Research on supply and demand matching model of transportation modes in MaaS system of integrated passenger transport hub based on deep learning

<jats:title>Abstract</jats:title>         <jats:p>Advances in artificial intelligence and data acquisition technology are growing, the research on deep learning algorithm has gone deep into various fields. At this stage, the demand supply matching model under the comprehensive passenger transport hub travel system is obtained by analyzing the travel mode data. This paper takes traffic time as the research direction,uses machine learning and complex network theory to conduct in-depth learning algorithm research, respectively discusses, explores and forecasts the traffic supply and demand mode data, and explores the traffic mode supply and demand model under the comprehensive passenger transport hub travel service system. It is shown in traffic data that using the prediction form of deep learning to predict traffic conditions and travel pressure can enable traffic managers to master traffic dynamics and lead the direction for future traffic development. Finally, from the perspective of MaaS system, the paper uses big data and information processing methods to explore the supply and demand matching model in terms of transportation modes. The research shows that MaaS system can make overall planning in terms of traffic resources, provide reasonable travel modes for traffic travelers and match corresponding travel services. It not only makes overall planning and guarantee for travel services, but also promotes the sustainable development of the transportation supply and demand system.</jats:p>

section_content: With the continuous development of modern information technology, the travel and demand of passengers are also constantly improving. Therefore, the concept of MaaS has become a multi-level urban transport concept of passenger transport hub travel [1]. MaaS is a new concept under the form of transportation, focusing on the positioning form of transportation. This system regards transportation as a service form. Today, with the popularity of smart clients and mobile data, MaaS operators integrate the network form into the travel mode, and provide individual services according to the travel needs submitted by passengers [2]. While improving the travel e ciency of passengers, they also maintain the service quality. Since MaaS was proposed in 2021, its service elds and categories have been constantly innovated [3]. The development of the current MaaS system is also constantly facing impact and adjustment. Many scholars have analyzed the form of China's Internet and studied the opportunities for the rapid development of the transportation industry under this background, which also laid a foundation for the market formed by MaaS [4]. In the future, the challenges to be faced by MaaS system are analyzed in terms of system, law, policy, information security, travel route and other options. In the future, a complete and standardized framework mode is required to implement the system and ensure the development direction of travel services [5].
section_name: Introduction

section_content: It is proposed in the literature that the network structure similarity and characteristics of streets in ten European cities are compared and analyzed by the original map method. The dual graph method is proposed to construct the urban road complex network, and the structural fractal and self-similarity of the network under different scales are analyzed. It is proposed in the literature that the road networks of 50 cities are constructed, and the robustness of urban road networks is studied by using continuous deletion model and cascade model [6]. The literature investigates the quantitative value of bus users on qualitative factors, discusses the impact of service quality on the value of bus travel time and bus passenger ow, and points out that the improvement of service quality can be quanti ed as the reduction of passenger travel time, so as to obtain bene ts similar to the improvement of bus operation speed [7] ; By comparing the reduction of total travel time of passengers by different factors, the key factors affecting passenger satisfaction are obtained. The literature focuses on analyzing the satisfaction of passengers in the whole process of travel. Based on the data of passenger travel satisfaction in eight cities obtained from the survey, the whole process of multi-mode travel is retroactively evaluated. It is found that each segment of travel has different degrees of impact on the overall satisfaction of passengers, so as to identify the key segments of travel in the whole process of travel and obtain the key factors affecting travel satisfaction [8]. Through the estimation of the route model in the literature, it is found that the safety perception of transfer and the information service at the transfer place are the key in uencing factors of passengers' satisfaction with the multi-mode transfer service [9]. The literature mixed multiple logit model and mixed logit model comprehensively analyze the travel service preference of connecting passengers. By assessing the willingness of passengers to pay for a series of services, the bottleneck factors of air rail connecting service are found. It is found that reducing the transfer time and coordinating the operation schedule of air rail two ways play an important role in improving the attractiveness of air rail connecting service [10]. Taking Xinjiekou station of the subway as an example, based on the analysis of the passenger ow data, the literature describes the characteristics of the transfer passenger ow at Xinjiekou station, diagnoses the bottleneck of the transfer behavior of the subway passenger ow, and makes targeted optimization design of the transfer scheme. Based on the Rasch model, the literature analyzes the evaluation of Seoul rail transit passengers on the service quality of transfer facilities [11]. The evaluated service items are divided into ve categories: knowability, mobility, comfort, convenience and safety. Classify and analyze the satisfaction scores based on the travel purpose of passengers, identify the key factors that affect the rail transit passenger satisfaction with different travel purposes, and then put forward suggestions and measures that can improve the operation e ciency of Seoul rail transit and improve the passenger travel satisfaction [12] [13]. The literature uses the joint analysis method to predict the willingness of air rail passengers to pay for the development of air rail intermodal transport services at different levels, so as to obtain the key factors affecting the satisfaction of air rail intermodal transport, including the coordination schedule, baggage transport and delay compensation [14]. It is proposed in the literature that an optimization model of airport bus departure schedule is established to accelerate and expand the market share of airport buses. According to the literature, the perception and judgment of passengers are subjective measures of travel service quality, while the performance reports provided by bus operators are objective measures of travel service quality [15]. Based on this, they proposed a comprehensive evaluation method of transport service quality considering subjective and objective indicators. The average speed is an indicator to judge the travel speed. Due to the location and time period, the average speed is also divided into time average and interval average speed. The time average speed is the average speed of vehicles passing the road section, and the calculation formula is as follows: 2

In the formula, v t is the time average speed, N represents the actual number of vehicles. The speed of a speci c road section generated by the vehicle's teleportation speed is called the interval speed, which is calculated by the following formula: 3

In the formula, vs represents the average speed of the section, n represents the actual number of vehicles, and V α Indicates that the α speed of the car. 

Space occupancy refers to the ratio between the tra c ow and the length of the road section at a certain place within a certain time. The calculation formula is as follows: 4

In the formula, O s represents the spatial share, S car indicates the length of the tra c through the tra c, and s means the length of the road. 

The time share refers to the time and comparison value of the time and calculation time used at a certain place within a certain period of time. The formula is as follows:

O t in the formula represents the time share, T α represents the time of the section of the vehicle through the vehicle. 

(2) Matrix complex network In terms of matrix computing of large -scale networks, the matrix form has become a typical expression of complex networks. The three matrix expression modes with high utilization rates are adjacent matrix, associated matrix, and heavy matrix. 

The adjacent matrix describes whether there is connection between the nodes in the unpopular network, and the neighbor matrix is set: 6

The associated matrix describes the connection between nodes and its direction of nodes in the network. The associated matrix is set: 7

The weight matrix represents both the connection between the nodes, but also the number of nodes, also known as the weight of the edge. The heavy matrix of power is: 8

In the formula, wm ab is the node, and W ab is the weight between the connecting edges. 

(3) Network Statistics Index

The degree of the node is a comparative factor describing the nodes in the network. The edge data connected to the node in the unwanted network is calculated as follows:

With the direction of the edge in the network, the degree of nodes is divided into two types: input and outline. Remark the number of nodes as the amount of the end point side of the end point; call the node as the number of nodes at the number of nodes. The calculation formula of the input and output expression of nodes is as follows: 10 11 In the formula, EBA is the edge of the point to the node, and EAB is the edge of the node. 

The network density S means the strength value of the quanti ed network node, and its value is 0-1. When S = 0, it represents the zero map of the network. When S = 1, it represents the full picture of the network. In the universe network, the calculation formula of the network density is (12): 12

In the formula, V stands for the number of expressed in the network, and the value obtained by connecting the two nodes in the union network is connected in any two nodes. Calculate the directional and network density in the network (13):

The clustering coe cient is a coe cient that measures the degree of clustering of nodes in a network. The clustering coe cient of the node represents the ratio between the number of edges of the node and the connected node and the maximum number of connected edges, wherein the local clustering coe cient CC a of the node is expressed as formula ( 14 ):

In the formula, M a represents the edge value between two nodes connected by the node, and represents the triangle value formed by two adjacent nodes of the node. The global clustering coe cient CC is used to measure the aggregation of the network as a whole, and the numerical value of the connections between nodes is expressed as follows:

15

The resulting distance Z represents the shortest distance, and the average numerical expression between the two is: 16

In the formula, Zab is the distance between two points, and is the connection distance between edges. 

The shortest path represents the intermediate metric, and the ratio of the shortest distance in the network to the shortest distance of the nodes. When the node is on the shortest path or has no shortest distance, the expression of the node intermediate BC v is as follows: 17

3. 2 Quantitative model of passenger transport supply and demand coordination

In the passenger transport supply and demand system, the in uence of the parameter value of the system on the whole system is regarded as the development direction of the whole system, which makes the supply and demand system form a multi structure function problem. Therefore, the multi-objective function form is adopted to analyze and establish the supply-demand balance model. 

(1) Determine the evaluation indexes of each element in the subsystem of the comprehensive passenger transport supply and demand system. 

The evaluation indicators of system development are: Where j = s or j = D, the order degree of integrated passenger transport supply and demand subsystem can be expressed as:
section_name: Relevant Work

section_content: (2) Determine the evaluation index of comprehensive passenger transport supply and demand subsystem As a whole, the evaluation of the order degree of xji has an integrated effect on the order value of the subsystem. From a theoretical perspective, the form and performance of the supply and demand system only represent the value of the evaluation system value, and cannot determine the structure of the system. Therefore, different system structures are combined to make the composition different and the integration methods different, usually including two types:
section_name: 19

section_content: Weighted average method 21

The best value of the xji subsystem of the evaluation index is C 1. From the above two methods, it can be concluded that the greater the C 1, the greater the help of xji to the comprehensive passenger transport supply and demand system, the higher the order value, and the lower the opposite. In the weighted average method, the weight coe cient W i y i needs to analyze two factors: the real state and the objective of feedback to the passenger transport system within a certain period of time. 

(3) Establishment of equilibrium model of comprehensive passenger transport supply and demand system At the speci c time t 0 of the system, it is assumed that the system order degree of the comprehensive passenger transport subsystem evaluation index is y j, j is 1, 2,. . ., K. for the passenger transport supply and demand composite system, if the system order degree of the supply and demand subsystem evaluation index is y j, j is 1, 2,. . ., K, then XT is the equilibrium degree of the system:

The larger the XT value, the better the balance level and effect of the comprehensive passenger transport supply and demand system. 

(4) Evaluation of the balance degree between integrated passenger transport supply and demand subsystems Under the premise of the equilibrium mode, the overall equilibrium procedure of the passenger transport system is calculated, but the equilibrium value of the relative subsystem is not re ected. The equilibrium value of the subsystem re ected by the relative equilibrium value is calculated as follows:
section_name: Geometric average method 20

section_content: In the formula, U1 and U2 represent the subsystem equilibrium value. In the formula, the larger the Cr(I,j) value, the higher the equilibrium degree between the two subsystems, and vice versa. Cr(I,j) represents the degree of equalization among subsystems at a certain time. 

(5) Equilibrium model of supply and demand system

The passenger transport supply and demand system includes the supply system and the demand system. 

Different indicators are evaluated for the system, considering its safety, convenience, economy, comfort and other indicators. It is assumed that:

When X ij is the bigger the better evaluation index:

26 When X ij is the smaller the better evaluation index: 27 When X ij is a moderate evaluation index: 28 K1, K2 and K3 in the formula are all greater than 0. The function of the coe cient is to adjust the sensitivity. 

The larger the calculated value, the higher the sensitivity. The calculation formula of balanced development under the de ned system is:
section_name: 25

section_content: There two in uencing factors on the supply and demand of passengers' travel modes: production and consumption.
section_name: Analysis of in uencing factors of tra c and demand matching

section_content: From the above analysis, it can be seen that the urban tra c supply mainly includes several variables, mainly including social and economic development factors, urban population factors, demand factors of urban residents' travel, tra c supply factors, tra c operation mode factors and tra c policy factors. The relationship between them is shown in Fig. 1: 4. 3 Analysis of supply and demand of comprehensive passenger transport network

Under the system of MaaS, all kinds of tra c information are systematically imported and connected, so as to reasonably use tra c big data, facilitate accurate access to tra c information and select scienti c route forms. The data in Table 1 is obtained: The weight data of the demand system through the supply system in Table 2 can be seen that the relationship between the relationships between each indicator has no signi cant changes, and the effect of the effect is the same. Use the same method to analyze the heavy table of in uencing the power system of the demand system. The weight data of the supply system through the demand system in Table 3 can be seen that it is roughly the same as the impact re ected in Table 2, and there is no signi cant uctuation between the indicators.
section_name: module elements of tra c supply and demand

section_content: Private travel is a kind of private transportation, which can meet the needs of travelers for travel methods, but it is also a form of replacement of public transportation. Conventional travel methods, such as bus, subway, etc., are all transportation methods with huge operating volume. It relies more on government capital investment and policy support. The development and changes of non -balanced value in all aspects are shown in Fig. 2. 

As shown in Fig. 2, under different tra c famous travels, the degree of balance changes more obvious, and the form of private transportation and public transportation complement each other. The non -balanced ratio of public transportation is increasing and approaching the equilibrium value; the non -balanced value of private transportation is decreasing, which shows that the form of public transportation is constantly expanding and developing, attracting passengers to ride, choosing a private travel method The crowd gradually decreases. 

Private cars are also changing, and the state is also increasing policies to regulate. Relevant departments have also responded to the investment efforts of the transportation system, increased supply demand, and enhanced the service mechanism. In this environment of tra c development, the reason for the development of the taxi industry is still very stable because the service awareness has strengthened management.
section_name: Development trend simulation of the coordinated development trend of comprehensive passenger transportation

section_content: In order to verify the of the model, several indicators were selected, such as total passenger transport, total freight, total motor vehicle volume, and the stock of pollutants. As for the results of 2021, the results calculated by this model are shown in Table 4 : It can be seen from Table 4 that the population, scale, GDP, total passenger volume, total freight, total amount of motor vehicle, and the number of pollutants of the urban agglomeration are less than 6%, indicating that the credibility is high Comprehensive tra c analysis of urban agglomerations is more accurate.
section_name: Model validity test results analysis

section_content: Transportation For Maas
section_name: Suggestions For The Improvement Of Comprehensive Passenger

section_content: At this stage, urban transportation includes types of private transportation and public transportation, and the majority of private transportation travels. In the MaaS system, the tra c structure has changed. Different functional differentials will be divided into four different structures, including: MaaS user entities (system users), operators, main service providers of MaaS systems, and data providers of data. 

MaaS service provider is the core operation of the entire MaaS system. MaaS service provides a variety of transportation routes in the system, combined with data analysis of road conditions, matching results, and travel services that meet the conditions for supply and demand. The MaaS service is a service that is pushed by the MaaS system. The characteristics are as follows:

(1) Personalized customization: In terms of travel services and planning routes, travelers can submit routes according to their needs and import demand information. On the MaaS system, according to the matching travel preferences, customize the real -time road conditions information of the MaaS service, and adjust the planning route to users in time according to the supply and demand needs to maximize the supply and demand. 

(2) Data sharing: Big data analysis and optimization of travel service information. Comprehensive rationalized implementation information and successful implementation. The information of the data needs to be communicated and shared on the MAAS system. Tra c data is divided into ve data: static, real -time, demand, payment and prediction. Integrate data information on the MAAS system and analyze it to obey various users. The user refers to the enjoyment of the MaaS service. It is also a provider of information in addition to the customized service that is matched by the MaaS system. In terms of information sharing, you can provide travel services for reference to improve the satisfaction of future systems.
section_name: MaaS system advantage analysis

section_content: In recent years, the collection and sorting of tra c data has gradually become a research direction, and the transportation network at this stage has become more and more re ned, and it has also changed greatly in the organization of transportation. In the era of data, deep learning models are used to analyze tra c travel data prediction, and the current situation of tra c supply and demand is re ected in the data itself, and analyzed the hidden information in tra c events. Study the laws and patterns of tra c travel. Using the MAAS system's judgment of the tra c status and the xed -point prediction of future tra c travel data, it can provide real -time dynamic monitoring of tra c managers, which is conducive to better management of tra c status. Secondly, the use of MAAS system analysis to obtain travel data information also provides a reference for subsequent tra c improvement. The use of tra c induction and management measures to reduce tra c pressure and hidden dangers, and coordinate the form of tra c resources, increase the e ciency of road transportation, can reduce congestion and ensure smooth roads.
section_name: Conclusion

section_content: Compliance with Ethical Standards Figures
section_name: Declarations

section_name: Funding

section_content: This study was supported by "Research on the Theory and Methodology of Mobility as a Service (MaaS) System Planning for Integrated Passenger Transport Hub, Sichuan Science and Technology Plan Project (Grant No. 2021YJ0076 )".
section_name: Funding

section_content: The authors declare that they have no con ict of interests Ethical approval This article does not contain any studies with human participants performed by any of the authors.
section_name: Con ict of interest

section_name: Data Availability

section_content: Data will be made available on request.
section_name: Data Availability

section_name: References

section_content: The development trend of non -balancedness in each method
section_name: References