CN111210094A - Airport taxi automatic scheduling method and device based on real-time passenger flow prediction - Google Patents
Airport taxi automatic scheduling method and device based on real-time passenger flow prediction Download PDFInfo
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Abstract
The application relates to the technical field of intelligent traffic control, in particular to an airport taxi automatic scheduling method and device based on real-time passenger flow prediction. The method aims to solve the problems that social vehicles mistakenly enter a taxi waiting area, the queuing progress of the taxies is not clear, the passenger carrying order is disordered, the deviation of passenger flow data is predicted to be overlarge, and the dispatching is not timely and unreasonable. The method comprises the following steps: acquiring taxi information entering a storage yard to generate a taxi sequence; inputting the collected real-time passenger flow data and historical contemporaneous passenger flow data in the database into a passenger flow prediction model to calculate to obtain predicted passenger flow data after preset time; generating scheduling information according to the predicted passenger flow data and the taxi sequence, wherein the scheduling information is used for controlling an information screen to generate a license plate entering prompt of a taxi waiting area and controlling a barrier gate system; and according to the entering number plate prompt, the taxi enters a taxi waiting area through a barrier gate system controlled by the scheduling information.
Description
Technical Field
The application relates to the technical field of intelligent traffic control, in particular to an airport taxi automatic scheduling method and device based on real-time passenger flow prediction.
Background
The dispatching of the taxis in the airport refers to allocating the quantity of the taxis in a storage yard and a taxi waiting area according to the quantity of passengers waiting for the taxis so as to ensure the normal order of taking the taxis and leaving the airport. When the number of waiting passengers is large, taxis in a storage yard are dispatched to a taxi waiting area in order to queue for carrying passengers; or when the number of passengers waiting for the taxi is less, the taxi in the storage yard is prevented from being crowded to a taxi waiting area in large quantity to rush for passengers.
In some implementations of airport taxi dispatching methods, manual dispatching is generally adopted. And carrying out manual dispatching according to the monitoring video, or the personal experience of a manager and the rough number of passengers waiting at the airport. Performing timing scheduling at certain time intervals, such as once in half an hour; or at a certain threshold, such as 50 taxis per taxi.
However, when the dispatcher judges that the passenger flow has a large deviation or the dispatching command is not sent out in time, the taxi dispatching is delayed, the dispatching is unreasonable and cannot meet the requirements of passengers, the taxi cannot obtain the queuing progress, the taxi in the storage yard is crowded to the taxi waiting area to rush for passengers, and social vehicles mistakenly enter the taxi waiting area, so that the order disorder is caused.
Disclosure of Invention
The application provides an airport taxi automatic scheduling method and device based on real-time passenger flow prediction, and the method and device can solve the problems of mistaken entry of social vehicles into a taxi waiting area, unclear taxi queuing progress, disordered passenger carrying order, overlarge predicted passenger flow data deviation and untimely and unreasonable scheduling to a certain extent by acquiring taxi information, acquiring real-time passenger flow data, constructing a passenger flow prediction model, automatically generating scheduling information, generating an entry number plate prompt and controlling a barrier gate system.
The embodiment of the application is realized as follows:
a first aspect of an embodiment of the present application provides an airport taxi automatic scheduling method based on real-time passenger flow prediction, where the method includes:
acquiring taxi information entering a storage yard to generate a taxi sequence;
inputting the collected real-time passenger flow data and historical contemporaneous passenger flow data in the database into a passenger flow prediction model to calculate to obtain predicted passenger flow data after preset time;
generating scheduling information according to the predicted passenger flow data and the taxi sequence, wherein the scheduling information is used for controlling an information screen to generate a license plate entering prompt of a taxi waiting area and controlling a barrier gate system;
and according to the entering number plate prompt, the taxi enters a taxi waiting area through a barrier gate system controlled by the scheduling information.
A second aspect of the embodiments of the present application provides an airport taxi automatic dispatching device based on real-time passenger flow prediction, including a memory, a processor and a computer program stored on the memory, wherein the processor executes the computer program to perform the method according to any one of the aspects of the embodiments of the present application.
The beneficial effect of this application lies in: by collecting taxi information, taxi queuing informatization can be realized, and social vehicles are prevented from entering a waiting area; furthermore, by acquiring real-time passenger flow data and constructing a passenger flow prediction model, the accuracy of passenger flow data prediction can be improved, and the rationality of taxi scheduling is improved; furthermore, scheduling information is automatically generated, so that scheduling decision time can be reduced, and scheduling timeliness is improved; further, by controlling the entering number plate prompt and the barrier gate system, the taxi queuing progress information can be provided, and ordered passenger carrying of the taxi can be realized.
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Specifically, in order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments are briefly described below, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without any creative effort.
Fig. 1 is a schematic diagram illustrating an airport taxi automatic dispatching system 100 based on real-time passenger flow prediction according to an embodiment of the present application;
FIG. 2 illustrates a schematic diagram of an exemplary computing device 200 in an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating an airport taxi automatic-dispatching method based on real-time passenger flow prediction according to an embodiment of the application;
FIG. 4 shows a schematic flow chart of a passenger flow prediction model based on an XGboost algorithm in an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating an airport taxi dispatching job in accordance with an embodiment of the present application;
FIG. 6 shows a schematic block diagram of a computer implementation of an embodiment of the present application.
Detailed Description
Certain exemplary embodiments will now be described to provide an overall understanding of the principles of the structure, function, manufacture, and use of the devices and methods disclosed herein. One or more examples of these embodiments are illustrated in the accompanying drawings. Those of ordinary skill in the art will understand that the devices and methods specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the various embodiments of the present invention is defined solely by the claims. Features illustrated or described in connection with one exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are intended to be included within the scope of the present invention.
Reference throughout this specification to "embodiments," "some embodiments," "one embodiment," or "an embodiment," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in various embodiments," "in some embodiments," "in at least one other embodiment," or "in an embodiment" or the like throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, the particular features, structures, or characteristics shown or described in connection with one embodiment may be combined, in whole or in part, with the features, structures, or characteristics of one or more other embodiments, without limitation. Such modifications and variations are intended to be included within the scope of the present invention.
Fig. 1 is a schematic diagram of an airport taxi automatic dispatch system 100 based on real-time passenger flow prediction, according to some embodiments of the present application. An airport taxi automatic dispatch system 100 based on real-time passenger flow prediction is a platform that can automatically generate dispatch information according to real-time passenger flow data. An airport taxi automatic dispatch system 100 based on real-time passenger flow prediction may include a server 110, at least one storage device 120, at least one network 130, one or more passenger flow identification devices 150-1, 150-2. The server 110 may include a processing engine 112.
In some embodiments, the server 110 may be a single server or a group of servers. The server farm can be centralized or distributed (e.g., server 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, server 110 may access data stored in storage device 120 via network 130. Server 110 may be directly connected to storage device 120 to access the stored data. In some embodiments, the server 110 may be implemented on a cloud platform. The cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, multiple clouds, the like, or any combination of the above. In some embodiments, server 110 may be implemented on a computing device as illustrated in FIG. 2 herein, including one or more components of computing device 200.
In some embodiments, the server 110 may include a processing engine 112. Processing engine 112 may process information and/or data related to the service request to perform one or more of the functions described herein. For example, the processing engine 112 may be based on obtaining information collected by the passenger flow identification device 150 and sending the information to the storage device 120 via the network 130 for updating data stored therein. In some embodiments, processing engine 112 may include one or more processors. The processing engine 112 may include one or more hardware processors, such as a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), an image processor (GPU), a physical arithmetic processor (PPU), a Digital Signal Processor (DSP), a field-programmable gate array (FPGA), a Programmable Logic Device (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination of the above.
In some embodiments, the storage device 120 may be connected to a network 130 to enable communication with one or more components in the airport taxi automatic dispatch system 100 based on real-time passenger flow predictions. One or more components of the airport taxi automatic dispatch system 100 based on real-time passenger flow predictions may access data or instructions stored in the storage device 120 via the network 130. In some embodiments, the storage device 120 may be directly connected to or in communication with one or more components of the airport taxi automatic dispatch system 100 based on real-time passenger flow predictions. In some embodiments, storage device 120 may be part of server 110.
The network 130 may facilitate the exchange of information and/or data. In some embodiments, one or more components of the real-time passenger flow prediction based airport taxi automatic dispatch system 100 may send information and/or data to other components of the real-time passenger flow prediction based airport taxi automatic dispatch system 100 over the network 130. For example, the server 110 may obtain/obtain the request from the passenger flow identification device 150 via the network 130. In some embodiments, the network 130 may be any one of a wired network or a wireless network, or a combination thereof. In some embodiments, the network 130 may include one or more network access points. For example, the network 130 may include wired or wireless network access points, such as base stations and/or Internet switching points 130-1, 130-2, and so forth. Through the access point, one or more components of the airport taxi automatic dispatch system 100 based on real-time passenger flow predictions may be connected to the network 130 to exchange data and/or information.
The passenger flow identification device 150 may include a gate system, a video analysis server, a camera, and the like that can identify the flow of people. In some embodiments, the passenger flow identification device 150 may be used to collect data about the surroundings of the location, or the volume of people passing through its collection point. In some embodiments, passenger flow identification device 150 may transmit the various collected data information to one or more devices in airport taxi automatic dispatch system 100 based on real-time passenger flow predictions. For example, the passenger flow identification device 150 may collect the people flow data of the area and send the people flow data to the server 110 for processing, or store the people flow data in the storage device 120. In some embodiments, the passenger flow identification device may be disposed at an entrance and an exit of a taxi passenger area, and is used for data acquisition of the number of waiting passengers.
FIG. 2 is a schematic diagram of an exemplary computing device 200 shown in accordance with some embodiments of the present application. The server 110, storage device 120, and passenger flow identification device 150 may be implemented on the computing device 200. For example, the processing engine 112 may be implemented on the computing device 200 and configured to implement the functionality disclosed herein.
For ease of understanding, only one processor is exemplarily depicted in fig. 2. However, it should be noted that the computing device 200 in the present application may include multiple processors, and thus the operations and/or methods described in the present application that are implemented by one processor may also be implemented by multiple processors, collectively or independently. For example, if in the present application a processor of computing device 200 performs steps 1 and 2, it should be understood that steps 1 and 2 may also be performed by two different processors of computing device 200, either collectively or independently.
Fig. 3 shows a flow chart of an airport taxi automatic scheduling method based on real-time passenger flow prediction in an embodiment of the application.
In step 301, taxi information entering a storage yard is collected to generate a taxi sequence.
In the operation management of the airport, taxis need to be queued in sequence if new passengers need to be picked up according to the management rules. In general, taxi in an airport has great demands for passenger sending and receiving. If taxis are directly queued at the boarding points of passengers, the order of an airport is affected, so a taxi storage yard is usually arranged near or inside the airport, and the taxis are queued in the storage yard, then sequentially driven to a taxi waiting area to carry passengers, and finally leave the airport.
In some embodiments, a taxi waiting area is docked with a taxi passenger area, and taxis leave by carrying passengers in the waiting area; the storage yard is an area for temporarily parking and queuing taxis, wherein the taxis drive into a taxi waiting area in sequence to carry passengers, and the area of the storage yard is usually large so as to reserve a large number of taxis to meet peak demands of the taxis.
In some embodiments, the entrance and exit of the storage yard are each provided with a vehicle detector for collecting taxi information. The vehicle detector generally includes a number plate recognition device, a camera, a video analysis server, and the like, which can recognize vehicle information.
Carrying out clock verification on vehicle detectors at different positions, and setting a uniform data transmission frequency to transmit data to a background; the central server can obtain the taxi sequence in the storage yard by calculating and processing the transmitted data of the vehicle detector. The number, license plate number and queuing progress of each taxi in the storage yard can be obtained through the taxi sequence information. The taxi sequence information can be displayed on a plurality of display screens arranged in a storage yard smoothly, so that taxi drivers can know the waiting time and the queuing progress of passengers probably carried by the taxi drivers through the taxi sequence information.
The taxi license plate is usually in a special format, so that the social vehicles can be conveniently distinguished from entering a storage yard, the phenomenon that the social vehicles enter the storage yard by mistake to disturb passenger carrying order is prevented, and the unified management of the taxies is realized.
In step 302, the collected real-time passenger flow data and historical contemporaneous passenger flow data in the database are input into a passenger flow prediction model to calculate to obtain predicted passenger flow data after a preset time.
By using a machine learning technology, real-time passenger flow data is input into the passenger flow prediction model, historical contemporaneous passenger flow data in the database is called, and predicted passenger flow data after a preset time can be obtained, for example, the number of passengers taking a taxi one hour in the future of the current time of the day is predicted.
The real-time passenger flow data is generally acquired by passenger flow identification equipment arranged in a taxi passenger area, the real-time passenger flow data is counted in units of minutes, and then the acquired passenger flow data is stored in a passenger waiting information database, so that all original passenger flow data in the airport historical time period can be acquired.
In some embodiments, the taxi dispatching system further comprises a holiday information database which is separately stored for special passenger flow data such as weekends, legal holidays, and the like. The passenger flow data of the holidays and the festival and holidays are different from the daily passenger flow data in characteristics and usually have the characteristics of explosiveness and uncertainty, so the passenger flow data of the holidays and the festival and holidays are independently stored in a holiday and holiday information database for data research and use.
In some embodiments, the historical contemporaneous passenger flow data comprises: yesterday historical contemporaneous passenger flow data, last week historical contemporaneous passenger flow data and last year historical contemporaneous passenger flow number. Historical passenger flow data at the same time are analyzed and processed in a passenger waiting information database and a holiday information database, and the historical passenger flow data can be used as a training set for machine learning of a passenger flow prediction model.
In a training set for machine learning, yesterday history contemporaneous passenger flow data, last week history contemporaneous passenger flow data and last year history contemporaneous passenger flow data are used as characteristic values, and passenger flow data after the future preset time of a research moment is used as an output value, so that the training set is formed.
In some embodiments, the traffic prediction model is an XGBoost algorithm based traffic prediction model.
The XGBoost (eXtreme Gradient Boosting) algorithm is a machine learning technique that solves the regression and classification problem and produces a prediction model by integrating weak prediction models, such as decision trees.
XGboost is an integrated learning method based on CART (Classification and Regression Trees) and using negative gradient as learning strategy by passing a plurality of weak classifiers through a boost frame.
The XGboost traverses all feature division points, uses an objective function value as an evaluation function, and concretely comprises the steps that the gain of the objective function value after splitting is larger than that of an objective function of a single-son leaf node, and meanwhile, a threshold value is added for limiting the tree from growing too deeply, and splitting is carried out only when the gain is larger than the threshold value.
The XGboost supports a linear classifier, is a logistic regression classification problem and a linear regression algorithm with L1 and L2 regular terms, the regular terms comprise the number of leaf nodes and the L2 norm of the output values of the leaf nodes, and through a constraint tree structure, the variance of the model is reduced and overfitting is prevented; the method comprises the steps of supporting second-order Taylor expansion on a target function and supporting a user-defined loss function; XGboost supports column sampling and row sampling by using a random forest algorithm, so that the overfitting risk can be reduced, and the calculation resources can be reduced; the XGboost is insensitive to missing values, the splitting direction of the XGboost can be automatically learned, a reduction factor is introduced in each step, the influence of a single tree on the result is reduced, a subsequent model has a larger optimization space, and overfitting is further prevented; the XGboost sorts and stores data into a block in advance before training, and the block is repeatedly used in subsequent iteration, so that calculation is reduced, and meanwhile, parallel calculation can be performed when division points are calculated; the XGboost can be used for parallel approximate histogram algorithm, when the tree nodes are split, the gain of each node needs to be calculated, if the data volume is large, the characteristics of all the nodes are sequenced, the optimal segmentation points are obtained through traversal, the approximate histogram algorithm is introduced and used for generating efficient segmentation points, namely, a certain value after splitting is used for subtracting a certain value before splitting to obtain the gain, in order to limit the increase of the tree, a threshold value is introduced, and when the gain is larger than the threshold value, splitting is performed.
The XGB OST algorithm is used for passenger flow prediction, the algorithm makes full use of multi-core CPU parallel computing, fast Gradient Boosting (Gradient Boosting) is achieved, and the XGB OST algorithm is suitable for large-scale data processing of airports. Before the algorithm is applied, the original data set needs to be preprocessed to obtain corresponding data characteristic values, then an XGB OST model is constructed, and finally effective prediction of passenger travel quantity is achieved.
Fig. 4 shows a schematic flow chart of a passenger flow prediction model based on an XGBoost algorithm in the embodiment of the present application.
Raw passenger flow data from a passenger waiting information database and a holiday information database is obtained in step 401.
The passenger waiting information database is composed of passenger flow data collected by passenger flow identification equipment arranged in a taxi passenger area, and is counted by taking minutes as a unit, and then stored in the database. The holiday information database is used for independently storing special passenger flow data such as weekends, legal holidays and the like. The passenger flow data of the holidays and the festival and holidays are different from the daily passenger flow data in characteristics and usually have the characteristics of explosiveness and uncertainty, so the passenger flow data of the holidays and the festival and holidays are independently stored in a holiday and holiday information database for data research and use.
In step 402, the original passenger flow data is preprocessed to obtain standard passenger flow data forming a training set.
And carrying out reasonability analysis on the original passenger flow data, and rejecting data which is obviously unqualified and has overlarge dispersion. The failing data includes all zero data that is significantly erroneous.
And (3) complementing the data without obvious errors, wherein the complementing of the data comprises the complementing of historical/future average values (median, mode), and normalizing the complemented data to obtain standard passenger flow data.
The standard passenger flow data are used for forming a training set of a passenger flow prediction model, the training set takes yesterday historical contemporaneous passenger flow data, last week historical contemporaneous passenger flow data and last year historical contemporaneous passenger flow data as characteristic values, and passenger flow data after the future preset time of the research moment is taken as an output value, so that the training set is formed.
In some embodiments, after obtaining the predicted passenger flow data after the preset time, generating a linear passenger flow statistical map based on the historical contemporaneous passenger flow data, the real-time passenger flow data, the predicted passenger flow data, the linear passenger flow statistical map being output for display. By analyzing the passenger flow linear statistical chart, the trends of the passenger flow data in different periods and the predicted trend at the future moment can be obtained, and the passenger flow linear statistical chart is used for providing data support for taxi dispatching.
When a passenger enters the area, the passenger flow identification system records the number of the passengers passing through the area, the statistics is carried out by taking minutes as a unit, finally, a linear statistical graph with the abscissa as time and the ordinate as the number of the passengers is formed in a taxi scheduling system, and meanwhile, a comparison curve graph of the number of the yesterday historical contemporary people, the number of the last week historical people and the number of the last year historical contemporary people can be obtained. And predicting the passenger flow at the current time of the day by a preset time in the future, for example, one hour in the future, based on the historical number of the current persons and the implementation passenger flow data.
In step 403, the XGBoost model is trained based on the training set to obtain a passenger flow prediction model.
And training the XGboost model by using the training set, and obtaining the passenger flow prediction model based on the XGboost after the model training reaches a convergence condition, wherein the passenger flow prediction model has optimal parameters, and the convergence condition can be set as iteration times or an accuracy threshold value.
In some embodiments, the passenger flow prediction model based on the XGBoost algorithm is arranged in the cloud, receives standard passenger flow data which is preprocessed from the passenger waiting information database and the holiday information database through the arranged data interface, and sends the real-time passenger flow data to the data interface of the passenger flow prediction model after training is completed, so as to return the predicted passenger flow data in real time.
Based on the collected real-time passenger flow data, statistical analysis of the passenger flow data can be realized, and the time-space distribution unbalanced characteristic and the periodic variation rule of the passenger flow data are obtained, so that the passenger flow prediction model can be iterated in time, the passenger flow prediction accuracy is improved, and finally more reasonable taxi scheduling is carried out.
In step 303, scheduling information is generated according to the predicted passenger flow data and the taxi sequence, and the scheduling information is used for controlling an information screen to generate a license plate entering prompt of a taxi waiting area and controlling a barrier gate system.
And a scheduling algorithm of the vehicle scheduling center automatically generates scheduling information of the taxi according to the predicted passenger flow data, wherein the scheduling information comprises information that the vehicle enters a waiting area of the taxi selected according to the serial number of the vehicle in a taxi sequence of the storage yard. The method comprises the steps that a display screen in a dispatching information control storage yard displays a number plate entering prompt, wherein the number plate entering prompt can reflect the number plate of a taxi needing to enter a barrier gate system at the moment and the serial queuing number of the taxi; the scheduling information is also configured to control the selective opening of the barrier gate system, when the allowed license plate number in the scheduling information passes through the barrier gate system, the barrier gate system can open the passing, and other vehicles can not pass through the barrier gate system when passing through the barrier gate system.
Fig. 5 shows a schematic diagram of airport taxi dispatching work according to the embodiment of the application.
The transport management department of the airport is in butt joint with two departments, namely a taxi company and a quality control center.
When a taxi enters a storage yard, the quality control center inputs related vehicle information, including a taxi license plate number, the entering time, the name of a taxi company to which the vehicle belongs and the like;
the taxi dispatching center generates taxi dispatching information according to the passenger flow predicting data and the vehicle information collected by the quality control center;
the transportation management department is responsible for stopping and dispatching the vehicles according to the dispatching information, and the information of the vehicles needing to be dispatched to the taxi waiting area is displayed on an information screen in the storage yard;
the taxi in the taxi storage yard sequentially enters a taxi waiting area through a barrier gate system according to the entering number plate prompt displayed by the information screen, wherein the entering number plate prompt comprises a taxi serial number and a taxi plate number;
the taxi in the taxi waiting area is used as a passenger for conveying the taxi to leave the taxi taking area in sequence, the passenger can select the taxi in the waiting area in the period, and a manager is responsible for maintaining the on-site order;
after the taxi leaves the field, the taxi leaving information is stored at the transportation and management place; if the taxi is complained by the passenger for some reasons after leaving, the fortune management department feeds back the opinion to the taxi company and processes the report;
after the taxi carrying the passenger to leave is served, the passenger can enter the storage yard again.
In some embodiments, the entry number plate prompt is displayed on the display screen in turn, and the content of the entry number plate prompt comprises the number plate of the taxi which can enter the barrier gate system and the serial number thereof, and the number plate of the waiting taxi and the serial number thereof. The taxi driver can know the queuing progress through the number plate and the serial number of the waiting taxi displayed on the display screen, and further estimate the time of leaving the airport. The waiting taxi license plate number and the serial number are configured to be used for displaying all taxis in a waiting state in the storage yard.
In step 304, according to the entry number plate prompt, the taxi enters a taxi waiting area through a barrier gate system controlled by the scheduling information.
The taxi leaves the storage yard through the barrier gate system and enters the waiting area to carry passengers and leaves the airport in sequence and timely according to the prompt of the number plate entering of the display screen in the storage yard.
A barrier gate system is a passage management device that is disposed at an entrance and an exit of a road or a site to restrict the travel of a motor vehicle, and is generally widely used in areas such as a highway toll station, a parking lot, a cell, an airport, and a train station. In this embodiment, the electric barrier of the barrier system can realize the automatic release function through the linkage of the scheduling information. The brake bar of the barrier can be a straight bar, a 90-degree curved bar, a 180-degree folding bar, or a fence, etc.
The taxi can timely carry away passengers to be arrived according to the scheduling information displayed by the display screen, and can be effectively managed through the control of a barrier gate system of a storage yard and the reminding of an information screen, so that the long-time idling, the passenger robbing and the illegal operation of the taxi are avoided.
The application also provides an airport taxi automatic scheduling device based on real-time passenger flow prediction, which comprises a memory, a processor and a computer program stored on the memory, wherein the processor executes any airport taxi automatic scheduling method based on real-time passenger flow prediction according to the embodiment of the application when executing the computer program. The method has been described in detail in the foregoing, and is not described in this section.
Referring now to FIG. 6, there is illustrated a schematic block diagram of a computing environment 600 in accordance with the subject specification. The system 600 includes one or more clients 602 (e.g., computers, smart phones, tablets, cameras, PDAs). The client(s) 602 can be hardware and/or software (e.g., threads, processes, computing devices). The client(s) 602 can employ the specification to accommodate cookie(s) and/or associated contextual information, for example.
The system 600 also includes one or more server(s) 604. The server(s) 604 can also be hardware or hardware in combination with software (e.g., threads, processes, computing devices). The servers 604 can house threads to perform transformations of media items, such as by employing aspects of the subject application. One possible communication between a client 602 and a server 604 can be in the form of a data packet adapted to be transmitted between two or more computer processes, where the data packet can include a code analysis headspace and/or input. The data packet can include a cookie and/or associated contextual information, for example. The system 600 includes a communication framework 606 (e.g., a global communication network such as the internet) that can be employed to facilitate communications between the client(s) 602 and the server(s) 604.
Communications can be facilitated via a wired (including optical fiber) and/or wireless technology. The client(s) 602 are operatively connected to one or more client data store(s) 608 that can be employed to store information local to the client(s) 602 (e.g., cookie(s) and/or associated contextual information). Similarly, the server(s) 604 are operatively connected to one or more server data store(s) 610 that can be employed to store information local to the servers 604.
In one exemplary implementation, the client 602 can communicate encoded files (e.g., encoded media items) to the server 604. The server 604 can store the file, decode the file, or transmit the file to another client 602. It is to be appreciated that, in accordance with the subject application, the client 602 can also pass uncompressed files to the server 604, and the server 604 can compress the files and/or transform the files. Likewise, the server 604 can encode information and transmit the information to one or more clients 602 via the communication framework 606.
The beneficial effect of this application: by collecting taxi information, taxi queuing informatization can be realized, and social vehicles are prevented from entering a waiting area; furthermore, by acquiring real-time passenger flow data and constructing a passenger flow prediction model, the accuracy of passenger flow data prediction can be improved, and the rationality of taxi scheduling is improved; furthermore, scheduling information is automatically generated, so that scheduling decision time can be reduced, and scheduling timeliness is improved; further, by controlling the entering number plate prompt and the barrier gate system, the taxi queuing progress information can be provided, and ordered passenger carrying of the taxi can be realized.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data blocks," modules, "" engines, "" units, "" components, "or" systems. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Claims (10)
1. An airport taxi automatic scheduling method based on real-time passenger flow prediction is characterized by comprising the following steps:
acquiring taxi information entering a storage yard to generate a taxi sequence;
inputting the collected real-time passenger flow data and historical contemporaneous passenger flow data in the database into a passenger flow prediction model to calculate to obtain predicted passenger flow data after preset time;
generating scheduling information according to the predicted passenger flow data and the taxi sequence, wherein the scheduling information is used for controlling an information screen to generate a license plate entering prompt of a taxi waiting area and controlling a barrier gate system;
and according to the entering number plate prompt, the taxi enters a taxi waiting area through a barrier gate system controlled by the scheduling information.
2. The method of claim 1, wherein said real-time passenger flow data is counted in minutes.
3. The method of real-time passenger flow prediction based automatic airport taxi dispatching of claim 1, wherein said historical contemporaneous passenger flow data comprises: yesterday historical contemporaneous passenger flow data, last week historical contemporaneous passenger flow data and last year historical contemporaneous passenger flow number.
4. The method for automatically dispatching taxis at airports based on real-time passenger flow prediction as claimed in claim 1, wherein the passenger flow prediction model is a passenger flow prediction model based on XGboost algorithm.
5. The method for automatically dispatching taxis at airports based on real-time passenger flow prediction as claimed in claim 4, wherein the XGboost algorithm-based passenger flow prediction model is constructed by the following steps:
acquiring original passenger flow data from a passenger waiting information database and a holiday information database;
preprocessing the original passenger flow data to obtain standard passenger flow data forming a training set;
and training the XGboost model based on the training set to obtain a passenger flow prediction model.
6. The method of claim 1, wherein after obtaining predicted passenger flow data after a preset time, generating a linear passenger flow statistical map based on said historical contemporaneous passenger flow data, real-time passenger flow data, predicted passenger flow data, said linear passenger flow statistical map being output for display.
7. The method for automatic airport taxi dispatching based on real-time passenger flow prediction according to claim 1, wherein the real-time passenger flow data is acquired through a passenger flow identification device arranged in a taxi passenger area.
8. The method of claim 1, wherein said entry number plate prompt is displayed on said information screen in turn, and the content of said entry number plate prompt comprises: the taxi license plate number and the serial number thereof, and the waiting taxi license plate number and the serial number thereof which can enter the barrier gate system.
9. The method of real-time passenger flow prediction based automatic taxi dispatching at an airport of claim 8, wherein the number plate and serial number of waiting taxis are configured to display all taxis in a waiting state in the storage yard.
10. An airport taxi automatic dispatch device based on real-time passenger flow prediction, comprising a memory, a processor and a computer program stored on the memory, wherein the processor executes the computer program to perform the method according to any one of claims 1 to 9.
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