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CN111160722B - A bus route adjustment method based on passenger flow competition - Google Patents

A bus route adjustment method based on passenger flow competitionDownload PDF

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CN111160722B
CN111160722BCN201911273607.6ACN201911273607ACN111160722BCN 111160722 BCN111160722 BCN 111160722BCN 201911273607 ACN201911273607 ACN 201911273607ACN 111160722 BCN111160722 BCN 111160722B
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李海波
翁邵源
孙萌萌
王成
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Huaqiao University
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Abstract

Translated fromChinese

本发明涉及一种基于客流量竞争关系的公交路线调整方法，通过对公交站点的所有停靠公交线路的客流量时间序列进行相关性分析，可求解正负相关线路集，即获得影响关系的强弱，即通过分析停靠公交站点的公交线路之间对客流量的竞争关系，反映各公交线路的设置对整个公交网的客流吞吐量的影响，有助于改进和优化线路和站点设置。本发明不依赖于特定场景，更加通用，可用于各大中城市的公交网络。

Figure 201911273607

The invention relates to a bus route adjustment method based on the passenger flow competition relationship. By performing correlation analysis on the passenger flow time series of all bus stops at the bus station, the positive and negative correlation line sets can be solved, that is, the strength of the influence relationship can be obtained. , that is, by analyzing the competition relationship between the bus lines that stop at the bus station on the passenger flow, it reflects the impact of the settings of each bus line on the passenger throughput of the entire bus network, which is helpful to improve and optimize the line and station settings. The present invention does not depend on a specific scenario, is more general, and can be used in public transport networks in large and medium-sized cities.

Figure 201911273607

Description

Translated fromChinese
一种基于客流量竞争关系的公交路线调整方法A bus route adjustment method based on passenger flow competition

技术领域technical field

本发明涉及数据分析在交通规划、交通运输管理的应用，更具体地说，涉及一种基于客流量竞争关系的公交路线调整方法。The invention relates to the application of data analysis in traffic planning and traffic management, and more particularly, to a bus route adjustment method based on the competition relationship of passenger flow.

背景技术Background technique

公交站点的客流量受随机因素影响很大，除天气、突发事件等外在因素外，在该站点停靠的各线路公交车之间存在着竞争关系。这是因为乘客出行时，往往有多条线路能到达目的地，公交线路选择并不唯一，乘客通常选择最快停靠的线路出行。这些可替代线路对客流量就形成了竞争关系，但从整个公交线网对客流量的吞吐能力角度，这些线路之间是合作关系。The passenger flow of a bus station is greatly affected by random factors. Except for external factors such as weather and emergencies, there is a competitive relationship between the buses of various lines that stop at the station. This is because when passengers travel, there are often multiple routes to reach their destination, and the choice of bus routes is not unique. Passengers usually choose the fastest route to travel. These alternative lines form a competitive relationship for passenger flow, but from the perspective of the throughput capacity of the entire bus network to passenger flow, these lines are cooperative.

对公交线路间的影响关系，现有技术的方法主要有：调整发车间隔消除公交车辆聚集、以车辆相遇次数最大为目标建立最大协同换乘模型、同步换乘、建立常规公交和轨道交通竞争模型、基于线路位置和重叠站点数量描述竞争和合作关系等方法。On the influence relationship between bus lines, the methods in the prior art mainly include: adjusting the departure interval to eliminate the aggregation of bus vehicles, establishing a maximum coordinated transfer model with the goal of maximizing the number of vehicle encounters, synchronizing transfers, and establishing a competition model for conventional bus and rail transit. , methods to describe competition and partnership based on line location and number of overlapping stations.

现有技术未提供公交站点之间的相互关系对公交网的吞吐量的影响，即从公交站点的各条停靠公交线路间的竞争关系角度获得公交线路之间的影响度。The prior art does not provide the influence of the mutual relationship between the bus stops on the throughput of the bus network, that is, the degree of influence between the bus lines is obtained from the perspective of the competitive relationship between the bus lines that stop at the bus stop.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于克服现有技术的不足，提供一种基于客流量竞争关系的公交路线调整方法，基于客流量竞争关系，对公交站点分析公交线路间影响关系，可提高整个公交线网的客流吞吐量。The purpose of the present invention is to overcome the deficiencies of the prior art, and to provide a bus route adjustment method based on the passenger flow competition relationship. Based on the passenger flow competition relationship, the impact relationship between bus lines is analyzed for bus stops, and the passenger flow of the entire bus network can be improved. throughput.

本发明的技术方案如下：The technical scheme of the present invention is as follows:

一种基于客流量竞争关系的公交路线调整方法，步骤如下：A bus route adjustment method based on passenger flow competition, the steps are as follows:

1)设共有m辆车停靠该站点，所述的m辆车分属n条不同公交线路，为L＝{li|0≤i≤n}，每辆车的历史刷卡数据按时间顺序形成一个客流量时间序列F＝(f1,f2,…,fm)，L和F的关系满足：F中任意fi对应的公交车都属于某公交线路lj∈L，其中，n≤m；1) Suppose a total of m vehicles stop at the site, and the m vehicles belong to n different bus lines, which is L={li |0≤i≤n}, and the historical card swiping data of each vehicle is formed in chronological order A passenger flow time series F=(f1 , f2 ,..., fm ), the relationship between L and F satisfies: the bus corresponding to any fi in F belongs to a certain bus line lj ∈ L, where n≤ m;

获取所有成对的公交线路li和lj在设定的时段内停靠在任意站点的客流量时间序列D＝{LF(li,lj)|0≤i≤n,0≤j≤n}；Obtain the passenger flow time series of all pairs of bus lines li and lj that stop at any stop within the set period D={LF(li ,lj )|0≤i≤n,0≤j≤n };

2)拆分D中的所有LF(li,lj)，形成两个序列

Figure BDA0002314911080000021
和
Figure BDA0002314911080000022
分属公交线路li和lj；2) Split all LF(li ,lj ) in D to form two sequences
Figure BDA0002314911080000021
and
Figure BDA0002314911080000022
belong to bus lines li and lj respectively;

3)计算任意

Figure BDA0002314911080000023
和
Figure BDA0002314911080000024
之间的相关度
Figure BDA0002314911080000025
3) Calculate any
Figure BDA0002314911080000023
and
Figure BDA0002314911080000024
correlation between
Figure BDA0002314911080000025

其中，

Figure BDA0002314911080000026
为
Figure BDA0002314911080000027
和
Figure BDA0002314911080000028
的协方差，
Figure BDA0002314911080000029
与
Figure BDA00023149110800000210
为
Figure BDA00023149110800000211
和
Figure BDA00023149110800000212
的方差；in,
Figure BDA0002314911080000026
for
Figure BDA0002314911080000027
and
Figure BDA0002314911080000028
the covariance of ,
Figure BDA0002314911080000029
and
Figure BDA00023149110800000210
for
Figure BDA00023149110800000211
and
Figure BDA00023149110800000212
Variance;

4)选取D中任意

Figure BDA00023149110800000213
和
Figure BDA00023149110800000214
在N天内的所有相关度
Figure BDA00023149110800000215
K＝1,2,…,N；4) Select any of D
Figure BDA00023149110800000213
and
Figure BDA00023149110800000214
All correlations in N days
Figure BDA00023149110800000215
K=1,2,...,N;

5)按条件

Figure BDA00023149110800000216
和
Figure BDA00023149110800000217
把相关度向量集合V分为集合V+和集合V-，且
Figure BDA00023149110800000218
5) By condition
Figure BDA00023149110800000216
and
Figure BDA00023149110800000217
Divide the correlation vector set V into a set V+ and a set V-, and
Figure BDA00023149110800000218

6)对于步骤5)获得的集合V+和集合V-，分别使用密度聚类方法进行聚类，分别获得正相关簇集C+和负相关簇集C-；6) For the set V+ and set V- obtained in step 5), use the density clustering method to perform clustering, respectively, to obtain a positive correlation cluster set C+ and a negative correlation cluster set C-;

其中，正相关簇集C+中的公交路线之间对客流量具有合作关系，负相关簇集C-中的公交路线之间对客流量具有竞争关系；Among them, the bus routes in the positive correlation 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)根据正相关簇集C+和负相关簇集C-，对公交网中的公交路线进行调整，提高公交网的客流吞吐量。7) According to the positive correlation cluster C+ and the negative correlation cluster C-, adjust the bus routes in the public transport network to improve the passenger flow throughput of the public transport network.

作为优选，步骤1)具体为：As preferably, step 1) is specifically:

1.1)选取任意一对公交线路li∈L和lj∈L，从F中抽取出属于公交线路li和lj的历史刷卡数据，并按原顺序形成一个客流量时间序列LF(li,lj)＝(f'1,f'2,…,f'q)，满足：对LF(li,lj)中任意f'k和f'k+1，在F中都存在ft和ft+p与之对应，k＜q，t＜q-p，表示历史刷卡数据的相对顺序在L和LF中保持相对顺序不变；1.1) Select any pair of bus lines li ∈ L and lj ∈ L, extract the historical credit card data belonging to bus lines li and lj from F, and form a passenger flow time series LF (lii ,lj )=(f'1 ,f'2 ,...,f'q ), satisfying: For any f'k and f'k+1 in LF(li ,lj ), f exists in Ft and ft+p correspond to them, k < q, t < qp, indicating that the relative order of historical card swiping data remains unchanged in L and LF;

1.2)合并LF(li,lj)中的客流量形成新序列LF(li,lj)＝(f'1,f'2,…,f't)，t≤q，满足：f'2s-1和f'2s是分属不同公交线路li和lj的历史刷卡数据，

Figure BDA00023149110800000219
1.2) Combine the passenger flow in LF(li ,lj ) to form a new sequence LF(li ,lj )=(f'1 ,f'2 ,...,f't ), t≤q, satisfying: f '2s-1 and f'2s are the historical credit card data belonging to different bus lines li and lj ,
Figure BDA00023149110800000219

1.3)重复步骤1.1)、步骤1.2)，获取所有成对的公交线路li和lj在设定的时段内停靠在任意站点的客流量时间序列D＝{LF(li,lj)|0≤i≤n,0≤j≤n}；1.3) Repeat steps 1.1) and 1.2) to obtain the passenger flow time series D= {LF(li, lj )| 0≤i≤n, 0≤j≤n};

进而，步骤2)中，

Figure BDA0002314911080000031
Then, in step 2),
Figure BDA0002314911080000031

作为优选，步骤2)中，如果

Figure BDA0002314911080000032
与
Figure BDA0002314911080000033
的长度不同，则舍弃
Figure BDA0002314911080000034
或
Figure BDA0002314911080000035
中最后一个元素，使得
Figure BDA0002314911080000036
与
Figure BDA0002314911080000037
的长度相同。Preferably, in step 2), if
Figure BDA0002314911080000032
and
Figure BDA0002314911080000033
are different in length, discard
Figure BDA0002314911080000034
or
Figure BDA0002314911080000035
the last element in , such that
Figure BDA0002314911080000036
and
Figure BDA0002314911080000037
of the same length.

作为优选，步骤4)具体为：As preferably, step 4) is specifically:

4.1)初始化相关度向量集合

Figure BDA0002314911080000038
4.1) Initialize the correlation vector set
Figure BDA0002314911080000038

4.2)选取D中任意

Figure BDA0002314911080000039
和
Figure BDA00023149110800000310
在N天内的所有相关度
Figure BDA00023149110800000311
K＝1,2,…,N；4.2) Select any of D
Figure BDA0002314911080000039
and
Figure BDA00023149110800000310
All correlations in N days
Figure BDA00023149110800000311
K=1,2,...,N;

4.3)按时间顺序，建立相关度向量(ρ1,ρ2,…,ρN)，形成V←(ρ1,ρ2,…,ρN)。4.3) In time sequence, establish a correlation vector (ρ1 ,ρ2 ,...,ρN ) to form V←(ρ1 ,ρ2 ,...,ρN ).

作为优选，步骤3)中，

Figure BDA00023149110800000312
Figure BDA00023149110800000313
As preferably, in step 3),
Figure BDA00023149110800000312
Figure BDA00023149110800000313

其中，

Figure BDA00023149110800000314
与
Figure BDA00023149110800000315
为
Figure BDA00023149110800000316
和
Figure BDA00023149110800000317
的平均差。in,
Figure BDA00023149110800000314
and
Figure BDA00023149110800000315
for
Figure BDA00023149110800000316
and
Figure BDA00023149110800000317
average difference.

本发明的有益效果如下：The beneficial effects of the present invention are as follows:

本发明所述的基于客流量竞争关系的公交路线调整方法，通过对公交站点的所有停靠公交线路的客流量时间序列进行相关性分析，可求解正负相关线路集，即获得影响关系的强弱，即通过分析停靠公交站点的公交线路之间对客流量的竞争关系，反映各公交线路的设置对整个公交网的客流吞吐量的影响，有助于改进和优化线路和站点设置。The bus route adjustment method based on the passenger flow competition relationship of the present invention can solve the positive and negative correlation line set by performing correlation analysis on the passenger flow time series of all bus stops at the bus station, that is, the strength of the influence relationship can be obtained. , that is, by analyzing the competition relationship between the bus lines that stop at the bus stop on the passenger flow, it can reflect the impact of the settings of each bus line on the passenger flow throughput of the entire bus network, which is helpful to improve and optimize the line and station settings.

本发明不依赖于特定场景，更加通用，可用于各大中城市的公交网络。The present invention does not depend on a specific scenario, is more general, and can be used in public transport networks in large and medium-sized cities.

附图说明Description of drawings

图1是本发明的流程示意图；Fig. 1 is the schematic flow sheet of the present invention;

图2是创建客流量序列的实现过程的示意图；Fig. 2 is the schematic diagram of the realization process of creating passenger flow sequence;

图3是实施例中，2019年10月10日塘边站各线路客流量的示意图；3 is a schematic diagram of the passenger flow of each line of Tangbian Station on October 10, 2019 in the embodiment;

图4是实施例中，2019年10月10日951路线路客流量；Figure 4 shows the passenger flow ofRoute 951 on October 10, 2019 in the embodiment;

图5是实施例中，2019年10月10日27路线路客流量的示意图；Figure 5 is a schematic diagram of the passenger flow of 27 routes on October 10, 2019 in the embodiment;

图6是实施例中，27路与951路多天的相关度的示意图；6 is a schematic diagram of the multi-day correlation between 27 routes and 951 routes in an embodiment;

图7是实施例中，线路之间相关度之和为负的线路集合的示意图；7 is a schematic diagram of a set of lines whose sum of correlations between lines is negative in an embodiment;

图8是实施例中，线路之间相关度之和为正的线路集合的示意图；8 is a schematic diagram of a set of lines whose sum of correlations between lines is positive in an embodiment;

图9是实施例中，具有合作关系的线路的示意图；9 is a schematic diagram of a line with a cooperative relationship in an embodiment;

图10是实施例中，具有竞争关系的线路的示意图；FIG. 10 is a schematic diagram of lines having a competitive relationship in an embodiment;

图11是实施例中，各站点正、负相关的可视化的示意图；11 is a schematic diagram of the visualization of the positive and negative correlations of each site in an embodiment;

图12是实施例中，全岛各站点正、负相关的可视化的示意图。FIG. 12 is a schematic diagram of the visualization of positive and negative correlations of each site on the whole island in the embodiment.

具体实施方式Detailed ways

以下结合附图及实施例对本发明进行进一步的详细说明。The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

本发明所述的基于客流量竞争关系的公交路线调整方法，如图1所示，步骤如下：The bus route adjustment method based on the passenger flow competition relationship according to the present invention, as shown in Figure 1, the steps are as follows:

1)设共有m辆车停靠该站点，所述的m辆车分属n条不同公交线路，为L＝{li|0≤i≤n}，每辆车的历史刷卡数据按时间顺序形成一个客流量时间序列F＝(f1,f2,…,fm)，L和F的关系满足：F中任意fi对应的公交车都属于某公交线路lj∈L，其中，n≤m；1) Suppose a total of m vehicles stop at the site, and the m vehicles belong to n different bus lines, which is L={li |0≤i≤n}, and the historical card swiping data of each vehicle is formed in chronological order A passenger flow time series F=(f1 , f2 ,..., fm ), the relationship between L and F satisfies: the bus corresponding to any fi in F belongs to a certain bus line lj ∈ L, where n≤ m;

获取所有成对的公交线路li和lj在设定的时段内停靠在任意站点的客流量时间序列D＝{LF(li,lj)|0≤i≤n,0≤j≤n}。Obtain the passenger flow time series of all pairs of bus lines li and lj that stop at any stop within the set period D={LF(li ,lj )|0≤i≤n,0≤j≤n }.

2)拆分D中的所有LF(li,lj)，形成两个序列

Figure BDA0002314911080000041
和
Figure BDA0002314911080000042
分属公交线路li和lj。由于
Figure BDA0002314911080000043
与
Figure BDA0002314911080000044
的长度不一样相同，如果
Figure BDA0002314911080000045
与
Figure BDA0002314911080000046
的长度不同，则舍弃
Figure BDA0002314911080000047
或
Figure BDA0002314911080000048
中最后一个元素，使得
Figure BDA0002314911080000049
与
Figure BDA00023149110800000410
的长度相同。2) Split all LF(li ,lj ) in D to form two sequences
Figure BDA0002314911080000041
and
Figure BDA0002314911080000042
They belong to bus lines li and lj . because
Figure BDA0002314911080000043
and
Figure BDA0002314911080000044
The lengths are not the same as if
Figure BDA0002314911080000045
and
Figure BDA0002314911080000046
are different in length, discard
Figure BDA0002314911080000047
or
Figure BDA0002314911080000048
the last element in , such that
Figure BDA0002314911080000049
and
Figure BDA00023149110800000410
of the same length.

3)计算任意

Figure BDA00023149110800000411
和
Figure BDA00023149110800000412
之间的相关度
Figure BDA00023149110800000413
3) Calculate any
Figure BDA00023149110800000411
and
Figure BDA00023149110800000412
correlation between
Figure BDA00023149110800000413

其中，

Figure BDA00023149110800000414
为
Figure BDA00023149110800000415
和
Figure BDA00023149110800000416
的协方差，
Figure BDA00023149110800000417
与
Figure BDA00023149110800000418
为
Figure BDA00023149110800000419
和
Figure BDA00023149110800000420
的方差。具体地，
Figure BDA00023149110800000421
in,
Figure BDA00023149110800000414
for
Figure BDA00023149110800000415
and
Figure BDA00023149110800000416
the covariance of ,
Figure BDA00023149110800000417
and
Figure BDA00023149110800000418
for
Figure BDA00023149110800000419
and
Figure BDA00023149110800000420
Variance. specifically,
Figure BDA00023149110800000421

其中，

Figure BDA00023149110800000422
与
Figure BDA00023149110800000423
为
Figure BDA00023149110800000424
和
Figure BDA00023149110800000425
的平均差。in,
Figure BDA00023149110800000422
and
Figure BDA00023149110800000423
for
Figure BDA00023149110800000424
and
Figure BDA00023149110800000425
average difference.

4)选取D中任意

Figure BDA0002314911080000051
和
Figure BDA0002314911080000052
在N天内的所有相关度
Figure BDA0002314911080000053
K＝1,2,…,N。4) Select any of D
Figure BDA0002314911080000051
and
Figure BDA0002314911080000052
All correlations in N days
Figure BDA0002314911080000053
K=1,2,...,N.

当乘客有多个公交线路可选时，停靠同一公交站点的不同公交线路对客流量实质上是竞争关系。这种竞争关系会受到多种因素影响，比如节假日、天气等因素，一个时段难以准确反映这种竞争关系，需要N天同时段综合考虑，其中，N＞1。进而，步骤4)具体为：When passengers have multiple bus routes to choose from, different bus routes that stop at the same bus stop are essentially a competitive relationship for passenger flow. This competitive relationship will be affected by a variety of factors, such as holidays, weather and other factors. It is difficult to accurately reflect this competitive relationship in one time period, and it needs to be comprehensively considered for N days and the same time period, where N>1. Then, step 4) is specifically:

4.1)初始化相关度向量集合

Figure BDA0002314911080000054
4.1) Initialize the correlation vector set
Figure BDA0002314911080000054

4.2)选取D中任意

Figure BDA0002314911080000055
和
Figure BDA0002314911080000056
在N天内的所有相关度
Figure BDA0002314911080000057
K＝1,2,…,N；4.2) Select any of D
Figure BDA0002314911080000055
and
Figure BDA0002314911080000056
All correlations in N days
Figure BDA0002314911080000057
K=1,2,...,N;

4.3)按时间顺序，建立相关度向量(ρ1,ρ2,…,ρN)，形成V←(ρ1,ρ2,…,ρN)。4.3) In time sequence, establish a correlation vector (ρ1 ,ρ2 ,...,ρN ) to form V←(ρ1 ,ρ2 ,...,ρN ).

5)按条件

Figure BDA0002314911080000058
和
Figure BDA0002314911080000059
把相关度向量集合V分为集合V+和集合V-，且
Figure BDA00023149110800000510
5) By condition
Figure BDA0002314911080000058
and
Figure BDA0002314911080000059
Divide the correlation vector set V into a set V+ and a set V- , and
Figure BDA00023149110800000510

6)对于步骤5)获得的集合V+和集合V-，分别使用密度聚类方法进行聚类，分别获得正相关簇集C+和负相关簇集C-；6) For the set V+ and set V− obtained in step 5), use the density clustering method to perform clustering, respectively, to obtain a positive correlation cluster set C+ and a negative correlation cluster set C− ;

其中，正相关簇集C+中的公交路线之间对客流量具有合作关系，负相关簇集C-中的公交路线之间对客流量具有竞争关系。Among them, the bus routes in the positive correlation cluster C+ have a cooperative relationship with the passenger flow, and the bus routes in the negative correlation clusterC- have a competitive relationship with the passenger flow.

7)根据正相关簇集C+和负相关簇集C-，对公交网中的公交路线进行调整，提高公交网的客流吞吐量。当需要增加某条公交路线的客流量时，可基于正相关簇集C+，增加具有合作关系的相应的公交路线，或者减少具有竞争关系的相应的公交路线；反之同理。7) According to the positive correlation cluster C+ and the negative correlation cluster C− , adjust the bus routes in the public transport network to improve the passenger flow throughput of the public transport network. When the passenger flow of a certain bus route needs to be increased, based on the positive correlation cluster C+ , the corresponding bus routes with a cooperative relationship can be increased, or the corresponding bus routes with a competitive relationship can be decreased; and vice versa.

本发明中，步骤1)为数据预处理步骤，具体为：In the present invention, step 1) is a data preprocessing step, specifically:

1.1)选取任意一对公交线路li∈L和lj∈L，从F中抽取出属于公交线路li和lj的历史刷卡数据，并按原顺序形成一个客流量时间序列LF(li,lj)＝(f'1,f'2,…,f'q)，满足：对LF(li,lj)中任意f'k和f'k+1，在F中都存在ft和ft+p与之对应，k＜q，t＜q-p，表示历史刷卡数据的相对顺序在L和LF中保持相对顺序不变，如图2所示；1.1) Select any pair of bus lines li ∈ L and lj ∈ L, extract the historical credit card data belonging to bus lines li and lj from F, and form a passenger flow time series LF (lii ,lj )=(f'1 ,f'2 ,...,f'q ), satisfying: For any f'k and f'k+1 in LF(li ,lj ), f exists in Ft and ft+p correspond to them, k < q, t < qp, indicating that the relative order of historical card swiping data remains unchanged in L and LF, as shown in Figure 2;

1.2)合并LF(li,lj)中的客流量形成新序列LF(li,lj)＝(f'1,f'2,…,f't)，t≤q，满足：f'2s-1和f'2s是分属不同公交线路li和lj的历史刷卡数据，

Figure BDA00023149110800000511
其中，合并LF(li,lj)中的客流量所形成的新序列，了便于表达，仍然采用LF(li,lj)表示新序列。1.2) Combine the passenger flow in LF(li ,lj ) to form a new sequence LF(li ,lj )=(f'1 ,f'2 ,...,f't ), t≤q, satisfying: f '2s-1 and f'2s are the historical credit card data belonging to different bus lines li and lj ,
Figure BDA00023149110800000511
Among them, the new sequence formed by merging the passenger flow in LF(li , lj ) is still usedtorepresent the new sequence for convenience of expression.

1.3)重复步骤1.1)、步骤1.2)，获取所有成对的公交线路li和lj在设定的时段内停靠在任意站点的客流量时间序列D＝{LF(li,lj)|0≤i≤n,0≤j≤n}；1.3) Repeat steps 1.1) and 1.2) to obtain the passenger flow time series D= {LF(li, lj )| 0≤i≤n, 0≤j≤n};

进而，步骤2)中，

Figure BDA0002314911080000061
Then, in step 2),
Figure BDA0002314911080000061

具体实施时，因为高峰时段的客流行为相对固定，进而，步骤1)选定的时段通常选择高峰时段，高峰时段根据每个城市情况不同而设置，通常为：早上7:00-9:30。During the specific implementation, because the passenger flow behavior during peak hours is relatively fixed, further, the time period selected in step 1) usually selects the peak time period.

实施例Example

以厦门塘边站为例，取出塘边站单日的刷卡数据，塘边站共有17条线路停靠。塘边站单条线路的刷卡数据量有300多条，所有线路的总刷卡量有5248条。刷卡数据字段包括：线路号、车牌号、交易日期、刷卡卡号、交易时间、交易金额、车次、站点编号、行驶方向。Take Xiamen's Tangbian Station as an example, take out the single-day card swipe data at Tangbian Station, and there are a total of 17 lines stopping at Tangbian Station. There are more than 300 card swiping data for a single line at Tangbian Station, and a total of 5,248 card swiping data for all lines. The card swiping data fields include: line number, license plate number, transaction date, swiping card number, transaction time, transaction amount, train number, station number, and driving direction.

刷卡数据如表1所示。The credit card data is shown in Table 1.

表1：线路刷卡数据Table 1: Line Card Data

线路号line number车牌号number plate交易日期transaction date刷卡卡号Swipe card number交易时间transaction hour车次number of trips站点编号sitenumber行驶方向driving direction651651闽DZ9525Fujian DZ9525201810102018101011192285111922852580025800闽DZ5986Fujian DZ598623twenty three00

按各线路车次的到达顺序，统计同一天的早高峰期7：00-9：30之间每个线路车次的刷卡量，得到初始的站点客流量序列，如图3所示。According to the arrival order of the trains on each line, count the card swiping amount of each line train in the morning peak period of the same day between 7:00-9:30, and obtain the initial station passenger flow sequence, as shown in Figure 3.

从塘边站停靠的所有线路车次的客流量序列中，取每对线路的客流量序列，例如951路和27路的客流量序列，如图4、图5所示。From the passenger flow sequence of all lines and trains stopped at Tangbian Station, take the passenger flow sequence of each pair of lines, such as the passenger flow sequence of No. 951 and No. 27, as shown in Figure 4 and Figure 5.

对每对线路的客流量序列，将其变成等长序列。For the passenger flow sequence of each pair of lines, turn it into a sequence of equal length.

采用本发明求解不同日期的每对线路的客流量时间序列，求解不同日期的相关度，得到相关度向量，如图6所示。The present invention is used to solve the passenger flow time series of each pair of lines on different dates, to solve the correlation degree of different days, and to obtain the correlation degree vector, as shown in FIG. 6 .

对每对线路的相关度向量求和，当相关度和为正时，加入正相关度集合，相关度和为负时加入负相关度向量集合，如图7、图8所示。Sum the correlation vectors of each pair of lines. When the correlation sum is positive, add the positive correlation set, and when the correlation sum is negative, add the negative correlation vector set, as shown in Figure 7 and Figure 8.

分别对正、负相关度向量集合，利用密度聚类求解具相似相关度的线路集合。负相关簇集中所求得的簇代表线路之间为竞争关系。正相关簇集中所求得的簇代表线路之间为合作关系，如图9、图10所示。可见，当处于高峰时期时可增加具有合作关系的线路合作分担站点客流量压力，如增加线路27、437发车车次。当站点客流量较少时部分线路就可满足站点客流量对车次的需求，可考虑减少具有竞争关系的部分线路，如可减少线路27、34发车车次。For the sets of positive and negative correlation degree vectors respectively, use density clustering to solve the set of lines with similar degree of correlation. The clusters obtained in the negative correlation cluster set represent the competition relationship between the lines. The clusters obtained in the positive correlation cluster set represent the cooperative relationship between the lines, as shown in Figure 9 and Figure 10. It can be seen that when it is in the peak period, it is possible to increase the cooperation of lines with cooperative relations to share the passenger flow pressure of the site, such as increasing the number of departures oflines 27 and 437. When the passenger flow of the station is small, some lines can meet the demand of the passenger flow of the station for the number of trains. Consider reducing some lines with a competitive relationship, such as reducing the number of departures oflines 27 and 34.

本实施例中，公交线路的空间可视化如图11、图12所示。In this embodiment, the spatial visualization of the bus route is shown in FIG. 11 and FIG. 12 .

上述实施例仅是用来说明本发明，而并非用作对本发明的限定。只要是依据本发明的技术实质，对上述实施例进行变化、变型等都将落在本发明的权利要求的范围内。The above-mentioned embodiments are only used to illustrate the present invention, but not to limit the present invention. As long as it is in accordance with the technical essence of the present invention, changes, modifications, etc. to the above-described embodiments will fall within the scope of the claims 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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Citations (6)

* Cited by examiner, † Cited by third party
Publication numberPriority datePublication dateAssigneeTitle
AU2007333025A1 (en)*2006-12-132008-06-19Crown Equipment CorporationFleet management system
CN102156908A (en)*2010-12-172011-08-17东南大学Bus route and station adjusting method based on rail transit and bus integration
KR20170096715A (en)*2016-02-172017-08-25한양대학교 산학협력단Method for evaluating operation efficiency of late-night bus
CN109886491A (en)*2019-02-262019-06-14北京工业大学 A two-stage optimal scheduling method for ground public transport driven by massive operation data
CN110188938A (en)*2019-05-232019-08-30东南大学 A screening method for the adjustment of ground bus routes in the initial expectation of rail transit network formation
CN110458331A (en)*2019-07-182019-11-15南京行者易智能交通科技有限公司Optimization method and device is straightened around public bus network in a kind of mistake based on OD passenger flow data

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication numberPriority datePublication dateAssigneeTitle
AU2007333025A1 (en)*2006-12-132008-06-19Crown Equipment CorporationFleet management system
CN102156908A (en)*2010-12-172011-08-17东南大学Bus route and station adjusting method based on rail transit and bus integration
KR20170096715A (en)*2016-02-172017-08-25한양대학교 산학협력단Method for evaluating operation efficiency of late-night bus
CN109886491A (en)*2019-02-262019-06-14北京工业大学 A two-stage optimal scheduling method for ground public transport driven by massive operation data
CN110188938A (en)*2019-05-232019-08-30东南大学 A screening method for the adjustment of ground bus routes in the initial expectation of rail transit network formation
CN110458331A (en)*2019-07-182019-11-15南京行者易智能交通科技有限公司Optimization method and device is straightened around public bus network in a kind of mistake based on OD passenger flow data

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
竞争性常规公交与城市轨道交通衔接调整方法研究――以青岛市地铁3号线为例;房涛等;《城市公共交通》;20180815(第08期);全文*

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