Urban rail transit passenger travel behavior characteristic estimation method

Abstract

The invention belongs to the field of urban rail transit network passenger flow simulation, and particularly relates to an urban rail transit passenger travel behavior characteristic estimation method. Obtaining, analyzing and processing travel data; searching a feasible path set meeting the maximum transfer frequency constraint between the OD pairs by adopting a breadth-first algorithm with constraint; reconstructing a passenger trip chain; through a path time similarity index, passengers on a non-transfer single path and passengers on a single-transfer single path are distributed to a train, and then a travel chain of the two types of travel passengers is obtained; and taking the standard deviation of the train arrival time as the standard deviation of the waiting time of the passengers on the platform, and calculating to obtain passenger in-out walking time distribution and transfer walking time distribution. According to the passenger travel behavior characteristic estimation method for the urban rail transit, station entering and exiting walking time distribution of passengers in all stations in an urban rail transit network, transfer walking time distribution of the passengers in each transfer direction of a transfer station and waiting time distribution of each station can be accurately estimated.

Description

Technical Field

[0001] The present invention belongs to the field of urban rail transit line network passenger flow simulation, and in particular relates to a method for estimating travel behavior characteristics of urban rail transit passengers. Background Art

[0002] As the backbone of integrated urban public transportation networks, rail transit has long been a crucial mode of transport for alleviating urban traffic congestion and a key focus of urban transportation development. In recent years, rail transit in many cities has entered a networked operational phase. To better understand the operational status of rail network passenger flows, it is necessary to identify the travel characteristics of passengers within the system. To estimate these characteristics, it is necessary to reconstruct the travel trajectories of passengers on single routes with no transfers and single routes with only transfers within the urban rail network. This allows the location of the trains passengers board within the network and the spatial and temporal coordinates of their boarding and alighting locations upon arrival. The passenger entry and exit times are calculated using the single-route, single-route, single-route travel chains, and the transfer time distribution is calculated using the single-route, single-route travel chains. Furthermore, the platform waiting time distribution is calculated using the headway distribution of trains at stations calculated from the train schedule. Based on the additivity of independent Gaussian distributions, the walking time for passengers entering and exiting stations and the walking time for transfers within the urban rail transit system are calculated. Regarding the estimation of passenger travel status in urban rail transit networks, existing research at home and abroad mostly relies on manual surveys, field research, or calculations using station layouts and assumed travel speeds. Manual surveys and field research consume a lot of manpower and material resources and can only provide partial sampling. Calculations based on assumed passenger travel speeds can lead to deviations between the assumptions and actual travel status due to differences in passenger age and gender, reducing the accuracy of the estimated results. Summary of the Invention

[0003] Purpose of the invention: In response to the above problems, the method for estimating the travel behavior characteristics of urban rail transit passengers proposed in the present invention can provide a basis for passenger flow simulation and passenger travel behavior analysis of urban rail transit.

[0004] Technical solution: To achieve the purpose of the present invention, the technical solution adopted by the present invention is: a method for estimating the travel behavior characteristics of urban rail transit passengers.

[0005] The method for estimating the travel behavior characteristics of urban rail transit passengers of the present invention comprises the following steps:

[0006] S1. Acquire, analyze, and process travel data; this data includes: basic data on urban rail transit network stations and lines; passenger entry and exit transaction data recorded by the AFC system; and train operation schedule data.

[0007] S2: Construct a spatiotemporal topological model of the road network and use a constrained breadth-first algorithm to search for feasible paths between OD pairs. Based on the characteristics of the urban rail transit spatial network, a road network spatial topological structure is constructed. Train operation schedule information is then loaded into the road network topological model to construct a spatiotemporal topological model of the rail transit network. A constrained breadth-first algorithm is used to search for feasible paths between OD pairs that meet the maximum number of transfers.

[0008] S3, reconstructing the passenger travel chain;

[0009] Passengers are matched to ODs in the spatiotemporal topology model of the road network based on the trip OD pairs recorded in the passenger AFC transaction data in S1. Passengers are divided into the following categories according to the number of transfers and the number of feasible paths for the OD pairs: single path without transfers, single path with single transfers, single path with multiple transfers, and multi-path trips;

[0010] Passengers with a single path without transfers and passengers with a single path with only transfers are assigned to trains using the path time similarity index, thereby obtaining travel chains for the two types of passengers.

[0011] Estimate the passenger entry and exit time distribution at the station based on the single-path passenger travel chain without transfers.

[0012] Estimate the transfer time distribution of passengers at transfer stations based on the single-transfer single-path passenger travel chain;

[0013] S4, calculate the passenger's entry and exit walking time and transfer walking time, and calculate the passenger's entry and exit and transfer walking time distribution according to the passenger's entry and exit time distribution obtained in S3 and the train timetable,

[0014] Take half of the average train headway time as the average waiting time of passengers at the platform,

[0015] The standard deviation of train arrival time is used as the standard deviation of passengers’ waiting time at the platform.

[0016] Based on the additivity of independent Gaussian distributions, the distribution of passengers' walking time for entering and exiting the station and the distribution of passengers' walking time for transferring are calculated.

[0017] The method for estimating the travel behavior characteristics of urban rail transit passengers described in the present invention is as follows:

[0018] S1.1. Obtain basic data on urban rail transit network stations and lines, as follows:

[0019] Urban rail transit network station data includes four attribute values: station number, station longitude, station latitude, and the line to which it belongs, expressed as:

[0020] VS={[v1,Lon1,Lat 1, l1],…,[v i ,Loni ,Lat i ,l j ]}

[0021] Among them, VS represents the basic data of network sites and lines, i represents the site number, v i Indicates the site number, Lon indicates the site longitude, Lat indicates the site latitude, k indicates the line number, l j Indicates the line number to which the station belongs;

[0022] The urban rail transit network is divided into three levels: network, line and station. From high to low, they are "network, line and station". The network includes different operating lines.

[0023] N = {φ(L,V)} represents the urban rail transit network spatial model.

[0024] N represents the road network, L represents the set of routes, and V represents the set of stations. Routes and stations form a network through a nonlinear relationship φ;

[0025] The basic properties of the network can be deduced from the logical relationship between lines, stations, and network in the network N = {φ(L,V)}:

[0026] At the line level, line l j The site collection is represented as in, Indicates line l j The i-th station in, i=1,2,…,n, n is the line l j The number of sites in

[0027] At the network level, the line set is represented as L = {l1,l2,…,l m}, where l j ,j=1,2,…,m represents a certain route in the road network, and m is the total number of routes in the road network;

[0028] The set of all sites in the road network is represented as

[0029] Using φ(L,V) to represent the spatial relationship among stations, routes, and road networks, the road network spatial model is expressed as follows:

[0030] N={φ(L,V)}

[0031] In the model, line l j Contains two attribute values: line name and line rail transit clearing system ACC number; station Contains five attribute values: station name, line number, station ACC number, station line number, and whether it is a transfer station;

[0032] The station ACC number uniquely encodes all stations in the network. The station line number is encoded according to the order of the uplink and downlink directions of the line. The station code is not unique, and the code of the transfer station on different lines is different. Based on the attributes of the line and station, the query function of the station and line information in the network is realized.

[0033] S1.2. Obtain AFC transaction data. The transaction data recorded by the urban rail transit automatic ticket vending system includes the passenger's travel origin and destination points and entry and exit times. The AFC transaction data for a complete trip of passenger x is expressed as:

[0034] Trade x ={ID x ,v o ,ta,v d ,te}

[0035] Where, ID x represents the transaction number of passenger x recorded in the automatic ticket vending system, v o ,v d They represent the starting and ending stations of passengers in the urban rail transit system, ta and te represent the time it takes for passengers to pass through the entry and exit gates respectively;

[0036] S1.3, obtain train timetable data. Train timetable data includes the arrival and departure time information of operating trains on the line, expressed as:

[0037] T(L,TR)

[0038] Where L is the set of lines in the subway network, and TR is the set of trains in the subway network;

[0039] TR={tr1,tr2,…,tr k ,…,tr K}, k = 1, 2, ..., K, K is the total number of departures in the road network;

[0040] Line l j The set of trains dispatched from Indicates line l j The g-th train departs from the line l. j The number of departures.

[0041] In the method for estimating the travel behavior characteristics of urban rail transit passengers of the present invention, the construction of the spatiotemporal topology model of the rail transit network in S2 specifically includes:

[0042] S2.1, constructing a road network spatial topology model to search for feasible path sets between OD pairs in the road network;

[0043] According to the logical relationship between stations and lines in the road network N = {φ(L,V)}, the rail transit network is represented as a directed graph G(V,A);

[0044] Where V is the set of stations in the rail transit network, A is the set of directed edges in the rail transit network, and directed edges represent the train track areas between stations;

[0045] Construct the adjacency matrix M of G(V,A) c and the distance matrix M d ,

[0046]

[0047] In the formula, the adjacency matrix M c The matrix element c ij =1 indicates that sites i and j are directly connected, otherwise they are not directly connected, which is represented by 0; the distance matrix M d The matrix element d ij It represents the distance of the directed edge from site i to site j. If two sites are directly connected, the edge distance between adjacent sites is the distance a between the sites. ij , otherwise, it is +∞;

[0048] The logical relationship between stations and routes in the road network and the directed graph of the road network together constitute the road network spatial topology model:

[0049] O={φ(L,V),G(V,A)}

[0050] Where O represents the spatial topology model of the road network. In the network directed graph G(V,A), v∈V represents the nodes of the graph, and a∈A represents the edges of the graph. There are edges in both the upstream and downstream directions between adjacent stations. Virtual platforms are set at transfer stations to describe the transfer behavior of passengers within the transfer station.

[0051] S2.2. Construct a network train operation time information model T(L, V, TR). The information model includes the arrival and departure time information of trains running on different lines. The network time dimension information is extracted from the train timetable. The train arrival and departure information in the train operation timetable is expressed as:

[0052]

[0053] Where, Respectively represent the arrival time of trains in the up and down directions, Respectively represent the departure time of trains in the up and down directions,

[0054] Respectively represent line l j Go to the i-th site The kth column of vehicles Arrival time of up and down trains,

[0055] Respectively represent line l j Go to the i-th site The kth column of vehicles The departure time of up and down trains, m, n, q are the number of lines in the network, the number of stations in the line, and the number of train departures on the line respectively;

[0056] S2.3, load the road network train operation time information model T(L, V, TR) into the road network spatial directed graph constructed in S2.1, and obtain the road network spatiotemporal topology model as follows:

[0057] H={O={φ(L,V),G(V,A)},T(L,V,TR)}

[0058] Where H represents the road network spatiotemporal topology model, O = {φ(L,V),G(V,A)} represents the spatial topology model constructed in S2.1, and T(L,V,TR) represents the road network train operation time information model loaded into the spatiotemporal topology model.

[0059] T(L,V,TR) includes the arrival time and departure time of the train running on the line, which can be expressed as:

[0060]

[0061] Where, the constraint condition is equivalent to the logical relationship between stations and lines in the road network N = {φ(L,V)} in S2.1;

[0062] In a spatial directed graph, a constrained breadth-first algorithm is used to search for a set of feasible paths between OD pairs in the road network that meet the maximum number of transfers.

[0063] The method for estimating the travel behavior characteristics of urban rail transit passengers according to the present invention;

[0064] In S2.1, a virtual platform is set up at the transfer station to describe the passenger transfer behavior within the transfer station, including:

[0065] Line A and Line B at the transfer station v T Connect, then the transfer station v in the directed graph T Described as having stations on Line A and Line B and And with the virtual platform v T Two-way connection;

[0066] The walking time for passengers transferring between two lines is from the platform where the previous service train gets off to the virtual platform v TThe walking time plus the time from the virtual platform v T Walking time to the platform for the subsequent service train.

[0067] The method for estimating the travel behavior characteristics of urban rail transit passengers described in the present invention is as follows:

[0068] In S2.3, a constrained breadth-first algorithm is used to search for all feasible paths of OD pairs in the network. Specifically:

[0069] A1. Initialization: Load the road network directed graph G(V,A) constructed in S2.1, set the maximum number of transfers X for passengers traveling on the road network, and input the road network OD pair set R w ;

[0070] A2. Loop R w OD in w ij (v i ,v j ), and perform the following B1-B2 operations to generate the OD feasible path decision tree:

[0071] B1. Create an empty queue Q and use OD to get the starting point v i As the root node, put it into Q, define array C to represent the number of path transfers, and set C = 0;

[0072] B2. While queue Q is not empty, loop through operations C1 to C3.

[0073] C1. Take out the node in Q according to the permission, and determine whether the taken out node is the trip destination v j If yes, execute C3; otherwise, execute C2;

[0074] C2. Take the node taken from Q as the parent node v p , search for sites connected to the parent node in G(V,A) As its direct child, The nth child node of the sth parent node is placed in the queue Q, where n s is the child node number; determine whether the child node is a transfer station, if so, the number of transfers for the node is increased by one, otherwise, no operation is performed; the distance from the parent node station to the child node station is Number of transfers

[0075] Record the sub-node site number together Stored in the corresponding child node;

[0076] C3. End the search; determine the number of transfers c of the tree child nodes of the feasible path decision tree r, r=1,2,…, whether x is less than or equal to the maximum number of transfers, x is the number of leaf nodes, if so, keep the branch and store it in the feasible path set P of OD w Otherwise, prune the tree;

[0077] A3. Output the feasible path set of all OD pairs and the algorithm ends.

[0078] In the method for estimating the travel behavior characteristics of urban rail transit passengers of the present invention, in S3, the passenger's travel chain expression is as follows:

[0079]

[0080] Where λ x represents the passenger travel chain, x represents a passenger entering the urban rail transit system, ta represents the transaction time when the passenger passes through the entrance gate, and te represents the transaction time when the passenger passes through the exit gate; x v (tr) represents the time when passenger x boards train tr at station v, tr(x v ) represents the time when passenger x gets off train tr at station v;

[0081] In the method for estimating the travel behavior characteristics of urban rail transit passengers of the present invention, in step S3, AFC data and train schedule data are matched to trains, and the passenger's travel chain is inferred through the following steps:

[0082] Passengers are matched to trains using the acquired AFC data and train schedule data, and their travel chains are inferred, specifically:

[0083] S3.1, based on the obtained train schedule, define the train operation chain of path p:

[0084]

[0085] Where λ p represents the train operation chain of path p, They are the jth travel subchain of the train operation chain, and the travel subchain includes the starting point of the train at section j Departure time on and end point Arrival time on The train operation chain without transfer path contains only one train operation sub-chain, while the train operation chain with transfer path contains multiple train operation sub-chains;

[0086] S3.2, define the similarity between passenger travel time intervals and train operation time intervals on the route:

[0087]

[0088] Where, represents the time similarity between the travel chain of passenger x and the running chain of train tr on path p, is the length of the intersection of the passenger travel time interval and the train running time interval on the segment j of the path p, represents the travel time interval length of passenger x on segment j of path p, represents the length of the running time interval of train tr on section j of path p, and γ is a positive constant;

[0089] S3.3, based on the passenger transaction data recorded by the automatic ticket vending and checking system x , extract the passenger's entry and exit OD information, and then search the feasible shortest path set P of the road network w , classify passengers according to OD pairs of feasible paths and number of transfers,

[0090] According to the number of transfers along the route, passenger trips are divided into: single route with no transfer, single route with one transfer, single route with multiple transfers, and multi-route trips;

[0091] S3.4, for passengers with a single route without transfers, the trains taken by the passengers are matched using time similarity; the passenger's travel time interval is determined by the transaction data Trade x The transaction time of the passenger entering and exiting the station is obtained from the train timetable information T(L,TR). The similarity between the running time interval of the train tr on path p after the passenger enters the station and the travel time interval of passenger x is calculated, and the passenger is matched to the time interval similarity. The highest train goes up;

[0092] S3.5. Based on the matching results of passengers with a single path without transfers obtained in S3.4, calculate the time it takes for a passenger to enter the station through the gate and board the train, and the time it takes for a passenger to get off the train and exit the station through the gate using the following formula:

[0093]

[0094] Where ACT represents the time it takes for a passenger to enter the station through the gate and board the train tr, and x v (tr) is the time when the passenger boards the train tr at station v, ta is the transaction time when the passenger passes through the entrance gate; EGT is the time from when the passenger gets off the train to when he passes through the gate to exit the station, tr(x v ) is the time when the passenger gets off the train tr at station v, and te is the transaction time when the passenger passes through the exit gate;

[0095] Assume that the arrival and departure time distribution of passengers on a single route without transfer at station v represents the arrival and departure time distribution of passengers at station v, and calculate the Gaussian distribution of passenger arrival and departure times.

[0096] S3.6: To match passengers with a single transfer and single path to trains, the following relationship must be satisfied between the passenger travel chain and the train operation chain:

[0097]

[0098] Where, t d (l,tr,v) represents the time when train tr on line l leaves station v, t a (l, tr, v) represents the time when train tr running on line l arrives at station v, ACT min (l,v) represents the minimum entry time of a passenger at station v on line l, EGT min (l,v) represents the minimum exit time of passengers at station v on line l, t d (l′,tr′,v) represents the time when the transfer train tr′ on the transfer line l′ leaves the station v, TFT min (l,l′,v) represents the minimum transfer time for passengers to transfer from line l to line l′ at station v;

[0099] Match single-transfer single-path passengers to trains, and analyze the transfer time between transfer stations and lines in different transfer directions;

[0100] The minimum entry time ACT is obtained by calculating the entry and exit time distribution of passengers at the station through S3.5 min (l,v) and minimum exit time EGT min (l,v);

[0101] By analyzing the passenger's AFC transaction data and train schedule, and substituting them into constraint (2) in step S3.6, we can find the train that the passenger takes for the last segment of their trip sub-chain with a single transfer and a single path, and obtain the boarding time of the passenger's last segment of the trip sub-chain.

[0102] Taking the passenger boarding time of the last trip subchain as the estimated end time of the first trip subchain, and ta as the estimated start time of the first trip subchain, calculate the time similarity between the estimated time interval of the passenger's first trip subchain and the train operation chain of the section where the passenger's first trip subchain is located;

[0103] Match the passenger to the train with the highest time similarity to obtain the passenger's first travel subchain. Use the passenger's disembarkation time in the first travel subchain as the start time of the passenger's second travel subchain, and the passenger's exit time as its end time. Match the passenger to the train with the highest time similarity to obtain a complete travel chain for the single-transfer passenger.

[0104] Then calculate the passenger's transfer time. The transfer time calculation method is as follows:

[0105]

[0106] Where TFT represents the passenger transfer time, Indicates that the passenger boards the nth train on line l′ at transfer station v time, Indicates that the passenger transfers from the i-th train on line l at the transfer station v The time of getting off the bus; assuming that the transfer time distribution of single-transfer passengers at the station represents the transfer time distribution of transfer passengers, the transfer time distribution of transfer passengers at the station is obtained by calculating the transfer time of single-transfer passengers in each transfer direction at the station.

[0107] The method for estimating the travel behavior characteristics of urban rail transit passengers of the present invention estimates the walking time of passengers entering and exiting the station and the walking time of transfers in S4, and calculates the mean time headway of trains at the station:

[0108]

[0109] in, For line l station v direction The average train headway time, Respectively represent the running direction of line l station v The departure time of the k+1th and kth trains, where K is the number of train departures;

[0110] Assume that the average waiting time of passengers at the platform is half of the headway, that is,

[0111]

[0112] in, Indicates line l station v direction The average waiting time of passengers is Indicates line l station v direction Average train headway time;

[0113] Assume that the standard deviation of passenger waiting time is the standard deviation of train headway time, that is,

[0114]

[0115] in, For line l station v direction The standard deviation of the waiting time of passengers, For line l station v direction The headway of the i-th train, For line l station v direction Average train headway on ;

[0116] The distribution of the passenger's walking time into the station is calculated based on the additivity of independent Gaussian distributions and the estimated passenger entry and exit time distribution in claim 7. The distribution expression of the passenger's walking time into the station is as follows:

[0117]

[0118] in, represents the distribution of passengers’ walking time into the station, represents the passenger arrival time distribution, It represents the distribution of waiting time of passengers at the platform after entering the station. The relationship between the mean and standard deviation is:

[0119]

[0120] Calculate the distribution of passenger transfer walking time, specifically:

[0121]

[0122] in, represents the distribution of passengers’ transfer walking time, represents the distribution of passengers’ transfer time, represents the distribution of passengers' waiting time at the transfer station. The relationship between the mean and standard deviation is:

[0123]

[0124] Assuming that passengers walk to the exit gate immediately after getting off the bus at their destination, the exit walking time distribution of passengers is the exit time distribution.

[0125]

[0126] At this point, we have obtained the distribution of passengers' walking time in and out of the urban rail transit system, the distribution of transfer walking time in the transfer direction of each transfer station, and the distribution of passengers' waiting time at each station.

[0127] Beneficial effects:

[0128] The method for estimating the travel behavior characteristics of urban rail transit passengers described in the present invention can accurately estimate the distribution of passengers' walking time for entering and exiting all stations in the urban rail transit network, the distribution of transfer walking time for each transfer direction at transfer stations, and the distribution of waiting time at each station. It is conducive to the extraction of dynamic travel status characteristic information of passengers in the urban rail transit network and the analysis and simulation of dynamic passenger travel behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 Flow chart of the method of the present invention.

[0130] Figure 2It is the directed graph of the urban rail transit network of the present invention.

[0131] Figure 3 The feasible path set search algorithm between OD pairs of the present invention is OD pair w ij (v i ,v j ) is a feasible path decision tree between . DETAILED DESCRIPTION

[0132] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0133] The present invention provides a method for estimating the travel behavior characteristics of urban rail transit passengers, which can provide a basis for rail transit line network simulation.

[0134] The present invention comprises four modules, such as Figure 1 As shown, specifically:

[0135] The road network data loading module loads basic information of subway network stations and lines, AFC transaction data and train operation schedule data;

[0136] The road network spatiotemporal topology model is constructed and a constrained breadth-first path search algorithm module is used to realize the path search function;

[0137] The passenger travel chain reconstruction module defines the time and space coordinates of passengers getting on and off the bus on single routes without transfers and single routes with transfers in the road network;

[0138] The passenger travel characteristics estimation module estimates the passenger entry and exit time distribution based on the single-path passenger travel chain without transfers, and estimates the passenger transfer time distribution at the transfer station based on the single-path passenger travel chain with only transfers. The train timetable is then used to calculate the passenger platform waiting time distribution. Finally, based on the additivity of independent Gaussian distributions, the passenger entry and exit time distribution and the transfer time distribution are calculated.

[0139] The network data loading module reads and analyzes the basic information of subway network stations and lines, passenger entry and exit transaction information recorded by the AFC system, and train operation schedule information, and cleans the input data to obtain the data format required by the model; wherein:

[0140] Basic information of subway network stations and lines described in step S1:

[0141] Urban rail transit network station data mainly includes four attribute values: station number, station longitude, station latitude and the line to which it belongs, which can be expressed as:

[0142] VS={[v1,Lon1,Lat 1, l1],…,[v i ,Loni ,Lat i ,l k ]}

[0143] Where v represents the station number, Lon represents the longitude of the station, Lat represents the latitude of the station, and l represents the line number to which the station belongs.

[0144] The urban rail transit network is spatially divided into three levels: "network, line, and station" from high to low. The network contains different operating lines. The urban rail transit network spatial model is represented by N = {φ(L,V)}, where N represents the road network, L represents the line set, and V represents the station set. Lines and stations form a network through a nonlinear relationship φ.

[0145] The basic properties of the network can be deduced from the logical relationship between lines, stations, and network in the network N = {φ(L,V)}:

[0146] At the line level, multiple stations constitute a line. j The site collection is represented as in, Indicates line l j The i-th station in, i=1,2,…,n, n is the line l j The number of sites in

[0147] At the network level, all lines constitute a road network, and the line set is represented as

[0148] L={l1,l2,…,l m}

[0149] Where, l j ,j=1,2,…,m represents a certain route in the road network, and m is the total number of routes in the road network;

[0150] The road network contains all sites, and the set of all sites in the road network is represented as

[0151]

[0152] Use φ(L,V) to represent the spatial relationship among the above-mentioned stations, lines, and road networks, then we have the road network

[0153] N={φ(L,V)}

[0154] In the above road network spatial model N={φ(L,V)}, line l j Contains two attribute values: line name and line rail transit clearing system (hereinafter referred to as ACC) number;

[0155] station It contains five attribute values: station name, line number, station ACC number, station line number, and whether it is a transfer station. The station ACC number uniquely encodes all stations in the network, while the station line number is encoded according to the order of the uplink and downlink directions of the line. Station codes are not unique, and transfer stations have different codes on different lines. Based on the attributes of the line and station, the station and line information in the network can be queried.

[0156] The AFC transaction data described in step S1,

[0157] The transaction data recorded by the urban rail transit automatic ticket vending system includes the passenger's travel origin and destination as well as entry and exit times. The AFC transaction data for a complete trip of passenger x can be expressed as:

[0158] Trade x ={ID x ,v o ,ta,v d ,te}

[0159] Where, ID x represents the transaction number of passenger x recorded in the automatic ticket vending system, v o ,v d They represent the starting and ending stations of passengers’ travel in the urban rail transit system, and ta and te represent the time it takes for passengers to pass through the entry and exit gates respectively.

[0160] Claim 1, the train timetable data in step S1,

[0161] Train operation timetable data includes the arrival and departure time information of the trains on the line, which is expressed as

[0162] T(L,TR)

[0163] Where L is the set of lines in the subway network, TR is the set of trains in the subway network, and is expressed as TR =

[0164] {tr1,tr2,…,tr k ,…,tr Q}, k=1,2,…,K, K is the total number of vehicles dispatched in the network; each line in the network will dispatch different operating vehicles, line l j The set of trains dispatched from k is the line l j The number of departures.

[0165] The main invention contents of the road network spatiotemporal topology model construction module include constructing a road network spatial topology structure based on the characteristics of the urban rail transit network, using a constrained breadth-first algorithm to search for a set of feasible paths between OD pairs that meet the maximum number of transfer constraints, and selecting feasible paths between OD pairs; then constructing a rail transit road network spatiotemporal topology model based on loading train operation schedule information into the road network topology model; specifically:

[0166] 1. Construct a spatial topology model of the road network to search for feasible paths between OD pairs in the road network;

[0167] According to the logical relationship between stations and lines in the road network N = {φ(L,V)}, the subway network is represented as a directed graph G(V,A), as follows: Figure 2 As shown, where V is the set of stations in the subway network, and A is the set of directed edges (train track areas between stations) in the subway network; construct the adjacency matrix M of G(V,A) c and the distance matrix M d ,

[0168]

[0169] Where c ij =1 indicates that sites i and j are directly connected, otherwise they are not directly connected, which is represented by 0; d ij It represents the distance of the directed edge from site i to site j. If two sites are directly connected, the edge distance between adjacent sites is the distance a between the sites. ij , otherwise, it is +∞;

[0170] The logical relationship between stations and routes in the road network and the directed graph of the road network together constitute the road network spatial topology model:

[0171] O={φ(L,V),G(V,A)}

[0172] In the network directed graph G(V,A), v∈V represents the nodes of the graph, and a∈A represents the edges of the graph. There are edges in both the uplink and downlink directions between adjacent stations. Virtual platforms are set at transfer stations to describe the passenger transfer behavior in the transfer station. For example, Line A and Line B are connected at transfer station v. T Connect, then the transfer station v in the directed graph T Described as having stations on Line A and Line B and And with the virtual platform v T For a two-way connection, the walking time for passengers transferring between the two lines is from the platform where the previous service train gets off to the virtual platform v T The walking time plus the time from the virtual platform v T Walking time to the platform for the subsequent service train.

[0173] 2. Construct a network train operation time information model T(L,V,TR). The information model mainly includes the arrival and departure time information of trains running on different lines. The network time dimension information needs to be extracted from the train timetable. The train arrival and departure information in the train operation timetable can be expressed as:

[0174]

[0175]

[0176] Where, Respectively represent the arrival time of trains in the up and down directions, Respectively represent the departure time of trains in the up and down directions, Respectively represent line l j Go to the i-th site The kth column of vehicles Arrival time of up and down trains, Respectively represent line l j Go to the i-th site The kth column of vehicles The departure time of up and down trains, m, n, q are the number of lines in the network, the number of stations in the line, and the number of train departures on the line respectively;

[0177] 3. Load the road network train operation time information model T(L,V,TR) into the road network spatial topology model, and the obtained road network spatiotemporal topology model is expressed as follows:

[0178] H={O={φ(L,V),G(V,A)},T(L,V,TR)}

[0179] Where O = {φ(L,V),G(V,A)}, represents the spatial topology model constructed in step S22, and T(L,V,TR) represents the network train operation time information model loaded into the spatiotemporal topology model;

[0180] T(L,V,TR) includes the arrival time and departure time of the train running on the line, which can be expressed as:

[0181]

[0182] stl j ∈L

[0183]

[0184] Wherein, the constraint condition is equivalent to the logical relationship between stations and lines in the road network N = {φ(L,V)} in S21.

[0185] The constrained breadth-first algorithm is used to search for the feasible path set between all OD pairs in the network. The specific algorithm is as follows:

[0186] A1. Initialization: Load the road network directed graph G(V,A) constructed in S2.1, set the maximum number of transfers X for passengers traveling on the road network, and input the road network OD pair set R w ;

[0187] A2. Loop R w OD in w ij (v i ,v j ), and perform the following B1~B2 operations to generate Figure 3 The OD feasible path decision tree shown is:

[0188] B1. Create an empty queue Q and use OD to get the starting point v i As the root node, put it into Q, define array C to represent the number of path transfers, and set C = 0;

[0189] B2. While queue Q is not empty, loop through operations C1 to C3.

[0190] C1. Take out the node in Q according to the permission, and determine whether the taken out node is the trip destination v j If yes, execute C3; otherwise, execute C2;

[0191] C2. Take the node taken from Q as the parent node v p , search for sites connected to the parent node in G(V,A) As its direct child, The nth child node of the sth parent node is placed in the queue Q, where n s is the child node number; determine whether the child node is a transfer station, if so, the number of transfers for the node is increased by one, otherwise, no operation is performed; the distance from the parent node station to the child node station is Number of transfers Record the sub-node site number together Stored in the corresponding child node;

[0192] C3. End the search; determine the number of transfers c of the tree child nodes of the feasible path decision tree r , r=1,2,…, whether x is less than or equal to the maximum number of transfers, x is the number of leaf nodes, if so, keep the branch and store it in the feasible path set P of OD w Otherwise, prune the tree;

[0193] A3. Output the feasible path set of all OD pairs and the algorithm ends.

[0194] The passenger travel chain reconstruction module mainly includes the following invention contents: inputting passenger AFC transaction data, matching passengers to ODs in the road network spatiotemporal topology model based on the travel OD pairs recorded in the AFC transaction data, and classifying passengers into non-transfer, single-transfer, and multiple-transfer passengers according to the number of travel transfers; assigning non-transfer passengers to trains based on the path time similarity index, thereby obtaining a non-transfer passenger travel chain; calculating the station entry and exit time distribution based on the non-transfer passenger travel chain; matching passengers to trains on the travel sub-chain based on the station entry and exit time distribution, passenger travel chain constraints, and time similarity to reconstruct the single-transfer passenger travel chain, and calculating the transfer time distribution of different transfer directions at all transfer stations; specifically:

[0195] 1. Define the passenger trip chain,

[0196] The passenger's travel chain is represented as:

[0197]

[0198] In the formula, x represents a passenger entering the urban rail transit system, ta represents the transaction time when the passenger passes through the entrance gate, and te represents the transaction time when the passenger passes through the exit gate; x v (tr) represents the time when passenger x boards train tr at station v, tr(x v ) represents the time when passenger x gets off the train tr at station v. In the formula, the passenger travel chain may contain one or more travel sub-chains, such as It constitutes the travel sub-chain of a passenger taking a train in the urban rail transit system. The travel chain of passengers without transfers contains only one travel sub-chain, while the travel chain of passengers with transfers contains multiple travel sub-chains.

[0199] 2. Based on the train schedule obtained by S1 in claim 1 and the set of feasible paths searched in claim 4, define a train operation chain for path p in the road network;

[0200] Similar to the passenger trip chain, it can be defined as

[0201]

[0202] Where λ p represents the train operation chain of path p, They are the jth travel subchain of the train operation chain, and the travel subchain includes the starting point of the train at section j Departure time on and end point Arrival time on The train operation chain is similar to the passenger travel chain. The train operation chain without transfer path contains only one train operation sub-chain, while the train operation chain with transfer path contains multiple train operation sub-chains.

[0203] 3. Define the similarity between passenger travel time intervals and train operation time intervals on the route:

[0204]

[0205] Where, represents the time similarity between the travel chain of passenger x and the running chain of train tr on path p, is the length of the intersection of the passenger travel time interval and the train running time interval on the segment j of the path p, represents the travel time interval length of passenger x on segment j of path p, represents the length of the running time interval of train tr on section j of path p, and γ is a positive constant;

[0206] 4. Passenger travel type classification

[0207] Based on the passenger transaction data recorded by the automatic ticket vending system x , extract the passenger's entry and exit OD information, and then search the feasible path set P of the road network w , the passengers are classified according to the feasible path set and the number of transfers based on OD, and the passenger trips are divided into single path without transfer, single path with single transfer, single path with multiple transfers, and multi-path trips based on the number of transfers on the path;

[0208] 5. Reconstruction of the passenger travel chain

[0209] Reconstruction of a single-path passenger travel chain without transfers:

[0210] For passengers with a single route without transfers, the trains they take can be matched based on time similarity; the travel time interval of passengers can be determined by transaction data. x The transaction time of the passenger entering and exiting the station is obtained from the train timetable information T(L,TR). The similarity between the running time interval of the train tr on path p and the travel time interval of passenger x is calculated after the passenger enters the station, and the passenger is matched to the time interval similarity. The highest train goes up;

[0211] Reconstruction of single-transfer single-path passenger travel chain:

[0212] According to the matching results of passengers on a single path without transfer, the time from the passenger entering the station through the gate to boarding the train and the time from the passenger getting off the train to exiting the station through the gate are calculated, which can be expressed as

[0213]

[0214] Where ACT represents the time it takes for a passenger to enter the station through the gate and board the train tr, and xv (tr) is the time when the passenger boards the train tr at station v, ta is the transaction time when the passenger passes through the entrance gate; EGT is the time when the passenger gets off the train and passes through the gate to exit the station, tr(x v ) is the time when the passenger gets off the train tr at station v, and te is the transaction time when the passenger passes through the exit gate. Assuming that the arrival and exit time distribution of passengers without transfers at station v can represent the arrival and exit time distribution of passengers at station v, the distribution of passenger arrival and exit times is calculated.

[0215] To match passengers with a single transfer and single path to trains, the following relationship must be satisfied between the passenger travel chain and the train operation chain:

[0216]

[0217] Where, t d (l,tr,v) represents the time when train tr on line l leaves station v, t a (l, tr, v) represents the time when train tr running on line l arrives at station v, ACT min (l,v) represents the minimum entry time of a passenger at station v on line l, EGT min (l,v) represents the minimum exit time of passengers at station v on line l, t d (l′,tr′,v) represents the time when the transfer train tr′ on the transfer line l′ leaves the station v, TFT min (l,l′,v) represents the minimum transfer time for passengers to transfer from line l to line l′ at station v;

[0218] Matching analysis is performed on passengers with a single transfer path to obtain the transfer time in different transfer directions between transfer station lines; ACT is obtained based on the passenger entry and exit time distribution of the station. min (l,v) and EGT min (l, v); By analyzing the passenger's AFC transaction data and train schedule, and bringing them into the above formula (2), find the train that the single-transfer passenger takes on the last travel subchain, and obtain the boarding time of the passenger's last travel subchain. The passenger boarding time of the last travel subchain is used as the estimated end time of the first travel subchain, and ta is used as the estimated start time of the first travel subchain. The time similarity between the estimated time interval of the passenger's first travel subchain and the train operation chain of the section where the passenger's first travel subchain is located is calculated, so as to match the passenger to the train with the highest time similarity, obtain the passenger's first travel subchain, and thus obtain the complete travel chain of the single-transfer passenger;

[0219] Then calculate the passenger's transfer time. The transfer time calculation method is as follows:

[0220]

[0221] Where TFT represents the passenger transfer time, Indicates that the passenger boards the nth train on line l′ at transfer station v time, Indicates that the passenger transfers from the i-th train on line l at the transfer station v The time of getting off the bus; assuming that the transfer time distribution of single-transfer passengers at the station represents the transfer time distribution of transfer passengers, the transfer time distribution of transfer passengers at the station is obtained by calculating the transfer time of single-transfer passengers in each transfer direction at the station;

[0222] The passenger travel characteristics estimation module mainly includes calculating the mean time headway of trains at stations:

[0223]

[0224] in, For line l station v direction The average train headway time, Respectively represent the running direction of line l station v The departure time of the k+1th and kth trains, K is the number of train departures; suppose the average waiting time of passengers on the platform is half of the headway, that is

[0225]

[0226] in, Indicates line l station v direction The average waiting time of passengers is Indicates line l station v direction Average train headway time;

[0227] Assume that the standard deviation of passenger waiting time is the standard deviation of train headway time, that is,

[0228]

[0229] in, For line l station v direction The standard deviation of the waiting time of passengers, For line l station v direction The headway of the i-th train, For line l station v direction Average train headway on

[0230] The distribution of the walking time of passengers entering the station is calculated. According to the additivity of independent Gaussian distributions, using the estimated passenger entry and exit time distribution in claim 7, the distribution of the walking time of passengers entering the station can be obtained as follows:

[0231]

[0232] in, represents the distribution of passengers’ walking time into the station, N(μ ACT ,σ ACT ) represents the passenger arrival time distribution, It represents the waiting time distribution of passengers at the platform after entering the station. The relationship between the mean and standard deviation is:

[0233]

[0234] Calculate the distribution of passenger transfer walking time, specifically:

[0235]

[0236] in, represents the distribution of passengers’ transfer walking time, represents the distribution of passengers’ transfer time, It represents the waiting time distribution of passengers at the transfer station. The relationship between the mean and standard deviation is:

[0237]

[0238] Assuming that passengers walk to the exit gate immediately after getting off the bus at their destination, the exit walking time distribution of passengers is the exit time distribution.

[0239]

[0240] At this point, we have obtained the distribution of passengers' walking time in and out of the urban rail transit system, the distribution of transfer walking time in the transfer direction of each transfer station, and the distribution of passengers' waiting time at each station.

[0241] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above examples. The above examples and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications are possible without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for estimating travel behavior characteristics of urban rail transit passengers, characterized by: Here are the steps: S1. Acquire, analyze and process travel data; the data includes: basic data of urban rail transit network stations and lines; passenger entry and exit transaction data recorded by the AFC system; train operation timetable data; S2, constructing a road network spatiotemporal topological model and a constrained breadth-first algorithm to search for feasible path sets of OD pairs. According to the characteristics of the urban rail transit spatial network, the road network spatial topological structure is constructed, and then the train operation schedule information is loaded into the road network topological model to construct a rail transit road network spatiotemporal topological model; the constrained breadth-first algorithm is used to search for feasible path sets between OD pairs that meet the maximum number of transfer constraints; S3, reconstructing the passenger travel chain; Passengers are matched to ODs in the road network spatiotemporal topology model according to the travel OD pairs recorded in the passenger AFC transaction data in S1, and passengers are divided into the following types according to the number of transfers and the number of feasible paths for OD pairs: single path without transfers, single path with single transfers, single path with multiple transfers, and multi-path travel; The passengers of single path without transfer and single path with only transfer are assigned to trains by using the path time similarity index, and then the travel chains of the two types of passengers are obtained. Estimate the passenger entry and exit time distribution at the station based on the single-path passenger travel chain without transfers. Estimate the transfer time distribution of passengers at transfer stations based on the single-transfer single-path passenger travel chain; S4, calculate the passenger's entry and exit walking time and transfer walking time, and calculate the passenger's entry and exit and transfer walking time distribution according to the passenger's entry and exit time distribution obtained in S3 and the train timetable, The average waiting time of passengers at the platform is taken as half of the average time distance of the train head. The standard deviation of train arrival time is used as the standard deviation of passengers’ waiting time at the platform. The distribution of passengers' walking time for entering and exiting the station and the distribution of passengers' walking time for transferring are calculated.

2. The method for estimating the travel behavior characteristics of urban rail transit passengers according to claim 1, characterized in that: S1.

1. Obtain basic data on urban rail transit network stations and lines, as follows: The urban rail transit network station data includes four attribute values: station number, station longitude, station latitude and the line to which it belongs, expressed as: VS={[v1,Lon1,Lat 1, l1],…,[v i ,Lon i ,Lat i ,l j ]} VS represents the basic data of network sites and lines, i represents the site number, and v i represents the site number, Lon represents the longitude of the site, Lat represents the latitude of the site, k represents the line number, l j Indicates the line number to which the station belongs; The urban rail transit network is spatially divided into three levels: network, line, and station. N = {φ(L,V)} represents the spatial model of urban rail transit network. N represents the road network, L represents the set of lines, V represents the set of stations, and the lines and stations form a network through a nonlinear relationship φ; The basic properties of the network are derived from the logical relationship between lines, stations, and network in the network N = {φ(L,V)}: At the line level, line l j The site collection is represented as in, Indicates line l j The i-th station in, i=1,2,…,n, n is the line l j The number of sites in At the network level, the line set is represented by L = {l1,l2,…,l m }, where l j ,j=1,2,…,m represents a certain line in the road network, and m is the total number of lines in the road network; The set of all sites in the road network is represented as Using φ(L,V) to represent the spatial relationship among stations, lines, and road networks, the road network spatial model is expressed as follows: N={φ(L,V)} In the model, line l j Contains two attribute values: line name and line rail transit clearing system ACC number; station Contains five attribute values: station name, line number, station ACC number, station line number, and whether it is a transfer station; S1.2, obtain AFC transaction data. The transaction data recorded by the urban rail transit automatic ticket vending and checking system includes the passenger's travel starting and ending points and entry and exit times. The AFC transaction data of a complete trip of passenger x is expressed as: Trade x ={ID x ,v o ,t.a,v d ,t.e} Where, ID x represents the transaction number of passenger x recorded in the automatic ticket vending and checking system, v o ,v d They represent the starting and ending stations of passengers in the urban rail transit system, and ta and te represent the time when passengers pass through the entry and exit gates respectively; S1.3, obtain train timetable data. The train operation timetable data includes the arrival and departure time information of the operating trains on the line, which is expressed as: T(L,TR) In the formula, L is the set of lines in the subway network, TR is the set of trains in the subway network; TR={tr1,tr2,…,tr k ,…,tr K }, k = 1, 2, ..., K, K is the total number of departures in the road network; Line j The set of trains dispatched from Indicates line l j The g-th train departs from the line l. j The number of departures on.

3. The method for estimating travel behavior characteristics of urban rail transit passengers according to claim 1 or 2, characterized in that: The construction of the spatiotemporal topological model of the rail transit network in S2 specifically includes: S2.1, constructing a road network spatial topology model to search for feasible path sets between OD pairs in the road network; According to the logical relationship between stations and lines in the road network N = {φ(L,V)}, the rail transit network is represented as a directed graph G(V,A); Where V is the set of stations in the rail transit network, A is the set of directed edges in the rail transit network, and the directed edges represent the train track areas between stations; Construct the adjacency matrix M of G(V,A) c and the distance matrix M d , In the formula, the adjacency matrix M c The matrix element c ij =1 indicates that sites i and j are directly connected, otherwise, they are not directly connected, which is represented by 0; the distance matrix M d The matrix element d ij Represents the distance of the directed edge from site i to site j. If two sites are directly connected, the edge distance between adjacent sites is the distance a between the sites. ij , otherwise, it is +∞; The logical relationship between stations and routes in the road network and the directed graph of the road network together constitute the road network spatial topology model: O={φ(L,V),G(V,A)} Wherein, O represents the spatial topological model of the road network, in the network directed graph G(V,A), v∈V represents the nodes of the graph, and a∈A represents the edges of the graph. There are edges in the uplink and downlink directions between adjacent stations, and virtual platforms are set up at the transfer station to describe the transfer behavior of passengers in the transfer station. S2.2, construct the network train operation time information model T (L, V, TR), which includes the arrival and departure time information of trains running on different lines. The network time dimension information is extracted from the train timetable. The train arrival and departure information in the train operation timetable is expressed as: In the formula, Respectively represent the arrival time of trains in the up and down directions, Respectively represent the departure time of trains in the up and down directions, Respectively represent line l j The i-th site The kth vehicle The arrival time of up and down trains, Respectively represent line l j The i-th site The kth vehicle The departure time of up and down trains, m, n, q are the number of lines in the network, the number of stations in the line, and the number of train departures on the line respectively; S2.3, load the road network train operation time information model T(L,V,TR) into the road network space directed graph constructed in S2.1, and obtain the road network spatiotemporal topology model as follows: H={O={φ(L,V),G(V,A)},T(L,V,TR)} Where H represents the road network spatiotemporal topology model, O = {φ(L,V),G(V,A)} represents the spatial topology model constructed in S2.1, and T(L,V,TR) represents the road network train operation time information model.

4. The method for estimating travel behavior characteristics of urban rail transit passengers according to claim 1, characterized in that: In S3, the passenger's travel chain expression is as follows: In the formula, λ x represents the passenger travel chain, x represents a passenger entering the urban rail transit system, ta represents the transaction time when the passenger passes through the entry gate, and te represents the transaction time when the passenger passes through the exit gate; x v (tr) represents the time when passenger x boarded train tr at station v, tr(x v ) represents the time when passenger x gets off train tr at station v.

5. The method for estimating travel behavior characteristics of urban rail transit passengers according to claim 1, characterized in that: In S3, AFC data and train schedule data are matched to trains, and the passenger travel chain is inferred through the following steps: S3.1, according to the obtained train schedule, define the train operation chain of path p: In the formula, λ p represents the train operation chain of path p, are the jth travel subchain of the train operation chain, and the travel subchain includes the starting point of the train at section j Departure time on and end point Arrival time on The train operation chain without transfer path contains only one train operation sub-chain, while the train operation chain with transfer path contains multiple train operation sub-chains; S3.2, define the similarity between the passenger travel time interval and the train operation time interval on the path: In the formula, represents the time similarity between the travel chain of passenger x and the travel chain of train tr on path p, is the length of the intersection of the passenger travel time interval and the train operation time interval on segment j of path p, represents the travel time interval length of passenger x on segment j of path p, represents the length of the running time interval of train tr on section j of path p, and γ is a positive constant; S3.3, according to the number of transfers on the route, passenger trips are classified into: single route with no transfers, single route with one transfer, single route with multiple transfers, and multiple routes; S3.4, for passengers on a single route without transfer, the trains taken by the passengers are matched using time similarity; S3.5, based on the non-transfer single-path passenger matching results obtained in S3.4, the time from the passenger entering the station through the gate to boarding the train and the time from the passenger getting off the train to exiting the station through the gate are calculated by the following formula: In the formula, ACT represents the time from when the passenger enters the station through the gate to when he / she boards the train tr, and x v (tr) is the time when the passenger boards the train tr at station v, ta is the transaction time when the passenger passes through the entrance gate; EGT represents the time from when the passenger gets off the train to when he passes through the gate to exit the station, tr(x v ) is the time when the passenger gets off the train tr at station v, and te is the transaction time when the passenger passes through the exit gate; Assume that the passenger entry and exit time distribution of a single path without transfer at station v represents the passenger entry and exit time distribution at station v, and calculate the Gaussian distribution of passenger entry and exit time; S3.6, matching single-transfer single-path passengers to trains, the following relationship needs to be satisfied between the passenger travel chain and the train operation chain: In the formula, t d (l,tr,v) represents the time when train tr on line l leaves station v, t a (l,tr,v) represents the time when train tr running on line l arrives at station v, ACT min (l,v) represents the minimum entry time of a passenger at station v on line l, EGT min (l,v) represents the minimum exit time of passengers at station v on line l, t d (l′,tr′,v) represents the time when the transfer train tr′ running on the transfer line l′ leaves the station v, TFT min (l,l′,v) represents the minimum transfer time for passengers to transfer from line l to line l′ at station v; The minimum entry time ACT is obtained by calculating the entry and exit time distribution of passengers at the station through S3.5 min (l,v) and minimum exit time EGT min (l,v); By analyzing the passenger's AFC transaction data and train schedule, substituting them into constraint formula (2) in step S3.6, the boarding time of the passenger's last trip sub-chain is obtained according to the train taken by the passenger in the last trip sub-chain of single transfer and single path; Then the transfer time of passengers is calculated. The transfer time calculation method is as follows: Where TFT represents the passenger transfer time, Indicates that the passenger boards the nth train on line l′ at the transfer station v time, Indicates that the passenger transfers from the i-th train on line l at the transfer station v Time of getting off the bus; Assuming that the transfer time distribution of single-transfer passengers at the station represents the transfer time distribution of transfer passengers, the transfer time distribution of transfer passengers at the station is obtained by calculating the transfer time of single-transfer passengers in each transfer direction at the station.

6. The method for estimating travel behavior characteristics of urban rail transit passengers according to claim 1 or 5, characterized in that: In S4, the walking time for passengers entering and exiting the station and the walking time for transfers are estimated, and the mean headway time of trains at the station is calculated: in, For line l station v direction The average headway of trains, Respectively represent the running direction of line l station v The departure time of the k+1th and kth trains, where K is the number of train departures; Assume that the average waiting time of passengers at the platform is half of the headway time, that is, in, Indicates line l station v direction The average waiting time of passengers is Indicates line l station v direction Average headway of trains; Assume that the standard deviation of passenger waiting time is the standard deviation of train headway time, that is, in, For line l station v direction The standard deviation of the waiting time of passengers, For line l station v direction The headway of the i-th train, For line l station v direction Average headway of trains on The distribution expression of the passenger's walking time into the station is as follows: in, represents the distribution of passengers’ walking time into the station, represents the passenger arrival time distribution, It represents the waiting time distribution of passengers at the platform after entering the station. The relationship between the mean and standard deviation is: Calculate the distribution of passengers' transfer walking time, specifically: in, represents the distribution of passengers' transfer walking time, represents the distribution of passengers’ transfer time, represents the waiting time distribution of passengers at the transfer station, and the relationship between the mean and standard deviation is: Assuming that passengers get off at their destination and immediately walk to the exit gate, the passenger's exit walking time distribution is the exit time distribution. So far, we have obtained the distribution of passengers' walking time in and out of the urban rail transit system, the distribution of transfer walking time in the transfer direction of each transfer station, and the distribution of passengers' waiting time at each station.

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