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CN111160722B - Bus route adjusting method based on passenger flow competition relationship - Google Patents

Bus route adjusting method based on passenger flow competition relationship Download PDF

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CN111160722B
CN111160722B CN201911273607.6A CN201911273607A CN111160722B CN 111160722 B CN111160722 B CN 111160722B CN 201911273607 A CN201911273607 A CN 201911273607A CN 111160722 B CN111160722 B CN 111160722B
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李海波
翁邵源
孙萌萌
王成
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Abstract

The invention relates to a bus route adjusting method based on passenger flow competition relationship, which can solve a positive and negative correlation line set by carrying out correlation analysis on the passenger flow time sequence of all bus lines stopped at a bus stop, namely, the strength of the influence relationship is obtained, namely, the influence of the setting of each bus line on the passenger flow throughput of the whole bus network is reflected by analyzing the competition relationship of the bus lines stopped at the bus stop on the passenger flow, and the method is favorable for improving and optimizing the line and stop setting. The invention is independent of specific scenes, is more universal and can be used for public transport networks of large and medium-sized cities.

Description

Bus route adjusting method based on passenger flow competition relationship
Technical Field
The invention relates to application of data analysis in traffic planning and traffic transportation management, in particular to a bus route adjusting method based on passenger flow competition relationship.
Background
The passenger flow of the bus stop is greatly influenced by random factors, and except for external factors such as weather, emergencies and the like, competitive relations exist among buses on all lines stopped at the bus stop. This is because when a passenger goes out, there are often a plurality of routes to reach a destination, the bus route selection is not unique, and the passenger usually selects the route at the fastest stop to go out. These alternative lines form a competitive relationship to the traffic, but from the perspective of throughput of the entire public traffic network to the traffic, there is a cooperative relationship between these lines.
For the influence relationship among the bus lines, the prior art method mainly comprises the following steps: adjusting departure intervals to eliminate bus aggregation, establishing a maximum cooperative transfer model with the maximum number of vehicle meeting times as a target, synchronously transferring, establishing a conventional bus and rail transit competition model, describing competition and cooperation relations based on line positions and the number of overlapped stations and the like.
The prior art does not provide the influence of the mutual relation among the bus stops on the throughput of the public transport network, namely, the influence degree among the bus lines is obtained from the competitive relation among all the stop bus lines of the bus stops.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a bus route adjusting method based on a passenger flow competition relationship.
The technical scheme of the invention is as follows:
a bus route adjusting method based on passenger flow competition relationship comprises the following steps:
1) a total of m vehicles are set to stop at the station, wherein the m vehicles belong to n different bus lines and are L ═ LiI is more than or equal to 0 and less than or equal to n, and historical card swiping data of each vehicle form a passenger flow time sequence F-F (F is the time sequence of the passenger flow time sequence F)1,f2,…,fm) The relationship between L and F satisfies: any of F in FiThe corresponding buses all belong to a certain bus line ljBelongs to L, wherein n is less than or equal to m;
obtaining all paired bus routes liAnd ljThe passenger flow time sequence D ═ LF (l) for stopping at any station in a set time periodi,lj)|0≤i≤n,0≤j≤n}；
2) Splitting all LF (l) in Di,lj) Form two sequences
Figure BDA0002314911080000021
And
Figure BDA0002314911080000022
belonging to a public transport line liAnd lj；
3) Calculate an arbitrary
Figure BDA0002314911080000023
And
Figure BDA0002314911080000024
degree of correlation between
Figure BDA0002314911080000025
Wherein,
Figure BDA0002314911080000026
is composed of
Figure BDA0002314911080000027
And
Figure BDA0002314911080000028
the covariance of (a) of (b),
Figure BDA0002314911080000029
and
Figure BDA00023149110800000210
is composed of
Figure BDA00023149110800000211
And
Figure BDA00023149110800000212
the variance of (a);
4) selecting any of D
Figure BDA00023149110800000213
And
Figure BDA00023149110800000214
all correlations within N days
Figure BDA00023149110800000215
K＝1,2,…,N；
5) According to the condition
Figure BDA00023149110800000216
And
Figure BDA00023149110800000217
the set V of relevance vectors is divided into a set V + and a set V-, and
Figure BDA00023149110800000218
6) for the set V obtained in step 5)+And the sum set V-is respectively clustered by using a density clustering method to respectively obtain positive correlation cluster sets C+And negative correlation cluster C-;
wherein, the positive correlation cluster C+The bus routes in the cluster C-have a cooperative relationship with the passenger flow, and the bus routes in the negative correlation cluster C-have a competitive relationship with the passenger flow;
7) clustering according to positive correlation+And a negative correlation cluster C-, adjusting the bus route in the bus network, and improving the passenger flow throughput of the bus network.
Preferably, step 1) is specifically:
1.1) selecting any pair of bus lines liE.g. L and LjBelongs to the bus line L extracted from the F by belonging to the LiAnd ljThe historical card swiping data of the passenger card form a passenger flow time sequence LF (l) according to the original sequencei,lj)＝(f'1,f'2,…,f'q) Satisfies the following conditions: for LF (l)i,lj) Of any of f'kAnd f'k+1In F are all presenttAnd ft+pCorrespondingly, k is less than q, t is less than q-p, and the relative sequence of the historical card swiping data is kept unchanged in L and LF;
1.2) merging LF (l)i,lj) In (1) traffic volume formation of a new sequence LF (l)i,lj)＝(f'1,f'2,…,f't) And t is less than or equal to q, and satisfies the following conditions: f'2s-1And f'2sBelong to different bus lines liAnd ljThe historical card-swiping data of the card,
Figure BDA00023149110800000219
1.3) repeating step 1.1) and step 1.2), obtaining all paired bus lines liAnd ljAt the settingTime sequence D ═ LF (l) of passenger flow volume parked at any station in time intervali,lj)|0≤i≤n,0≤j≤n}；
Furthermore, in the step 2),
Figure BDA0002314911080000031
preferably, in step 2), if
Figure BDA0002314911080000032
And
Figure BDA0002314911080000033
if the lengths of the two are different, discarding
Figure BDA0002314911080000034
Or
Figure BDA0002314911080000035
In the last element, such that
Figure BDA0002314911080000036
And
Figure BDA0002314911080000037
are the same length.
Preferably, the step 4) is specifically:
4.1) initializing a set of relevance vectors
Figure BDA0002314911080000038
4.2) selecting any of D
Figure BDA0002314911080000039
And
Figure BDA00023149110800000310
all correlations within N days
Figure BDA00023149110800000311
K＝1,2,…,N；
4.3) establishing a vector of correlation degrees (p) in time sequence1,ρ2,…,ρN) Forming V ← (ρ)1,ρ2,…,ρN)。
Preferably, in step 3),
Figure BDA00023149110800000312
Figure BDA00023149110800000313
wherein,
Figure BDA00023149110800000314
and
Figure BDA00023149110800000315
is composed of
Figure BDA00023149110800000316
And
Figure BDA00023149110800000317
the average difference of (a).
The invention has the following beneficial effects:
according to the bus route adjusting method based on the passenger flow competition relationship, the correlation analysis is carried out on the passenger flow time sequences of all the stop bus routes of the bus stop, so that the positive and negative correlation route sets can be solved, namely the strength of the influence relationship is obtained, namely the influence of the setting of each bus route on the passenger flow throughput of the whole bus network is reflected by analyzing the competition relationship of the bus routes stopping at the bus stop on the passenger flow, and the improvement and optimization of the route and stop setting are facilitated.
The invention is independent of specific scenes, is more universal and can be used for public transport networks of large and medium-sized cities.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of an implementation process for creating a passenger flow sequence;
FIG. 3 is a schematic diagram of passenger flow of each line of a pond side station in 10 months and 10 days in 2019 in the embodiment;
FIG. 4 shows the traffic volume of a 951 route in 10 months and 10 days in 2019 in an embodiment;
FIG. 5 is a schematic diagram of 27 route passenger flow in 2019, 10 months and 10 days;
FIG. 6 is a graph showing the correlation between 27 lanes and 951 lanes for a plurality of days in the example;
FIG. 7 is a diagram of a line set in which the sum of the correlations between lines is negative in the embodiment;
FIG. 8 is a diagram of a line set in which the sum of correlations between lines is positive in the embodiment;
FIG. 9 is a schematic diagram of a circuit having a cooperative relationship in the embodiment;
FIG. 10 is a diagram illustrating a circuit with contention relationships in an embodiment;
FIG. 11 is a visual diagram of positive and negative correlations between sites in the embodiment;
FIG. 12 is a visual diagram showing the positive and negative correlations between sites in the whole island in the embodiment.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention relates to a bus route adjusting method based on passenger flow competition relationship, which comprises the following steps as shown in figure 1:
1) a total of m vehicles are set to stop at the station, wherein the m vehicles belong to n different bus lines and are L ═ LiI is more than or equal to 0 and less than or equal to n, and historical card swiping data of each vehicle form a passenger flow time sequence F-F (F is the time sequence of the passenger flow time sequence F)1,f2,…,fm) The relationship between L and F satisfies: any of F in FiThe corresponding buses all belong to a certain bus line ljBelongs to L, wherein n is less than or equal to m;
obtaining all paired bus routes liAnd ljThe passenger flow time sequence D ═ LF (l) for stopping at any station in a set time periodi,lj)|0≤i≤n,0≤j≤n}。
2) Splitting all LF (l) in Di,lj) Form two sequences
Figure BDA0002314911080000041
And
Figure BDA0002314911080000042
belonging to a public transport line liAnd lj. Due to the fact that
Figure BDA0002314911080000043
And
Figure BDA0002314911080000044
are different in length if
Figure BDA0002314911080000045
And
Figure BDA0002314911080000046
if the lengths of the two are different, discarding
Figure BDA0002314911080000047
Or
Figure BDA0002314911080000048
In the last element, such that
Figure BDA0002314911080000049
And
Figure BDA00023149110800000410
are the same length.
3) Calculate an arbitrary
Figure BDA00023149110800000411
And
Figure BDA00023149110800000412
degree of correlation between
Figure BDA00023149110800000413
Wherein,
Figure BDA00023149110800000414
is composed of
Figure BDA00023149110800000415
And
Figure BDA00023149110800000416
the covariance of (a) of (b),
Figure BDA00023149110800000417
and
Figure BDA00023149110800000418
is composed of
Figure BDA00023149110800000419
And
Figure BDA00023149110800000420
the variance of (c). In particular, the amount of the solvent to be used,
Figure BDA00023149110800000421
wherein,
Figure BDA00023149110800000422
and
Figure BDA00023149110800000423
is composed of
Figure BDA00023149110800000424
And
Figure BDA00023149110800000425
the average difference of (a).
4) Selecting any of D
Figure BDA0002314911080000051
And
Figure BDA0002314911080000052
all within N daysDegree of correlation
Figure BDA0002314911080000053
K＝1,2,…,N。
When a passenger has a plurality of selectable bus lines, different bus lines stopping at the same bus stop are substantially in competition relation with passenger flow. The competitive relationship is influenced by various factors, such as holidays, weather and the like, the competitive relationship is difficult to accurately reflect in one time period, and N days and N periods are comprehensively considered, wherein N is more than 1. Further, the step 4) is specifically:
4.1) initializing a set of relevance vectors
Figure BDA0002314911080000054
4.2) selecting any of D
Figure BDA0002314911080000055
And
Figure BDA0002314911080000056
all correlations within N days
Figure BDA0002314911080000057
K＝1,2,…,N；
4.3) establishing a vector of correlation degrees (p) in time sequence1,ρ2,…,ρN) Forming V ← (ρ)1,ρ2,…,ρN)。
5) According to the condition
Figure BDA0002314911080000058
And
Figure BDA0002314911080000059
dividing a set V of relevance vectors into sets V+And set V-And is and
Figure BDA00023149110800000510
6) for the set V obtained in step 5)+And set V-Respectively clustering by using a density clustering method to respectively obtain positive correlation cluster sets C+And negative correlation cluster C-；
Wherein, the positive correlation cluster C+The public transportation routes in the cluster have a cooperative relationship with the passenger flow and are negatively related to the cluster C-The bus routes in (1) have a competitive relationship to passenger flow.
7) Clustering according to positive correlation+And negative correlation cluster C-And the bus route in the bus network is adjusted, so that the passenger flow throughput of the bus network is improved. When the passenger flow of a certain bus route needs to be increased, the cluster C can be based on positive correlation+Increasing corresponding bus routes with a cooperative relationship or reducing corresponding bus routes with a competitive relationship; otherwise, the same principle is applied.
In the invention, the step 1) is a data preprocessing step, and specifically comprises the following steps:
1.1) selecting any pair of bus lines liE.g. L and LjBelongs to the bus line L extracted from the F by belonging to the LiAnd ljThe historical card swiping data of the passenger card form a passenger flow time sequence LF (l) according to the original sequencei,lj)＝(f'1,f'2,…,f'q) And satisfies the following conditions: for LF (l)i,lj) Of any of f'kAnd f'k+1In F are all presenttAnd ft+pCorrespondingly, k is less than q, t is less than q-p, and the relative sequence of the historical card swiping data is kept unchanged in L and LF, as shown in FIG. 2;
1.2) merging LF (l)i,lj) In (1) traffic volume formation of a new sequence LF (l)i,lj)＝(f'1,f'2,…,f't) And t is less than or equal to q, and satisfies the following conditions: f'2s-1And f'2sBelong to different bus lines liAnd ljThe historical card-swiping data of the card,
Figure BDA00023149110800000511
wherein LF (l) is combinedi,lj) The new sequence formed by passenger flow in (1) is convenient for expression and still adopts LF (l)i,lj) To representA new sequence.
1.3) repeating step 1.1) and step 1.2), obtaining all paired bus lines liAnd ljThe passenger flow time sequence D ═ LF (l) for stopping at any station in a set time periodi,lj)|0≤i≤n,0≤j≤n}；
Furthermore, in the step 2),
Figure BDA0002314911080000061
in specific implementation, because the passenger flow behavior at the peak time is relatively fixed, the peak time is usually selected in the time selected in step 1), and the peak time is set according to different urban conditions, and is usually: morning from 7:00 to 9: 30.
Examples
Taking a building door and a pond side station as an example, the card swiping data of the pond side station in a single day is taken out, and the pond side station has 17 lines for stopping. The card swiping data volume of a single line of the pond side station is 300, and the total card swiping volume of all lines is 5248. The data field of punching the card includes: line number, license plate number, transaction date, card swiping number, transaction time, transaction amount, train number, station number and driving direction.
The card swiping data is shown in table 1.
Table 1: line card swiping data
Line number License plate number Date of transaction Card number for swiping card Transaction time Number of vehicles Site numbering Direction of travel
651 Min DZ9525 20181010 11192285 25800 Min DZ5986 23 0
According to the arrival sequence of the train numbers of all lines, counting the early peak period 7 of the same day: 00-9:30, the initial station passenger flow sequence is obtained by the card swiping amount of each line train number, as shown in fig. 3.
From the passenger flow sequences of all the line train numbers stopped at the pond side station, the passenger flow sequence of each pair of lines, for example, the passenger flow sequence of 951 lines and 27 lines, is taken, as shown in fig. 4 and 5.
And changing the passenger flow sequence of each pair of lines into an equal-length sequence.
By adopting the method and the device, the passenger flow time sequence of each pair of lines on different dates is solved, the correlation degree on different dates is solved, and the correlation degree vector is obtained, as shown in figure 6.
And summing the correlation vectors of each pair of lines, and adding a positive correlation set when the correlation sum is positive and adding a negative correlation vector set when the correlation sum is negative, as shown in fig. 7 and 8.
And respectively solving the line sets with similar correlation degrees by utilizing density clustering on the positive correlation degree vector set and the negative correlation degree vector set. The clusters obtained in the negative correlation cluster set represent lines in a competitive relationship. The clusters obtained in the positive correlation cluster set represent cooperative links, as shown in fig. 9 and 10. It can be seen that the cooperative line sharing site traffic pressure can be increased when in peak hours, such as increasing the number of departure cars on the line 27, 437. When the passenger flow of the station is less, the requirement of the passenger flow of the station on the train number can be met by part of lines, and the reduction of part of lines with competitive relation can be considered, for example, the train number of departure of the lines 27 and 34 can be reduced.
In this embodiment, the space visualization of the bus route is as shown in fig. 11 and 12.
The above examples are provided only for illustrating the present invention and are not intended to limit the present invention. Changes, modifications, etc. to the above-described embodiments are intended to fall within the scope of the claims of the present invention as long as they are in accordance with the technical spirit of the present invention.

Claims (5)

1. A bus route adjusting method based on passenger flow competition relationship is characterized by comprising the following steps:
1) the method is characterized in that a total of m parking stations are arranged, the m vehicles belong to n different bus lines, and L is equal to { L ═ LiI is more than or equal to 0 and less than or equal to n, and historical card swiping data of each vehicle form a passenger flow time sequence F-F (F is the time sequence of the passenger flow time sequence F)1,f2,…,fm) The relationship between L and F satisfies: any of F in FiThe corresponding buses all belong to a certain bus line ljBelongs to L, wherein n is less than or equal to m;
obtaining all paired bus routes liAnd ljThe passenger flow time sequence D ═ LF (l) for stopping at any station in a set time periodi,lj)|0≤i≤n,0≤j≤n}；
2) Splitting all LF (l) in Di,lj) Form two sequences
Figure FDA0002314911070000011
And
Figure FDA0002314911070000012
belonging to a public transport line liAnd lj；
3) Calculate an arbitrary
Figure FDA0002314911070000013
And
Figure FDA0002314911070000014
degree of correlation between
Figure FDA0002314911070000015
Wherein,
Figure FDA0002314911070000016
is composed of
Figure FDA0002314911070000017
And
Figure FDA0002314911070000018
the covariance of (a) of (b),
Figure FDA0002314911070000019
and
Figure FDA00023149110700000110
is composed of
Figure FDA00023149110700000111
And
Figure FDA00023149110700000112
the variance of (a);
4) selecting any of D
Figure FDA00023149110700000113
And
Figure FDA00023149110700000114
all correlations within N days
Figure FDA00023149110700000115
5) According to the condition
Figure FDA00023149110700000116
And
Figure FDA00023149110700000117
dividing a set V of relevance vectors into sets V+And set V-And is made of
Figure FDA00023149110700000118
6) For the set V obtained in step 5)+And set V-Respectively clustering by using a density clustering method to respectively obtain positive correlation cluster sets C+And negative correlation cluster C-；
Wherein, the positive correlation cluster C+The public transportation routes in the cluster have a cooperative relationship with the passenger flow and are negatively related to the cluster C-The bus routes have a competitive relationship with passenger flow;
7) clustering according to positive correlation+And negative correlation cluster C-And the bus route in the bus network is adjusted, so that the passenger flow throughput of the bus network is improved.
2. The bus route adjusting method based on the passenger flow competition relationship as recited in claim 1, wherein the step 1) is specifically as follows:
1.1) selecting any pair of bus lines liE.g. L and LjBelongs to the bus line L extracted from the F by belonging to the LiAnd ljThe historical card swiping data of the passenger card form a passenger flow time sequence LF (l) according to the original sequencei,lj)＝(f′1,f′2,…,f′q) And satisfies the following conditions: for LF (l)i,lj) Of any of f'kAnd f'k+1In F are all presenttAnd ft+pCorrespondingly, k is less than q, t is less than q-p, and the relative sequence of the historical card swiping data is kept unchanged in L and LF;
1.2) merging LF (l)i,lj) In (1) traffic volume formation of a new sequence LF (l)i,lj)＝(f′1,f′2,…,f′t) And t is less than or equal to q, and satisfies the following conditions: f'2s-1And f'2sBelong to different bus lines liAnd ljThe historical card-swiping data of the card,
Figure FDA0002314911070000021
1.3) repeating step 1.1) and step 1.2), obtaining all paired bus lines liAnd ljThe passenger flow time sequence D ═ LF (l) for stopping at any station in a set time periodi,lj)|0≤i≤n,0≤j≤n}；
Furthermore, in the step 2), the step of,
Figure FDA0002314911070000022
3. the bus route adjusting method based on the passenger flow competition relationship as claimed in claim 2, wherein in the step 2), if the passenger flow competition relationship is satisfied, the bus route is adjusted
Figure FDA0002314911070000023
And
Figure FDA0002314911070000024
if the lengths of the two are different, discarding
Figure FDA0002314911070000025
Or
Figure FDA0002314911070000026
In the last element, such that
Figure FDA0002314911070000027
And
Figure FDA0002314911070000028
are the same length.
4. The bus route adjusting method based on the passenger flow competition relationship as recited in claim 2, wherein the step 4) is specifically as follows:
4.1) initializing a set of relevance vectors
Figure FDA0002314911070000029
4.2) selecting any of D
Figure FDA00023149110700000210
And
Figure FDA00023149110700000211
all correlations within N days
Figure FDA00023149110700000212
4.3) establishing a vector of correlation degrees (p) in time sequence1,ρ2,…,ρN) Forming V ← (ρ)1,ρ2,…,ρN)。
5. The bus route adjusting method based on the passenger flow competition relationship as recited in claim 1, wherein in step 3),
Figure FDA00023149110700000213
wherein,
Figure FDA00023149110700000214
and
Figure FDA00023149110700000215
is composed of
Figure FDA00023149110700000216
And
Figure FDA00023149110700000217
the average difference of (a).
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