CN111882475B - Visual analysis method for travel mode of urban rail transit station - Google Patents
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Abstract
The invention discloses a visual travel mode analysis method for urban rail transit stations, which analyzes travel behavior modes of urban residents from two layers of time and space. Firstly, mining and visualizing the mobility of different sections and the passenger flow distribution conditions of different sections of a city from the perspective of space; secondly, based on the previous content, the change condition of the fluidity of urban residents in different time periods is mined and visualized from the time point of view, and the foundation can be provided for the guidance opinion of urban traffic management and standardization departments, the formulation of a rail traffic operation organization scheme and the planning of urban land utilization.
Description
Technical Field
The invention belongs to the field of visual analysis, and particularly relates to visual analysis of urban rail transit travel modes. The travel mode of the urban rail transit station is mined from two aspects of time and space, and then visualized and analyzed.
Background
With the rapid development of the economic society, the level of urbanization in China is increasingly improved. The degree of motorization of China cities is also continuously improved, and traffic systems are also continuously perfected. Meanwhile, the traveling behaviors of residents are more and more complex, and the urban traffic problem is more and more serious. Current traffic means have failed to meet the demands of urban residents. Traffic problems have become one of the major problems affecting the rapid and healthy development of the economic society. At the same time, problems with land utilization can also lead to unhealthy economic developments.
The travel behaviors of residents have important values in various application fields such as city planning, social behavior analysis and the like. Therefore, it is of great importance to study the travel behaviors of residents. First, urban traffic is the transportation of people and goods from a starting point to an ending point using different transportation facilities. Therefore, the travel characteristics of residents are researched, and the travel behaviors and rules of the residents are found, so that the method has important influence on urban traffic management and planning. Secondly, the purpose of the resident's trip is different, such as shopping, work, etc. The purpose of the resident's travel determines to some extent the choice of destination and source. Therefore, urban land utilization affects the traveling behavior of residents to some extent. Meanwhile, when a large number of residents have specific travel demands in a certain area, the development and utilization of the land in the area are driven. Therefore, the traveling behavior of residents influences the development and utilization of the land to a certain extent. Therefore, the travel characteristics of residents are researched, the behaviors and the rules of the residents are found, and the existing land utilization conditions of the city can be better known. Thirdly, the resident trip behavior research has important significance for urban traffic management and land utilization planning, and also has certain reference significance for public safety management and infectious disease prevention and control.
With the development and maturity of information technology, it becomes more convenient to acquire a large amount of detailed and accurate resident trip data. Currently, the main sources of resident trip data include GPS data, cell phone data, and smart card data. Many scholars analyze resident trip patterns based on GPS data, cell phone data, and smart card data. The invention researches resident trip modes based on smart card data.
Smart card data of an automatic toll gate (AFC) is widely used by traffic departments to manage incomes and collect a large amount of passenger boarding and alighting information (boarding location, boarding time, alighting location, alighting time) in a decentralized manner. More and more researchers use data recorded by AFC systems to mine the behavior of residents. The invention uses the Beijing urban rail transit intelligent card records from 2013, 6 months to 2013, 7 months, and contains 483,614,919 entry and exit records obtained from 15081258 transit cards and 227 stations.
Zhao et al explored the resident's travel patterns in three ways, time, space and space, using Shenzhen's smart card data. And analyzing the resident trip mode based on a statistical method and an unsupervised clustering method. Based on a statistical method, distribution of resident travel mode characteristics is analyzed, and travel behaviors of resident individuals are summarized. Based on an unsupervised clustering method, the residents are classified according to the similarity of travel modes. Horses and the like have found a space-time travel pattern of individual residents, including residence, departure place, departure time and the like, by using smart card data of Beijing and various data mining techniques. And identifying public transportation travel crowd by adopting a spatial clustering and multi-standard decision analysis method. Le et al propose a passenger classification method based on smart card data. After the travel route based on the smart card data is reconstructed, the behavior pattern of each resident is analyzed based on a DBSCAN clustering algorithm, and the residents are classified into four types according to the existing knowledge.
In the past, studies on resident travel pattern analysis have mainly focused on resident individual travel patterns. In the present invention, the main focus is on the mass-flowing travel mode.
Disclosure of Invention
The invention aims to provide a visual analysis method for a travel mode of an urban rail transit station. The travel behavior patterns of urban residents are mainly analyzed from two layers of time and space. Firstly, mining and visualizing the mobility of different sections and the passenger flow distribution conditions of different sections of a city from the perspective of space; next, based on the foregoing, urban residents are mined and visualized from the time point of view of the change in fluidity in different time periods.
The technical proposal adopted by the invention is a visual analysis method for the travel mode of urban rail transit stations, which comprises the following steps,
step 1: mining and visualizing the fluidity of different sections and the passenger flow distribution of different sections of a city from the perspective of space comprises the following steps:
step 1.1: space aggregation is performed based on smart card data.
Step 1.1.1: the city is divided into m regions. First, the center point O of the city needs to be determined, with O as the center point, and the whole city can be regarded as a two-dimensional plane with 0 ° to 360 ° clockwise with the point just above O as the starting point. In order to uniformly divide the area, the longitude and latitude coordinates of O are shown as formula (1) and formula (2), respectively, where max_lng is the maximum longitude of the city and min_lng is the minimum longitude of the city; max_lat is the largest dimension of the city and min_lat is the smallest dimension of the city.
Wherein each region occupies an angular range in the two-dimensional plane as shown in formula (3), wherein m refers to the number of regions to be divided, start_angle is the start angle, i e [0, m).
Step 1.1.2: the city is divided into m areas, and all urban rail transit stations are respectively divided into the m areas according to the physical positions of the urban rail transit stations. And determining the area where the urban rail transit station is located by calculating the included angle between the line segment between the urban rail transit station and the O and the ray which is upwards from the point O.
Step 1.1.3: and after all the urban rail transit stations are aggregated into m areas, the urban rail transit stations contained in each area are obtained. The traffic between these m zones is now calculated, and the traffic between any two zones is the sum of the traffic from the stations contained in one zone to the stations contained in the other zone. Region R o And region R d The passenger flow volume between them is shown in formula (4).
flow(R O ,R d )＝∑flow(s i ,s j )
(4)
Step 1.2: based on the step 1.1, the passenger flow volume among all the areas is obtained, and then a space-time chord diagram is designed to visualize the space-aggregated smart card data.
Chordal graphs are widely used to represent relationships between objects. If it is desired to represent the relationship between 5 objects (A, B, C, D, E), a 5X 5 matrix needs to be entered. It should be noted that (a, B) and (B, a) are different in meaning, although they both represent the relationship between the object a and the object B in the matrix. When the origin is a and the end point is B, the relationship between them is expressed by the value of (a, B). When the origin is B and the end point is a, the relationship between them is expressed by the value of (B, a). Fig. 2 (a) is a chord chart showing the relationship between all objects. Hovering a mouse over node C in FIG. 2 (a) results in all relationships associated with object C shown in FIG. 2 (b). In the chord graph, the arc length occupied by node a is the sum of all elements of the row of the matrix in which object a is located, i.e., (a, a), (a, B), (a, C), (a, D), and (a, E). Thus, the arc length of node A is used to represent all traffic that starts with object A. The chord between node C and node D represents the association between object C and object D, the value of arc length C connecting the nodes is effectively (C, D) representing the flow from C to object D, and likewise the arc length connected to node D is effectively the value of (D, C) representing the flow from object D to C.
To better express spatial information, a spatial chord graph is designed based on the chord graph. The space chord graph is also composed of two parts of nodes and chords. The arc length of a node represents the spatial position of the region, as opposed to the arc length of a node in a chord graph. In a conventional chord graph, the arc length of a node represents the flow from that node. Therefore, the arc length occupied by each node needs to be calculated.
Step 1.2.1: the inflow flow rate of each region was calculated as described below.
flow(R i )＝∑flow(R i ,R j )
Step 1.2.2: after the inflow rates of the respective areas are obtained, the maximum value max_flow thereof is determined. Based on max_flow, the arc length occupied by one unit of passenger flow is calculated, wherein m is the number of areas.
Step 1.2.3: the arc length occupied by the flow from the node is determined as follows.
randianOfChord＝radianOfUnitFlow×flow
Step 2: mining and visualizing the change of fluidity of urban residents in different time periods from the time point of view comprises the following steps:
step 2.1: based on the above description, the method of space division has been clarified, and the fluidity of the person between two areas is now determined, as described below.
Step 2.2: a city resident flow coefficient is determined for a period of time. Based on the above space division method, different space division results can appear at different initial angles. Traversing from 0 to 360 degrees, determining a region division method with the maximum personnel mobility and a region division method with the minimum personnel mobility in a period of time by using a formula (8), and determining resident flow coefficients according to the region division method and the region division method, wherein the description is as follows:
flowIndex＝max_degree-min_degree
(9)
the invention can provide basis for the establishment of the rail traffic operation organization scheme and the planning of urban land utilization.
Drawings
Fig. 1 is a schematic diagram of spatial distribution of a Beijing urban rail transit station.
Fig. 2 (a) -2 (b) are chord graphical representations.
Fig. 3 (a) -3 (b) are space-time chord graphical representations.
Fig. 4 (a) -4 (c) are diagrams showing the flow of personnel over time.
Detailed Description
In order to make the objects, technical solutions and features of the present invention more apparent, the present invention will be further described in detail below with reference to specific examples of embodiments and with reference to the accompanying drawings. The invention discloses a city track traffic card swiping record based on Beijing city, which comprises the following specific embodiments:
step 1: the fluidity of different sections of a city and the passenger flow distribution of different sections are excavated and visualized from the perspective of space.
Step 1.1: and determining urban space division of Beijing city.
Let m be 4, start_angle be 0, determine R0 e [0 °,90 °), R1 e [90 °,180 °), R2 e [180 °,270 °), R4 e [270 °,0 °) according to equation 2. The stations included in R0 are { Yonghe station, home station, R1 is { ten river station }, R2 is { beijing south station }, R3 is { people university station }. The results are shown in FIG. 1.
Step 1.2: passenger flow between the individual zones is determined.
The passenger traffic between the site included in the region R0 and the site included in the region R1 is shown in table 1. The passenger flow between R0 and R1 can be obtained according to equation 4, and the results are shown in Table 2. By the same method, the passenger flow volume among other areas can be obtained.
TABLE 1 passenger flow volume between sites within region R0 and sites within region R1
| Yonghong station | Calling building station | Ten-Li river station | |
| Yonghong station | 0 | 1000 | 1500 |
| Calling building station | 2000 | 0 | 2500 |
| Ten-Li river station | 3000 | 3500 | 0 |
TABLE 2 passenger flow volume between region R0 and region R1
| R0 | R1 | |
| R0 | 3500 | 4000 |
| R1 | 6500 | 0 |
Step 2: the change condition of fluidity of urban residents in different time periods is mined and visualized from the time point of view.
The time is divided into several time periods, the size of the time interval being mainly dependent on the correlation of the specific time period. In the present invention, to analyze the resident travel pattern every day, the time will be divided by day and with one hour as an interval. For each time period, the arbitrary flow coefficient for that period is calculated using the method described in the summary.
The entire process of determining the minimum flowability of an interval over a period of time is described in Algorithm1, with the maximum flowability of an interval being equally available.
Based on the above method, the change of fluidity of urban residents in different time periods can be obtained as shown in fig. 4 (a) - (c). Fig. 4 (a) shows the variation of the fluidity of residents in different time periods during a working day. From 0 time to 4 time, the instability of the urban rail transit system is 0. That is, the traffic system is very stable during this period. This is because urban rail transit is not running and the passenger flow is zero in this period. And 5, the instability degree of the urban rail transit system starts to rise. The instability of the urban rail transit system decreases from 8. The instability degree of the urban rail transit system starts to be stable from ten times. The degree of instability of the urban rail transit system starts to rise from fourteen hours. From 17, the degree of instability of the urban rail transit system begins to decrease. They form two peaks. The first peak occurs in the morning and the peak point occurs at 8 am. The second peak occurs at the afternoon and the peak occurs at 5 pm. During these two periods, urban rail transit systems are the least stable, with a large number of passengers flowing between areas. When another working day is selected, the instability change of the urban rail transit system is shown in fig. 4 (b). Comparing fig. 4 (a) with fig. 4 (b), it is evident that there is a similar trend on different workdays. When weekends are selected, the instability of the urban rail transit system changes as shown in fig. 4 (c). Fig. 4 (c) is significantly different from fig. 4 (a) and fig. 4 (b). First, the instability of urban rail transit over weekends is less than a weekday for each time period. Then, there is no significant evening peak on weekends.
Claims (2)
1. A visual analysis method for the travel mode of urban rail transit stations is characterized by comprising the following steps,
step 1: performing spatial aggregation based on smart card data;
step 1.1: dividing a city into m areas; firstly, determining a center point O of a city, taking the O as the center point, and regarding the whole city as a two-dimensional plane which takes the position right above the O point as the starting point and is 0-360 degrees clockwise; in order to uniformly divide the area, the longitude and latitude coordinates of O are shown as formula (1) and formula (2), respectively, where max_lng is the maximum longitude of the city and min_lng is the minimum longitude of the city; max_lat is the largest dimension of the city, and min_lat is the smallest dimension of the city;
wherein each region occupies an angle range in the two-dimensional plane as shown in formula (3), wherein m refers to the number of regions to be divided, start_angle is a starting angle, i e [0, m);
step 1.2: dividing a city into m areas, and dividing all urban rail transit stations into the m areas according to the physical positions of the urban rail transit stations; the area where the urban rail transit station is located can be determined by calculating the included angle between the line segment between the urban rail transit station and O and the ray which is upwards from the point O;
step 1.3: after all urban rail transit stations are aggregated into m areas, urban rail transit stations contained in each area are obtained; the passenger flow between the m areas is calculated, and the passenger flow between any two areas is the sum of the passenger flow from the station contained in one area to the station contained in the other area; region R o And region R d The passenger flow volume between the two is shown in the formula (4);
flow(R O ,R d )＝∑flow(s i ,s j ) (4)
step 2: based on the step 1.1, obtaining the passenger flow volume among all areas, and then designing a space-time chord diagram to visualize the space-aggregated smart card data;
designing a space chord graph based on the chord graph; the space chord graph consists of two parts of nodes and chords; the arc length of the node represents the spatial position of the region, and the meaning expressed by the arc length of the node in the chord graph is different; in the chord graph, the arc length of a node represents the flow from the node, and the arc length occupied by each node is calculated;
step 1.2.1: calculating inflow flow rate of each region, as described below;
flow(R i )＝∑flow(R i ,R j ) (5)
step 1.2.2: after the inflow flow of each area is obtained, determining the maximum max_flow in the inflow flow; based on max_flow, calculating the arc length occupied by a unit of passenger flow volume, wherein m is the number of areas;
step 1.2.3: the arc length occupied by the flow from the node is determined as follows:
randianOfChord＝radianOfUnitFlow×flow (7)。
2. the visual analysis method for the travel mode of the urban rail transit station according to claim 1, wherein the change condition of the mobility of urban residents in different time periods is mined and visualized from the time point of view, and the mobility of personnel between two areas is determined as follows;
determining a city resident flow coefficient over a period of time; based on the space division method, different initial angles can generate different space division results; traversing from 0 degrees to 360 degrees, determining a region division method with the maximum personnel mobility and a region division method with the minimum personnel mobility in a time period by using a formula (8), and determining resident flow coefficients according to the region division method and the region division method with the minimum personnel mobility, wherein the description is as follows;
flowIndex＝max_degree-min_degree (9)。
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| CN110111575A (en) * | 2019-05-16 | 2019-08-09 | 北京航空航天大学 | A kind of Forecast of Urban Traffic Flow network analysis method based on Complex Networks Theory |
| CN110633307A (en) * | 2019-08-19 | 2019-12-31 | 北京建筑大学 | Urban public bicycle connection subway space-time analysis method |
| CN110716935A (en) * | 2019-10-09 | 2020-01-21 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Track data analysis and visualization method and system based on online taxi appointment travel |
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| CN110111575A (en) * | 2019-05-16 | 2019-08-09 | 北京航空航天大学 | A kind of Forecast of Urban Traffic Flow network analysis method based on Complex Networks Theory |
| CN110633307A (en) * | 2019-08-19 | 2019-12-31 | 北京建筑大学 | Urban public bicycle connection subway space-time analysis method |
| CN110716935A (en) * | 2019-10-09 | 2020-01-21 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Track data analysis and visualization method and system based on online taxi appointment travel |
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