A method for dividing a station attraction range of a suburban section of a city rail transit
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING JIAOTONG UNIV
- Filing Date
- 2022-08-29
- Publication Date
- 2026-06-12
Smart Images
Figure CN115423286B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of rail transit operation and management technology, and in particular to a method for dividing the attraction range of suburban sections of urban rail transit stations. Background Technology
[0002] Urban rail transit refers to passenger rail transit lines within the urban area of large cities, serving the city and suburbs, central cities and satellite cities, and key towns. Suburban sections are characterized by larger station spacing, higher operating speeds, and smaller passenger capacity. Traveler preferences reflect the inherent preferences of different travelers for different modes of transportation, thus exhibiting a somewhat irrational travel mode selection behavior.
[0003] With the accelerating pace of interconnectivity in rail transit, integrated operation technology for multi-mode rail transit networks, including urban, intercity, and intercity lines, has become crucial for supporting future network operations. Currently, my country's urban rail transit development is characterized by urban rail transit as the primary mode, supplemented by other modes. In terms of new rail transit lines, the development is gradually transitioning from a single development model focused on urban rail construction to a trend of interconnectivity among multiple modes, including urban rail, urban rail transit, intercity lines, and intercity railways. Taking Guangzhou as an example, as the core of the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou has planned to construct and put into operation multiple urban rail transit lines within the next five years. A new pattern of coordinated development, with urban rail and urban rail transit as the main modes and other modes complementing each other, has emerged, bringing both significant opportunities and challenges to the coordinated development of multi-mode rail transit systems.
[0004] Against the backdrop of integrated operation of multi-mode rail transit networks, the integration of urban rail transit and conventional urban rail networks is rapidly advancing. Accurately delineating the attraction range of its stations has become crucial for urban rail transit construction planning. With the commissioning of urban rail transit, the topology of the rail transit network changes, altering the original travel patterns of the entire rail transit system. Travelers connect to urban rail transit lines through various means, significantly expanding the attraction range of urban rail lines compared to traditional urban rail stations. To rationally plan the scale and distribution of urban rail transit stations and maximize passenger flow, it is necessary to accurately delineate the attraction range of the proposed urban rail transit lines.
[0005] Based on this, developing a passenger flow forecasting method for suburban stations of urban rail transit under multi-mode rail transit regional collaborative transportation is of great significance for supporting the efficient operation of the integrated rail transit network and comprehensively improving the level of operation and management.
[0006] Regarding the integrated operation of regional multi-mode rail transit, as early as the late 1970s, scholars proposed that intercity railways and suburban railways operate in different areas with inconsistent route forms, and that transfer stations should be set up at the intersections of intercity and suburban railways to improve operational efficiency. Domestically, scholars have proposed a basic model for constructing an integrated passenger rail transit system, including alternative use, shared rail transport, and hub transfers. The degree of coordination between different rail transit modes is divided into four levels, and five coordinated transport organization models, including the integrated transport organization of multi-mode rail transit on suburban lines, are proposed. Based on typical domestic and international cases, this paper explores the implementation path of regional multi-mode rail transit organization and proposes relevant suggestions.
[0007] Determining the passenger attraction range of rail transit stations is crucial for guiding land planning and utilization around these stations. Early scholars, both domestically and internationally, largely relied on empirical values or qualitative methods to describe this range, typically selecting a 400m-1000m radius around the urban rail transit station as their research area. However, this method of defining the attraction range by a fixed radius suffers from significant deviations in practical applications, and assigning the same attraction range to all stations lacks persuasiveness. Further research has seen many scholars, both domestically and internationally, utilize software and model building to study passenger attraction ranges, including model-based theoretical studies and data-driven case studies.
[0008] In model-based studies of attraction range, buffer zone analysis is used to determine the direct passenger flow attraction range around rail transit stations by analyzing the proportion of roads surrounding the stations and simulating travelers' routes. Multiple regression is employed to analyze the relationship between factors such as population and employment around rail transit stations and the attraction range. Constraint equations and GIS buffer zone algorithms are also widely used in defining station radiation ranges and passenger flow attraction areas. Furthermore, a choice behavior model based on the theory of maximizing overall travel utility has some application in analyzing the competitive relationships between different modes of transportation and the intensity of station attraction.
[0009] The shortcomings of the existing urban rail transit attraction range methods mentioned above include: Delineating the passenger flow attraction range of rail transit stations is crucial for the planning of station scale and layout, and for guiding the construction of connecting facilities, and has accumulated rich theoretical and practical experience. However, as a new type of rail transit system, suburban rail transit has significantly different passenger flow characteristics compared to traditional urban rail transit, and currently lacks a method for delineating the attraction range that can be directly applied to suburban rail transit stations.
[0010] Current methods for determining the attraction range of urban rail transit rely on historical passenger flow data from existing stations and actual connecting passenger flow data. However, the passenger attraction range of suburban stations in urban rail transit systems differs from that of conventional urban rail stations. As a new type of urban rail system, urban rail transit lacks historical passenger flow data for reference when its stations open, making existing methods for defining the attraction range insufficient to guarantee the accuracy of predictions. Facing the significant challenges brought about by the coordinated development of multiple rail transit systems, existing methods for defining the attraction range are difficult to directly apply to suburban stations in urban rail transit networks. As a mode of rail transit serving suburban commuting, urban rail transit has large station spacing and a wide attraction range in its suburban areas. Travelers typically use multiple connecting methods to reach urban rail transit stations for suburban travel. Different connecting methods exhibit varying attractiveness within different spatial ranges due to differences in the spatial characteristics of the station areas, posing a significant challenge to defining the attraction range of urban rail transit.
[0011] Predicting passenger flow to and from suburban stations of urban rail transit first requires determining the station's attraction range. Existing methods for defining station attraction ranges mostly focus on already operational urban rail transit stations. For pedestrian and bicycle attraction ranges, most studies directly define them using empirical thresholds or rely solely on survey data to build theoretical models. The resulting pedestrian attraction ranges are often standardized circular areas centered on the station, suffering from long timeframes, incomplete data, and discrepancies with reality. For motor vehicle attraction ranges such as buses, private cars, and taxis, most studies directly use attenuation models, requiring extensive actual connection data from existing stations. This ignores the strong heterogeneity and random fluctuations in the spatial distribution of connection demand, as well as the lack of actual connection data before the opening of urban rail transit. Therefore, this paper proposes a multi-connection mode attraction range delineation method that considers spatial heterogeneity by obtaining travelers' connection preference data, combining it with land use in the surrounding area and spatial topological distance to the station, and analyzing the spatial heterogeneity of the area. Simultaneously, a method for calculating attraction intensity is proposed, and a method for classifying the spatial distribution differences of passenger flow attraction points within the attraction range of suburban stations of urban rail transit is given. Summary of the Invention
[0012] The embodiments of the present invention provide a method for dividing the attraction range of suburban sections of urban rail transit stations, so as to achieve a reasonable and accurate division of the attraction range of suburban sections of urban rail transit stations.
[0013] To achieve the above objectives, the present invention adopts the following technical solution.
[0014] A method for delineating the attraction range of suburban sections of urban rail transit stations, including:
[0015] The MNL model is selected to characterize travelers' connection behavior to urban rail transit stations, and the utility function of the MNL model is established.
[0016] Divide the station area, create the station attraction range envelope and divide it into equally spaced intervals. Calculate the selection probability of each connection method within each equally spaced interval of the envelope based on the utility function of the MNL model for each connection method, and establish a selection probability dataset for each connection method.
[0017] Based on land use, spatial topology, and selection probability datasets for each connection mode, the spatial heterogeneity of the area surrounding the station is analyzed, the demand intensity distribution is calculated, and the selection probability datasets for each connection mode are weighted and accumulated using the land use relationship between equally spaced intervals and the spatial attribute weight vector between rail transit stations to obtain a first-accumulated connection demand intensity distribution matrix within the station envelope.
[0018] The attraction intensity of a station is obtained by spatially fitting the cumulative connection demand intensity distribution matrix within the station's envelope, and the attraction range of the station is determined based on the attraction intensity.
[0019] Preferably, the selection of the MNL model to characterize travelers' connection behavior to urban rail transit stations and the establishment of the MNL model utility function include:
[0020] The actual connection methods are defined as including non-motorized vehicle connection methods and motorized vehicle connection methods. Non-motorized vehicle connection methods include walking and cycling, while motorized vehicle connection methods include buses, private cars, and taxis. Based on the survey results of travelers' preferences for each connection method, the MNL model is selected to characterize travelers' connection behavior to urban rail transit stations. An MNL model alternative solution set for rail transit connection behavior is constructed, which includes non-motorized vehicle connection alternative solutions and motorized vehicle connection alternative solutions. The utility function of the MNL model is established.
[0021] Preferably, the method of selecting an MNL model to characterize travelers' connection behavior to rail transit stations based on survey results of travelers' preferences for each mode of transportation, constructing an MNL model alternative solution set for rail transit connection behavior, including non-motorized vehicle connection alternative solutions and motorized vehicle connection alternative solutions, and establishing an MNL model utility function, including:
[0022] The probability P that traveler n chooses connecting route i in It manifests in the following form:
[0023] P ni =Prob(U ni >U nj i ≠ j, j ∈ A n (1)
[0024] Among them, U in For the utility of connection scheme i to traveler n; A n Let n be the set of choices for traveler n;
[0025] The utility function U of traveler n to shuttle solution i in The calculation method is shown in equation (2):
[0026] U ni =V ni +ε ni (2)
[0027]
[0028] Among them, V in For traveler n, the utility function of choosing connection option i is fixed, ε in The random term of the utility function for traveler n choosing connection scheme i, θ = (θ1, ..., θ2). K ) represents the vector of unknown parameters corresponding to the attribute; X ni =(X ni1 X ni2 , ..., X niK () represents the vector of factors influencing traveler n's choice of connecting route i;
[0029] The probability that traveler n chooses connecting route i when traveling is:
[0030]
[0031] The calculation method for the fixed term of the utility function of each connection scheme is as follows:
[0032]
[0033]
[0034]
[0035]
[0036]
[0037] in, These are the fixed terms of the utility functions for walking shuttle, bicycle shuttle, public transport shuttle, private car shuttle, and taxi shuttle, respectively; θ t θ is the connection time coefficient. d θ is the connection distance coefficient; f T is the connection cost coefficient; walk T bike T bus T car Ttaxi These are the connection times for walking shuttle, bicycle shuttle, bus shuttle, private car shuttle, and taxi shuttle, respectively; D walk D bike D bus D car D taxi These are the connection distances for walking shuttle, bicycle shuttle, bus shuttle, private car shuttle, and taxi shuttle, respectively; F bus F car F taxi These are the shuttle fees for public transportation, private car transportation, and taxi transportation, respectively; ASC walk , ASC bike , ASC car , ASC taxi These are respectively pedestrian shuttle, bicycle shuttle, private car shuttle, and taxi shuttle with inherent mute elements.
[0038] Based on the fixed terms of the utility function above, the calculation method for the connection probability of each connection scheme is as follows:
[0039]
[0040]
[0041]
[0042]
[0043]
[0044] in, Probability of walking connection; Probability of bicycle shuttle service; For the probability of bus connections; The probability of private car shuttle service; This represents the probability of taxi pick-up.
[0045] Preferably, the process of dividing the station area, creating a station attraction range envelope and dividing it into equally spaced intervals, calculating the selection probability of each connection method within each equally spaced interval of the envelope based on the MNL model utility function for each connection method, and establishing a selection probability dataset for each connection method includes:
[0046] The Grey Distance Attenuation (GDD) model was used to fit the connection demand within a certain distance range. The GDD model obtained the spatial connection range of each station by analyzing the intensity distribution of connection demand in space. Based on different connection methods, the attraction range was divided into primary attraction range and secondary attraction range. The primary attraction range is the connection range for travelers reaching the urban rail transit station via non-motorized vehicle connections; the secondary attraction range is the connection range for travelers reaching the urban rail transit station via motorized vehicle connections.
[0047] An envelope is created radiating outwards from the rail transit station by a radius of R meters. The radius R includes the primary and secondary attraction ranges of the station. The space within the radius R is divided into n equally spaced intervals of length r, forming an equally spaced spatial matrix R. n :
[0048] R n = (R1, R2, ..., R k , ..., R n (15)
[0049] The method for calculating the distance from the center of the k-th equally spaced interval to the station is as follows:
[0050] R k =R·kR / 2, k∈[1,n] (16)
[0051] Using electronic maps, the vector of influencing factor parameters from the midpoint of each equally spaced interval to the rail transit station is crawled. These parameters include connection topology distance, connection time, and connection cost. Based on each connection mode, the utility function of the MNL model is selected to calculate the selection probability of each connection mode for each equally spaced interval, thus constructing the original sequence of the selection probability of the i-th connection mode for each equally spaced interval.
[0052]
[0053] The original sequence of probabilities of choosing all connection methods in each equally spaced interval. Construct the original connection probability distribution matrix P 0 :
[0054]
[0055] P i (k) represents the probability of choosing the i-th connection method in the k-th equally spaced interval, where i∈[1, z] represents z connection methods respectively.
[0056] Based on the original sequence of selection probabilities and the original connection probability distribution matrix P 0 Quantify travelers' connection preferences.
[0057] Preferably, the step of analyzing the spatial heterogeneity of the area surrounding the station based on land use, spatial topology, and the selection probability dataset of each connection mode, calculating the demand intensity distribution, and using the land use relationship of equally spaced intervals and the spatial attribute weight vector between the urban rail transit stations to perform a weighted summation of the selection probability dataset of each connection mode, to obtain a first-supplemented connection demand intensity distribution matrix within the station envelope, includes:
[0058] By considering the spatial heterogeneity of each equally spaced interval using improved grey system theory, this paper analyzes the land use relationships, topological distances to rail transit stations, and spatial attributes of road network density in different equally spaced intervals, and introduces a spatial attribute weight vector ω. 0 = (ω1, ω2, ..., ω k ,…,ω n To reflect the impact of spatial attributes on the intensity of connection demand, Performing a weighted summation yields the weighted summation sequence shown in Equation 19.
[0059]
[0060]
[0061] in, It represents the weighted cumulative connection demand intensity within the first k equally spaced intervals.
[0062] A weighted cumulative sequence of all connection methods for each equally spaced interval. Construct a cumulative connection demand intensity distribution matrix P 1 :
[0063]
[0064] Preferably, the step of spatially fitting the cumulative connection demand intensity distribution matrix within the station's envelope to obtain the station's attraction intensity, and determining the station's attraction range based on the attraction intensity, includes:
[0065] Calculate the cumulative connection frequency of each connection method within a distance of k*r from the station. The calculation methods are shown in equations 21 and 22:
[0066]
[0067]
[0068] Where, N i The total connection demand for urban rail transit station connection mode i;
[0069] The cumulative connection frequency of each connection method within the range of k*r Convert to cumulative connection frequency outside the k*r range
[0070]
[0071] By comparing quadratic, exponential, Gaussian, and Logistic distance attenuation models, the cumulative connection frequency was analyzed. The fitting effect with distance k*r is used to determine the optimal distance decay fitting function to identify the attraction range of each connection method. The cumulative connection probability function q for each connection method within a distance l from the station is obtained from the distance decay fitting results. i (l), q i (l) If the connection is continuous and differentiable, then the connection probability function for connection mode i at a distance l from station is q. i The derivative of (l):
[0072]
[0073] The attraction intensity of connection in each area is directly proportional to the connection probability. The attraction intensity S of connection mode i at a distance l from the station is... i (l) The calculation method is shown in equation (25):
[0074] S i (l)=α·q′ i (l) (25)
[0075] S i (l) Normalization is performed to eliminate the spatial discretization effect of the scaling factor α:
[0076]
[0077] The 95% connection intensity threshold is selected to divide the station into the attraction range for each connection mode. The attraction range of non-motorized vehicle connection is the primary attraction range, and the attraction range of motorized vehicle connection is the secondary attraction range. The attraction intensity of each area within the attraction range is calculated according to formula (26).
[0078] As can be seen from the technical solutions provided by the embodiments of the present invention, the method of the present invention can divide the passenger flow attraction range of different connection methods according to the spatial heterogeneity of the station, fully reflect the traveler's choice preference, and has high applicability to rail transit systems such as suburban rail transit lines. The method of dividing the attraction range is more reasonable and accurate.
[0079] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description
[0080] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0081] Figure 1 A flowchart illustrating a method for dividing the attraction range of suburban stations in urban rail transit, provided in an embodiment of the present invention;
[0082] Figure 2 This is a schematic diagram illustrating the steps for dividing the potential attraction range of a station based on connection selection behavior and considering spatial heterogeneity, as provided in an embodiment of the present invention. Detailed Implementation
[0083] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0084] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.
[0085] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.
[0086] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.
[0087] With the development of multi-modal rail transit integration, urban rail transit has become an important mode of transportation for suburban residents of large cities. Suburban rail transit stations, as major attractions for suburban travelers, will draw a large number of passengers to these stations after the opening of new lines. To rationally plan the layout of suburban rail transit stations, support the efficient operation of the integrated rail transit network, and comprehensively improve operational management, it is necessary to develop a method for defining the attraction range of suburban rail transit stations under the coordinated development of multi-modal rail transit, achieving precise delineation of the attraction range of suburban stations.
[0088] To address the uncertainties in connection demand at suburban stations on urban rail transit lines and the lack of historical connection data, this invention analyzes the spatial heterogeneity of land use, topological distance, and traveler connection preferences within the vicinity of urban rail transit stations. It introduces the Grey Models (GM) model, which can uncover the underlying patterns of complexity in data representation, to spatially weight and accumulate connection demand at urban rail transit stations, improving the quality of connection demand analysis data. Based on the accumulated connection demand, the spatial distribution patterns of urban rail transit station connection demand are explored. A distance decay model is introduced to calibrate the decreasing trend of connection demand with increasing distance from a station within a certain range. Finally, Thiessen polygons are used to classify stations with overlapping attraction ranges.
[0089] Urban rail transit connection demand refers to the demand of travelers within a certain space to reach urban rail transit stations from their origin via connecting methods such as walking, non-motorized vehicles, buses, taxis, and private cars, and then take urban rail transit for their journeys. Disaggregated behavior models take individual travelers as the research object, using stochastic utility theory and consumer demand theory to characterize the travel decision-making behavior of individual travelers and construct a transportation choice behavior model.
[0090] The processing flow of a method for dividing the attraction range of suburban stations in urban rail transit provided by an embodiment of the present invention is as follows: Figure 1 As shown, the processing steps include the following:
[0091] Step S10: Select the MNL model to characterize the traveler's connection behavior to the urban rail transit station, establish the utility function of the MNL model, and calibrate the MNL model.
[0092] The present invention defines actual connection methods as including non-motorized vehicle connection methods and motorized vehicle connection methods. Non-motorized vehicle connection methods include walking and cycling, while motorized vehicle connection methods include buses, private cars and taxis.
[0093] Based on the survey results of travelers' preferences for each mode of transportation, the MNL model is selected to characterize travelers' connection behavior to urban rail transit stations. An MNL model alternative scheme set for urban rail transit connection behavior is constructed, which includes non-motorized vehicle connection alternative schemes and motorized vehicle connection alternative schemes.
[0094] A utility function for the MNL model is established, which can be calculated based on the actual connection method in practical applications. The method proposed in this invention is applicable to all connection methods. The input for this step is the survey results of travelers' preferences for each connection method, and the output is the calibration results of the MNL model parameters.
[0095] Step S20: Divide the station area, create the station attraction range envelope, select the MNL model utility function according to each connection method to calculate the selection probability of each connection method within the envelope, and establish a selection probability dataset for each connection method.
[0096] First, an envelope is created radiating outwards by R meters from the city's rail transit station, ensuring that the radiation range R is large enough to completely encompass the station's primary and secondary attraction ranges. The space within the radiation range R is then divided into n equally spaced intervals of length r, forming an equally spaced spatial matrix R. n :
[0097] R n = (R1, R2, ..., R k , ..., R n (15)
[0098] The method for calculating the distance from the center of the k-th equally spaced interval to the station is as follows:
[0099] R k =R·kR / 2, k∈[1,n] (16)
[0100] The differences in the actual spatial distribution of each equally spaced interval lead to discrepancies in the actual connection parameters between intervals and stations within the same radiation range. By crawling an electronic map, we obtain the vector of influencing factors from the midpoint of each equally spaced interval to the municipal rail transit station, including connection topology distance, connection time, and connection cost. Based on each connection method, we select the utility function of the MNL model to calculate the selection probability of each connection method for each equally spaced interval, thus constructing the original sequence of the selection probability of the i-th connection method for each equally spaced interval. The original sequence of probabilities of choosing all connection methods in each equally spaced interval. Construct the original connection probability distribution matrix P 0 Based on the above selection probability original sequence and the original connection probability distribution matrix P 0 It can quantify travelers' connection preferences.
[0101] This step takes as input the set of alternative routes from the station's envelope to the station, including the connection time, distance, and cost. Alternative routes include, but are not limited to, all non-motorized vehicle routes such as walking and cycling, and all motorized vehicle routes such as buses, private cars, and taxis. Specifically, the inputs are the connection time, distance, and cost for each traveler's walking, cycling, bus, private car, and taxi connections. The output is the original sequence of the selection probabilities of the alternative routes. and the original connection probability distribution matrix P 0 This step, which calculates the connection probability of each interval using equally spaced intervals of the envelope, is a unique technical feature of this invention.
[0102] Step S30: Analyze the spatial heterogeneity of the area surrounding the station based on land use, spatial topology, and traveler connection preferences obtained in Step S20, and calculate the demand intensity distribution. Analyze spatial attributes such as land use relationships, topological distance to urban rail transit stations, and road network density in different equally spaced intervals, and introduce a spatial attribute weight vector ω. 0 = (ω1, ω2, ..., ω k ,…,ω n An improved cumulative transformation method in grey system theory is proposed, utilizing spatial attribute weight vectors to... Performing a weighted summation yields a weighted summation sequence. A weighted cumulative sequence of all connection methods for each equally spaced interval. Construct a cumulative connection demand intensity distribution matrix P 1 .
[0103] The input for this step is the spatial attributes such as the land use relationship of equally spaced intervals, the topological distance to the urban rail transit station, and the road network density, as well as the original sequence of selection probabilities from the alternative scheme set output by S20. and the original connection probability distribution matrix P 0 The output is a spatial attribute weight vector and a first-order weighted cumulative sequence. The cumulative connection demand intensity distribution matrix P 1 The spatial attribute weight vector and the improved gray system theory cumulative transformation method described in this step are unique technical features of this invention.
[0104] Step S40: Spatial fitting of the distribution of connection demand intensity within the station envelope to reflect the station's attraction intensity, and determination of the station's attraction range based on the attraction intensity.
[0105] First, based on the weighted cumulative sequence output by S30... The cumulative connection demand intensity distribution matrix P 1 The cumulative connection frequency within the station envelope is fitted using the distance attenuation (GDD) model, and the attraction intensity S of connection mode i at a distance l from the station within the envelope is calculated based on the fitted function. i (l) A 95% connection intensity threshold was selected to divide the attraction range for each connection method, and the attraction intensity of each area within the attraction range was calculated. The attraction range for pedestrian and bicycle connections constitutes the non-motorized vehicle connection attraction range, i.e., the primary attraction range; the attraction range for buses, private cars, taxis, and other motorized vehicles constitutes the motorized vehicle connection attraction range, i.e., the secondary attraction range. For areas with overlapping attraction ranges, adjacent edge analysis was performed using Thiessen polygon theory to delineate the overlapping areas.
[0106] The input for this step is a weighted cumulative sequence. The cumulative connection demand intensity distribution matrix P 1 The output is the attraction intensity S of connection method i at a distance l from the station. i (l) and the station's attraction range. The distance attenuation model and Thiessen polygon used in this step are existing technologies. The unique technical feature of this invention is that it uses a probability-weighted cumulative sequence to reflect the attraction intensity of each connection method.
[0107] Furthermore, step S10 specifically includes: the present invention calculates the connection demand of the new suburban rail transit line within a certain range, and therefore selects the MNL (Multinomial Logit) model to characterize the connection behavior of travelers to the urban rail transit station, calibrates the model parameters according to the traveler's travel choice preferences, and determines the values of each parameter in the MNL model.
[0108] MNL is based on the theory of stochastic utility maximization, and its probability P of a traveler n choosing connecting route i is... in It manifests in the following form:
[0109] P ni =Prob(U ni >U nj i ≠ j, j ∈ A n (1)
[0110] Among them, U ni For the utility of connection scheme i to traveler n; A n Let n be the set of choices for traveler n.
[0111] Stochastic utility theory posits that utility is a random variable, dividing the utility function into a fixed term and a random term, and assuming a linear relationship between the two. Let U be the utility of traveler n for shuttle option i. in The calculation method is shown in equation (2).
[0112] U ni =V ni +ε ni (2)
[0113]
[0114] Among them, V ni The fixed term in the utility function for traveler n choosing connection scheme i; ε ni Let θ be the random term of the utility function for choosing connection scheme i for traveler n. K ) represents the vector of unknown parameters corresponding to the attribute; X ni =(X ni1 X ni2 , ..., X niK Let be the vector of factors influencing the choice of connecting route i for traveler n.
[0115] The Logit model assumes that the random terms follow a double exponential distribution. Based on this, the probability that traveler n chooses connecting route i when traveling is calculated as follows:
[0116]
[0117] The MNL model for urban rail transit connection behavior constructed in this invention includes all non-motorized vehicle connection options such as walking and cycling, and all motorized vehicle connection options such as buses, private cars, and taxis. A model utility function is established. This invention only uses walking, cycling, buses, private cars, and taxis as examples for illustration; in actual application, calculations can be performed according to the actual connection method. The method proposed in this invention is applicable to all connection methods. The probability of a traveler choosing each connection option is calculated based on the utility function.
[0118] (1) Walking connection: Travelers walk from their departure point to the urban rail transit station and then take the urban rail transit to complete their suburban trip. This alternative is suitable for travel connections where the departure point is close to the urban rail transit station.
[0119] (2) Bicycle shuttle: Travelers can take bicycles or shared bicycles from their starting point to the city rail transit station and then take the city rail transit to complete their suburban trip. This option is suitable for travelers whose starting point is relatively close to the city rail transit station or for travelers with a high level of environmental awareness.
[0120] (3) Bus connection: Travelers arrive at the bus stop from their departure point, take a bus to the urban rail transit station, and then take the urban rail transit to complete their suburban trip. Bus-subway connections have the advantages of low travel costs and fast travel speed, and are suitable for urban rail transit stations with relatively dense surrounding public transportation.
[0121] (4) Private car shuttle: Travelers drive from their starting point to the urban rail transit station, park, and then use the urban rail transit to complete their suburban journey. This method is efficient and fast, combining the advantages of private cars for short-distance travel with the advantages of urban rail transit for suburban travel, greatly expanding the attractiveness of urban rail transit.
[0122] (5) Taxi shuttle: Travelers take a taxi from their departure point to the city rail transit station, and then take the city rail transit to complete their suburban trip. This method avoids the parking problem of private car shuttles, effectively saves taxi fares, and offers a high level of comfort.
[0123] The calculation method for the fixed term of the utility function of each connection scheme is as follows:
[0124]
[0125]
[0126]
[0127]
[0128]
[0129] in, These are the fixed terms of the utility functions for walking shuttle, bicycle shuttle, public transport shuttle, private car shuttle, and taxi shuttle, respectively; θ t θ is the connection time coefficient. d θ is the connection distance coefficient; f T is the connection cost coefficient; walk T bike T bus T car T taxi These are the connection times for walking shuttle, bicycle shuttle, bus shuttle, private car shuttle, and taxi shuttle, respectively; D walk D bike D bus D car D taxi These are the connection distances for walking shuttle, bicycle shuttle, bus shuttle, private car shuttle, and taxi shuttle, respectively; F bus F car F taxi These are the shuttle fees for public transportation, private car transportation, and taxi transportation, respectively; ASC walk , ASC bike , ASC car , ASC taxiThese are respectively pedestrian shuttle, bicycle shuttle, private car shuttle, and taxi shuttle with inherent mute elements.
[0130] Based on the fixed terms of the utility function above, the calculation method for the connection probability of each connection scheme is as follows:
[0131]
[0132]
[0133]
[0134]
[0135]
[0136] in, Probability of walking connection; Probability of bicycle shuttle service; For the probability of bus connections; The probability of private car shuttle service; This represents the probability of taxi pick-up.
[0137] Furthermore, step S20 specifically includes: Figure 2 This invention provides a schematic diagram illustrating the steps for dividing the potential attraction range of a station based on spatial heterogeneity and considering connection selection behavior, as provided in an embodiment of the present invention. The method for dividing the distribution of connection demand and attraction range considering spatial heterogeneity, as provided in this embodiment of the invention, includes: using a GDD (Grey Distance Delay) model to fit the connection demand within a certain distance range to obtain the spatial patterns of this data. The GDD model obtains the spatial connection range of each station by analyzing the intensity distribution of connection demand in space. Based on different connection methods, the attraction range is divided into primary attraction range and secondary attraction range. The primary attraction range is the connection range for travelers reaching the urban rail transit station via non-motorized connections such as walking and bicycles; the secondary attraction range is the connection range for travelers reaching the urban rail transit station via motorized connections such as buses, private cars, and taxis.
[0138] First, the station area is divided. An envelope is created radiating outwards by R meters from the city rail transit station, ensuring the radius R is large enough to completely encompass the station's primary and secondary attraction zones. The space within the radius R is then divided into n equally spaced intervals of length r, forming an equally spaced spatial matrix R. n :
[0139] R n = (R1, R2, ..., R k , ..., R n (15)
[0140] The method for calculating the distance from the center of the k-th equally spaced interval to the station is as follows:
[0141] R k =R·kR / 2, k∈[1,n] (16)
[0142] The differences in the actual spatial distribution of each equally spaced interval lead to differences in the actual connection parameters between intervals and stations within the same radiation range. Using an electronic map, we crawled the vector of influencing factor parameters from the midpoint of each equally spaced interval to the municipal rail transit station. These influencing factor parameters include connection topology distance, connection time, and connection cost. Based on Equations 3 and 4, we calculated the selection probability of each connection method for each equally spaced interval, forming the original sequence of the selection probability of the i-th connection method for each equally spaced interval.
[0143]
[0144] The original sequence of probabilities of choosing all connection methods in each equally spaced interval. Construct the original connection probability distribution matrix P 0 :
[0145]
[0146] Among them, P i (k) represents the probability of choosing the i-th connection method in the k-th equally spaced interval, where i∈[1,z] represents z connection methods such as walking, bicycle, bus, private car, and taxi.
[0147] Furthermore, step S30 specifically includes: due to P i Selecting probabilities for each connection method within each equally spaced interval does not represent the actual number of connection demands. Actual connection demands are related to spatial characteristics such as land use, spatial topological distance, and connection convenience within each interval. This invention improves upon grey system theory by considering the spatial heterogeneity of each equally spaced interval, analyzing spatial attributes such as land use relationships, topological distance to urban rail transit stations, and road network density within different equally spaced intervals, and introducing a spatial attribute weight vector ω. 0 = (ω1, ω2, ..., ω k ,…,ω n This reflects the impact of spatial attributes on the intensity of connection demand. An improved cumulative transformation method from grey system theory is used to... Performing a weighted summation yields the weighted summation sequence shown in Equation 19.
[0148]
[0149]
[0150] in, It represents the weighted cumulative connection demand intensity within the first k equally spaced intervals.
[0151] A weighted cumulative sequence of all connection methods for each equally spaced interval. Construct a cumulative connection demand intensity distribution matrix P 1 :
[0152]
[0153] Furthermore, step S40 specifically includes: to determine the attraction range and attraction intensity of the urban rail transit station, it is necessary to calculate the connection probability of each area within the urban rail transit station's range. First, the cumulative connection frequency of each connection mode within a distance of k*r from the station is calculated. The calculation methods are shown in equations 21 and 22:
[0154]
[0155]
[0156] Where, N i The total connection demand for urban rail transit station connection mode i.
[0157] Assuming the cumulative probability of each connection mode within a distance of k*r from the station equals its frequency, the connection probabilities are fitted using a distance attenuation model. The cumulative connection frequency of each connection mode within the k*r range is then calculated. Convert to cumulative connection frequency outside the k*r range
[0158]
[0159] By comparing quadratic, exponential, Gaussian, and Logistic distance attenuation models, the cumulative connection frequency was analyzed. The optimal distance decay fitting function is determined by comparing the fitting results with the distance k*r to identify the attraction range of each connection method. The cumulative connection probability function q for each connection method within a distance l from the station is obtained from the distance decay fitting results. i (l), q i (l) If the connection is continuous and differentiable, then the connection probability function for connection mode i at a distance l from station is q. i The derivative of (l):
[0160]
[0161] Within the connection range, the greater the connection attraction intensity, the greater the connection probability. Therefore, the connection attraction intensity of each area is directly proportional to the connection probability. The attraction intensity S of connection mode i at a distance l from the station is... i (l) The calculation method is as shown in equation (25):
[0162] S i (l)=α·q′ i (l) (25)
[0163] S i (l) Normalization is performed to eliminate the spatial discretization effect of the scaling factor α:
[0164]
[0165] A 95% connection intensity threshold was selected to divide the attraction range of each connection method, and the attraction intensity of each area within the attraction range was calculated according to formula (26). Among them, the attraction range of non-motorized vehicle connections such as walking and bicycles constitutes the non-motorized vehicle connection attraction range, which is the primary attraction range; the attraction range of motorized vehicles such as buses, private cars, and taxis constitutes the motorized vehicle connection attraction range, which is the secondary attraction range.
[0166] For regions with overlapping attraction ranges, adjacent edge analysis is performed using Thiessen polygon theory to delineate the overlapping areas. A Thiessen polygon, also known as a von Rooi diagram, is a set of continuous polygons composed of perpendicular bisectors of line segments connecting two adjacent points. The distance from any point in a Thiessen polygon to a control point within that polygon is less than its distance to control points of other polygons. The principles for dividing Thiessen polygons are as follows:
[0167] (1) Each Thiessen polygon contains only one discrete point;
[0168] (2) The distance from any point within the Thiessen polygon to the corresponding discrete point is the shortest, that is, if any point (x) in the region is closest to the corresponding discrete point. 1 y 1 ) is located in (x i y j There exists an inequality inside the polygon. Established;
[0169] The distances from the boundary points of the Thiessen polygon to the discrete points on both sides are equal, that is, if any point (x) in the region is equidistant from the boundary points, then... 1 y 1 ) is located in (x i y j For a polygon with a common edge, there exists an equation... Established.
[0170] Based on the above methodological framework, the steps for delineating the potential attraction range of stations considering spatial heterogeneity based on connection selection behavior are as follows: Figure 2 As shown.
[0171] First, a Mode of Link (MNL) model for connecting traveler selection is constructed, and the model parameters are calibrated based on travelers' connecting traveler preferences. Using the calibrated MNL model, the probability of choosing each connecting traveler within each equally spaced interval of the station's attraction range envelope is calculated. The probability of choosing each connecting traveler within each interval is statistically analyzed, creating a dataset of the probability of choosing each connecting traveler. Spatial attribute features of the intervals are obtained, and spatial attribute weight vectors are constructed for each interval. Based on grey system theory, a first-order weighted cumulative sequence of the grey system is constructed, and the demand intensity distribution is calculated. Finally, the spatial fitting of the connecting traveler demand intensity distribution within the station's envelope is performed, an attraction intensity distance attenuation model is calibrated, the station's attraction range is determined based on the attraction intensity, and Thiessen polygons are used to divide overlapping areas.
[0172] In summary, this invention proposes a method for delineating the passenger flow attraction range of suburban stations in urban rail transit based on the selection behavior of multiple connecting modes, taking into account spatial heterogeneity. This invention can obtain travelers' preferences for various connecting modes of urban rail transit before its opening, and by analyzing the spatial heterogeneity of the area surrounding the station, delineate the passenger flow attraction range of suburban stations in urban rail transit in terms of both spatial scope and attraction intensity.
[0173] Compared with the existing methods for dividing the attraction range of urban rail transit, the method for dividing the attraction range of suburban stations of urban rail transit proposed in this invention can divide the passenger flow attraction range of different connection methods according to the spatial heterogeneity of the station, fully reflect the traveler's choice preferences, and has high applicability to rail transit systems such as suburban rail lines. The method for dividing the attraction range is more reasonable and accurate.
[0174] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.
[0175] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.
[0176] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.
[0177] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for dividing the attraction range of suburban sections of urban rail transit stations, characterized in that, include: The MNL model is selected to characterize travelers' connection behavior to urban rail transit stations, and the utility function of the MNL model is established. Divide the station area, create the station attraction range envelope and divide it into equally spaced intervals. Calculate the selection probability of each connection method within each equally spaced interval of the envelope based on the utility function of the MNL model for each connection method, and establish a selection probability dataset for each connection method. Based on land use, spatial topology, and selection probability datasets for each connection mode, the spatial heterogeneity of the area surrounding the station is analyzed, the demand intensity distribution is calculated, and the selection probability datasets for each connection mode are weighted and accumulated using the land use relationship between equally spaced intervals and the spatial attribute weight vector between rail transit stations to obtain a first-accumulated connection demand intensity distribution matrix within the station envelope. The attraction intensity of a station is obtained by spatially fitting the cumulative connection demand intensity distribution matrix within the station's envelope, and the attraction range of the station is determined based on the attraction intensity.
2. The method according to claim 1, characterized in that, The selection of the MNL model to characterize travelers' connection behavior to urban rail transit stations and the establishment of the MNL model utility function include: The actual connection methods are defined as including non-motorized vehicle connection methods and motorized vehicle connection methods. Non-motorized vehicle connection methods include walking and cycling, while motorized vehicle connection methods include buses, private cars, and taxis. Based on the survey results of travelers' preferences for each connection method, the MNL model is selected to characterize travelers' connection behavior to urban rail transit stations. An MNL model alternative solution set for rail transit connection behavior is constructed, which includes non-motorized vehicle connection alternative solutions and motorized vehicle connection alternative solutions. The utility function of the MNL model is established.
3. The method according to claim 2, characterized in that, The method described above selects an MNL model to characterize travelers' connection behavior at rail transit stations based on survey results of travelers' preferences for each mode of transportation. It constructs a set of MNL model alternatives for rail transit connection behavior, including alternatives for non-motorized vehicle connections and alternatives for motorized vehicle connections. An MNL model utility function is established, including: The probability that traveler n chooses connecting route i It manifests in the following form: (1) in, The utility of connection scheme i for traveler n; The utility of connection scheme j for traveler n; Let n be the set of choices for traveler n; The utility function of traveler n to shuttle solution i The calculation method is shown in equation (2): (2) , (3) in, The utility function for traveler n choosing connection option i is a fixed term. The random term of the utility function for choosing connection scheme i for traveler n. This is the vector of unknown parameters for the corresponding attribute; Let i be the vector of factors influencing the choice of connecting route i for traveler n. The probability that traveler n chooses connecting route i when traveling is: (4) The calculation method for the fixed term of the utility function of each connection scheme is as follows: (5) (6) (7) (8) (9) in, , , , , These are the fixed terms of the utility functions for walking shuttle, bicycle shuttle, public transport shuttle, private car shuttle, and taxi shuttle, respectively. For connection time coefficient; This is the connection distance coefficient; This is the connection cost coefficient; , , , , The connection times are for walking shuttle, bicycle shuttle, bus shuttle, private car shuttle, and taxi shuttle, respectively. , , , , These are the connection distances for walking shuttle, bicycle shuttle, bus shuttle, private car shuttle, and taxi shuttle, respectively. , , These are the shuttle fees for bus transfers, private car transfers, and taxi transfers, respectively. , , , These are respectively pedestrian shuttle, bicycle shuttle, private car shuttle, and taxi shuttle with inherent mute elements; Based on the fixed terms of the utility function above, the calculation method for the connection probability of each connection scheme is as follows: (10) (11) (12) (13) (14) in, Probability of walking connection; Probability of bicycle shuttle service; For the probability of bus connections; The probability of private car shuttle service; This represents the probability of taxi pick-up.
4. The method according to claim 3, characterized in that, The process involves dividing the station area, creating a station attraction range envelope and dividing it into equally spaced intervals, calculating the selection probability of each connection method within each equally spaced interval of the envelope based on the MNL model utility function for each connection method, and establishing a selection probability dataset for each connection method, including: The grey distance attenuation (GDD) model is used to fit the connection demand within a certain distance range. The GDD model obtains the spatial connection range of each station by analyzing the intensity distribution of connection demand in space. According to the different connection methods, the attraction range is divided into primary attraction range and secondary attraction range. The primary attraction range is the connection range for travelers to reach the urban rail transit station by non-motorized vehicle connection; the secondary attraction range is the connection range for travelers to reach the urban rail transit station by motorized vehicle connection. An envelope is created radiating outwards from the rail transit station by a radius of R meters. The radius R includes the primary and secondary attraction ranges of the station. The space within the radius R is divided into n equally spaced intervals of length r, forming an equally spaced spatial matrix. : (15) The method for calculating the distance from the center of the k-th equally spaced interval to the station is as follows: (16) Using electronic maps, the vector of influencing factor parameters from the midpoint of each equally spaced interval to the rail transit station is crawled. These parameters include connection topology distance, connection time, and connection cost. Based on each connection mode, the utility function of the MNL model is selected to calculate the selection probability of each connection mode for each equally spaced interval, thus constructing the original sequence of the selection probability of the i-th connection mode for each equally spaced interval. , (17) The original sequence of probabilities of choosing all connection methods in each equally spaced interval. Construct the original connection probability distribution matrix : (18) Let represent the probability of choosing the i-th connection method in the k-th equally spaced interval. These represent z different connection methods; Based on the original sequence of selection probabilities and the original connection probability distribution matrix Quantify travelers' connection preferences.
5. The method according to claim 4, characterized in that, The analysis of spatial heterogeneity in the area surrounding the station, based on land use, spatial topology, and selection probability datasets for each connection mode, calculates the demand intensity distribution. It then uses the land use relationships within equally spaced intervals and the spatial attribute weight vectors between urban rail transit stations to perform a weighted summation of the selection probability datasets for each connection mode, resulting in a first-sum summation matrix of connection demand intensity distribution within the station envelope. This includes: By considering the spatial heterogeneity of each equally spaced interval using improved grey system theory, this paper analyzes the land use relationships, topological distances to rail transit stations, and spatial attributes of road network density in different equally spaced intervals, and introduces spatial attribute weight vectors. To reflect the impact of spatial attributes on the intensity of connection demand, Performing a weighted summation yields the weighted summation sequence shown in equation (19). : (19) in, This represents the weighted cumulative connection demand intensity within the first k equally spaced intervals. A weighted cumulative sequence of all connection methods for each equally spaced interval. Construct a cumulative connection demand intensity distribution matrix : (20)。 6. The method according to claim 5, characterized in that, The method of spatially fitting the cumulative connection demand intensity distribution matrix within the station's envelope to obtain the station's attraction intensity, and determining the station's attraction range based on the attraction intensity, includes: Calculate the distance from the station as Cumulative connection frequency of each connection method within the range The calculation methods are shown in equations (21) and (22): (21) (22) in, The total connection demand for urban rail transit station connection mode i; Will Cumulative connection frequency of each connection method within the range Convert to Cumulative connection frequency outside the range : (23) By comparing quadratic, exponential, Gaussian, and Logistic distance attenuation models, the cumulative connection frequency was analyzed. With distance The fitting effect is analyzed to determine the optimal distance decay fitting function, which then determines the attraction range of each connection method. The cumulative connection probability function for each connection method within a distance *l* from the station is obtained from the distance decay fitting results. , If the connection is continuous and differentiable, then the connection probability function for connection mode i at a distance l from station is: The derivative: (24) The attraction intensity of connection in each area is directly proportional to the connection probability. The attraction intensity of connection mode i at a distance l from the station is... The calculation method is shown in equation (25): (25) Will Normalization is performed to eliminate the scaling factor. Spatial discretization effect: (26) The 95% connection intensity threshold is selected to divide the station into the attraction range for each connection mode. The attraction range of non-motorized vehicle connection is the primary attraction range, and the attraction range of motorized vehicle connection is the secondary attraction range. The attraction intensity of each area within the attraction range is calculated according to formula (26).