CN112380398A - Many modes of transportation of air port passenger transfer trip chain founds device - Google Patents
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Abstract
The invention provides a construction device for a multi-traffic mode transfer travel chain of air port passengers. The method comprises the following steps: the method comprises the steps that a travel preference model construction device based on passenger figures predicts travel behaviors of passengers in an airport; the travel full-coverage segmented time-consuming estimation device estimates the travel time inside and outside the traffic transfer room on the land side of the arriving passengers; the passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module analyzes the passenger gathering and scattering rule in the land side traffic hub and predicts the waiting time of different traffic modes; the multi-target construction method and device for the passenger travel chain comprehensively optimize and generate the passenger travel chain by taking the passenger travel preference, the travel whole-course time, the passenger flow convergence and dispersion distribution, the traffic mode transport capacity and the like as influence factors. The invention can predict the traveling preference of passenger groups with different traveling purposes, the same number of people, age structures and the like, accurately estimates the walking time of passengers in the terminal building, the traveling time and waiting time of the terminal building, and recommends an efficient and comfortable traveling scheme for passengers arriving at the airport.
Description
Technical Field
The invention relates to the technical field of airport passenger travel management, in particular to a construction device for a multi-traffic mode transfer travel chain of airport passengers.
Background
With more and more passengers transported by civil aviation in China and the ever-increasing business of airlines at home and abroad, China has become the second largest air transportation system in the world. However, the air transportation industry in China has a larger gap compared with developed countries, and how to improve the airport service quality and improve the passenger experience of riding in the airplane is an important ring for improving the competitiveness of civil aviation transportation. At present, a large airport land side traffic transfer center is connected with various traffic modes such as rail transit, urban public transport, airport buses, taxis, private cars and the like, so that travel choices can be added for passengers, and the travel range of the passengers is expanded. But the interweaving of multiple transportation modes in a limited space range also increases the complexity of passenger travel. Meanwhile, a large airport lacks the capability of collecting and pushing travel information such as travel time, traffic capacity, real-time passenger seat rate and the like in various traffic modes, and cannot provide comprehensive and objective information for passenger travel decisions. The main functions of the existing LBS (Location Based Services) software are limited to an urban traffic travel transfer strategy and travel time estimation outside an airport terminal, and the existing LBS software cannot link the travel paths inside and outside the terminal to optimally construct a passenger travel chain with a full-coverage travel.
At present, no trip chain optimization construction device for full coverage of indoor and outdoor trips of airport passengers exists in the prior art.
Disclosure of Invention
The embodiment of the invention provides a construction device for a multi-traffic mode transfer travel chain of airport passengers, which is used for effectively predicting travel preference of passenger groups with different travel purposes, different numbers of people in the same row, different age structures and the like.
In order to achieve the purpose, the invention adopts the following technical scheme.
The utility model provides a many modes of transportation of airport passenger transfer trip chain constructs device, includes: the system comprises a passenger portrait-based travel preference model construction module, a travel full-coverage segmented time-consuming estimation module, a passenger flow convergence and dispersion rule and multi-mode traffic capacity coupling module and a passenger travel chain multi-target construction module:
the travel preference model building module based on the passenger portrait is used for building a consistency relation between the characteristics of the passengers and the selection of travel modes and predicting the personalized travel preference of the passengers with different travel purposes, income levels, age structures and baggage numbers;
the travel full-coverage segmented time-consuming estimation module is used for constructing a segmented terminal building internal and external travel model structure and estimating the time of the airport passenger in the terminal building traveling process and the terminal building external traveling process included in the land side traffic transfer;
the passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module is used for acquiring a selection rule of passenger gathering and scattering traffic modes in an airport traffic transfer center according to historical passenger flow and real-time passenger flow data and estimating waiting time of passengers taking different traffic modes by combining traffic mode traffic capacity change and a running schedule;
the passenger travel chain multi-target construction module is used for realizing the customization of the travel chain of the group/single passenger based on the prediction result of the travel preference of the passenger, the estimation result of the travel time and the waiting time of the passenger and by combining passenger flow distribution and the transportation capacity level of the transportation mode.
Preferably, the travel preference model construction module based on passenger portrait is specifically used for carrying out multi-dimensional passenger clustering and passenger feature classification processing based on travel information related to passenger age structure, income level, travel purpose and baggage quantity, carrying out passenger feature classification by using a K-means clustering method, establishing a consistency relation between passenger features and passenger travel mode selection to obtain a passenger travel preference prediction model, training the passenger travel preference prediction model according to passenger historical travel data, inputting actual features of passengers to be processed into the trained passenger travel preference prediction model, and outputting prediction results of travel behaviors of the passengers to be processed by the passenger travel preference prediction model.
Preferably, the travel full-coverage segmented time-consuming estimation module is specifically configured to consider an airport passenger travel process as accumulation of a travel process in a terminal, a waiting process and a travel process out of the terminal, abstract a passenger travel path into a streamline and a node, and establish a travel BPR model in the terminal according to passenger flow density and different traffic facility lengths, widths and bearing capacity attributes; estimating the travel time of passengers taking airports, buses, private cars and taxis according to the state of the section outside the terminal and the traffic time index TTI, and estimating the time of taking subways according to a subway running schedule;
adopting a waiting time estimation method based on a flight wave traffic schedule to link indoor and outdoor travel stages of passengers at an airport, searching a travel scheme with the shortest time consumption, and constructing a whole travel path from the airport to an arrival gate to a destination of the passengers; when the passenger arrives and searches the travel route, the travel time of the whole travel routes is estimated by accumulating the time spent of different sections, and the travel route with the shortest time is recommended to the passenger.
Preferably, the travel full-coverage segmented time-consuming estimation module includes: a traveling time estimation model, a waiting time estimation model and an out-of-flight time estimation model which are connected by the passing facilities;
the traffic facility connection traveling time estimation model is used for establishing a BPR model of a walking channel, an escalator and a gate machine traffic facility in an airport terminal building, calculating traveling time by adopting the BPR model, abstracting a route from an arrival port to a platform taking a vehicle into a traveling network consisting of a plurality of crossed line segments and nodes with certain spatial distribution, wherein the nodes in the traveling network represent a region where people flow crosses, the line segments represent the length, direction and type attributes of a traveling path, the length, direction and type of the traveling path are obtained by actual measurement in the airport, the road resistance attribute of the traveling path reflects the carrying capacity of the traffic facility to passenger flow, and is represented by the time for a pedestrian to travel on the traffic facility for a unit length;
the passenger waiting time estimation model is used for comprising two parts of queuing ticket buying time and waiting departure time, the process of queuing ticket buying for passengers and the process of waiting for taxi for passengers are regarded as a queuing system, the process of passenger arrival obeys the poisson process, and the waiting departure time is obtained by estimating the passenger arrival time and the operation schedule;
the estimation model for the time consumption of the out-going navigation station building is used for calculating the road travel time based on the real-time traffic operation index TTI, training a BP (back propagation) neural network by using real traffic state data at different moments, estimating the vehicle travel time by using the trained BP neural network, wherein the length of a road section and the traffic state level are input of the BP neural network, 9 nodes are arranged in the middle layer, and the output of the BP neural network is the travel time of a vehicle on the road section.
Preferably, the passenger flow convergence and divergence rule and multi-mode traffic capacity coupling module is specifically used for analyzing passenger flow change conditions in different time periods of the airport traffic transfer center according to historical passenger flow and real-time passenger flow statistical data, and predicting a passenger convergence and divergence traffic mode selection rule by using a BP neural network; and (3) giving a passenger flow and traffic capacity matching optimization guidance strategy by combining a traffic mode capacity change and operation schedule dynamic coupling method.
Preferably, the passenger travel chain multi-target construction module is used for constructing a multi-target optimization function based on a prediction result of passenger travel preference, an estimation result of passenger travel time and waiting time, combining passenger flow distribution and traffic mode transport capacity level, optimizing a passenger travel scheme by utilizing the multi-target optimization function to synthesize various influence factors of passenger preference, travel time consumption and traffic capacity matching, realizing the construction of a customized travel chain of a group/single passenger, and realizing the seamless transfer of the passenger between different traffic modes.
According to the technical scheme provided by the embodiment of the invention, the method can predict the travel preference of passenger groups with different travel purposes, the same number of people, age structures and other differences, accurately estimate the walking time of passengers in the terminal building, the traveling time and waiting time of the terminal building, recommend an efficient and comfortable travel scheme for passengers arriving at an airport, and solve the problem of airport passenger flow congestion.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a construction device for a multi-mode transportation transfer trip chain of an airport passenger according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a travel mode selection prediction of a passenger according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating an implementation principle of a travel full-coverage segmented time-consumption estimation module according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a travel full-coverage segmented time-consumption estimation module according to an embodiment of the present invention;
fig. 5 is a schematic diagram of multi-objective optimization, travel mode, time and comfort provided by the embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, modules, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, modules, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The embodiment of the invention provides a construction device of a multi-transportation mode transfer trip chain of airport passengers, which is used for constructing the multi-transportation mode transfer trip chain of the airport passengers so as to realize construction and platform application of the trip chain with full coverage of airport passenger travel.
The schematic structural diagram of the construction device for the multi-transportation mode transfer trip chain of the airport passenger provided by the embodiment of the invention is shown in fig. 1, and the construction device comprises a trip preference model construction module based on passenger portraits, a travel full-coverage segmented time consumption estimation module, a passenger flow convergence and dispersion rule and multi-mode traffic capacity coupling module and a passenger trip chain multi-target construction module.
The passenger flow convergence and divergence rule and multi-mode traffic capacity coupling module is associated with a travel preference model building module and a travel full-coverage segmented time-consuming estimation module based on passenger figures, and the passenger travel chain multi-target building module is associated with the travel preference model building module, the travel full-coverage segmented time-consuming estimation module, the passenger flow convergence and divergence rule and multi-mode traffic capacity coupling module based on the passenger figures.
The travel preference model construction module based on the passenger portrait is used for establishing a consistency relation between the passenger characteristics and travel mode selection. The passenger portrait is based on travel requirements of statistical passengers, basic attributes and behavior information of the passengers are recorded, mining and reconstruction of passenger characteristic data are achieved, passenger groups are divided according to the passenger characteristic data, and a targeted passenger characteristic data set is constructed. The personalized travel preference of the passengers with different travel purposes, income levels, age structures and luggage quantity is predicted based on the images of the passengers, and differentiated marketing is realized.
The travel full-coverage segmented time-consuming estimation module is used for constructing a segmented model structure of the inner and outer travel of the terminal building and estimating the time of the travel process of the airport passengers in the terminal building and the travel process of the airport passengers out of the terminal building, wherein the travel full-coverage segmented time-consuming estimation module is used for estimating the time of the travel process of the airport passengers in the terminal building and the travel process of the airport passengers out of the terminal building.
The passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module is used for acquiring a selection rule of passenger gathering and scattering traffic modes in an airport traffic transfer center according to historical passenger flow and real-time passenger flow data, and estimating waiting time of passengers taking different traffic modes by combining traffic mode traffic capacity change and a running schedule.
Analyzing passenger flow change conditions in different time periods of an airport traffic transfer center according to historical passenger flow and real-time passenger flow statistical data, and predicting a passenger gathering and scattering traffic mode selection rule by using a BP neural network; and (3) giving a passenger flow and traffic capacity matching optimization guidance strategy by combining a traffic mode capacity change and operation schedule dynamic coupling method.
The passenger travel chain multi-target construction module is used for realizing the customization of the travel chain of the group/single passenger based on the prediction result of the travel preference of the passenger, the estimation result of the travel time and the waiting time of the passenger and by combining passenger flow distribution and the transportation capacity level of the transportation mode.
Based on the prediction result of the passenger travel preference, the estimation result of the passenger travel time and the waiting time, a multi-objective optimization function is constructed by combining passenger flow distribution and traffic mode capacity level, the passenger travel scheme is optimized by utilizing various influence factors such as passenger preference, travel time consumption, traffic capacity matching and the like through the multi-objective optimization function, the construction of a customized travel chain of a group/single passenger is realized, and the seamless transfer of the passenger among different traffic modes is realized.
In order to overcome the defects that in the prior art, real-time information of a traffic hub is insufficient, people flow evacuation difficulty in an interlaced area, accidents, weather, traffic control and other uncertain factors have large influence on travel time, the embodiment of the invention analyzes the influence of travel route distribution of passengers on the travel time, estimates travel time and transportation time of the passengers in a terminal, constructs the whole travel route from an airport to a destination of the passengers, and provides multi-traffic mode transfer autonomous service for the passengers by taking self-service equipment as a carrier.
According to the embodiment of the invention, the traveling process time of passengers in the terminal building and the traveling process time of passengers taking transportation means are respectively estimated by analyzing the influence of the traveling path distribution of the passengers in the airport on the traveling time. The waiting time estimation method based on the flight wave traffic schedule is adopted to link the indoor and outdoor travel stages of passengers in the airport, search the travel scheme with the shortest time consumption and construct the whole travel path of the passengers from the airport to the destination.
The travel preference model building module based on the passenger portrait is further specifically used for selecting a travel mode of the passenger. Fig. 2 is a schematic diagram illustrating a travel mode selection prediction of a passenger according to an embodiment of the present invention. The method mainly comprises the steps of setting sample points, calculating Euclidean distances and correcting a clustering center, and finally explaining clustering results of the passenger characteristics according to a statistical method and historical experience. On the basis of passenger characteristic analysis, a consistency relation between passenger characteristics and passenger travel mode selection is established to obtain a passenger travel preference prediction model, and the passenger travel preference prediction model is trained according to passenger historical travel data. And then, inputting the actual characteristics of the passenger to be processed into a trained passenger trip preference prediction model, and outputting the prediction result of the trip behavior of the passenger to be processed by the passenger trip preference prediction model.
The travel full-coverage segmented time-consuming estimation module is further specifically configured to estimate segmented time-consuming of a passenger's coverage full-travel. Fig. 3 is a schematic diagram of an implementation principle of a passenger travel full-coverage segmented time-consumption estimation module according to an embodiment of the present invention, and a schematic structural diagram of the module is shown in fig. 4, and includes: the system comprises a traveling time estimation model, a waiting time estimation model and an out-of-flight time estimation model of the connection of the passing facilities. The module considers the travel process of the airport passengers as the accumulation of the travel process in the terminal, the waiting process and the travel process out of the terminal, and respectively estimates the travel process of the passengers in the terminal and the travel process time of taking a vehicle by analyzing the influence of the travel path distribution of the airport passengers on the travel time. The waiting time estimation method based on the flight wave traffic schedule is adopted to link the indoor and outdoor travel stages of passengers in the airport, search the travel scheme with the shortest time consumption and construct the whole travel path of the passengers from the airport to the destination. When the passenger arrives and searches the travel route, the travel time of the whole travel routes is estimated by accumulating the time spent of different sections, and the travel route with the shortest time is recommended to the passenger.
The traveling time estimation model linked with the passing facilities is specifically used for establishing a BPR (Road resistance function) model of the passing facilities such as a walking channel, an escalator and a gate in the terminal building, and calculating the traveling time by using the BPR model. When calculating the indoor traveling time, the route from the arrival port to the platform taking the vehicle is relatively fixed, and can be abstracted into a network consisting of a plurality of crossed line segments and nodes with certain spatial distribution. Nodes in the network represent the areas where people flow intersects, and can describe the interlacing phenomenon of people flow; the line segments represent attributes such as length, direction and type of the path. Since the walking path includes walking channels, escalators, gates and other traffic facilities, and the different spatial distributions of the different traffic facilities, such as width, position and the like, cause different passenger carrying capacities and passenger traffic times of the different traffic facilities, the abstract walking network not only has length attributes corresponding to the journey, but also should have road resistance attributes reflecting the traffic capacities. The length, direction and type of the walking path are fixed and can be obtained through actual measurement at an airport. The road resistance attribute of the travel route reflects the carrying capacity of the traffic facility for passenger flow, and can be represented by the time taken by a pedestrian to walk on the traffic facility for a unit length. The method is characterized in that the method is obtained by adopting a manual investigation method, the total duration is 3 hours, the investigation time of each group is 30s, and investigation contents comprise pedestrian flow, passing time at a walking channel, an ascending and descending escalator and a gate, and channel parameters such as the length and the width of a passing facility. The condition that the pedestrian flow is free flow is set to be v less than or equal to 30 people/(m & min), and the running speed of the escalator is considered to be the free flow speed of passengers on the escalator. And the data analysis uses the data of pedestrian flow and traffic time obtained by investigation to fit the BPR function parameters, and the relation between the walking time and the pedestrian flow of different traffic facilities can be obtained according to a BPR function curve.
The passenger waiting time estimation model is specifically used for the two parts of queuing ticket buying time and waiting departure time. The process of queuing and booking tickets for passengers can be regarded as a queuing system, and taxis arriving at an airport as a continuous stream, so that passengers waiting for taxis can also be regarded as a queuing system. A queuing system consists of three main parts: input process, queuing rules and service platform. The arrival process of passengers obeys the poisson process, and as the airport bus and the subway operate according to a fixed schedule, the waiting departure time can be estimated through the arrival time and the operation schedule of the passengers.
The estimation model of the Time consumption of the out-going navigation station building is specifically used for calculating the road Travel Time based on the real-Time traffic operating condition Index, TTI (Travel Time Index) is an evaluation Index which uses the most extensive urban congestion degree, and is based on the traffic congestion Index, namely the ratio of the actual Travel Time on the road to the free Travel Time. The actual travel time is the average travel time of all vehicles on a section of road at the current time, and this data is typically acquired by traffic detectors or floating cars. The free flow speed is a road running speed of a vehicle under the condition that the vehicle is not influenced by other vehicles, is related to factors such as a road construction level and a road width, is an observed value in a certain period, and is generally acquired under the road condition that the distance between vehicles is large and the traffic density is sparse. The Traffic Time Index (TTI) and the traffic state level have a direct relation with the travel time of the vehicle, and the travel time of the vehicle is estimated by using a BP (back propagation) neural network.
Fig. 5 is a schematic diagram of multi-objective optimization, travel mode, time and comfort provided by the embodiment of the invention. Forecasting the personalized travel preference of the passengers with different travel purposes, income levels, age structures and baggage numbers through the consistency relation between the characteristics of the passengers and the travel mode selection; analyzing passenger flow change conditions in different time periods of an airport traffic transfer center according to historical passenger flow and real-time passenger flow statistical data, and exploring a passenger gathering and scattering traffic mode selection rule; and (3) giving a passenger flow and traffic capacity matching optimization guidance strategy by combining a traffic mode capacity change and operation schedule dynamic coupling method. When the passenger arrives and searches for the travel route, the platform estimates the whole travel time of different travel routes by accumulating the time spent on different sections, and the travel route which takes the shortest time is recommended to the passenger. The method comprises the steps of estimating the traveling time in the terminal building by using a BPR model, estimating the waiting time by using running schedules and queuing theory of different traffic modes, and estimating the traveling time out of the terminal building by using a BP neural network. Based on the passenger travel preference prediction, the segmented time-consuming estimation model, the passenger flow distribution and the transportation capacity level of the transportation mode, various influence factors such as the passenger preference, the travel time consumption, the transportation capacity matching and the like are integrated to optimize a passenger travel scheme, the customized travel chain construction of a group/single passenger is realized, the seamless transfer of the passenger among different transportation modes is realized, and the land-side transportation transfer efficiency of the airport is improved.
In conclusion, the embodiment of the invention can predict the travel preference of group/individual passengers, and measure the passenger riding intention according to the factors such as the number of airport passengers, age structure, income level and the like; estimating the time consumption of the passenger travel sectional track according to the real-time traffic conditions inside and outside the terminal building and the multi-traffic mode running schedule; meanwhile, the airport passenger travel chain construction with multi-objective optimization is realized by combining the passenger gathering and scattering rule and the traffic capacity matching result.
The invention can predict the travel preference of passenger groups with different travel purposes, the same number of people, age structures and other differences, accurately estimate the walking time of passengers in the terminal building, the traveling time and waiting time of the terminal building, recommend an efficient and comfortable travel scheme for passengers arriving at an airport, and solve the problem of crowded passenger flow in the airport. Thereby improving the service level of passengers and improving the land side traffic transfer efficiency of airports.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The utility model provides a many modes of transportation of airport passenger transfer trip chain constructs device which characterized in that includes: the system comprises a passenger portrait-based travel preference model construction module, a travel full-coverage segmented time-consuming estimation module, a passenger flow convergence and dispersion rule and multi-mode traffic capacity coupling module and a passenger travel chain multi-target construction module:
the travel preference model building module based on the passenger portrait is used for building a consistency relation between the characteristics of the passengers and the selection of travel modes and predicting the personalized travel preference of the passengers with different travel purposes, income levels, age structures and baggage numbers;
the travel full-coverage segmented time-consuming estimation module is used for constructing a segmented terminal building internal and external travel model structure and estimating the time of the airport passenger in the terminal building traveling process and the terminal building external traveling process included in the land side traffic transfer;
the passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module is used for acquiring a selection rule of passenger gathering and scattering traffic modes in an airport traffic transfer center according to historical passenger flow and real-time passenger flow data and estimating waiting time of passengers taking different traffic modes by combining traffic mode traffic capacity change and a running schedule;
the passenger travel chain multi-target construction module is used for realizing the customization of the travel chain of the group/single passenger based on the prediction result of the travel preference of the passenger, the estimation result of the travel time and the waiting time of the passenger and by combining passenger flow distribution and the transportation capacity level of the transportation mode.
2. The apparatus of claim 1, wherein:
the travel preference model building module based on the passenger portrait is specifically used for carrying out multi-dimensional passenger grouping and passenger feature classification processing based on travel information related to passenger age structures, income levels, travel purposes and baggage quantity, carrying out passenger feature classification by using a K-means clustering method, establishing a consistency relation between passenger features and passenger travel mode selection, obtaining a passenger travel preference prediction model, training the passenger travel preference prediction model according to passenger historical travel data, inputting actual features of passengers to be processed into the trained passenger travel preference prediction model, and outputting prediction results of travel behaviors of the passengers to be processed by the passenger travel preference prediction model.
3. The apparatus of claim 2, wherein:
the travel full-coverage segmented time-consuming estimation module is specifically used for considering the travel process of the airport passengers as the accumulation of the travel process, the waiting process and the travel process out of the airport terminal, abstracting the travel path of the passengers into a streamline and nodes, and establishing a travel BPR model in the airport terminal according to passenger flow density and different traffic facility lengths, widths and bearing capacity attributes; estimating the travel time of passengers taking airports, buses, private cars and taxis according to the state of the section outside the terminal and the traffic time index TTI, and estimating the time of taking subways according to a subway running schedule;
adopting a waiting time estimation method based on a flight wave traffic schedule to link indoor and outdoor travel stages of passengers at an airport, searching a travel scheme with the shortest time consumption, and constructing a whole travel path from the airport to an arrival gate to a destination of the passengers; when the passenger arrives and searches the travel route, the travel time of the whole travel routes is estimated by accumulating the time spent of different sections, and the travel route with the shortest time is recommended to the passenger.
4. The apparatus of claim 3, wherein the run-time full-coverage segment-time estimation module comprises: a traveling time estimation model, a waiting time estimation model and an out-of-flight time estimation model which are connected by the passing facilities;
the traffic facility connection traveling time estimation model is used for establishing a BPR model of a walking channel, an escalator and a gate machine traffic facility in an airport terminal building, calculating traveling time by adopting the BPR model, abstracting a route from an arrival port to a platform taking a vehicle into a traveling network consisting of a plurality of crossed line segments and nodes with certain spatial distribution, wherein the nodes in the traveling network represent a region where people flow crosses, the line segments represent the length, direction and type attributes of a traveling path, the length, direction and type of the traveling path are obtained by actual measurement in the airport, the road resistance attribute of the traveling path reflects the carrying capacity of the traffic facility to passenger flow, and is represented by the time for a pedestrian to travel on the traffic facility for a unit length;
the passenger waiting time estimation model is used for comprising two parts of queuing ticket buying time and waiting departure time, the process of queuing ticket buying for passengers and the process of waiting for taxi for passengers are regarded as a queuing system, the process of passenger arrival obeys the poisson process, and the waiting departure time is obtained by estimating the passenger arrival time and the operation schedule;
the estimation model for the time consumption of the out-going navigation station building is used for calculating the road travel time based on the real-time traffic operation index TTI, training a BP (back propagation) neural network by using real traffic state data at different moments, estimating the vehicle travel time by using the trained BP neural network, wherein the length of a road section and the traffic state level are input of the BP neural network, 9 nodes are arranged in the middle layer, and the output of the BP neural network is the travel time of a vehicle on the road section.
5. The apparatus of claim 1, wherein:
the passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module is specifically used for analyzing passenger flow change conditions in different time periods of an airport traffic transfer center according to historical passenger flow and real-time passenger flow statistical data and predicting passenger gathering and scattering traffic mode selection rules by using a BP neural network; and (3) giving a passenger flow and traffic capacity matching optimization guidance strategy by combining a traffic mode capacity change and operation schedule dynamic coupling method.
6. The apparatus of claim 1, wherein:
the passenger travel chain multi-objective construction module is used for constructing a multi-objective optimization function based on prediction results of passenger travel preference, estimation results of passenger travel time and waiting time, passenger flow distribution and traffic mode transport capacity levels are combined, passenger travel schemes are optimized by utilizing the multi-objective optimization function to synthesize various influence factors of passenger preference, travel time consumption and traffic transport capacity matching, construction of customized travel chains of groups/single passengers is achieved, and seamless transfer of passengers between different traffic modes is achieved.
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