CN109978267B - Urban microcirculation bus route planning method based on urban rail transit data - Google Patents
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Abstract
The invention discloses an urban microcirculation bus route planning method based on urban rail transit data. The method of the invention comprises the following steps: s1, AFC card swiping data of urban rail transit is obtained, and time-space analysis is conducted on trips with the riding distance of 4 stations or less in the data to determine time-space distribution of each trip interval of each rail transit; s2, selecting a track traffic travel interval with densely distributed short-distance travel, and inspecting each station in the interval and roads and land in the attraction range of the station; s3, performing semi-actualization on the road network in the examination range; s4, dividing the land in the semi-actual road network into cells, and inspecting the land property of each cell; s5, selecting a bus route starting point and a bus route ending point within the attraction range of the upstream and downstream rail transit stations to generate a candidate route set; s6, generating a site set corresponding to the candidate line; s7, distributing passenger flow to each cell and each bus stop; s8, distributing passenger flow of each cell to each bus stop; and S9, generating a line operation scheme, selecting an optimal scheme, and ending the steps.
Description
The technical field is as follows:
the invention relates to an urban microcirculation bus route planning method based on urban rail transit data, and belongs to the technical field of urban traffic planning management.
Background art:
with the rapid development of the urbanization process in China and the continuous enlargement of the area of the urban built-up area, all the places pay attention to the complete construction of the urban public transport system. And the urban rail transit as the urban public transport trunk bears more and more traffic, so that partial compartments are full of people. The microcirculation public transport is as a part of city public transport system, provides short-distance swift trip service for city residents, can replace the track traffic trip to a certain extent in the aspect of the short-distance trip inside the city. Therefore, the method has very important significance for scientifically and reasonably planning the microcirculation bus route.
At the present stage, the number of cities for operating the microcirculation bus lines is small, and the cities which are developed for rail transit construction lack attention to short-distance travel in the cities, so that if the reasonable microcirculation bus lines are planned and laid to be matched with the rail transit lines for running, multi-mode travel selection can be provided for travelers, and the service level of the urban public transit system is well improved.
The existing microcirculation bus route planning method is mostly based on an empirical method, or the data analysis of resident OD trip data and the existing ground bus network, lacks the consideration of the development of the urban bus system, and cannot take the short-distance trip passenger flow in the rail transit network into consideration. Therefore, in order to attract short-distance travel passenger flow in the rail transit network, a microcirculation bus route planning method which is based on rail transit data and accords with travel characteristics of the rail transit data needs to be found out urgently.
Disclosure of Invention
The invention aims to solve the existing problems and provides an urban microcirculation bus route planning method based on urban rail transit data.
The above purpose is realized by the following technical scheme:
an urban microcirculation bus route planning method based on urban rail transit data comprises the following steps:
s1, AFC card swiping data of urban rail transit is obtained, and time-space analysis is conducted on trips with the riding distance of 4 stations or less in the data to determine time-space distribution of each trip interval of each rail transit;
s2, selecting a track traffic travel interval with densely distributed short-distance travel, and inspecting each station in the interval and roads and land in the attraction range of the station;
s3, performing semi-actualization on the road network within the examination range;
s4, dividing the land in the semi-actual road network into cells, and investigating land use attributes of the cells;
s5, selecting a bus route starting point and a bus route ending point within the attraction range of the upstream and downstream rail transit stations to generate a candidate route set;
s6, generating a site set corresponding to the candidate line;
s7, distributing passenger flow to each cell and each bus stop;
s8, distributing passenger flow of each cell to each bus stop;
and S9, generating a line operation scheme, selecting an optimal scheme, and ending the steps.
The urban microcirculation bus route planning method based on the urban rail transit data comprises the following specific steps of: loading lines without stops generated in the candidate line set, and setting the radius R of the bus stop attraction range; drawing a circle with the starting point as the center of the circle and R as the radius, and inspecting the intersection point P of the circle and the farthest end of the line in the advancing direction 1 Setting the bus station as a first bus station; and then, a loop is developed, the current bus stop is taken as the center of a circle, R is taken as the radius to draw a circle, and the intersection point P of the circle and the farthest end of the path advancing direction is n And setting bus stops until the route end point is included in the garden, and ending the cycle to generate a candidate route stop set.
The urban microcirculation bus route planning method based on urban rail transit data comprises the following specific steps of S7: according to the land property of each cell, the development intensity of each cell is inspected, and the passenger flow generation and attraction coefficients lambda of each cell are respectively marked Oi 、λ Di (ii) a Investigating the distance l between each cell and the nearest rail transit station i (ii) a Defining a cell passenger flow contribution rate gamma i The value is a cell generation and attraction coefficient lambda i Distance l from cell to nearest rail transit station i The ratio of (A) to (B):
occurrence/attraction contribution rate: gamma ray i ＝λ i /l i ；
For different time periods, the contribution rate attributes corresponding to all cells are different, and during the early peak period, the residential area cell is the occurrence contribution rate, and the office area cell is the attraction contribution rate; in the evening peak period, the residential district is the attraction contribution rate, and the office district is the occurrence contribution rate;
distributing the passenger flow of each station in the research range to each cell according to the proportion of the occurrence contribution rate in the sum; distributing the passenger flow of each cell to each cell of the terminal according to the occupation ratio of the attraction contribution rate of each cell in the terminal range in the sum; and completing the distribution of the passenger flow to the cell.
The urban microcirculation bus route planning method based on urban rail transit data comprises the following specific steps of generating a route operation scheme in step S9 and selecting an optimal scheme: two aspects of passenger trip expense and enterprise operation expense are considered. Wherein the passenger trip cost is divided into a passenger walking time cost C A Passenger riding time fee C B Passenger waiting time fee C W (ii) a Passenger walking time fee C A Passenger riding time fee C B Determined as the line goes to site layout determination. Wherein C is A The walking time from the centroid of the cell to the nearest bus stop is formed by the distance from the centroid of the cell to the road network and the distance from the point to the nearest bus stop; c B The running time between the nearest bus stops in the starting and ending point cells is set;
the passenger waiting time and expense are changed along with the operation scheduling, generally, the passenger waiting time obeys the poisson distribution, and if T is a line departure interval, the passenger waiting time interval is [0, T ] to ensure that the maximum waiting time does not exceed the bus departure interval time, then:
in the formula:
t: passenger arrival time;
lambda can be understood as the average waiting time of the bus station, and then
Then:
random passenger waiting times according to the distribution;
operating cost C of operating enterprise R Can be expressed as:
C R ＝(Lm)/(vT)
wherein L is the length of the line, m is the operation cost of the vehicle, v is the running speed of the vehicle, and T is the departure interval of the line;
and (4) considering the sum of the trip cost of the passengers and the operation cost of the enterprise, and selecting a scheme with the minimum sum as a planned route.
Has the advantages that:
compared with the prior art, the invention provides an urban microcirculation bus route planning method based on urban rail transit data, which has the following technical effects:
1. according to the method, the actual short-distance travel characteristics are obtained through analysis of the actual rail transit data, and the daily short-distance travel requirements of urban residents can be met more accurately through the developed urban internal microcirculation route planning.
2. The invention provides a heuristic algorithm which generates and determines the line trend, station layout and operation scheduling between the starting point and the ending point, thereby improving the coverage range of the line and the scientificity of line station selection and having higher application value for public transportation planning and operating enterprises.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a process of constructing a semi-realistic road network to an inclined road in the method of the present invention.
Fig. 3 is a road network under investigation in an embodiment of the invention.
Fig. 4 is a study area semi-realistic road network in an embodiment of the invention.
Fig. 5 is a semi-realistic road network cell partitioning in an embodiment of the invention.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the examples.
An urban microcirculation bus route planning method based on urban rail transit data is disclosed, as shown in figure 1, and comprises the following steps:
s1, AFC card swiping data of urban rail transit are obtained, travel with a riding distance of 4 stations or less in the data is subjected to space-time analysis, and space-time distribution of each travel interval of each rail transit is determined; the method comprises the steps of moving to a local rail transit operation enterprise, obtaining urban rail transit historical AFC card swiping data, screening trips with distances of 4 stations and less in the data, recording starting and ending points and time of entering and leaving the stations, completing space-time distribution analysis of all data, and determining distribution density of short-distance trips in each interval and each time period.
S2, selecting a track traffic travel interval with densely distributed short-distance travel, and inspecting each station in the interval and roads and land in the attraction range of the station; selecting the interval with high short-distance travel density in the step S1 and the corresponding time period thereof, taking the interval as a current research object, and investigating each rail transit station and attraction range in the interval, wherein the research range is determined by generally taking 800 meters; inspecting the road network in the research range, and determining the road network for the bus to run;
s3, performing semi-actualization on the road network in the examination range; a semi-realistic road with a road network in a research range similar to a rectangular square grid can be converted into a zigzag semi-realistic road for an oblique road as shown in fig. 2;
s4, dividing the land in the semi-actual road network into cells, and inspecting the land property of each cell; the land property can be divided into residence, business, office, education and the like according to the functions of different cells;
s5, selecting a bus route starting and ending point within the attraction range of the upstream and downstream rail transit stations to generate a candidate route set; selecting proper bus route starting and ending points in the rail transit attraction ranges at the two ends in the research range, loading route length limits by using a deep search method, and determining a candidate route set;
s6, generating a station set corresponding to the candidate line(ii) a Loading lines of stations which are not generated in the candidate line set, and setting the radius R of the bus station attraction range to be 300 meters generally; drawing a circle with the starting point as the center of the circle and R as the radius, and inspecting the intersection point P of the circle and the farthest end of the line in the advancing direction 1 Setting the bus station as a first bus station; and then, a loop is developed, the current bus stop is taken as the center of a circle, R is taken as the radius to draw a circle, and the circle and the intersection point P of the farthest end of the advancing direction of the path are connected n And setting bus stops until the route end point is included in the garden, and ending the circulation to generate a candidate route stop set.
S7, distributing passenger flow to each cell; according to the land property of each cell, the development intensity is inspected, and the passenger flow generation and attraction coefficients lambda are respectively marked Oi 、λ Di (ii) a The distance l from each cell to the nearest rail transit station is examined i (ii) a Defining a cell passenger flow contribution rate gamma i The value is a cell generation and attraction coefficient lambda i Distance l from cell to nearest rail transit station i The ratio of (A) to (B):
occurrence/attraction contribution rate: gamma ray i ＝λ i /l i ；
For different periods, the contribution rate attributes corresponding to each cell are different, for example, during the early peak period, the residential area cell is the occurrence contribution rate, and the office area cell is the attraction contribution rate.
Distributing the passenger flow of each station in the research range to each cell according to the proportion of the occurrence contribution rate in the sum; distributing the passenger flow of each cell to each cell of the terminal according to the proportion of the attraction contribution rate of each cell in the terminal range in the sum; and completing the distribution of the passenger flow to the cell.
S8, distributing passenger flow of each cell to each bus stop; and according to a nearby distribution principle, distributing passenger flows of all the districts to the bus stops closest to the districts. If the distance exceeds a predetermined value, the cell is discarded without being within the line attraction range.
S9, generating a line operation scheme and selecting an optimal scheme; two aspects of passenger trip expense and enterprise operation expense are considered. Wherein the passenger trip cost is divided into a passenger walking time cost C A Passenger riding time fee C B Passenger waiting time fee C W ；
Passenger walking time fee C A Passenger riding time fee C B Determined as the line goes to site layout determination. Wherein C A The walking time from the centroid of the cell to the nearest bus stop is formed by the distance from the centroid of the cell to the road network and the distance from the point to the nearest bus stop; c B The running time between the nearest bus stops in the starting and ending point cells is set;
the passenger waiting time and expense are changed along with the operation scheduling, generally, the passenger waiting time obeys poisson distribution, T is set as a line departure interval, and the passenger waiting time interval is [0, T ] so as to ensure that the maximum waiting time does not exceed the bus departure interval time, then:
in the formula:
t: passenger arrival time;
lambda can be understood as the average waiting time at a bus stop, and can be taken
Then:
passengers may randomly wait time according to the distribution.
Operating cost C of operating enterprise R Can be expressed as:
C R ＝(Lm)/(vT)
wherein L is the length of the line, m is the operation cost of the vehicle, v is the running speed of the vehicle, and T is the departure interval of the line; and (4) considering the sum of the trip cost of the passengers and the operation cost of the enterprise, and selecting a scheme with the minimum sum as a planned route.
The invention is described below by way of example:
the first embodiment is as follows: taking a rail transit network of a certain city in China and a central city area thereof as an example, the micro-circulation bus route in the city is developed and planned.
1. Local AFC historical card swiping data are obtained, trips with the multiplication distance being less than or equal to 4 stations are screened, the time-space characteristics of the trips are analyzed, and partial results are as shown in the following table 1:
table 1 early peak swipe data example
2. Selecting a short-distance travel dense interval as a research range; the interval from the station 11 to the station 25 is selected as a research range, and the interval comprises four stations 11, 10, 9 and 25. Inspecting roads and land around the station; the road network for public transportation vehicles to pass through in the research range is determined, and the result is shown in fig. 2:
3. the road network (fig. 3) within the range of investigation was semi-implemented, and the results are shown in fig. 4: wherein the number is a road network node number.
4. And dividing the semi-actualized road network into cells, and inspecting the land property of each cell. The result of cell division is shown in fig. 5, in which the numbers are the numbers of the centroid of the corresponding cells;
the attributes of some of the cells are shown in table 2 below:
table 2 partial cell land attributes
5. Examining the research range, and selecting road nodes 5 and 59 as starting and ending points of a line; a route length limit of 22 is set in a semi-realistic road network, and all feasible routes between nodes 5 and 59 are determined by utilizing deep search to form a candidate route set.
6. And loading paths of sites which are not generated in the candidate path set, wherein the example path trend is as follows: [59,47,42,63,81,35,69,72,33,29,73,75,28,10,21,77,20,16,12,8,0,1,2,3,4,80,5]. Using the starting point 59 as the center of a circle and 300 meters as the radius(radius 2 in correspondence with a semi-realistic road network), draw a circle, and examine its intersection point P with the farthest end in the path advancing direction 1 The coordinate is [2,2.73 ]]Setting the bus station as a first bus station; and drawing a circle by taking the current bus station as the center of the circle, observing the intersection point of the circle and the farthest end of the path advancing direction, and setting the intersection point as the bus station until the circle contains the terminal point 5. Example path site locations are as follows in table 3:
TABLE 3 candidate Path site layout coordinates example
7. According to the land property, development intensity and the distance between each cell and the corresponding rail transit station listed in the table 2, the passenger flow generation/attraction coefficient lambda of each cell is calculated Oi ，
The passenger flow generation coefficient of the cell 8 is lambda O8 ＝1÷6＝0.183；
The passenger flow attraction coefficient of the cell 133 is λ D133 ＝1÷3＝0.333；
The passenger flow occurrence attraction coefficient of all cells can be obtained. The results are given in table 4 below:
table 4 passenger flow occurrence attraction coefficients of all cells
The passenger flow was distributed to the cells according to the occurrence/attraction coefficients in table 4:
taking the rail transit station 25 as an example, the sum of the generation coefficients is:
0.183+0.183+0.2+0.25+0.333＝1.149
the traffic 29 corresponding to the interval 25-11 is distributed to each cell:
and a cell 8: 29X 0.183 ÷ 1.149 ≈ 5
Cell 17: 29X 0.183 ÷ 1.149 ≈ 5
Cell 18: 29X 0.2 ÷ 1.149 ≈ 5
Cell 27: 29X 0.25 ÷ 1.149 ≈ 6
Cell 28: 29X 0.333 ÷ 1.149 ≈ 8
Completing the work of distributing the flow to the cell; then, the traffic of each cell is allocated to each end point cell, taking the cell 28 as an example, the traffic 8 corresponding to the cell 28 in the interval 25-11 is allocated to each end point cell:
the total attraction of the rail transit station 11 is:
0.333+0.25+0.5+1＝2.083
cell 133: 9X 0.333 ÷ 2.083 ≈ 2
Cell 134: 9X 0.25 ÷ 2.083 ≈ 1
Cell 144: 9X 0.5 ÷ 2.083 ≈ 2
Cell 155: 9X 1 ÷ 2.083 ≈ 4
The traffic allocation of the cell 28 is completed, and the traffic allocation of the remaining cells is obtained in the same manner.
8. According to the principle of proximity, connecting each cell to the nearest bus station, the results are as follows in table 5:
TABLE 5 nearest bus station in cell
9. Calculating the fees, wherein the fees have the following cost 6:
TABLE 6 cost of each item
Taking the passenger flows 28-144 of the cell 28 as an example, the charges are as follows:
walking time cost:
the method is characterized by comprising two parts, namely a part that a cell 28 (1.5, 3.5) walks to a bus stop 2 (2, 2.732) and a part that a bus stop 12 (10, 16.823) walks to a cell 144 (8.5, 14.5) in a centroid way, the distance calculated along the path is respectively 1.268 and 1.177, the distance is divided by a walking speed 6, and the walking speed is multiplied by the walking speed 6The walking time C can be obtained from the walking time value 15 A ＝(1.268+1.177)÷6×15＝6.113
Passenger riding cost:
calculating the distance between the bus stop 2 (2, 2.732) and the bus stop 12 (10, 16.823) to be 23 along the path, dividing the distance by the running speed 40 of the bus and multiplying the distance by the waiting time value 12 to obtain the riding time C B ＝23÷40×12＝6.9
Passenger waiting fee:
if the interval between the taking and dispatching of the vehicles is 10, the poisson distribution can be calculated
Randomly generating random numbers in the interval of 0-10, multiplying the Poisson distribution of the matching values by the waiting time value to obtain C W Value, C W ＝6×12＝72；
Enterprise operation cost:
taking the departure interval as 10, calculating the line length L =36, and calculating the enterprise operation expense as C R ＝(36×300)/(40×10/60)＝1620。
And sequentially calculating walking, riding and waiting costs of passengers of all passenger flows, summing the walking, riding and waiting costs with the operation costs of enterprises to obtain total costs, comparing the total costs corresponding to all paths and departure intervals, and taking the minimum total cost as the optimal path.
Claims (3)
1. An urban microcirculation bus route planning method based on urban rail transit data is characterized by comprising the following steps:
s1, AFC card swiping data of urban rail transit is obtained, and time-space analysis is conducted on trips with the riding distance of 4 stations or less in the data to determine time-space distribution of each trip interval of each rail transit;
s2, selecting a track traffic travel interval with densely distributed short-distance travel, and inspecting each station in the interval and roads and lands in an attraction range of the station;
s3, performing semi-actualization on the road network in the examination range;
s4, dividing the land in the semi-actual road network into cells, and inspecting the land property of each cell;
s5, selecting a bus line starting and ending point within the attraction range of the upstream and downstream rail transit stations to generate a candidate line set;
s6, generating a station set corresponding to the candidate line;
s7, distributing passenger flow to each cell and each bus stop;
s8, distributing passenger flow of each cell to each bus stop;
s9, generating a line operation scheme, selecting an optimal scheme, and ending the step;
the specific method for generating the line operation scheme and selecting the optimal scheme in step S9 is as follows: considering two aspects of passenger travel cost and enterprise operation cost, wherein the passenger travel cost is divided into passenger walking time cost C A Passenger riding time fee C B Passenger waiting time fee C W ；
Passenger walking time fee C A Passenger riding time fee C B Determined by the line-oriented site layout determination, wherein C A The walking time from the centroid of the cell to the nearest bus stop is formed by the distance from the centroid of the cell to the road network and the distance from the centroid to the nearest bus stop; c B The running time between the nearest bus stops in the starting and ending point cell is set;
the passenger waiting time and expense are changed along with the operation scheduling, the passenger waiting time obeys the poisson distribution, T is set as a line departure interval, the passenger waiting time interval is [0, T ] so as to ensure that the maximum waiting time does not exceed the bus departure interval time, and then:
in the formula:
t: passenger arrival time;
lambda can be understood as the average waiting time at a bus stop, and can be taken
Then:
obtaining the random waiting time of passengers according to the distribution;
operating cost C of operating enterprise R Can be expressed as:
C R ＝(Lm)/(vT)
wherein L is the length of the line, m is the operation cost of the vehicle, v is the running speed of the vehicle, and T is the departure interval of the line;
and (4) considering the sum of the trip cost of the passengers and the operation cost of the enterprise, and selecting a scheme with the minimum sum as a planned route.
2. The urban microcirculation bus route planning method based on urban rail transit data according to claim 1, wherein the specific method for generating the station sets corresponding to the candidate routes in step S6 is as follows: loading lines without stops generated in the candidate line set, and setting the radius R of the bus stop attraction range; drawing a circle with the starting point as the center of the circle and R as the radius, and inspecting the intersection point P of the circle and the farthest end of the line in the advancing direction 1 Setting the bus station as a first bus station; and then, a loop is developed, the current bus stop is taken as the center of a circle, R is taken as the radius to draw a circle, and the intersection point P of the circle and the farthest end of the path advancing direction is n And setting bus stops until the route end point is included in the garden, and ending the cycle to generate a candidate route stop set.
3. The urban microcirculation bus route planning method based on urban rail transit data according to claim 1, wherein the specific method for distributing passenger flow to each cell and each bus stop in step S7 is as follows: according to the land property of each cell, the development intensity is inspected, and the passenger flow generation and attraction coefficients lambda are respectively marked Oi 、λ Di (ii) a The distance l from each cell to the nearest rail transit station is examined i (ii) a Defining a cell passenger flow contribution rate gamma i The value is a cell generation and attraction coefficient lambda i Distance l from cell to nearest rail transit station i The ratio of (A) to (B):
occurrence/attraction contribution rate: gamma ray i ＝λ i /l i ；
For different time periods, the contribution rate attributes corresponding to all cells are different, during the early peak period, residential area cells are used as the occurrence contribution rate, and office areas are used as the attraction contribution rate; in the evening peak period, the residential district is the attraction contribution rate, and the office district is the occurrence contribution rate;
distributing the passenger flow of each station in the research range to each cell according to the proportion of the occurrence contribution rate in the sum; distributing the passenger flow of each cell to each cell of the terminal according to the occupation ratio of the attraction contribution rate of each cell in the terminal range in the sum; and finishing the passenger flow distribution to the cells.
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