrival time of 7: 00. 7: 30. 10: 00. 10: 30. 13:00, etc.

After generating the grid matrix, mapping each historical code brushing data in the historical code brushing data set to a corresponding grid area respectively based on the longitude and latitude information of the code brushing data contained in the historical code brushing data, and obtaining a second mapping relation between the code brushing machine and the grid area;

In the embodiment of the application, when the historical code brushing data is mapped to the corresponding grid area based on the code brushing longitude and latitude information, if the grid matrix shown in fig. 5 is adopted, the mapping is directly performed based on the code brushing longitude and latitude information, and if the grid matrix shown in fig. 6 is adopted, the code brushing longitude and latitude information is converted into grid coordinates and the mapping is performed based on the grid coordinates.

For the convenience of calculation, when each historical code brushing data in the historical code brushing data set is mapped to a corresponding grid area respectively, the historical digital data of the same code brushing machine are mapped to different grid areas by taking the code brushing machine as a unit; therefore, before each history code brushing data in the history code brushing machine data set is mapped to a corresponding grid area, the history code brushing data in the history code brushing data set is classified according to the code brushing machine identification, and a history code brushing data subset corresponding to each code brushing machine is obtained; for example, the historical code brushing data of one day of a code brushing machine are sequenced and combined according to the historical code brushing position information and the code brushing time;

The fact that the site position on the route is not changed by the same code brushing machine is considered, and only the code brushing time corresponding to the code brushing machine is different is considered, so that the historical code brushing data subset can be further subjected to data processing, and a historical code brushing time set corresponding to each code brushing position is determined; as shown in table 4:

TABLE 4 Table 4

Therefore, when the historical brushing data of the same brushing machine are mapped into different grid areas, a plurality of pieces of historical brushing information of the same brushing machine can be mapped into different grid areas only based on the brushing position information. Fig. 8 is a schematic diagram illustrating mapping of historical brush code information to grid areas according to an embodiment of the present application.

In the third mapping relationship between the vehicle and the grid area, which is obtained by the embodiment of the application: one grid area corresponds to a plurality of vehicles, and one vehicle corresponds to a plurality of grid areas; in the second mapping relation between the code brushing machine and the grid area, which is obtained by the embodiment of the application: one grid area corresponds to a plurality of code brushing machines, and one code brushing machine corresponds to a plurality of grid areas.

After the second mapping relation and the third mapping relation are obtained, determining a first mapping relation between the code brushing machine and the vehicle based on the second mapping relation and the third mapping relation.

In the following, a description will be given of determining a mapping relationship between a code brushing machine i and a corresponding target vehicle, taking the code brushing machine i as an example.

Firstly, for one code brushing machine i, determining a plurality of target grid areas corresponding to the code brushing machine i based on a second mapping relation between the code brushing machine and the grid areas, and determining historical code brushing data of the code brushing machine i corresponding to each target grid area;

Determining candidate vehicles corresponding to each target area in the plurality of target areas based on a third mapping relation between the vehicles and the grid areas so as to obtain historical vehicle data corresponding to the candidate vehicles;

Acquiring historical code brushing data of a code brushing machine i in a target grid area and historical vehicle data corresponding to at least one candidate vehicle; determining all historical code brushing time and corresponding historical code brushing position information of a code brushing machine i in a target grid area, and determining historical arrival time and corresponding historical site position information of a candidate vehicle in the target grid area;

Then, for one candidate vehicle j, matching the historical brushing time of the brushing machine i with the historical arrival time of the candidate vehicle j to obtain a first matching result, and matching the historical brushing position information of the brushing machine i with the historical site position information of the candidate vehicle j to obtain a second matching result; determining a target number of successful first matching results and successful second matching results, and taking the target number as a first historical vehicle data value;

then, adding the first historical vehicle data values corresponding to the at least one target grid area respectively to obtain a first historical vehicle data comprehensive value; and determining a second historical vehicle data composite value for all candidate vehicles in the at least one target grid region, the second historical vehicle data composite value being a sum of the first historical vehicle data composite values;

Finally, determining a target score for the candidate vehicle based on the ratio between the first historical vehicle data integrated value and the second historical vehicle data integrated value; the target score is used for representing the matching degree of the candidate vehicle and one code brushing machine; and selecting the candidate vehicle with the largest target score from all the candidate vehicles as the target vehicle.

In another possible implementation, for the historical barcode brushing data under one barcode brushing machine, the historical barcode brushing data of the barcode brushing machine in the same grid and the historical vehicle data of a plurality of candidate vehicles under the same grid are acquired first. The method comprises the steps of carrying out time position clustering segmentation on historical code brushing data of a code brushing machine, for example, dividing points with a distance of 100 meters and a time of 5 minutes into one section, respectively counting historical vehicle data of candidate vehicles meeting conditions in each section, marking the candidate vehicles meeting the conditions, recording the number of the historical code brushing data of the covered code brushing machine, filtering the historical code brushing data, generating a mapping list from the code brushing machine to a plurality of candidate vehicles for each grid, counting the number of the historical code brushing data of the covered code brushing machine for all the candidate vehicles, and determining the target score of each candidate vehicle: Where m is the total number of the grid matrix, n is the number of segments of a candidate vehicle in one grid, gps_cnt is the historical brushing data number corresponding to the brushing machine in a certain segment, and total_cnt is the historical brushing data number of the total brushing machine.

In the embodiment of the application, in order to ensure the accuracy of the first mapping relation between the code brushing machine and the vehicle, a plurality of days of statistical verification is required. And (3) checking the result of a plurality of days, such as 30 days, of the same code brushing machine, wherein more than 80% of days are mapped to the same vehicle, and the code brushing machine and the vehicle are considered to be in a one-to-one mapping relation. And (3) carrying out coverage statistics on vehicles on the same line, and outputting a mapping table from the code brushing machine to the vehicle if the code brushing machine appearing on the same day can cover 80% of real-time vehicle data.

Step S403, determining a target code brushing machine corresponding to the vehicle to be predicted based on a first mapping relation between the code brushing machine and the vehicle, and predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine.

In one possible implementation manner, when a vehicle arrival time query instruction input by a target object for a vehicle to be predicted is received, but an urban traffic server fails or a network signal is interrupted, and real-time vehicle data cannot be acquired from the urban traffic server, determining a target code brushing machine corresponding to the vehicle to be predicted based on a first mapping relation between the vehicle and the code brushing machine, acquiring target code brushing data corresponding to the target code brushing machine, and then predicting the arrival time of the vehicle to be predicted based on the acquired target code brushing data.

In consideration of the fact that the target brushing data are generated when the vehicle arrives at the station, the brushing position information in the target brushing data and the station position information in the route of the vehicle to be predicted need to be matched, the target station position information which is successfully matched is obtained, and the arrival time of the vehicle to be predicted is predicted based on the target station position information, the query station position information in the vehicle arrival time query instruction and the brushing time corresponding to the target station position.

Illustratively, a predicted Arrival time (ESTIMSTED TIME of Arrival, ETA) service is invoked, a historical travel time from the target site location information to the query site location information is determined based on the historical data, and a Arrival time of the vehicle to be predicted is predicted based on the historical travel time and a corresponding brush time for the target site location.

In one possible implementation manner, in order to ensure the accuracy of the prediction result, after the target code brushing data corresponding to the target code brushing machine is acquired, invalid data in the target code brushing data is filtered, so as to obtain code brushing data which accords with a real-time vehicle data source approximately acquired from the urban traffic server.

After obtaining the target brushing data, firstly performing site position matching, and discarding the brushing position information and the brushing time corresponding to the brushing position information if the distance between the brushing position information in the target brushing data is greater than a set first distance threshold compared with the nearest site position information in the line of the vehicle to be predicted; and if the brushing position information in the latest acquired target brushing data is compared with the brushing position information in the previous target brushing data, discarding the latest acquired target brushing data to filter the abnormal redundant data when the distance between the brushing position information and the brushing position information is smaller than a set second distance threshold value.

The first distance threshold and the second distance threshold are set according to actual conditions.

Referring to fig. 9, fig. 9 is a flowchart of a specific implementation method for predicting the arrival time of a vehicle according to an embodiment of the present application, including the following steps:

step S900, at least one historical brushing data and at least one historical vehicle data in the same time period are obtained;

step S901, dividing at least one historical code brushing data and at least one historical vehicle data according to the city information to obtain a historical code brushing data set and a historical vehicle data set corresponding to each city respectively;

step S902, determining the longitude and latitude information of the maximum station and the longitude and latitude information of the minimum station in a historical vehicle data set corresponding to one city;

Step S903, determining a vehicle comprehensive area based on the maximum station longitude and latitude information and the minimum station longitude and latitude information, and dividing the vehicle comprehensive area into a plurality of grid areas;

Step S904, based on the longitude and latitude information of the historical site in the historical vehicle data, mapping the historical vehicle data in the historical vehicle data set corresponding to one city to corresponding grid areas respectively, and determining a third mapping relation between the vehicle and the grid areas;

Step S905, based on the longitude and latitude information of the historical code brushing in the historical code brushing data, mapping the historical code brushing data in the historical code brushing data set corresponding to one city to corresponding grid areas respectively, and determining a second mapping relation between the code brushing machine and the grid areas;

Step S906, determining at least one candidate vehicle corresponding to the code brushing machine based on the second mapping relation and the third mapping relation;

step S907, selecting a target vehicle meeting target conditions from at least one candidate vehicle, and determining a first mapping relationship between a code brushing machine and the target vehicle based on the selected target vehicle;

Step S908, when a vehicle arrival time query instruction input by the target object for the vehicle to be predicted is received, but the urban traffic server fails or the network signal is interrupted, determining a target code brushing machine corresponding to the vehicle to be predicted based on a first mapping relationship between the code brushing machine and the vehicle, and predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine.

In order to accurately predict the arrival time of a vehicle based on the code brushing data, the application firstly determines a first mapping relation between the vehicle and a code brushing machine based on the historical code brushing data and the historical vehicle data so as to ensure that the code brushing data corresponding to the code brushing machine can be smoothly used as the supplementary data of the corresponding vehicle; then, determining a target code brushing machine corresponding to the vehicle to be predicted based on the first mapping relation, and acquiring target code brushing data of the target code brushing machine; and finally, predicting the arrival time of the vehicle to be predicted based on the target brushing data. Under the condition that the data source of the urban traffic server is interrupted, the prediction of the arrival time of the vehicle is carried out, the use experience is improved, and the availability of the real-time vehicle query server is further improved.

Based on the same inventive concept, the embodiment of the present application further provides a vehicle arrival time prediction apparatus 1000, as shown in fig. 10, the apparatus 1000 includes:

An obtaining unit 1001, configured to obtain at least one historical barcode brushing data and at least one historical vehicle data; wherein each history brush code data includes: the code brushing machine identification, the historical code brushing time and the historical code brushing position information; each historical vehicle data includes: vehicle identification, historical arrival time and historical station location information; the time period of the at least one historical brushing code data and the at least one historical vehicle data are consistent;

A determining unit 1002 configured to determine a first mapping relationship between the barcode brusher and the vehicle based on at least one historical barcode brushing data and at least one historical vehicle data;

The prediction unit 1003 is configured to determine a target code brushing machine corresponding to the vehicle to be predicted based on a first mapping relationship between the code brushing machine and the vehicle, and predict an arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine.

In one possible implementation, the determining unit 1002 is specifically configured to:

Determining at least one vehicle integrated area based on historical site location information included in each of the at least one historical vehicle data, and dividing the at least one vehicle integrated area into at least one grid area respectively;

Based on the history code brushing position information respectively included in the history code brushing data, mapping the history code brushing data to corresponding grid areas respectively to obtain a second mapping relation between the code brushing machine and the grid areas;

mapping at least one historical vehicle data to a corresponding grid area respectively based on the historical site position information respectively included in the at least one historical vehicle data to obtain a third mapping relation between the vehicle and the grid area;

and determining a first mapping relation between the code brushing machine and the vehicle based on the second mapping relation and the third mapping relation.

In one possible implementation, the historical site location information includes city information and site latitude and longitude information; the determining unit 1002 is specifically configured to:

classifying at least one historical vehicle data based on the city information to obtain at least one historical vehicle data set corresponding to the city information;

for at least one of the affiliated city information, the following operations are performed: selecting the longitude and latitude information of the maximum station and the longitude and latitude information of the minimum station from a historical vehicle data set corresponding to the city information; and determining a vehicle comprehensive area corresponding to the city information based on the maximum station longitude and latitude information and the minimum station longitude and latitude information.

In a possible implementation manner, the determining unit 1002 is further configured to:

After dividing at least one vehicle comprehensive area into at least one grid area respectively, before mapping at least one history brush code data to the corresponding grid area respectively, determining a first grid coordinate value based on a first difference value between maximum site longitude information and minimum site longitude information and a configured first grid size;

Determining a second grid coordinate value based on a second difference between the maximum site latitude information and the minimum site latitude information in a configured second grid size;

based on the first grid coordinate value and the second grid coordinate value, respective grid coordinates of at least one grid region are determined.

In one possible implementation, the determining unit 1002 is specifically configured to:

determining at least one target grid area corresponding to one code brushing machine based on the second mapping relation;

Determining at least one candidate vehicle corresponding to each of the at least one target grid region based on the third mapping relation;

Determining all candidate vehicles corresponding to one code brushing machine based on at least one target grid area corresponding to one code brushing machine and at least one candidate vehicle corresponding to each of the at least one target grid area;

And selecting a target vehicle meeting the target condition from all the candidate vehicles, and determining a first mapping relation between the code brushing machine and the target vehicle based on the selected target vehicle.

In one possible implementation, the determining unit 1002 is specifically configured to:

For one of all the candidate vehicles, the following operations are performed, respectively:

Determining a first historical vehicle data composite value for a candidate vehicle in at least one target grid region; and a second historical vehicle data composite value for all candidate vehicles in the at least one target grid region;

determining a target score for a candidate vehicle based on a ratio between the first historical vehicle data synthesis value and the second historical vehicle data synthesis value; the target score is used for representing the matching degree of the candidate vehicle and one code brushing machine;

and selecting the candidate vehicle with the largest target score from all the candidate vehicles as the target vehicle.

In one possible implementation, the determining unit 1002 is specifically configured to:

For at least one target grid region, the following operations are performed:

determining all historical code brushing times and corresponding historical code brushing position information of a code brushing machine in a target grid area, and determining historical arrival times and corresponding historical site position information of a candidate vehicle in the target grid area;

matching the historical brushing time with the historical arrival time to obtain a first matching result, and matching the historical brushing position information with the historical site position information to obtain a second matching result;

Determining a target number of successful first matching results and successful second matching results, and taking the target number as a first historical vehicle data value;

and adding the first historical vehicle data values corresponding to the at least one target grid region respectively to obtain a first historical vehicle data comprehensive value.

In one possible implementation, the prediction unit 1003 is specifically configured to:

Matching the target brushing code position information in the target brushing code data with the station position information of the route to which the vehicle to be predicted belongs to obtain target station position information successfully matched;

and predicting the arrival time of the vehicle to be predicted based on the successfully matched target station position information.

For convenience of description, the above parts are respectively described as functionally divided into units (or modules). Of course, the functions of each unit (or module) may be implemented in the same piece or pieces of software or hardware when implementing the present application.

Having described the vehicle arrival time prediction method and apparatus of an exemplary embodiment of the present application, another exemplary embodiment of the present application computing device is described next.

Those skilled in the art will appreciate that the various aspects of the application may be implemented as a system, method, or program product. Accordingly, aspects of the application may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

In one possible implementation, a computing device provided by an embodiment of the present application may include at least a processor and a memory. The memory stores program code that, when executed by the processor, causes the processor to perform any of the steps of the vehicle arrival time prediction methods of the various exemplary embodiments of the present application.

In this embodiment, the structure of the computing device may be as shown in fig. 11, including: communication component 1110, memory 1120, display unit 1130, camera 1140, sensor 1150, audio circuit 1160, bluetooth module 1170, processor 1180, and the like.

The communication component 1110 is for communicating with a server. In some embodiments, a circuit wireless fidelity (WIRELESS FIDELITY, WIFI) module may be included, the WiFi module belonging to a short-range wireless transmission technology, through which the computing device may help the user to send and receive information.

Memory 1120 may be used to store software programs and data. The processor 1180 performs various functions of the terminal device and data processing by executing software programs or data stored in the memory 1120. Memory 1120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one type of disk storage device, flash memory device, or other volatile solid-state storage device. The memory 1120 stores an operating system that enables the terminal device to operate. The memory 1120 of the present application may store an operating system and various applications, and may also store code for performing the vehicle arrival time prediction method according to the embodiment of the present application.

The display unit 1130 may also be used to display information input by a user or information provided to the user and a graphical user interface (GRAPHICAL USER INTERFACE, GUI) of various menus of the terminal device. In particular, the display unit 1130 may include a display 1132 disposed on a front surface of the terminal device. The display 1132 may be configured in the form of a liquid crystal display, a light emitting diode, or the like.

The display unit 1130 may also be used to receive input numeric or character information, generate signal inputs related to user settings and function controls of the terminal device, and in particular, the display unit 1130 may include a touch screen 1131 provided on the front of the terminal device, and may collect touch operations on or near the user, such as clicking buttons, dragging scroll boxes, and the like.

The touch screen 1131 may cover the display screen 1132, or the touch screen 1131 may be integrated with the display screen 1132 to implement input and output functions of the terminal device, and the integrated touch screen may be simply referred to as a touch screen. The display unit 1130 may display application programs and corresponding operation steps in the present application.

The camera 1140 may be used to capture still images. The number of cameras 1140 may be one or more. The object generates an optical image through the lens and projects the optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (charge coupled device, CCD) or a Complementary Metal Oxide Semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then passed to a processor 1180 for conversion into a digital image signal.

The terminal device may further comprise at least one sensor 1150, such as an acceleration sensor 1151, a distance sensor 1152, a fingerprint sensor 1153, a temperature sensor 1154. The terminal device may also be configured with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, light sensors, motion sensors, and the like.

Audio circuitry 1160, speaker 1161, microphone 1162 may provide an audio interface between a user and a terminal device. The audio circuit 1160 may transmit the received electrical signal converted from audio data to the speaker 1161, and may be converted into a sound signal by the speaker 1161 to be output. The terminal device may also be configured with a volume button for adjusting the volume of the sound signal. On the other hand, the microphone 1162 converts the collected sound signals into electrical signals, which are received by the audio circuit 1160 and converted into audio data, which are output to the communication component 1110 for transmission to, for example, another terminal device, or to the memory 1120 for further processing.

The bluetooth module 1170 is used for exchanging information with other bluetooth devices with bluetooth module through bluetooth protocol. For example, the terminal device may establish a bluetooth connection with a wearable computing device (e.g., a smart watch) that also has a bluetooth module through the bluetooth module 1170, thereby performing data interaction.

The processor 1180 is a control center of the terminal device, connects various parts of the entire terminal using various interfaces and lines, and performs various functions of the terminal device and processes data by running or executing software programs stored in the memory 1120, and calling data stored in the memory 1120. In some embodiments, the processor 1180 may include one or more processing units; the processor 1180 may also integrate an application processor that primarily processes operating systems, user interfaces, applications, etc., with a baseband processor that primarily processes wireless communications. It will be appreciated that the baseband processor described above may not be integrated into the processor 1180. The processor 1180 of the present application may run an operating system, applications, user interface displays and touch responses, as well as the vehicle arrival time prediction method of embodiments of the present application. In addition, a processor 1180 is coupled to the display unit 1130.

As shown in fig. 12, components of computing device 1200 may include, but are not limited to: at least one processor 1201, at least one memory 1202, a bus 1203 connecting the different system components, including the memory 1202 and the processor 1201.

Memory 1202 may be used to store software programs and data. The processor 1201 implements the vehicle arrival time prediction method of the embodiment of the present application by executing software programs or data stored in the memory 1202 to perform various functions and data processing.

Bus 1203 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a processor, and a local bus using any of a variety of bus architectures.

Memory 1202 may include readable media in the form of volatile memory, such as Random Access Memory (RAM) 12021 and/or cache memory 12022, and may further include Read Only Memory (ROM) 12023.

Memory 1202 may also include a program/utility 12025 having a set (at least one) of program modules 12024, such program modules 12024 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

The computing device 1200 may also communicate with one or more external devices 1204 (e.g., keyboard, pointing device, etc.), one or more devices that enable a user to interact with the computing device 1200, and/or any devices (e.g., routers, modems, etc.) that enable the computing device 1200 to communicate with one or more other computing apparatuses. Such communication may occur through an input/output (I/O) interface 1205. Moreover, computing device 1200 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 1206. As shown in fig. 12, network adapter 1206 communicates with other modules for computing device 1200 via bus 1203. It should be appreciated that although not shown in fig. 12, other hardware and/or software modules may be used in connection with computing device 1200, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

In some possible embodiments, aspects of the vehicle arrival time prediction method provided by the present application may also be implemented in the form of a program product comprising program code for causing a computer device to carry out the steps of the vehicle arrival time prediction method according to the various exemplary embodiments of the application as described herein above when the program product is run on the computer device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The program product of the vehicle arrival time prediction of embodiments of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code and may run on a computing device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with a command execution system, apparatus, or device.

The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with a command execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's equipment, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the elements described above may be embodied in one element in accordance with embodiments of the present application. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied.

Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program commands may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the commands executed by the processor of the computer or other programmable data processing apparatus produce means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program commands may also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the commands stored in the computer readable memory produce an article of manufacture including command means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (12)

1. A method for predicting vehicle arrival time, the method comprising:

Acquiring at least one historical brush code data and at least one historical vehicle data; wherein each history brush code data includes: the code brushing machine identification, the historical code brushing time and the historical code brushing position information; each historical vehicle data includes: vehicle identification, historical arrival time and historical station location information; the time period of the at least one historical brushing data and the at least one historical vehicle data are consistent;

determining a first mapping relationship of the barcode brusher and the vehicle based on the at least one historical barcode brusher data and the at least one historical vehicle data;

and determining a target code brushing machine corresponding to the vehicle to be predicted based on the first mapping relation between the code brushing machine and the vehicle, and predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine.

2. The method of claim 1, wherein the determining a first mapping of the barcode brusher and the vehicle based on the at least one historical barcode brusher data and the at least one historical vehicle data comprises:

Determining at least one vehicle integrated area based on historical site location information respectively included in the at least one historical vehicle data, and dividing the at least one vehicle integrated area into at least one grid area respectively;

Based on the history code brushing position information respectively included in the at least one history code brushing data, mapping the at least one history code brushing data to corresponding grid areas respectively, and obtaining a second mapping relation between a code brushing machine and the grid areas;

mapping the at least one historical vehicle data to corresponding grid areas respectively based on the historical site position information respectively included in the at least one historical vehicle data to obtain a third mapping relation between the vehicle and the grid areas;

And determining a first mapping relation between the code brushing machine and the vehicle based on the second mapping relation and the third mapping relation.

3. The method of claim 2, wherein the historical site location information includes city information and site latitude and longitude information;

the determining at least one vehicle integration area based on the historical site location information included in each of the at least one historical vehicle data includes:

classifying the at least one historical vehicle data based on the city information to obtain at least one historical vehicle data set corresponding to the city information;

For the at least one city information, respectively executing the following operations: selecting the longitude and latitude information of the maximum station and the longitude and latitude information of the minimum station from a historical vehicle data set corresponding to the city information; and determining a vehicle comprehensive area corresponding to the city information based on the latitude and longitude information of the maximum station and the latitude and longitude information of the minimum station.

4. The method of claim 3, wherein after dividing the at least one vehicle integration area into at least one grid area, respectively, before mapping the at least one historical brush code data to the corresponding grid area, respectively, comprises:

determining a first grid coordinate value based on a first difference between the maximum and minimum longitude information and a configured first grid size;

Determining a second grid coordinate value according to the configured second grid size based on a second difference value between the maximum site latitude information and the minimum site latitude information;

And determining respective grid coordinates of the at least one grid region based on the first grid coordinate value and the second grid coordinate value.

5. The method of claim 2, wherein the determining the first mapping of the barcode brusher and the vehicle based on the second mapping and the third mapping comprises:

Determining at least one target grid area corresponding to one code brushing machine based on the second mapping relation;

Determining at least one candidate vehicle corresponding to each of the at least one target grid region based on the third mapping relation;

Determining all candidate vehicles corresponding to the one code brushing machine based on at least one target grid area corresponding to the one code brushing machine and at least one candidate vehicle corresponding to each of the at least one target grid area;

And selecting a target vehicle meeting target conditions from all the candidate vehicles, and determining a first mapping relation between the one code brushing machine and the target vehicle based on the selected target vehicle.

6. The method of claim 5, wherein the selecting a target vehicle among the candidate vehicles that satisfies a target condition comprises:

for one of the all candidate vehicles, the following operations are performed respectively:

Determining a first historical vehicle data composite value for the one candidate vehicle in the at least one target grid region; and a second historical vehicle data composite value for all candidate vehicles in the at least one target grid area;

Determining a target score for the one candidate vehicle based on a ratio between the first historical vehicle data synthesis value and the second historical vehicle data synthesis value; wherein the target score is used for representing the matching degree of the candidate vehicle and the one code brushing machine;

and selecting the candidate vehicle with the largest target score from all the candidate vehicles as the target vehicle.

7. The method of claim 6, wherein said determining a first historical vehicle data composite value for a candidate vehicle in said at least one target grid region comprises:

For at least one target grid region, the following operations are performed:

determining all historical brushing times and corresponding historical brushing position information of the one brushing machine in the one target grid area, and determining historical arrival times and corresponding historical site position information of the one candidate vehicle in the one target grid area;

matching the historical brushing time with the historical arrival time to obtain a first matching result, and matching the historical brushing position information with the historical site position information to obtain a second matching result;

Determining a target number of successful first matching results and successful second matching results, and taking the target number as the first historical vehicle data value;

And adding the first historical vehicle data values corresponding to the at least one target grid region respectively to obtain the first historical vehicle data comprehensive value.

8. The method according to any one of claims 1 to 7, wherein predicting the arrival time of the vehicle to be predicted based on the target brush code data corresponding to the target digital machine includes:

matching the target brushing code position information in the target brushing code data with the station position information of the route of the vehicle to be predicted to obtain target station position information successfully matched;

And predicting the arrival time of the vehicle to be predicted based on the successfully matched target station position information.

9. A vehicle arrival time prediction apparatus, characterized in that the apparatus comprises:

An acquisition unit for acquiring at least one history barcode data and at least one history vehicle data; wherein each history brush code data includes: the code brushing machine identification, the historical code brushing time and the historical code brushing position information; each historical vehicle data includes: vehicle identification, historical arrival time and historical station location information; the time period of the at least one historical brushing data and the at least one historical vehicle data are consistent;

A determining unit, configured to determine a first mapping relationship between the code brushing machine and the vehicle based on the at least one historical code brushing data and the at least one historical vehicle data;

The prediction unit is used for determining a target code brushing machine corresponding to the vehicle to be predicted based on the first mapping relation between the code brushing machine and the vehicle, and predicting the arrival time of the vehicle to be predicted based on target code brushing data corresponding to the target code brushing machine.

10. A computing device, the computing device comprising: a memory and a processor, wherein:

The memory is used for storing a computer program;

The processor being adapted to execute the computer program for implementing the steps of the method according to any one of claims 1 to 8.

11. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the steps of the method according to any of claims 1-8.

12. A computer program product comprising computer instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 8.