CN112380398B - Airport passenger multi-traffic mode transfer travel chain construction device - Google Patents
Airport passenger multi-traffic mode transfer travel chain construction device Download PDFInfo
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Abstract
The invention provides a device for constructing a transfer travel chain of airport passengers in a multi-traffic mode. Comprising the following steps: the travel preference model building device is used for predicting travel behaviors of airport passengers based on the passenger portraits; the full-coverage subsection time-consuming estimation device estimates the indoor and outdoor journey time of the arrival passenger land-side traffic transfer; the passenger flow gathering and scattering law and multi-mode traffic capacity coupling module is used for analyzing the passenger gathering and scattering law in the land-side transportation junction and predicting the waiting time of different transportation modes; the multi-objective construction method and device for the passenger travel chain take passenger travel preference, travel whole-course time, passenger flow gathering and distributing, traffic mode transportation capacity and the like as influence factors, and comprehensively optimize and generate the passenger travel chain. The method can predict the travel preference of the passenger group with different travel purposes, the number of the same person, the age structure and the like, accurately estimate the walking time of the passenger in the terminal building, the travel time outside the terminal building and the waiting time, and recommend an efficient and comfortable travel scheme for the airport arriving passengers.
Description
Technical Field
The invention relates to the technical field of airport passenger travel management, in particular to a device for constructing a transfer travel chain of airport passengers in a multi-traffic mode.
Background
At present, the land-side transportation transfer center of the large airport is connected with various transportation modes such as rail transportation, urban buses, airport buses, taxis, private cars and the like, so that travel selection can be increased for passengers, and the travel range of the passengers is expanded. However, the interleaving of multiple traffic patterns in a limited space increases the complexity of the traveler's travel. Meanwhile, the large airport lacks travel information collection and pushing capabilities 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 travel decisions of passengers. The existing LBS (Location Based Services, location-based service) software has the main functions of being limited to an urban traffic travel transfer strategy and travel time estimation outside an airport terminal building, and cannot be connected with travel paths inside and outside the terminal building, so that a travel full-coverage passenger travel chain is optimally constructed.
At present, no travel chain optimizing and constructing device for fully covering indoor and outdoor journey of airport passengers exists in the prior art.
Disclosure of Invention
The embodiment of the invention provides a multi-traffic transfer travel chain construction device for airport passengers, which is used for effectively predicting travel preference of passenger groups with different travel purposes, the number of the same person, the age structure and the like.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
An airport passenger multi-transportation transfer travel chain construction device comprises: travel preference model building module based on passenger portrait, travel full-coverage segmentation time consumption estimation module, passenger flow gathering and scattering rule, multi-mode traffic capacity coupling module and passenger travel chain multi-target building module:
the travel preference model construction module based on the passenger portrait is used for establishing a consistency relation between the characteristics of the passengers and travel mode selection and predicting personalized travel preferences of the passengers with different travel purposes, income levels, age structures and baggage numbers;
the travel full-coverage segmentation time-consuming estimation module is used for constructing a segmented internal and external travel model structure of the terminal building and estimating the travel process of airport passengers in the terminal building and the travel process time of the terminal building, which are contained in 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 the 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 capacity change and operation timetable;
the passenger travel chain multi-target construction module is used for realizing customization of a group/single passenger travel chain based on a prediction result of passenger travel preference, an estimation result of passenger travel time and waiting time and combining passenger flow distribution and traffic mode transport capacity level.
Preferably, the travel preference model building module based on the passenger portrait is specifically configured to perform multidimensional passenger classification and passenger feature classification based on passenger age structure, income level, travel purpose and baggage quantity related travel information, perform passenger feature classification by using a K-means clustering method, establish a consistency relationship between passenger features and passenger travel mode selection, obtain a passenger travel preference prediction model, train the passenger travel preference prediction model according to passenger history travel data, input actual features of the passenger to be processed into the trained passenger travel preference prediction model, and output a prediction result of travel behaviors of the passenger to be processed by the passenger travel preference prediction model.
Preferably, the journey full-coverage segmentation time-consuming estimation module is specifically configured to consider an airport passenger travel process as accumulation of an in-terminal travel process, a waiting process and an out-terminal travel process, abstract a passenger travel path into flow lines and nodes, and establish an in-terminal travel BPR model according to passenger flow density and different transit facility length, width and bearing capacity attributes; estimating travel time of passengers taking airport buses, private cars and taxis according to the states of road sections outside the airport terminal and the traffic time index TTI, and estimating subway taking time according to a subway running schedule;
adopting a waiting time estimation method based on a flight wave traffic schedule to link up the internal and external travel stages of the airport passenger room, searching a travel scheme with the shortest time consumption, and constructing a whole travel path from an airport arrival port to a destination of the passenger; when the traveler arrives and searches the travel route, estimating the whole travel time of the different travel routes by accumulating the time spending of the different sections, and recommending the travel route with the shortest spending time to the traveler.
Preferably, the journey full coverage segmentation time-consuming estimation module comprises: a travel time estimation model, a waiting time estimation model and an airport terminal building outbound travel time consumption estimation model which are connected by the transit facility;
the running time estimation model is used for establishing a BPR model of a walking channel, an escalator and a gate running facility in a terminal building, calculating running time by adopting the BPR model, abstracting a route of a passenger from an arrival port to a platform of a riding transportation means into a running network formed by a plurality of intersecting line segments and nodes with certain space distribution, wherein the nodes in the running network represent a human flow intersecting area, the line segments represent the length, the direction and the type of a running path, the length, the direction and the type of the running path are obtained through actual measurement at an airport, and the road resistance attribute of the running path reflects the bearing capacity of the running facility on the passenger flow and is represented by the time used by the pedestrian running on the running facility for a unit length;
the passenger waiting time estimation model is used for comprising two parts of queuing ticket purchasing time and waiting taxi taking time, wherein the passenger queuing ticket purchasing process and the passenger taxi waiting process are regarded as a queuing system, the passenger arriving process is subject to a poisson process, and the waiting taxi taking time is estimated and obtained through the passenger arriving time and an operation schedule;
the model is used for calculating road journey time based on real-time traffic running index TTI, training BP neural network by using real traffic state data at different moments, estimating vehicle journey time by using the trained BP neural network, wherein the road section length 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 journey time of the vehicle in the road section.
Preferably, the passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module is specifically configured to analyze 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 predict a passenger gathering and scattering traffic mode selection rule by using a BP neural network; and providing a passenger flow and traffic capacity matching optimization induction strategy by combining the traffic capacity change and the running schedule dynamic coupling method.
Preferably, the multi-objective construction module of the passenger travel chain is used for constructing a multi-objective optimization function based on the prediction result of the passenger travel preference, the estimation result of the passenger travel time and the waiting time, combining the passenger flow distribution and the traffic mode transport capacity level, optimizing the passenger travel scheme by utilizing the multi-objective optimization function to integrate the passenger preference, the journey time and the traffic transport capacity to match various influencing factors, realizing the construction of the customized travel chain of the group/single passenger, and realizing the seamless transfer among different traffic modes.
According to the technical scheme provided by the embodiment of the invention, the travel preference of the passenger group with different travel purposes, the number of the same person, the age structure and the like can be predicted, the walking time of the passenger in the terminal building, the travel time outside the terminal building and the waiting time can be accurately estimated, an efficient and comfortable travel scheme is recommended for the airport arrival passengers, and the problem of crowded airport passenger flow is solved.
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.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a device for constructing a transfer travel chain of airport passengers in a multi-traffic mode according to an embodiment of the present invention;
fig. 2 is a schematic diagram of travel mode selection prediction of a passenger according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an implementation principle of a travel full-coverage segmentation time-consuming estimation module according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a travel full-coverage segmentation time-consuming 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 according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for 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 expressly stated otherwise, as understood by those skilled in the art. 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. The term "and/or" as used herein 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 purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The embodiment of the invention provides an airport passenger multi-traffic mode transfer travel chain construction device which is used for constructing an airport passenger multi-traffic mode transfer travel chain so as to realize airport passenger travel full-coverage travel chain construction and platform application.
The structural schematic diagram of the airport passenger multi-traffic-mode transfer travel chain construction device provided by the embodiment of the invention is shown in fig. 1, and the device comprises a travel preference model construction module based on passenger portraits, a travel full-coverage segmentation time consumption estimation module, a passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module and a passenger travel chain multi-target construction module.
The passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module is associated with a travel preference model building module based on passenger portraits and a journey full-coverage segmentation time consumption estimation module, and the passenger travel chain multi-target building module is associated with the travel preference model building module based on the passenger portraits, the journey full-coverage segmentation time consumption estimation module, the passenger flow gathering and scattering rule and multi-mode traffic capacity coupling module.
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 refers to the construction of a targeted passenger characteristic data set by recording basic attribute and behavior information of passengers based on the travel demands of the statistical passengers, realizing the mining reconstruction of passenger characteristic data and dividing passenger groups according to the passenger characteristic data. And predicting individual travel preferences of passengers with different travel purposes, income levels, age structures and baggage numbers based on the passenger images, so as to realize differentiated marketing.
The travel full-coverage segmentation time-consuming estimation module is used for constructing a segmented internal and external travel model structure of the terminal building and estimating the travel process of airport passengers in the terminal building and the travel process time of the terminal building contained in 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 the 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 capacity change and operation timetable.
Analyzing the passenger flow change conditions in different time periods of the airport traffic transfer center according to the historical passenger flow and the real-time passenger flow statistical data, and predicting a passenger gathering and dispersing traffic mode selection rule by using a BP neural network; and providing a passenger flow and traffic capacity matching optimization induction strategy by combining the traffic capacity change and the running schedule dynamic coupling method.
The passenger travel chain multi-target construction module is used for realizing travel chain customization of groups/single passengers based on the prediction result of the passenger travel preference, the estimation result of the passenger travel time and the waiting time and combining the passenger flow distribution and the traffic mode transport capacity level.
Based on the prediction result of the travel preference of the passengers, the estimation result of the travel time and the waiting time of the passengers, the passenger flow distribution and the traffic mode transport capacity level are combined to construct a multi-objective optimization function, and the passenger travel scheme is optimized by utilizing the multi-objective optimization function to synthesize various influencing factors such as the passenger preference, the journey time consumption and the traffic capacity matching, so that the construction of customized travel chains of groups/single passengers is realized, and the seamless transfer of the passengers among different traffic modes is realized.
In order to overcome the defects that in the prior art, the real-time information of a transportation junction is insufficient, the influence of uncertain factors such as traffic evacuation difficulty, accidents, weather, traffic control and the like on travel time is large, and the like, the embodiment of the invention estimates the travel time and the transportation time of passengers in a terminal building by analyzing the influence of the travel route distribution of the passengers on the travel time, constructs the whole travel route of the passengers from an airport to a destination, and provides multi-transportation-mode transfer autonomous service for the passengers by taking self-service equipment as a carrier.
According to the embodiment of the invention, the travel process of the passengers in the terminal building and the travel process time of the riding vehicles are estimated respectively 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 the airport passenger room, find the travel scheme with the shortest time consumption and construct the whole travel path from the airport arrival port to the destination.
The travel preference model building module based on the passenger portrait is also specifically used for selecting a travel mode of the passenger. Fig. 2 is a schematic diagram of travel mode selection prediction of a passenger according to an embodiment of the present invention. Selecting travel-related passenger characteristics from an airport business level, performing multidimensional passenger grouping and passenger characteristic classification processing based on the travel-related information such as passenger age structure, income level, travel purpose, luggage quantity and the like, performing passenger characteristic classification by using a K-means clustering method, mainly comprising the processes of setting sample points, calculating Euclidean distance and correcting a clustering center, and finally explaining the clustering result of the passenger characteristics according to a statistical method and historical experience. On the basis of passenger characteristic analysis, establishing a consistency relation between passenger characteristics and passenger travel mode selection to obtain a passenger travel preference prediction model, and training the passenger travel preference prediction model according to passenger history travel data. And then, inputting the actual characteristics of the passengers to be processed into a trained passenger travel preference prediction model, and outputting a prediction result of the travel behaviors of the passengers to be processed by the passenger travel preference prediction model.
The journey full coverage segmentation time consumption estimation module is further specifically used for estimating the segmentation time consumption of the passenger covering the full journey. Fig. 3 is a schematic implementation diagram of a full-coverage and segmented time-consuming estimation module for a passenger journey according to an embodiment of the present invention, where a schematic structural diagram of the module is shown in fig. 4, and includes: a travel time estimation model, a waiting time estimation model and an airport terminal outbound travel time consumption estimation model for the connection of the transit facilities. The module regards the travel process of the airport passengers as accumulation of travel process, waiting process and travel process of the airport terminal, and estimates travel process time of passengers in the airport terminal and riding vehicles respectively by analyzing influence of travel path distribution of the airport passengers on 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 the airport passenger room, find the travel scheme with the shortest time consumption and construct the whole travel path from the airport arrival port to the destination. When the traveler arrives and searches the travel route, estimating the whole travel time of the different travel routes by accumulating the time spending of the different sections, and recommending the travel route with the shortest spending time to the traveler.
The running time estimation model connected with the traffic facilities is particularly used for establishing a BPR (Bureau of Public Road, road resistance function of the United states road bureau) model of traffic facilities such as a walking channel, an escalator, a gate and the like in a terminal building, and calculating the running time by adopting the BPR model. When the indoor travel time is calculated, the route of the passenger from the arrival port to the platform of the riding transportation means is relatively fixed, and the network formed by a plurality of intersecting line segments and nodes with certain spatial distribution can be abstracted. Nodes in the network represent areas where people flow meet, and can depict the interweaving phenomenon of the people flow; line segments represent the length, direction, type, etc. of the path. The travel path comprises travel facilities such as a walking channel, an escalator, a gate and the like, and the passenger carrying capacity and the passenger travel time of different travel facilities are different due to different spatial distribution of the width, the position and the like of different travel facilities, so that the abstract travel network has the length attribute corresponding to the travel and the road resistance attribute reflecting the travel capacity. The length, direction and type of the travel path are relatively fixed and can be obtained by actual measurements at the airport. The travel path has a road resistance property which reflects the carrying capacity of the passing facility on the passenger flow, and the passenger flow can be represented by the time taken by a pedestrian to walk on the passing facility for a unit length. The method is obtained by adopting a manual investigation method for 3 hours, each group of investigation time is 30s, and the investigation contents comprise pedestrian flow and passing time at a walking channel, an up-down escalator and a gate, and channel parameters such as the length, the width and the like of passing facilities. The condition that the pedestrian flow is free flow is set as v is less than or equal to 30 times/(m.min), and the running speed of the escalator is considered to be the free flow speed of the passengers on the escalator. And fitting the BPR function parameters by using the data of pedestrian traffic and traffic time obtained by investigation through data analysis, and obtaining the relationship between the walking time and pedestrian traffic of different traffic facilities according to the BPR function curve.
The passenger waiting time estimation model is particularly used for two parts of queuing ticket purchasing time and waiting departure time. The process of queuing passengers for ticket purchase can be regarded as a queuing system, and waiting passengers for taxis can also be regarded as a queuing system because taxis at airports arrive as a continuous stream. A queuing system consists of three main components: input processes, queuing rules, and service platforms. The arrival process of passengers is subject to the poisson process, and since airport buses and subways operate according to a fixed schedule, waiting for departure time can be estimated by the arrival time of passengers and the operation schedule.
The model is specifically used for calculating the road journey time based on a real-time traffic running condition index, and the TTI (Travel Time Index, traffic running index) is an evaluation index of the most widely used urban congestion degree and is based on the traffic congestion index, namely the ratio of the actual journey time on the road to the free epidemic journey time. The actual travel time is the average travel time of all vehicles on a road at the current time, and this data is typically acquired by traffic detectors or floating vehicles. The free flow speed is the road running speed of the vehicle without being influenced by other vehicles, is related to factors such as road construction level, road width and the like, is an observed value in a certain period, and is generally acquired under road conditions of large vehicle distance and sparse vehicle flow density. Traffic Time Index (TTI) and traffic status level have a direct relationship with the travel time of the vehicle, which is estimated using a BP (back propagation) neural network.
Fig. 5 is a schematic diagram of multi-objective optimization, travel mode, time and comfort according to an embodiment of the present invention. Predicting individual travel preferences of passengers with different travel purposes, income levels, age structures and baggage numbers through the consistency relation between the characteristics of the passengers and travel mode selection; analyzing the passenger flow change conditions in different time periods of the airport traffic transfer center according to the historical passenger flow and the real-time passenger flow statistical data, and exploring the selection rule of the passenger gathering and scattering traffic mode; and providing a passenger flow and traffic capacity matching optimization induction strategy by combining the traffic capacity change and the running schedule dynamic coupling method. When the traveler arrives and searches the travel route, the platform estimates the whole travel time of the different travel routes by accumulating the time spending of the different sections, and the travel route with the shortest spending time is recommended to the traveler. Estimating the travel time in the terminal building by using a BPR model, estimating the waiting time by using operation timetables and queuing theory of different traffic modes, and estimating the travel time out of the terminal building by using a BP neural network. Based on the passenger travel preference prediction, the segmentation time consumption estimation model, the passenger flow distribution and the traffic mode transport capacity level, the passenger travel scheme is optimized by integrating various influence factors such as passenger preference, travel time consumption, traffic transport capacity matching and the like, the customized travel chain construction of groups/single passengers is realized, the seamless transfer of passengers among different traffic modes is realized, and the airport land side traffic transfer efficiency is improved.
In summary, the embodiment of the invention can predict the travel preference of group/individual passengers, and measure the riding intention of the passengers according to the factors such as the luggage quantity, the age structure, the income level and the like of the airport passengers; estimating the time consumption of the travel subsection track of the passenger according to the real-time traffic conditions inside and outside the terminal building and the multi-traffic mode operation schedule; and 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 method can predict the travel preference of the passenger group with different travel purposes, the number of the same person, the age structure and the like, accurately estimate the walking time of the passengers in the terminal building, the travel time outside the terminal building and the waiting time, recommend an efficient and comfortable travel scheme for the airport arrival passengers, and solve the problem of airport passenger flow crowding. Thereby improving the passenger service level and the airport land side traffic transfer efficiency.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (1)
1. The utility model provides a multi-traffic mode transfer trip chain construction device of airport passenger, its characterized in that includes: travel preference model building module based on passenger portrait, travel full-coverage segmentation time consumption estimation module, passenger flow gathering and scattering rule, multi-mode traffic capacity coupling module and passenger travel chain multi-target building module:
the travel preference model construction module based on the passenger portrait is used for establishing a consistency relation between the characteristics of the passengers and travel mode selection and predicting personalized travel preferences of the passengers with different travel purposes, income levels, age structures and baggage numbers;
the travel full-coverage segmentation time-consuming estimation module is used for constructing a segmented internal and external travel model structure of the terminal building and estimating the travel process of airport passengers in the terminal building and the travel process time of the terminal building, which are contained in 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 the 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 capacity change and operation timetable;
the multi-objective building module of the passenger travel chain is used for realizing the customization of the travel chain of a group/single passenger based on the prediction result of the passenger travel preference, the estimation result of the passenger travel time and the waiting time and combining the passenger flow distribution and the traffic mode transport capacity level,
the travel preference model construction module based on the passenger portrait is particularly used for carrying out multidimensional passenger grouping and passenger characteristic classification processing based on the passenger age structure, income level, travel purpose and travel information related to the quantity of baggage, carrying out passenger characteristic classification by using a K-means clustering method, establishing a consistency relation between passenger characteristics and passenger travel mode selection to obtain a passenger travel preference prediction model, training the passenger travel preference prediction model according to passenger history travel data, inputting the actual characteristics of the passenger to be processed into the trained passenger travel preference prediction model, outputting the prediction result of the travel behavior of the passenger to be processed by the passenger travel preference prediction model,
the travel full-coverage segmentation time-consuming estimation module is specifically used for regarding the travel process of airport passengers as the accumulation of the travel process in the terminal building, the waiting process and the travel process out of the terminal building, abstracting the travel path of the passengers into flow lines and nodes, and establishing a travel BPR model in the terminal building according to the passenger flow density, the length, the width and the bearing capacity attribute of different passing facilities; estimating travel time of passengers taking airport buses, private cars and taxis according to the states of road sections outside the airport terminal and the traffic time index TTI, and estimating subway taking time according to a subway running schedule;
adopting a waiting time estimation method based on a flight wave traffic schedule to link up the internal and external travel stages of the airport passenger room, searching a travel scheme with the shortest time consumption, and constructing a whole travel path from an airport arrival port to a destination of the passenger; when the traveler arrives and searches the travel route, estimating the whole travel time of the different travel routes by accumulating the time spending of the different segments, recommending the travel route with the shortest spending time to the traveler,
the travel full-coverage segmentation time-consuming estimation module comprises: a travel time estimation model, a waiting time estimation model and an airport terminal building outbound travel time consumption estimation model which are connected by the transit facility;
the running time estimation model is used for establishing a BPR model of a walking channel, an escalator and a gate running facility in a terminal building, calculating running time by adopting the BPR model, abstracting a route of a passenger from an arrival port to a platform of a riding transportation means into a running network formed by a plurality of intersecting line segments and nodes with certain space distribution, wherein the nodes in the running network represent a human flow intersecting area, the line segments represent the length, the direction and the type of a running path, the length, the direction and the type of the running path are obtained through actual measurement at an airport, and the road resistance attribute of the running path reflects the bearing capacity of the running facility on the passenger flow and is represented by the time used by the pedestrian running on the running facility for a unit length;
the passenger waiting time estimation model is used for comprising two parts of queuing ticket purchasing time and waiting taxi taking time, wherein the passenger queuing ticket purchasing process and the passenger taxi waiting process are regarded as a queuing system, the passenger arriving process is subject to a poisson process, and the waiting taxi taking time is estimated and obtained through the passenger arriving time and an operation schedule;
the model is used for calculating the road journey time based on the real-time traffic running index TTI, training the BP neural network by using the real traffic state data at different moments, estimating the vehicle journey time by using the trained BP neural network, wherein the road section length and the traffic state level are the inputs of the BP neural network, the middle layer is provided with 9 nodes, the output of the BP neural network is the journey time of the vehicle at the road section,
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 a passenger gathering and scattering traffic mode selection rule by using a BP neural network; the dynamic coupling method of traffic capacity change and operation schedule is combined to give the optimized guidance strategy of passenger flow and traffic capacity matching,
the multi-objective building module of the passenger travel chain is used for building a multi-objective optimization function based on the prediction result of the passenger travel preference, the estimation result of the passenger travel time and the waiting time, combining the passenger flow distribution and the traffic mode transport capacity level, optimizing the passenger travel scheme by utilizing the multi-objective optimization function to integrate the passenger preference, the journey time consumption and the traffic capacity to match various influencing factors, realizing the building of customized travel chains of groups/single passengers, and realizing the seamless transfer of passengers among different traffic modes.
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