CN116701495B - Subway-bus composite network key station identification method - Google Patents

Subway-bus composite network key station identification method Download PDF

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CN116701495B
CN116701495B CN202310984508.9A CN202310984508A CN116701495B CN 116701495 B CN116701495 B CN 116701495B CN 202310984508 A CN202310984508 A CN 202310984508A CN 116701495 B CN116701495 B CN 116701495B
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郑乐
张毅萌
陈学武
宋波
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a subway-bus composite network key station identification method, which is used for calculating the trip amount of each time period OD pair in a subway-bus composite network by using a trip chain OD back-push method based on the minimum entropy rate based on subway and bus line and station data and historical card swiping data; taking the travel quantity of the OD pairs in each period as input, taking the shortest travel path length of passengers as a target, and acquiring the passenger flow quantity of each side of each period in the subway-bus composite network; comprehensively considering the travel quantity of OD pairs in each period and the passenger flow quantity of each side, and calculating the parameters of each station in each period aiming at a subway-bus weighted time-varying network; and calculating the comprehensive importance of each station in each period by using an entropy weight method, and identifying key subways and bus stations in each period based on the comprehensive importance. The invention helps public transportation operation enterprises rapidly identify the passenger flow trip bottleneck stations in the network of each period, and provides decision support for network planning, station layout, capacity configuration and emergency plans under emergencies.

Description

Subway-bus composite network key station identification method
Technical Field
The invention relates to a subway-bus composite network key station identification method, and belongs to the technical field of public transportation planning control.
Background
With the increasing of the land development intensity and population density of large and medium-sized cities in China, the planning and construction of urban rail transit mainly comprising subways are quickened in all large cities. The multi-mode public transportation system taking rail transportation as backbone and ground public transportation as main body is actively propelled.
In the multi-mode public transportation system, the subway has the characteristics of high speed, large transportation capacity, high quasi-point rate and the like, and can serve as a skeleton network of urban public transportation to serve the travel of long and medium lines of residents; the conventional bus has the characteristics of wide coverage, high accessibility, low travel cost and the like, and can serve as a backbone network of urban public transportation for middle-short distance travel of residents; the composite network formed by the integration of the subway network and the public transport network is complementary in function, so that urban residents travel is greatly facilitated, and the overall service level of urban public transport is improved. However, with the gradual expansion of the scale of the subway-bus composite network, the interaction gradually increases, and higher requirements are also put on the robustness of the network. Especially critical site failures can greatly affect the transport efficiency of the composite network and even trigger cascading failures. Therefore, the identification of the key sites in the composite network has important significance for guiding network planning, site layout and capacity configuration, and can also provide decision support for emergency guarantee plans under sudden public events.
The existing key site identification method mainly has three-point problems:
firstly, most of the existing researches focus on the research of a subway or a public transportation single network, and the research of a composite network key station identification method formed by the subway and the public transportation is lacked.
Secondly, most of the existing researches are remained in analysis of the topological characteristics of the unauthorized network, and the spatial heterogeneity of the actual passenger flow in the network is not considered, so that the identified key stations cannot reflect the actual passenger flow rule.
Thirdly, most of the existing researches assume that the network is a static network, the time heterogeneity of the passenger flow in the actual network is not considered, and the time-varying characteristic of the actual network cannot be truly reflected.
Therefore, from the viewpoint of actual passenger flow, a person skilled in the art is needed to solve the technical problem of the current "compound-weighted-time-varying" network key station identification.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a subway-bus composite network key station identification method which is used for assisting public transportation operators in identifying key stations in each period in a composite network, so as to guide network planning, station layout and transport capacity configuration of public transportation.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
a planning method of a multi-mode bus travel path comprises the following steps:
step 1: and acquiring subway and bus route and station data, searching bus stations within the walking reachable range of the subway station, and establishing a subway-bus composite unauthorized network.
Step 2: based on historical card swiping data of subways and buses, calculating the trip amount of the OD pairs in each period in the subway-bus composite unauthorized network by adopting a trip chain OD reverse-push method based on the minimum entropy rate.
Step 3: and taking the travel quantity of the OD pairs in each period as input, taking the shortest travel path length of the passengers as a target, and acquiring the passenger flow quantity of each side of each period in the network based on the built subway-bus composite unauthorized network.
Step 4: and comprehensively considering the travel quantity of the OD pairs in each period and the passenger flow quantity of each side, and establishing a subway-bus weighted time-varying network.
Step 5: and calculating the flow access degree value of each station in each period aiming at the subway-bus weighted time-varying network.
Step 6: and calculating the flow medium value of each station in each time period aiming at the subway-bus weighted time-varying network.
Step 7: based on the flow access value and the flow medium value of each station in each period, the comprehensive importance of each station in each period is calculated by using an entropy weight method, and key subways and bus stations in each period are identified based on the comprehensive importance.
Preferably, the step 1 specifically includes:
step 1.1: based on subway and bus route and station data, a Space L method is adopted to respectively establish a subway unauthorized networkPublic transport unauthorized network->。
Wherein,representing a subway unauthorized network; />Representing a subway station set; />A set of edges representing a subway network;representing a public transport unauthorized network; />Representing a bus stop set; />Representing a collection of edges of a public transportation network.
Step 1.2: method for searching subway station set by adopting space connectionWithin the range of walking>Bus stops of rice, obtaining subway stop sets ∈>And bus stop set->The method comprises the steps of (1) establishing a space mapping relation of transfer edges of a subway network and a bus network, wherein the transfer edges are in a set of +.>。
Step 1.3: building subway-bus composite unauthorized networkThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Characterizing all public transit and subway station sets, +.>And (5) representing an edge set and a transfer edge set of the subway and bus network.
The step 2 specifically includes:
step 2.1: reading historical card swiping data of buses and subways in the same time period, wherein the bus card swiping data comprises card numbers, boarding time, boarding stations, boarding lines and vehicle numbers of passengers; the subway card swiping data comprises the card number, the inbound time, the inbound station, the outbound time and the outbound station of passengers.
Step 2.2: and extracting historical card swiping data of all subways and buses of passengers within a certain time range according to the card numbers.
Step 2.3: sequencing the travel of passengers according to the boarding time of the passengers, and constructing an initial travel chain set，/>Denoted as->。
Wherein,Lindicating the length of the travel chain of the passengers, the total journey number of the passengers isL/2；Indicate the->Number of individual sites, when->When it is odd->For getting on the station, when->If even, the case is->Is a get-off station; for the bus stop at the bus trip, the value is currently unknown, and the initial value is set to be null.
Step 2.4: selecting an initial travel chain setThe%>And judging whether the record is the first journey of the current day, if so, recording the number of the upper station of the journey and jumping to the step 2.10, and if not, jumping to the step 2.5.
Step 2.5: and judging whether the next station point of the last travel of the current travel is empty, if so, jumping to the step 2.6, and if not, jumping to the step 2.10.
Step 2.6: and acquiring a station in the bus route of the last journey, which is closest to the current journey station, as a potential departure point of the last journey, and estimating the arrival time of the potential departure point based on AVL data of the vehicle.
Step 2.7: calculating the space distance between the current travel boarding point and the potential alighting point of the previous travel according to the potential alighting point of the previous travel and the arrival time of the potential alighting pointdTime differencetd。
Step 2.8: determining spatial distancedTime differencetdWhether or not to simultaneously be smaller than the distance thresholdDIf so, marking the potential get-off point as the get-off point of the previous journey, and assigning the station number to the stationIf no, then->Still empty.
Step 2.9: judging whether the current journey is the current dayIf yes, and the current journey is a bus trip, selecting a bus stop closest to the bus stop on the first journey on the current journey in the bus route of the current journey as a bus stop, and assigning the stop number to the bus stopThe method comprises the steps of carrying out a first treatment on the surface of the If not, go to step 2.10.
Step 2.10: judging the currentkWhether or not to equal the total stroke numberL/2The method comprises the steps of carrying out a first treatment on the surface of the If not, assignk=k+1, and jumping to the step 2.4; if yes, outputting updatedAnd marked in->Middle->The departure point which is still empty is +.>。
Step 2.11: searching for the currentIn all stops with empty stop numbers +.>Recorded as a collectionEMAggregation ofEMThe number of the elements is recorded as n。
Step 2.12: for collectionsEMAll elements in (1)Searching for bus stops meeting all constraints as +.>The corresponding set of possible departure stops is marked +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein, set->The number of the elements is->。
Step 2.13: based on all acquiredCorresponding feasible station-down point set +.>By being added from each->Selecting one element to construct a possible permutation and combination of the getting-off station, traversing each +.>Constructing all possible permutation and combination of the lower station points, and putting all possible permutation and combination into a set +.>In (a) and (b); wherein, set->The number of combinations in->，/>Representing the product symbol.
Step 2.14: for collectionsAssigning it to +.>In the corresponding departure station number is empty>And calculate the travel chain set after assignment +.>Entropy rate of->。
Step 2.15: finding collectionsEntropy rate in all combinations->Minimum combination and apply minimum combination pair +.>Updating and outputting final passenger travel chain set +.>。
Step 2.16: based on all final passenger travel chain setsAccording to the boarding time and boarding and alighting station point numbers of passengers, counting each periodtThe trip amount of the inner OD pair is used for constructing a trip amount matrix of passengers +. >。
Wherein,is->Matrix (S)>The number of stations in the subway-bus composite unauthorized network is the number of stations; trip amount matrix->The middle element is marked as->，/>Indicating that the boarding time is in a period of timetIn the interior, the boarding station is a stationiThe get-off station is a stationjIs a travel amount of (a).
Preferably, all the constraints include:
。
wherein, the station number of the get-off pointStation number of the station which must be located at the boarding point->After that, the time of getting off is>Must get on time->Afterwards, the bus route to which the departure point belongs is +.>Bus route which must be associated with the departure point ∈ ->Similarly, the vehicle number to which the departure point belongs +.>Bus route which must be associated with the departure point ∈ ->The same applies.
Preferably, the entropy rateThe calculation formula of (2) is as follows:
。
in the method, in the process of the invention,representation->In positioniIs>；/>Representation->Is from the position of (2)iTo the point ofi+m-1; />Representation->Is from the position of (2)jTo the point ofj+m-1; />Representation->Entropy rate of (2);Lindicating the length of the passenger travel chain.
Preferably, the step 3 specifically includes:
step 3.1: for all ofInitializing, and setting an initial value to 0; wherein (1)>For passenger flow data set->Element(s) of->Representing a time periodtInner passing edge->Is a passenger flow volume of the vehicle.
Step 3.2: searching for a traffic matrix All->Is numbered and the total number is recorded as OD pairs of (2)。
Step 3.3: for number ofkOD pair of (d)By site->As departure site, use site->As a destination station, solving the shortest path of the destination station by using a Dijkstra algorithm based on the constructed subway-bus composite unauthorized network; the shortest path is marked->Wherein->Side comprising all paths of shortest path +.>Is a set of (3).
Step 3.4: for the purpose ofSide of middle way->Update->Corresponding->，/>。
Step 3.5: judging the currentkWhether or not to be equal toThe method comprises the steps of carrying out a first treatment on the surface of the If not, assignk=k+1, and jumping to the step 3.3; if yes, outputting the final passenger flow volume data set of each side +.>。
As a preferred solution, the subway-bus weighted time-varying network includes:
the subway-bus weighted time-varying network is expressed as:wherein:，/>characterizing all public transit and subway station sets, +.>Representing the first in a subway-bus weighted time-varying networkiA site.
，/>Edge set representing subway and public transport network, transfer edge set, and +.>Representing the information by site->To site->The connecting edge is formed; />，/>Characterization of the period of timetTrip volume of all OD pairs in +.>Is expressed in time periodtInner by site->To site->Is a travel amount of (a).
，/>Characterization of the period of time tPassenger flow through each side in the interior, +.>Is expressed in time periodtInner passing edge->Is a passenger flow volume of the vehicle.
Preferably, the flow rate in-out degree value includes: the flow inlet value and the flow outlet value are calculated according to the following formulas:
。
wherein,and->Respectively express subway or bus station->In the time periodtFlow in value and flow out value in +.>Representing bordering->In the time periodtPassenger flow volume within; />Representing bordering->In the time periodtPassenger flow volume within.
Preferably, the flow medium value is calculated according to the following formula:
。
wherein,representing subway or bus stop->In the time periodtFlow medium value in->Is expressed in time periodtThe internal starting point is site->The end point is site->Is a trip amount of the vehicle; />Is expressed in time periodtThe internal starting point is site->The end point is site->Is a passenger flow volume of the vehicle.
Preferably, the step 7 specifically includes:
step 7.1: based on each time periodtThe flow inlet value, the flow outlet value and the flow medium value of each site in the network are established to establish a comprehensive evaluation matrix of the siteThe calculation formula is as follows:
。
step 7.2: for comprehensive evaluation matrixNormalization processing is carried out to obtain a normalized comprehensive evaluation matrix ∈>The calculation formula is as follows:
。
step 7.3: calculating information entropy of three indexes of flow input value, flow output value and flow medium value 、/>、/>The calculation formula is as follows:
。
step 7.4: calculating the weights of three indexes of a flow input value, a flow output value and a flow medium value、、/>The calculation formula is as follows:
。
step 7.5: computing each siteIs->The method comprises the steps of carrying out a first treatment on the surface of the The comprehensive importance of all stations is ordered according to the order from high to low, the stations which are more front are key stations in the subway-bus composite network, wherein the comprehensive importance is +.>The calculation formula is as follows:
。
the beneficial effects are that: aiming at the defect that the current key station identification method considers the space-time heterogeneity of the multimode composite network and the actual passenger flow, the subway-bus composite network key station identification method acquires the travel quantity of the OD pairs in each period and the passenger flow of each side in the network by deep mining of public traffic historical data and a travel chain OD reverse-push method based on the minimum entropy rate, and establishes a subway-bus weighted time-varying network and an identification method of the network key station on the basis.
The method is based on city public transportation network data and historical card swiping travel data, and the OD travel amount between stations and the passenger flow amount between stations in different time periods are rapidly calculated. Meanwhile, the key station identification method comprehensively considers the time-varying characteristics, the local and global characteristics and the transfer characteristics of the passenger flow of the station, and can help public transport operation enterprises to quickly identify the passenger flow trip bottlenecks in the network of each period, so that decision support is provided for network planning, station layout, capacity configuration and emergency plans under emergencies.
Drawings
Fig. 1 is a schematic flow chart of a planning method of a travel path of the present invention.
Fig. 2 is a diagram of a bus stop recognition method within a certain range of a subway stop in the invention.
Fig. 3 is a schematic diagram of a subway-bus composite unauthorized network in an embodiment of the invention.
Fig. 4 is a schematic flow chart of an OD back-extrapolation method of a travel chain based on a minimum entropy rate in the present invention.
FIG. 5 is a graph of a top 100 site distribution diagram of a 6:00-8:30 composite importance level in an embodiment of the present invention.
FIG. 6 is a graph of a top 100 site distribution diagram of an 8:30-16:30 composite importance level in an embodiment of the present invention.
FIG. 7 is a graph of a top 100 site distribution diagram of a 16:30-18:30 composite importance level in an embodiment of the present invention.
FIG. 8 is a diagram of a top 100 site distribution diagram of 18:30-24:30 composite importance in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully by reference to the accompanying drawings, in which embodiments of the invention are shown, and in which it is evident that the embodiments shown are only some, but not all embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention.
The invention will be further described with reference to specific examples.
Example 1:
as shown in fig. 1, this embodiment describes a method for planning a multi-mode bus travel path, including the following steps:
step 1: and acquiring subway and bus route and station data, searching bus stations within the walking reachable range of the subway station, and establishing a subway-bus composite unauthorized network.
Step 2: based on historical card swiping data of subways and buses, calculating the trip amount of OD pairs in each period in the subway-bus composite unauthorized network by adopting a trip chain OD (ORIGIN DESTINATION) reverse-push method based on the minimum entropy rate.
Step 3: and taking the travel quantity of the OD pairs in each period as input, taking the shortest travel path length of the passengers as a target, and acquiring the passenger flow quantity of each side of each period in the network based on the built subway-bus composite unauthorized network.
Step 4: and comprehensively considering the travel quantity of the OD pairs in each period and the passenger flow quantity of each side, and establishing a subway-bus weighted time-varying network.
Step 5: and calculating the flow access degree value of each station in each period aiming at the subway-bus weighted time-varying network.
Step 6: and calculating the flow medium value of each station in each time period aiming at the subway-bus weighted time-varying network.
Step 7: based on the flow access value and the flow medium value of each station in each period, the comprehensive importance of each station in each period is calculated by using an entropy weight method, and key subways and bus stations in each period are identified based on the comprehensive importance.
Further, the method for establishing the subway-bus composite unauthorized network in the step 1 specifically includes:
step 1.1: based on subway and bus route and station data, a Space L method is adopted to respectively establish a subway unauthorized networkPublic transport unauthorized network->。
Wherein,representing a subway unauthorized network; />Representing a subway station set; />A set of edges representing a subway network;representing a public transport unauthorized network; />Representing a bus stop set; />Representing a collection of edges of a public transportation network.
Step 1.2: method for searching subway station set by adopting space connectionWithin the range of walking>Bus stops of rice, e.g.)>Setting 500 meters to obtain subway station set +.>And bus stop set->The method comprises the steps of (1) establishing a space mapping relation of transfer edges of a subway network and a bus network, wherein the transfer edges are in a set of +.>。
Step 1.3: building subway-bus composite unauthorized networkThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Characterizing all public transit and subway station sets, +.>And (5) representing an edge set and a transfer edge set of the subway and bus network.
As an optimal scheme, the method for calculating the trip amount of the OD pairs in each period in the subway-bus composite unauthorized network in the step 2 specifically comprises the following steps:
step 2.1: and reading historical card swiping data of buses and subways in the same time period, wherein the bus card swiping data comprises card numbers, boarding time, boarding stations, boarding lines and vehicle numbers of passengers. The subway card swiping data comprises the card number, the inbound time, the inbound station, the outbound time and the outbound station of passengers.
Step 2.2: and extracting all subway and bus card swiping records of passengers within a certain time range according to the card numbers.
Step 2.3: sequencing the travel of passengers according to the boarding time of the passengers, and constructing an initial travel chain set，/>Denoted as->。
Wherein,Lindicating the length of the travel chain of the passengers, the total journey number of the passengers isL/2；Indicate the->Number of individual sites, when->When it is odd->For getting on the station, when->If even, the case is->Is a get-off station; for the bus stop at the bus trip, the value is currently unknown, and the initial value is set to be null.
Step 2.4: selecting an initial travel chain setThe%>Travel of [ ]k1) and judging whether the record is the first journey of the current day, if so, recording the upper station number of the journey and jumping to the step 2.10, otherwise jumping to the step 2.5.
Step 2.5: and judging whether the departure station point of the last journey of the current journey is empty, namely whether the departure station point is a bus trip. If yes, go to step 2.6, if not, go to step 2.10.
Step 2.6: the stop in the bus route of the last journey, which is closest to the current journey on-board point, is obtained as the potential off-board point of the last journey, and the arrival time of the potential off-board point is estimated based on AVL (Automatic Vehicle Locator) data of the vehicle.
Step 2.7: calculating the space distance between the current travel boarding point and the potential alighting point of the previous travel according to the potential alighting point of the previous travel and the arrival time of the potential alighting pointdTime differencetd。
Step 2.8: determining spatial distancedTime differencetdWhether or not to simultaneously be smaller than the distance thresholdDIf so, marking the potential get-off point as the get-off point of the previous journey, and assigning the station number to the stationIf no, then->Still empty.
Step 2.9: judging whether the current journey is the last journey of the day, if so, selecting a bus stop closest to the first journey on the day in the current journey bus route as a get-off point, and assigning the stop number to the bus stopThe method comprises the steps of carrying out a first treatment on the surface of the If not, go to step 2.10.
Step 2.10: judging the currentkWhether or not to equal the total stroke numberL/2The method comprises the steps of carrying out a first treatment on the surface of the If not, assignk=k+1, and jumping to the step 2.4; if yes, outputting updatedAnd marked in->Middle->The departure point which is still empty is +.>。
Step 2.11: searching for the currentIn all stops with empty stop numbers +.>Recorded as a collectionEMAggregation ofEMThe number of the elements is recorded as n。
Step 2.12: for collectionsEMAll elements in (1)Searching for a bus stop satisfying the constraint conditions of all the formula (1) as +.>The corresponding set of possible departure stops is marked +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein, set->The number of the elements is->。
The constraints of equation (1) include: get-off point station numberStation number of the station which must be located at the boarding pointAfter that, the time of getting off is>Must get on time->Afterwards, the bus route to which the departure point belongs is +.>Bus route which must be associated with the departure point ∈ ->The same vehicle number to which the departure point belongsBus route which must be associated with the departure point ∈ ->The same applies.
（1）。
Step 2.13: based on all acquiredCorresponding feasible station-down point set +.>By being added from each->Selecting one element to construct a possible permutation and combination of the getting-off station, traversing each +.>Constructing all possible permutation and combination of the lower station points, and putting all possible permutation and combination into a set +.>In (a) and (b); wherein, set->The number of combinations in->，/>Representing the product symbol.
Step 2.14: for collectionsAssigning it to +.>In the corresponding departure station number is empty>And calculate the assigned travel chain set ++using equation (2) and equation (3) >Entropy rate of (2)。
（2）。
（3）。
In the method, in the process of the invention,representation->In positioniIs>；/>Representation->Is from the position of (2)iTo the point ofi+m-1; />Representation->Is from the position of (2)jTo the point ofj+m-1; />Representation->Entropy rate of (2);Lindicating the length of the passenger travel chain.
Step 2.15: finding collectionsEntropy rate in all combinations->Minimum combination and apply minimum combination pair +.>Updating and outputting final passenger travel chain set +.>。
Step 2.16: based on all final passenger travel chain setsAccording to the boarding time and boarding and alighting station point numbers of passengers, counting each periodtThe trip amount of the inner OD pair is used for constructing a trip amount matrix of passengers +.>。
Wherein,is->Matrix (S)>The number of stations in the subway-bus composite unauthorized network is the number of stations; trip amount matrix->The middle element is marked as->，/>Indicating that the boarding time is in a period of timetIn the interior, the boarding station is a stationiThe get-off station is a stationjIs a travel amount of (a).
As a preferred solution, the method for obtaining the passenger flow volume at each side of each period of the subway-bus composite unauthorized network in the step 3 specifically includes:
based on the constructed subway-bus composite unauthorized network, establishing a network edge setPassenger flow volume data set of each time period in one-to-one correspondence>. Passenger flow volume data set- >The basic element of (2) is->Representing a period of timetInner passing edge->Passenger flow volume of->The acquisition steps of (a) are as follows:
step 3.1: for all ofInitialization is performed, and the initial value is set to 0.
Step 3.2: searching for a traffic matrixAll->Is numbered and the total number is recorded as OD pairs of (2)。
Step 3.3: for number ofkOD pair of (d)（kInitial value of 1) to site->As departure site, use site->As a destination station, solving the shortest path of the destination station by using a Dijkstra algorithm based on the constructed subway-bus composite unauthorized network; the shortest path is marked->Wherein->Side comprising all paths of shortest path +.>Is a set of (3).
Step 3.4: for the purpose ofSide of middle way->Update->Corresponding->，/>。
Step 3.5: judging the currentkWhether or not to be equal to. If not, assignk=k+1 and jumps to step 3.3. If yes, outputting the final passenger flow volume data set of each side +.>。
As a preferred scheme, the building of the subway-bus weighted time-varying network in the step 4 specifically includes:
the subway-bus weighted time-varying network is expressed as:wherein:，/>characterizing all public transit and subway station sets, +.>Representing the first in a subway-bus weighted time-varying networkiA site; />，/>Edge set representing subway and public transport network, transfer edge set, and +. >Representing the information by site->To site->The connecting edge is formed; />，Characterization of the period of timetTrip volume of all OD pairs in +.>Is expressed in time periodtInner by site->To site->Is a trip amount of the vehicle; />，/>Characterization of the period of timetPassenger flow through each side in the interior, +.>Is expressed in time periodtInner passing edge->Is a passenger flow volume of the vehicle.
As a preferred solution, in the step 5, the flow access value of each site in each period is calculated according to the following formula:
（4）。
（5）。
wherein,and->Respectively express subway or bus station->In the time periodtThe traffic inflow value and the traffic outflow value in the network reflect the local importance of the station in the network, and the larger the traffic inflow value and the traffic outflow value of the station indicate that the station is more closely connected with the passenger flows of other nodes; />Representing bordering->In the time periodtPassenger flow volume within; />Representing bordering->In the time periodtPassenger flow volume within.
As a preferable solution, in the step 6, the flow medium value of each station in each period is calculated as follows:
（6）。
wherein,representing subway or bus stop->In the time periodtThe traffic medium value in the network reflects the global influence of the station in the network, and the larger the value of the traffic medium value is, the larger the passenger flow of the station as the shortest path is;is expressed in time periodtThe internal starting point is site- >The end point is site->Is a trip amount of the vehicle; />Is expressed in time periodtThe internal starting point is site->The end point is site->Is a passenger flow volume of the vehicle.
As a preferred solution, the method for calculating the comprehensive importance of each site in each period by using the entropy weight method in the step 7 includes the following steps:
step 7.1: based on each time periodtThe flow inlet value, the flow outlet value and the flow medium value of each site in the network are established to establish a comprehensive evaluation matrix of the siteThe calculation formula is as follows:
（7）。
step 7.2: for comprehensive evaluation matrixNormalization processing is carried out, absolute values of all indexes are converted into relative values, and a normalized comprehensive evaluation matrix is obtained>The calculation formula is as follows:
（8）。
step 7.3: calculating information entropy of three indexes of flow input value, flow output value and flow medium value by using (9) - (11)、/>、/>The calculation formula is as follows:
（9）。
（10）。
（11）。
step 7.4: calculating the weights of three indexes of flow rate inflow degree, flow rate outflow degree and flow medium number by using the steps (12) - (14)、/>、/>The calculation formula is as follows: />
（12）。
（13）。
（14）。
Step 7.5: calculating each site by using (15)Is of (2)Importance->The method comprises the steps of carrying out a first treatment on the surface of the The comprehensive importance of all stations is ordered according to the order from high to low, the stations which are more front are key stations in the subway-bus composite network, wherein the comprehensive importance is +. >The calculation formula is as follows:
（15）。
example 2:
the embodiment introduces a process of specifically applying a multi-mode bus travel path planning method to a subway-bus network in a certain city and identifying key stations.
1: and acquiring subway and bus route and station data, searching bus stations within the walking reachable range of the subway station, and establishing a subway-bus composite unauthorized network.
Wherein, bus/subway station and line data include following attribute: the specific data of five items of site number, site name, site longitude, site latitude, line name are shown in table 1 and table 2.
Table 1 bus stop and route data examples
Table 2 subway station and route data examples
Based on the data, a Space L method is adopted to establish a public transport unauthorized directed network and a subway unauthorized directed networkPublic transport unauthorized network->Wherein (1)>Representing a subway unauthorized network; />Representing a subway station set; />A set of edges representing a subway network; />Representing a public transport unauthorized network; />Representing a bus stop set; />Representing an edge set of a public transport network; in the process of establishing the subway and bus unauthorized network, stations with the same station names and different lines are regarded as the same station.
On the basis, as shown in fig. 2, a buffer zone with a radius of 500 meters and a subway station as a center is established, bus stations in the buffer zone range are identified by using a space connection method, a mapping relation between the subway station and the bus stations is established, and a transfer edge set of a subway network and a bus network is obtained。
Based on the peripheral station identification method, a subway-bus composite unauthorized network shown in fig. 3 is established. The network is a directed network and comprises 4437 stations, namely 4278 bus stations and 159 subway stations. The bus network comprises 12233 sides, 10887 sides of the bus network, 328 sides of the subway network and 1018 sides of the transfer between the subway network and the bus network.
2: based on historical card swiping data of subways and buses, calculating the trip amount of the OD pairs in each period in the network by adopting a trip chain OD back-push method based on the minimum entropy rate.
And selecting the bus and subway card swiping data from 2021, 5 months and 1 to 2021, 5 months and 31 months as a study time range. The bus data comprises a card number, a boarding time, a boarding station, a boarding line and a vehicle number of passengers, and 40036065 pieces of data in total. The subway data includes 39314358 pieces of data in total including the card number of the passenger, the arrival time, the arrival station, the arrival time, and the arrival station. Specific examples of the data are shown in tables 3 and 4.
Table 3 bus swiping data example
Table 4 subway card swipe data example
Extracting all subway and bus card swiping records of passengers according to the card numbers of the passengers, sorting the trips according to time, and constructing an initial trip chain set，/>Denoted as->. Wherein,Lindicating the length of the travel chain of the passengers, the total journey number of the passengers isL/2；/>Indicate the->Number of individual sites, when->When it is odd->For getting on the station, when->If even, the case is->Is a get-off station; for a get-off station of a bus trip, the value is currently unknown, and an initial value is set to be empty; a specific example is shown in fig. 4.
In one embodiment, STEP1: and constructing an initial travel chain set. The travel chain length in this example is 20, which contains 10 trips in total. The numbers in the coding representation method represent numbers of subways or bus stops, and initial values of bus departure points are empty.
In an initial travel chain set corresponding to a passenger card number in STEP1And the initial values of the train stations 2, 4, 6, 8, 14, 16, 18 and 20 are the train stations for traveling of the buses, and are unknown.
In one embodiment, STEP2: and updating part of the get-off points by adopting an OD back-pushing method based on the travel chain.
Aiming at an initial travel chain set, an OD back-pushing method based on the travel chain is firstly adopted to infer the bus departure point of passengers and travel chains of the passengers The get-off point is updated, and the distance threshold value in the methodDTime thresholdTDSet at 500 meters and 30 minutes, respectively. In this example, ">The bus departure point number obtained by the OD back-pushing method based on the travel chain comprises the following steps: 2,1,4,6."/>"is a departure point which cannot be estimated.
All bus departure points which are the station points of the empty departure can be deduced by adopting an OD back-pushing method, but due to the irregularity of the travel of passengers, the fact that the OD back-pushing method cannot deduce the bus departure points of partial empty departure stations is possibly brought, and the initial travel chain set in STEP2 is providediThe departure stops for the buses 14, 18 travel are not known.
One embodiment is: STEP3: and constructing a final travel chain set by adopting an OD back-pushing method based on the minimum entropy rate.
Aiming at the departure points which cannot be presumed by the OD back-pushing method based on the travel chains, the departure points which are still blank are complemented by adopting the OD back-pushing method based on the minimum entropy rate, and a final passenger travel chain set is constructed。
STEP3 inThe number of the feasible station is 4, 1,/or->Equal to 2./>The number of the feasible station for getting off is 3, 2 #>Equal to 2.
From the slaveTaking out 4%>And 3, constructing one possible arrangement combination 4,3 of the next station points.
Similarly, traverse、/>Constructing all possible permutation and combination of the lower station points, and putting all possible permutation and combination into a set +.>Set->The following are provided:
。
aggregationThe combined number of (2) is->。
Will be assembled4 combinations assign +.>And calculating entropy rate of all combinations +.>。
Combining: the departure points are station 4 and station 3, and the entropy rate is 1.086.
And (2) combining two: the departure points are station 1 and station 3, and the entropy rate is 1.538.
And (3) combining three: the departure points are station 4 and station 2, and the entropy rate is 0.579.
Combination four: the departure points are station 1 and station 2, and the entropy rate is 1.229.
In this example, the combination two is the lowest entropy rate, and the numbers 1 and 3 of the corresponding departure stations in the combination two are assigned to the departure station points of the bus trip、/>Finish final passenger travel chain set +.>。
The running time of the whole day is divided into four time periods, namely: (1) early peak: 6:00-8:30, denoted ast 1 The method comprises the steps of carrying out a first treatment on the surface of the (2) early flat peak: 8:30-16:30, denoted ast 2 The method comprises the steps of carrying out a first treatment on the surface of the (3) late peak: 16:30-18:30, denoted ast 3 The method comprises the steps of carrying out a first treatment on the surface of the (4) late plain: 18:30-24:00, noted ast 4 . Final passenger travel chain set based on all passengersAnd counting the trip amount of the OD pairs in each period in the subway-bus composite network according to the boarding time of passengers. Based on the actual card swiping data of the south Beijing metro buses, the total OD pair 1089139 pairs are identified. Wherein, the bus passenger flow OD is 1067029 to, and the subway passenger flow OD is 22110 to. Table 5 shows the statistical result of the daily average of the amount of travel for each period OD. Build up of time periods according to Table 5 tThe matrix of the amount of travel of the inner OD pair is marked as +.>。
TABLE 5 daily average trip amount of OD pairs of each period in subway-bus composite network
3: and taking the trip quantity of each time period OD as input, taking the shortest trip path length of the passengers as a target, and acquiring the passenger flow of each side of each time period in the network based on the built subway-bus composite unauthorized network.
Based on the constructed subway-bus composite unauthorized network, establishing a network edge setPassenger flow volume data set of each time period in one-to-one correspondence>. Its basic element->Representing a time periodtInner transit station side->The value is obtained by counting the number of times the OD shortest path of each period passes through the network side. Table 6 is an example of average daily passenger flow through each side at each time period in a south-Beijing subway-bus composite network.
TABLE 6 daily average passenger flow volume at each side of each time period in subway-bus composite network
4: and comprehensively considering the travel quantity of the OD pairs in each period and the passenger flow quantity of each side, and establishing a subway-bus weighted time-varying network.
The subway-bus weighted time-varying network is expressed as:wherein:，/>characterizing all public transit and subway station sets, +.>Representing the first in a subway-bus weighted time-varying networkiA site; />，/>Characterizing subway and public transport network Edge set and transfer edge set, +.>Representing the information by site->To site->The connecting edge is formed; />，Characterization of the period of timetTrip volume of all OD pairs in +.>Is expressed in time periodtInner by site->To site->Is a trip amount of the vehicle; />，/>Characterization of the period of timetPassenger flow through each side in the interior, +.>Is expressed in time periodtInner passing edge->Is a passenger flow volume of the vehicle.
5: and calculating the flow access degree value of each station in each period aiming at the subway-bus weighted time-varying network.
Time-varying network based on constructed subway-bus weightingThe flow rate and the flow rate of each station in different periods are obtained by the formulas (4) and (5), and the obtained example results are shown in table 7.
Table 7 station flow in-out value for each period of time in subway-bus composite network
6: and calculating the flow medium value of each station in each time period aiming at the subway-bus weighted time-varying network.
Time-varying network based on constructed subway-bus weightingThe flow medium values of the stations in different time periods are obtained by the formula (6), and the obtained example results are shown in table 8.
Table 8 station flow medium value of each period in subway-bus composite network
7: based on the flow access value and the flow medium value of each station in each period, calculating the comprehensive importance of each station in each period by using an entropy weight method, and identifying key stations in the subway-bus composite network based on the comprehensive importance.
Based on the traffic ingress value, traffic egress value and traffic medium value of the stations in each period. First, a comprehensive evaluation matrix is established as shown in the formula (7). And then carrying out normalization processing on the matrix to obtain a normalized comprehensive evaluation matrix. Next, the information entropy of the flow rate inflow, the flow rate outflow, and the flow medium for each period is calculated using equations (9) to (11), and the calculation results are shown in table 9. Based on this, the weights of the flow rate inflow, the flow rate outflow, and the flow medium number in each period were calculated by the formulas (12) to (14), and the calculation results are shown in table 10. Finally, the overall importance of each site for each period is calculated based on equation (15), and example results are shown in table 11. The comprehensive importance of all stations is ordered according to the order from high to low, and the effect of the stations in the subway-bus composite network becomes more critical as the ranking is closer. Fig. 5 to 8 are graphs of the top 100 site distribution map for each period of comprehensive importance plotted based on the calculation results of table 11.
TABLE 9 information entropy of flow into, out of and between each period
Table 10 weights of flow ingress, flow egress, and flow medians for each period
Table 11 comprehensive importance of stations at each time period in subway-bus composite network
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.

Claims (9)

1. A subway-bus composite network key station identification method is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring subway and bus route and station data, searching bus stations within the walking reachable range of the subway station, and establishing a subway-bus composite unauthorized network;
step 2: based on historical card swiping data of subways and buses, calculating the travel quantity of OD pairs in each period in the subway-bus composite unauthorized network by adopting a travel chain OD reverse-push method based on the minimum entropy rate;
step 3: taking the travel quantity of the OD pairs in each period as input, taking the shortest travel path length of passengers as a target, and acquiring the passenger flow quantity of each side of each period in the network based on the built subway-bus composite unauthorized network;
step 4: comprehensively considering the travel quantity of OD pairs in each period and the passenger flow quantity of each side, and establishing a subway-bus weighted time-varying network;
step 5: aiming at a subway-bus weighted time-varying network, calculating the flow access degree value of each station in each period;
Step 6: aiming at a subway-bus weighted time-varying network, calculating the flow medium value of each station in each period;
the flow medium value is calculated according to the following formula:
wherein,representing subway or bus station v i A flow medium value during period t, +.>Indicating that the start point is station v in period t s The end point is site v t Is a trip amount of the vehicle; />Indicating that the start point is station v in period t s The end point is site v t And the shortest path passes v i Is a passenger flow volume of (1);
step 7: based on the flow access value and the flow medium value of each station in each period, the comprehensive importance of each station in each period is calculated by using an entropy weight method, and key subways and bus stations in each period are identified based on the comprehensive importance.
2. The subway-bus composite network key station identification method according to claim 1, wherein the method comprises the following steps: the step 1 specifically includes:
step 1.1: based onSubway, bus route and station data, and establishing a subway unauthorized network G by adopting Space L method m ＝(V m ，E m ) Public transport unauthorized network G b ＝(V b ，E b )；
Wherein G is m Representing a subway unauthorized network; v (V) m Representing a subway station set; e (E) m A set of edges representing a subway network; g b Representing a public transport unauthorized network; v (V) b Representing a bus stop set; e (E) b Representing an edge set of a public transport network;
Step 1.2: method for searching subway station set V by adopting space connection m Zeta meter bus station within walking reach to obtain subway station set V m With public transport station set V b The space mapping relation of transfer edges of the subway network and the bus network is established, and a transfer edge set E of the subway network and the bus network is established mb ；
Step 1.3: building subway-bus composite unauthorized network
The method comprises the steps of carrying out a first treatment on the surface of the Wherein,
V m-b ＝V m ∪V b ，V m-b characterizing all the collection of bus and subway stations,
E m-b ＝E m ∪E b ∪E mb ，E m-b and (5) representing an edge set and a transfer edge set of the subway and bus network.
3. The subway-bus composite network key station identification method according to claim 2, wherein the method comprises the following steps: the step 2 specifically includes:
step 2.1: reading historical card swiping data of buses and subways in the same time period, wherein the bus card swiping data comprises card numbers, boarding time, boarding stations, boarding lines and vehicle numbers of passengers; the subway card swiping data comprises a card number of a passenger, an inbound time, an inbound station, an outbound time and an outbound station;
step 2.2: extracting historical card swiping data of all subways and buses of passengers within a certain time range according to the card numbers;
step 2.3: sequencing the travel of passengers according to the boarding time of the passengers, and constructing an initial travel chain set Represented as { x } 1 ，x 2 ，...，x i ，...，x L-1 ，x L }；
Wherein L represents the length of a passenger travel chain, and the total travel number of passengers is L/2; x is x i The number of the ith station of the travel chain, and when i is odd, x i For boarding the station, when i is even, x i Is a get-off station; for a departure station point of a bus trip, the value of the departure station point of the bus trip is currently unknown, and an initial value is set to be empty;
step 2.4: selecting an initial travel chain setJudging whether the kth travel is the first travel of the same day, if so, recording the last station number of the travel and jumping to the step 2.10, otherwise jumping to the step 2.5;
step 2.5: judging whether the next station point of the last travel of the current travel is empty or not, if so, jumping to the step 2.6, and if not, jumping to the step 2.10;
step 2.6: acquiring a stop in a bus route of the last journey, which is closest to the current journey on-coming point, as a potential off-coming point of the last journey, and estimating the arrival time of the potential off-coming point based on AVL data of the vehicle;
step 2.7: calculating the space distance d and the time difference td between the current travel boarding point and the potential alighting point of the previous travel according to the potential alighting point of the previous travel and the arrival time of the potential alighting point;
Step 2.8: judging whether the spatial distance D and the time difference TD are simultaneously smaller than the distance threshold D and the time threshold TD, if so, marking the potential get-off point as the get-off point of the previous journey, and assigning the station number to x 2(k-1) If not, x 2(k-1) Is still empty;
step 2.9: judging whether the current journey is the last journey of the day, if so, selecting a bus stop closest to the first journey on the day in the current journey bus route as a get-off point, and assigning the stop number to x 2k The method comprises the steps of carrying out a first treatment on the surface of the If not, jumping to the step 2.10;
step 2.10: judging whether the current k is equal to the total stroke number L/2; if not, assigning k=k+1, and jumping to the step 2.4; if yes, outputting updatedAnd marked in->X in the middle i The departure point which is still empty is +.>
Step 2.11: searching for the currentIn all stops with empty stop numbers +.>The method comprises the steps of recording a set EM, wherein the number of elements in the set EM is recorded as n;
step 2.12: for all elements in the set EMSearching for bus stops meeting all constraints as +.>Corresponding feasible station-down point sets are marked as A i The method comprises the steps of carrying out a first treatment on the surface of the Wherein set A i The number of the elements is k i ；
Step 2.13: based on the acquiredAll ofCorresponding feasible station-down point set A i By from each A i Selecting one element to construct one possible permutation and combination of the get-off station, traversing each A i Constructing all possible permutation and combination of the next station point, and putting all possible permutation and combination into a set { EM }; wherein the number of combinations in the set { EM } isPi represents a product symbol;
step 2.14: for any combination in the set { EM }, assign it toIn the corresponding departure station number is empty>And calculate the travel chain set after assignment +.>Entropy rate of->
Step 2.15: finding entropy rate in all combinations of set { EM }Minimum combination and apply minimum combination pair +.>Updating and outputting final passenger travel chain set +.>
Step 2.16: based on all final passenger travel chain setsAccording to the boarding time and boarding and alighting station point numbers of passengers, counting the trip amount of OD pairs in each period t, and constructing a trip amount matrix of the passengers>
Wherein,n is N multiplied by N matrix, N is the number of stations in subway-bus composite unauthorized network; travel volume matrixThe middle element is marked as-> And the boarding time is represented in a period t, the boarding station is station i, and the alighting station is the trip amount of station j.
4. A method for identifying a key station of a subway-bus composite network according to claim 3, wherein: all constraints include:
s.t.
wherein, the station number of the get-off pointStop number stopID (x) i-1 ) After that, the time of getting off is>Must be timed up (x) i-1 ) Afterwards, the bus route to which the departure point belongs is +.>Must be associated with a bus Route (x) i-1 ) Similarly, the vehicle number to which the departure point belongs +.>Must be associated with the bus route Vehicle (x i-1 ) The same applies.
5. A method for identifying a key station of a subway-bus composite network according to claim 3, wherein: the entropy rateThe calculation formula of (2) is as follows:
in the method, in the process of the invention,representation->The longest matching length at position i, i=1, 2, … L; />Representation->From position i to i+m-1; />Representation->From position j to j+m-1; />Representation ofEntropy rate of (2); l represents the length of the passenger travel chain.
6. A method for identifying a key station of a subway-bus composite network according to claim 3, wherein: the step 3 specifically includes:
step 3.1: for all ofInitializing, and setting an initial value to 0; wherein (1) >For passenger flow data set->Element(s) of->Indicating the passing of edge e within period t ij Is a passenger flow volume of (1);
step 3.2: searching for a traffic matrixAll->Numbering it, the total number being noted as phi;
step 3.3: for OD pair numbered kTaking a station p as a departure station, taking a station q as a destination station, and solving the shortest path of the station p by using a Dijkstra algorithm based on the constructed subway-bus composite unauthorized network; the shortest Path is denoted as Path pq Wherein Path is pq Edge e comprising all paths of shortest path ij Is a collection of (3);
step 3.4: for Path pq Edge e of middle path ij Update e ij Corresponding to
Step 3.5: judging whether the current k is equal to phi; if not, assigning k=k+1, and jumping to the step 3.3; if yes, outputting the final passenger flow volume data set of each side
7. The subway-bus composite network key station identification method according to claim 6, wherein the method comprises the following steps: the subway-bus weighted time-varying network comprises:
the subway-bus weighted time-varying network is expressed as:wherein: v (V) m-b ＝V m ∪V b ＝{v i }，i＝1，2，...N，V m-b Characterizing all public transit and subway station sets, v i Representing an ith station in the subway-bus weighted time-varying network;
E m-b ＝E m ∪E b ∪E mb ＝{e ij }，i,j＝1,2,...N，E m-b edge set and transfer edge set for representing subway and public transport network, e ij ＝<v i ，v j >Representing the data represented by site v i To site v j The connecting edge is formed; characterizing the amount of travel of all OD pairs in period t, +.>Representing the time period t during which the station v is engaged i To site v j Is a trip amount of the vehicle; /> Characterizing the traffic flow per side during period t,/for each side>Indicating the passing of edge e within period t ij Is a passenger flow volume of the vehicle.
8. The subway-bus composite network key station identification method according to claim 7, wherein the method comprises the following steps: the flow access value includes: the flow inlet value and the flow outlet value are calculated according to the following formulas:
wherein,and->Representing subway or bus station v, respectively i Flow in and flow out values in period t, < >>Representing the connecting edge e ij Passenger flow volume during time period t; />Representing the connecting edge e ji Passenger flow volume during period t.
9. The subway-bus composite network key station identification method according to claim 8, wherein the method comprises the following steps: the step 7 specifically includes:
step 7.1: based on the flow inlet value, the flow outlet value and the flow medium value of each site in each period t, a comprehensive evaluation matrix X of the site is established t The calculation formula is as follows:
step 7.2: for comprehensive evaluation matrix X t Normalization processing is carried out to obtain a normalized comprehensive evaluation matrix The calculation formula is as follows:
step 7.3: calculating information entropy of three indexes of flow input value, flow output value and flow medium valueThe calculation formula is as follows:
step 7.4: calculating the weights of three indexes of a flow input value, a flow output value and a flow medium valueThe calculation formula is as follows:
step 7.5: calculating each site v i Is of integrated importance of (2)The comprehensive importance of all stations is ordered according to the order from high to low, the stations which are more front are key stations in the subway-bus composite network, wherein the comprehensive importance is +.>The calculation formula is as follows:
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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2950265A1 (en) * 2014-05-30 2015-12-02 Google, Inc. Detecting important transit stops for transit trip grouping
CN107563028A (en) * 2017-08-22 2018-01-09 南京邮电大学 A kind of public transport subway coupling network construction method based on spatial network
CN109446628A (en) * 2018-10-22 2019-03-08 太原科技大学 The building of multilayer city traffic network and key node recognition methods based on complex network
CN109472219A (en) * 2018-10-22 2019-03-15 中南大学 A kind of statistical method, device and the computer storage medium of the website volume of the flow of passengers
CN111598427A (en) * 2020-05-12 2020-08-28 东南大学 Urban rail transit station importance evaluation method
CN112002127A (en) * 2020-09-02 2020-11-27 西北工业大学 Method and device for evaluating importance of station in rail transit
CN113742870A (en) * 2021-07-29 2021-12-03 北京工业大学 Multi-mode public transport network key station identification method based on local heterogeneous influence
CN115169795A (en) * 2022-06-01 2022-10-11 西北工业大学 Method and device for evaluating importance of site
CN115759362A (en) * 2022-10-31 2023-03-07 北京航空航天大学 Subway-bus dependent network functional domain classification method based on elasticity evaluation
CN116504050A (en) * 2022-09-27 2023-07-28 东南大学 Special event city partitioning method based on multi-mode public transport trip data

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2950265A1 (en) * 2014-05-30 2015-12-02 Google, Inc. Detecting important transit stops for transit trip grouping
CN107563028A (en) * 2017-08-22 2018-01-09 南京邮电大学 A kind of public transport subway coupling network construction method based on spatial network
CN109446628A (en) * 2018-10-22 2019-03-08 太原科技大学 The building of multilayer city traffic network and key node recognition methods based on complex network
CN109472219A (en) * 2018-10-22 2019-03-15 中南大学 A kind of statistical method, device and the computer storage medium of the website volume of the flow of passengers
CN111598427A (en) * 2020-05-12 2020-08-28 东南大学 Urban rail transit station importance evaluation method
CN112002127A (en) * 2020-09-02 2020-11-27 西北工业大学 Method and device for evaluating importance of station in rail transit
CN113742870A (en) * 2021-07-29 2021-12-03 北京工业大学 Multi-mode public transport network key station identification method based on local heterogeneous influence
CN115169795A (en) * 2022-06-01 2022-10-11 西北工业大学 Method and device for evaluating importance of site
CN116504050A (en) * 2022-09-27 2023-07-28 东南大学 Special event city partitioning method based on multi-mode public transport trip data
CN115759362A (en) * 2022-10-31 2023-03-07 北京航空航天大学 Subway-bus dependent network functional domain classification method based on elasticity evaluation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China;Jianlin Jia 等;Physica A;第565卷;1-13 *
公交―地铁复合网络构建及网络特性分析;罗艺 等;交通运输系统工程与信息;第15卷(第05期);39-44 *

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