CN113177681A - Intercity travel passenger vehicle line correlation identification method - Google Patents

Intercity travel passenger vehicle line correlation identification method Download PDF

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CN113177681A
CN113177681A CN202110276738.0A CN202110276738A CN113177681A CN 113177681 A CN113177681 A CN 113177681A CN 202110276738 A CN202110276738 A CN 202110276738A CN 113177681 A CN113177681 A CN 113177681A
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傅顺开
李海波
曾省明
陈悦
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Abstract

The invention discloses a method for identifying correlation of lines of intercity travel passenger vehicles, which comprises the following steps: acquiring passenger flow time sequences of all operation lines; extracting passenger flow volume sequences of any pair of operation lines according to the time sequence to form a new time sequence; accumulating and combining passenger flow values belonging to different lines respectively, and forming a new passenger flow time sequence according to the original time sequence; until all the line pairs form a new passenger flow time sequence; splitting to obtain two different passenger flow time sequences, and calculating all correlation degree sequences in an observation period; respectively clustering positive and negative correlation degree sets to obtain positive correlation operation lines and negative correlation operation lines; and carrying out dispatching management on the passenger vehicles according to the positive correlation operation lines and the negative correlation operation lines. The method provided by the invention converts the passenger flow into the correlation sequence, obtains the positively correlated and negatively correlated operation lines by the clustering method, provides quantifiable competitive-combination relation, and further realizes accurate and reasonable dispatching and management of the passenger vehicles.

Description

Intercity travel passenger vehicle line correlation identification method
Technical Field
The invention relates to the field of vehicle scheduling, in particular to a method for identifying correlation of lines of intercity trip passenger vehicles.
Background
With the deep combination of customized passenger transport service and the internet +, the customized passenger transport between cities develops rapidly. At present, the management of an operation line of a passenger transport enterprise is single, passengers can directly arrive at a destination or get off in a city in the way to arrive at the destination, and the vehicle idle rate is high. In order to improve the overall full load rate of each operation enterprise, according to an originating city and a destination city of passenger travel, a passenger transfer link needs to be designed in an approach city, and operation vehicles are dispatched in real time by means of an efficient operation platform, so that the operation cost is reduced, and the purpose of fine management is achieved.
However, the transfer and multiplication between lines may cause a competitive relationship or a cooperative relationship between lines. The former is reflected in the opposite trend of the change of the passenger flow between the two lines, and the latter is reflected in the synchronous increase of the passenger flow between the two lines. This is related to the popularity of the city, the acceptance of passengers for customized passenger trips, and the like. Therefore, in order to achieve the purpose of fine management, the degree of the competitive relationship needs to be mined and quantified according to the original order data and the conversion data of the passengers, and the positive correlation between the lines, namely the cooperative relationship, is improved as much as possible through real-time scheduling, so that the passenger flow throughput of the customized passenger transport network can be integrally improved, and a basis is provided for fine management.
At present, in the existing method, the competitive relationship of the urban public transport lines is mainly mined aiming at the operation of the urban public transport lines, and the used method generally artificially designates overlapped stations as the competitive relationship. These methods make it difficult to schedule and manage passenger vehicles according to the trend of passenger flow.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art and provides a method for identifying the correlation of lines of intercity travel passenger vehicles, which converts a passenger flow into a correlation sequence and obtains positively correlated and negatively correlated operation lines by a clustering method so as to provide a basis for fine scheduling management. The invention can provide quantifiable competitive-combination relationship for the customized passenger transport enterprises, thereby realizing accurate and reasonable dispatching and management of the passenger transport vehicles.
The invention adopts the following technical scheme:
an intercity travel passenger vehicle line correlation identification method comprises the following steps:
s1: acquiring passenger flow time sequences of all operation lines;
s2: extracting the passenger flow volume sequences of any pair of operation lines according to the time sequence from the obtained passenger flow volume time sequences of all the operation lines to form a new time sequence;
s3: accumulating and combining passenger flow values belonging to different lines respectively, and forming a new passenger flow time sequence according to the original time sequence;
s4: repeating steps S2-S3 until all route pairs form a new passenger flow time series;
s5: splitting the new passenger flow time sequence obtained in the step S4 to obtain two different passenger flow time sequences, and calculating all correlation degree sequences in the observation period;
s6: clustering positive and negative correlation sets to obtain positive correlation operation lines and negative correlation operation lines;
s7: and carrying out dispatching management on the passenger vehicles according to the positive correlation operation lines and the negative correlation operation lines.
Specifically, the step S1 obtains the passenger flow time series of all the service lines, specifically:
let n different operation lines be denoted as L ═ LiI is more than or equal to |0 and less than or equal to n, n is an integer, a transfer order is screened out according to original order data of any day, and a basic passenger flow time sequence F is formed according to time sequence (F is equal to or less than F)1,f2,…,fn)。
Specifically, in step S2, the passenger flow volume sequence of any pair of service lines is extracted in time sequence from the passenger flow volume time sequences of all the obtained service lines, so as to form a new time sequence, specifically:
selecting any pair of operation lines L from LiAnd ljExtracting l from FiAnd ljJ is more than or equal to 0 and less than or equal to n, and the new time sequence LF (l) is formed by combiningi,lj)＝(f1′,f2′,…,fq') satisfies: for LF (l)i,lj) Of (1) to (f)k'and f'k+1In F are all presenttAnd ft+pCorrespondingly, k is less than or equal to t, and k +1 is less than or equal to t + p is less than or equal to n.
Specifically, the method accumulates and combines the passenger flow volume values belonging to different lines, and forms a new passenger flow time sequence according to the original time sequence, specifically comprising the following steps:
LF(li,lj) If any is adjacent to fs' and fs+1Are of the same line liThen add up fs' and fs+1', i.e. fs″＝fs′+fs+1', after which f is deleteds' and fs+1'; repeatedly executing until fs' and fs+1' not belonging to the same line li；
LF(li,lj) If any is adjacent to fs' and fs+1Are of the same line ljThen add up fs' and fs+1', i.e. fs″＝fs′+fs+1', after which f is deleteds' and fs+1', repeatedly executing until fs' and fs+1' not belonging to the same line lj；
Until LF (l)i,lj) All of fs'all merge to form a new time series LF' (l)i,lj)。
Specifically, in step S5, the new passenger flow time sequence obtained in step S4 is split to obtain two different passenger flow time sequences, and all correlation degree sequences in the observation period are calculated, specifically:
splitting a new passenger flow time sequence LF' (l)i,lj) Obtaining two sequences FiAnd FjIs divided into operation lines liAnd ljAnd F isiAnd FjThe traffic sequence remains LF' (l)i,lj) The relative order of (a);
if FiAnd FjIf the lengths are different, the last passenger flow value of the longer sequence is abandoned to obtain a sequence F with the same lengthiAnd Fj。
Specifically, the step S5 is to split the new passenger flow time sequence obtained in the step S4 to obtain two different passenger flow time sequences, and calculate all correlation degree sequences in the observation period, and specifically includes:
calculating any two lines F in the observation periodiAnd FjForm a correlation sequence (rho) in time sequence according to the correlation degree between the two1,ρ2,…,ρN) N is an observation period; and form a relevance sequence set V, V ← (rho)1,ρ2,…,ρN)；
According to the condition
Figure BDA0002976952170000031
And
Figure BDA0002976952170000032
dividing V into two sets V+And V-，
Figure BDA0002976952170000033
Specifically, the correlation formula is:
Figure BDA0002976952170000034
wherein, Cov (F)j，Fj) Is FiAnd the covariance of F; d (F)j),D(Fj) Are respectively FiAnd FjThe variance of (c).
Specifically, the step S6 clusters the positive and negative correlation sets to obtain a positive correlation operation line and a negative correlation operation line, specifically:
are respectively paired with V+And V-Clustering by adopting a hierarchical clustering method to obtain a cluster C+And C-Respectively representing positively correlated and negatively correlated service lines.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
(1) the invention provides a method for identifying the line correlation of intercity trip passenger vehicles, which converts a converted passenger flow into a correlation sequence, and obtains positively correlated and negatively correlated operation lines by a clustering method, thereby providing a basis for fine scheduling management.
(2) The passenger transport line competition and combination relationship quantification method provided by the invention can provide quantifiable competition and combination relationships for customized passenger transport enterprises, thereby realizing accurate and reasonable dispatching and management of passenger transport vehicles.
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FIG. 1 is a flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of the formation of a passenger flow sequence provided by an embodiment of the present invention;
fig. 3 is a schematic split view of a transition passenger flow sequence according to an embodiment of the present invention.
The invention is described in further detail below with reference to the figures and specific examples.
Detailed Description
The invention provides a method for identifying the line correlation of intercity trip passenger vehicles, which converts a converted passenger flow into a correlation sequence, and obtains positively correlated and negatively correlated operation lines by a clustering method, thereby providing a basis for fine scheduling management. The invention can provide quantifiable competitive-combination relationship for the customized passenger transport enterprises, thereby realizing accurate and reasonable dispatching and management of the passenger transport vehicles.
As shown in fig. 1, a flowchart of a method for identifying inter-city travel passenger vehicle route correlation according to an embodiment of the present invention includes the following specific steps:
s1: acquiring passenger flow time sequences of all operation lines;
the step S1 is to obtain the passenger flow time series of all the service lines, specifically:
let n different operation lines be denoted as L ═ LiI is more than or equal to |0 and less than or equal to n, n is an integer, a transfer order is screened out according to original order data of any day, and a basic passenger flow time sequence F is formed according to time sequence (F is equal to or less than F)1,f2,…,fn)。
As shown in fig. 2, which is a schematic diagram of the formation of a passenger flow sequence according to an embodiment of the present invention, a line 1: city 5-city C-city 1, line 2: city 4-city C-city 2, line 3: city 5-city C-city 2; and transferring in the city C to form a transfer passenger flow, and forming a basic passenger flow time sequence by transferring orders of all the roads in the city C according to the time sequence.
S2: extracting the passenger flow volume sequences of any pair of operation lines according to the time sequence from the obtained passenger flow volume time sequences of all the operation lines to form a new time sequence;
specifically, in step S2, the passenger flow volume sequence of any pair of service lines is extracted in time sequence from the passenger flow volume time sequences of all the obtained service lines, so as to form a new time sequence, specifically:
selecting any pair of operation lines L from LiAnd ljExtracting l from FiAnd ljJ is more than or equal to 0 and less than or equal to n, and the new time sequence LF (l) is formed by combiningi,lj)＝(f1′,f2′,…,fq') satisfies: for LF (l)i,lj) Of (1) to (f)k'and f'k+1In F are all presenttAnd ft+pCorrespondingly, k is less than or equal to t, and k +1 is less than or equal to t + p is less than or equal to n.
S3: accumulating and combining passenger flow values belonging to different lines respectively, and forming a new passenger flow time sequence according to the original time sequence;
specifically, the method accumulates and combines the passenger flow volume values belonging to different lines, and forms a new passenger flow time sequence according to the original time sequence, specifically comprising the following steps:
LF(li,lj) If any is adjacent to fs' and fs+1Are of the same line liThen add up fs' and fs+1', i.e. fs″＝fs′+fs+1', after which f is deleteds' and fs+1'; repeatedly executing until fs' and fs+1' not belonging to the same line li；
LF(li,lj) If any is adjacent to fs' and fs+1Are of the same line ljThen add up fs' and fs+1', i.e. fs″＝fs′+fs+1', after which f is deleteds' and fs+1', repeatedly executing until fs' and fs+1' not belonging to the same line lj；
Until LF (l)i,lj) All of fs'all merge to form a new time series LF' (l)i,lj)。
S4: repeating steps S2-S3 until all route pairs form a new passenger flow time series;
s5: splitting the new passenger flow time sequence obtained in the step S4 to obtain two different passenger flow time sequences, and calculating all correlation degree sequences in the observation period;
specifically, in step S5, the new passenger flow time sequence obtained in step S4 is split to obtain two different passenger flow time sequences, and all correlation degree sequences in the observation period are calculated, specifically:
splitting a new passenger flow time sequence LF' (l)i,lj) Obtaining two sequences FiAnd FjIs divided into operation lines liAnd ljAnd F isiAnd FjThe traffic sequence remains LF' (l)i,lj) The relative order of (a);
if FiAnd FjIf the lengths are different, the last passenger flow value of the longer sequence is abandoned to obtain a sequence F with the same lengthiAnd Fj。
Fig. 3 is a schematic split view of a sequence of converted passenger flows according to an embodiment of the present invention.
Specifically, the step S5 is to split the new passenger flow time sequence obtained in the step S4 to obtain two different passenger flow time sequences, and calculate all correlation degree sequences in the observation period, and specifically includes:
calculating any two lines F in the observation periodiAnd FjForm a correlation sequence (rho) in time sequence according to the correlation degree between the two1,ρ2,…,ρN) N is an observation period; and form a relevance sequence set V, V ← (rho)1,ρ2,…,ρN)；
According to the condition
Figure BDA0002976952170000061
And
Figure BDA0002976952170000062
dividing V into two sets V+And V-,
Figure BDA0002976952170000063
specifically, the correlation formula is:
Figure BDA0002976952170000064
wherein, Cov (F)j，Fj) Is FiAnd the covariance of F; d (F)j),D(Fj) Are respectively FiAnd FjThe variance of (c).
S6: clustering positive and negative correlation sets to obtain positive correlation operation lines and negative correlation operation lines;
specifically, the step S6 clusters the positive and negative correlation sets to obtain a positive correlation operation line and a negative correlation operation line, specifically:
are respectively paired with V+And V-Clustering by adopting a hierarchical clustering method to obtain a cluster C+And C-Respectively representing positively correlated and negatively correlated service lines.
S7: and carrying out dispatching management on the passenger vehicles according to the positive correlation operation lines and the negative correlation operation lines.
The following description will be made by specific examples.
(1) Taking fuzhou as an example, there are 10 routes to fuzhou, which are: the number of the line from Yongtai to Lianjiang, the line from Minhou to Changle, the line from Yongtai to Changle, the line from Minhou to Fuqing and other 6 lines are respectively L1, L2, … and L10. The initial order passenger flow to transfer to L1-L10 is shown in Table 1.
TABLE 1 time series of passenger flows on a certain day
Figure BDA0002976952170000065
(2) Taking lines L1 and L2 as examples, LF (L1, L2) is obtained as (1,1,1,2,1,1,1, 1) and the results are shown in table 2.
TABLE 2 merged passenger flow time series
Line L1 L2 L1 L1 L1 L2 L2 L2 L1 L1 L2
Passenger flow volume 1 1 1 2 1 1 1 1 1 1 1
(3) Merging and splitting the traffic, taking lines L1 and L2 as examples, obtaining F1 ═ 1,4,2 and F2 ═ 1,3, 1.
(4) And taking an observation period for 60 days, calculating to obtain a correlation sequence with the length of 60, and respectively obtaining a positive correlation sequence set and a negative correlation sequence set according to the sequence sum, wherein the positive correlation sequence set and the negative correlation sequence set are shown in a table 3.
TABLE 3 set of positively correlated line pairs
Line pair Sum of correlation Line pair Sum of correlation Line pair Sum of correlation
L1,L2 5.045 L2,L3 1.913 L3,L4 2.166
L1,L3 1.295 L2,L4 3.380 L3,L5 2.565
L1,L4 9.789 L2,L5 4.673 L3,L6 4.523
L1,L5 5.088 L2,L6 5.248
TABLE 4 set of inversely correlated line pairs
Line pair Sum of correlation Line pair Sum of correlation Line pair Sum of correlation
L6,L7 -0.352 L7,L8 -2.028 L8,L10 -6.646
L6,L8 -1.477 L7,L9 -5.676 L8,L9 -3.365
L6,L9 -2.081 L7,L10 -5.109 L6,L10 -2.624
(5) And (3) clustering the correlation sequence corresponding to the table 3 and the table 4 by adopting a hierarchical clustering method to obtain a positive correlation cluster set and a negative correlation cluster set, and respectively representing the operation line pairs with the cooperation and competition relations, as shown in the table 5 and the table 6.
TABLE 5 cooperative line pair correlation set
Figure BDA0002976952170000071
TABLE 6 Competition line pair correlation set
Figure BDA0002976952170000072
(6) According to the obtained operation line pair with cooperation and competition relationship, the passenger vehicles are dispatched and managed
The invention provides a method for identifying the correlation of lines of intercity travel passenger vehicles, which converts passenger flow into a correlation sequence, and obtains positively correlated and negatively correlated operation lines by a clustering method, thereby providing a basis for fine scheduling management; the passenger transport line competition and combination relationship quantification method provided by the invention can provide quantifiable competition and combination relationships for customized passenger transport enterprises, thereby realizing accurate and reasonable dispatching and management of passenger transport vehicles.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.

Claims (8)

1. An intercity travel passenger vehicle line correlation identification method is characterized by comprising the following steps:
s1: acquiring passenger flow time sequences of all operation lines;
s2: extracting the passenger flow volume sequences of any pair of operation lines according to the time sequence from the obtained passenger flow volume time sequences of all the operation lines to form a new time sequence;
s3: accumulating and combining passenger flow values belonging to different lines respectively, and forming a new passenger flow time sequence according to the original time sequence;
s4: repeating steps S2-S3 until all route pairs form a new passenger flow time series;
s5: splitting the new passenger flow time sequence obtained in the step S4 to obtain two different passenger flow time sequences, and calculating all correlation degree sequences in the observation period;
s6: respectively clustering positive and negative correlation degree sets to obtain positive correlation operation lines and negative correlation operation lines;
s7: and carrying out dispatching management on the passenger vehicles according to the positive correlation operation lines and the negative correlation operation lines.
2. The method for identifying inter-city travel passenger vehicle route correlation according to claim 1, wherein the step S1 is implemented by obtaining passenger flow time series of all operation routes, specifically:
let n different operation lines be denoted as L ═ LiI is more than or equal to |0 and less than or equal to n, n is an integer, a transfer order is screened out according to original order data of any day, and a basic passenger flow time sequence F is formed according to time sequence (F is equal to or less than F)1，f2，...，fn)。
3. The method for identifying inter-city travel passenger vehicle route correlation according to claim 2, wherein the step S2 is to extract the passenger flow volume sequence of any pair of operation routes in time sequence from the obtained passenger flow volume time sequences of all operation routes to form a new time sequence, specifically:
selecting any pair of operation lines L from LiAnd ljExtracting l from FiAnd ljJ is more than or equal to 0 and less than or equal to n, and the new time sequence LF (l) is formed by combiningi，lj)＝(f1′，f2′，...，fq') full ofFoot: for LF (l)i，lj) Of (1) to (f)k'and f'k+1In F are all presenttAnd ft+pCorrespondingly, k is less than or equal to t, and k +1 is less than or equal to t + p is less than or equal to n.
4. The method for line correlation identification of intercity travel passenger vehicles according to claim 3, characterized in that the passenger flow values belonging to different lines are accumulated and merged, and a new passenger flow time sequence is formed according to the original time sequence, specifically:
LF(li，lj) If any is adjacent to fs' and fs+1Are of the same line liThen add up fs' and fs+1', i.e. fs″＝fs′+fs+1', after which f is deleteds' and fs+1'; repeatedly executing until fs' and fs+1' not belonging to the same line li；
LF(li，lj) If any is adjacent to fs' and fs+1Are of the same line ljThen add up fs' and fs+1', i.e. fs″＝fs′+fs+1', after which f is deleteds' and fs+1', repeatedly executing until fs' and fs+1' not belonging to the same line lj；
Until LF (l)i，lj) All of fs'all merge to form a new time series LF' (l)i，lj)。
5. The method for identifying inter-city travel passenger vehicle line correlation according to claim 4, wherein the step S5 is to split the new passenger flow time series obtained in the step S4 to obtain two different passenger flow time series, and calculate all correlation degree series in an observation period, specifically:
splitting a new passenger flow time sequence LF' (l)i，lj) Obtaining two sequences FiAnd FjIs divided into operation lines liAnd ljAnd F isiAnd FjThe traffic sequence remains LF' (l)i，lj) The relative order of (a);
if FiAnd FjIf the lengths are different, the last passenger flow value of the longer sequence is abandoned to obtain a sequence F with the same lengthiAnd Fj。
6. The method for identifying inter-city travel passenger vehicle line correlation according to claim 4, wherein the step S5 is to split the new passenger flow time series obtained in the step S4 to obtain two different passenger flow time series, and calculate all correlation degree series in an observation period, and specifically comprises:
calculating any two lines F in the observation periodiAnd FjForm a correlation sequence (rho) in time sequence according to the correlation degree between the two1，ρ2，...，ρN) N is an observation period; and form a relevance sequence set V, V ← (rho)1，ρ2，...，ρN)；
According to the condition
Figure FDA0002976952160000021
And
Figure FDA0002976952160000022
dividing V into two sets V+And V-，
Figure FDA0002976952160000023
7. The method for identifying inter-city travel passenger vehicle route correlation according to claim 6, wherein the correlation formula is as follows:
Figure FDA0002976952160000024
wherein, Cov (F)j，Fj) Is FiAnd the covariance of F; d (F)j)，D(Fj) Are respectively FiAnd FjThe variance of (c).
8. The method for identifying inter-city travel passenger vehicle route correlation according to claim 6, wherein the step S6 clusters the positive and negative correlation sets to obtain positive correlation operation routes and negative correlation operation routes, and specifically comprises:
are respectively paired with V+And V-Clustering by adopting a hierarchical clustering method to obtain a cluster C+And C-Respectively representing positively correlated and negatively correlated service lines.
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CN111160722A (en) * 2019-12-12 2020-05-15 华侨大学 Bus route adjusting method based on passenger flow competition relationship

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* Cited by examiner, † Cited by third party
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CN118551900A (en) * 2024-07-24 2024-08-27 华侨大学 Inter-city dynamic carpooling scheduling optimization method and device based on two-stage scheduling
CN118551900B (en) * 2024-07-24 2024-10-11 华侨大学 Inter-city dynamic carpooling scheduling optimization method and device based on two-stage scheduling

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