CN114897213A - Historical block public transportation reachability measuring and calculating method and optimization method - Google Patents

Historical block public transportation reachability measuring and calculating method and optimization method Download PDF

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CN114897213A
CN114897213A CN202210340370.4A CN202210340370A CN114897213A CN 114897213 A CN114897213 A CN 114897213A CN 202210340370 A CN202210340370 A CN 202210340370A CN 114897213 A CN114897213 A CN 114897213A
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袁长伟
丁圣轩
冯健
张晨皓
马宁远
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Abstract

The invention relates to a method for measuring and calculating the reachability of a public traffic network in a historical block. The reachability calculation method considers historical block building conditions and travel requirements. On the basis of accessibility, on the basis of protecting the current built situation of a historical block, an objective function is built by reducing travel resistance, the line length, the average station distance, resident travel time consumption and the like are taken as models of constraint conditions, an ant colony algorithm is used for carrying out optimization design on a public transport network, the optimized public transport network improves the conventional public transport accessibility of the historical block, and on the basis of improving the level of public transport infrastructure, the land property and the traveler characteristic of the historical block are quantitatively depicted, so that the attraction of the public transport network is enhanced, and the environmental protection of the historical block is combined with the public transport network layout.

Description

Historical block public transportation reachability measuring and calculating method and optimization method
Technical Field
The disclosure relates to the technical field of traffic planning, in particular to a method for measuring and calculating public transport accessibility in a historical block and an optimization method.
Background
As the urbanization level and the motorized progress of China are continuously improved, the travel demand of a historical block is higher and higher, and the contradiction between the urban level and the motorized progress and the supply of traffic facilities and the protection of building environment in the block is more and more obvious. The traditional public transportation planning method takes the improvement of mobility as a guide, lacks quantitative description on the land property, characteristics of travelers and the like of a historical block, and does not place a main body of a trip at a primary position.
Disclosure of Invention
In view of the above, the invention aims to provide a method for measuring and calculating the reachability of public transportation networks in historical blocks, which quantitatively describes the land property and the characteristics of travelers of the historical blocks, calculates the reachability of the public transportation networks in each transportation cell in a research area based on positive effect and negative effect, and improves the individual reachability of conventional public transportation in the historical blocks while protecting the building environment of the historical blocks to the maximum extent; on the basis of accessibility measurement and calculation, the existing public transportation network is optimized by aiming at reducing travel resistance on the basis of protecting the current situation of the built historical block, so that the conventional public transportation accessibility of the historical block is improved, the environmental protection of the historical block is combined with the public transportation network layout, the public transportation travel convenience of residents in the historical block is improved, and the public transportation travel resistance of the residents is reduced.
Based on the above purpose, the invention provides the following technical scheme:
in a first aspect, the invention provides a method for measuring and calculating the reachability of a public traffic network, which comprises the following steps of calculating the reachability of the public traffic network of a traffic cell i by using the reachability formula:
Figure BDA0003578209610000011
in the formula:
di is the passenger flow attraction amount of the nearest station of the traffic cell i, and Ti is the travel resistance of the traffic cell i; the traffic cell is a set of nodes or connecting lines with certain traffic relevance and traffic similarity.
In the technical scheme, the method quantitatively describes the land property and the traveler characteristic of the historical block, calculates the reachability of the public transportation network of a traffic cell of the historical block by using positive effect and negative effect, and improves the individual reachability of conventional public transportation of the historical block while protecting the building environment of the historical block to the maximum extent. The method is simple, a person-oriented design idea is reflected, the higher the accessibility is, the higher the utilization rate of the historical block roads is, and the requirements of residents in traffic districts are met.
As an improvement of the above technical solution, in the method, the POI index of the traffic cell i is calculated by the following formula to facilitate metric calculation:
Figure BDA0003578209610000021
in the formula:
e is the total number of categories of POI; e is the category of POI; omega e The weight of the e-type POI in the traffic cell i; d ie The number of e-type POIs in the traffic cell i.
As an improvement of the above technical solution, in the method, a negative effect quantitative calculation manner is provided:
T i ＝w1 i ×T1 i +w2 i ×T2 i +w3 i ×T3 i
w1 i 、w2 i 、w3 i is a weight; t1 i The average walking time from the center position of the traffic cell i to the nearest station; t2 i For the center position of the traffic cell i to the nearest oneAnd (3) calculating the time of 1-time zero transfer by taking a bus according to the following formula:
Figure BDA0003578209610000022
in the formula: f is the departure frequency of the bus, and the unit is one hour; m is the number of times of riding of a traveler; delta is a deviation factor which represents the deviation between the actual departure time and the departure timetable of the bus; t3 i The average route riding time from the traffic cell i to the nearest station to each traffic cell is calculated by the following formula:
Figure BDA0003578209610000023
in the formula: n is the number of bus lines between bus stops; l ij The distance between a certain bus net stop j and the previous stop, which starts from the stop of the traffic cell i; v1 is the average running speed of the bus.
As an improvement of the above technical solution, in the method, the POI category may be obtained from a map, and includes: catering services, scenic spots, public facilities, shopping services, business residences, living services, financial insurance services, science and education culture services, sports and leisure services, medical services, government agencies and social groups, lodging services and transportation facilities, so as to measure the comprehensive service index of each traffic cell as comprehensively and accurately as possible.
As an improvement of the above technical solution, in the method, the weight of the POI is determined by a structure entropy weight method, and objective quantitative measurement analysis is performed on each index to make a reference basis for reachability measurement.
In a second aspect, the invention provides a method for optimizing a public traffic network, comprising the following steps:
s100, calculating the accessibility of each bus line according to the following formula, and setting the accessibility threshold value:
Figure BDA0003578209610000031
in the formula:
di is the POI index of the traffic cell i, and Ti is the travel resistance of the traffic cell i; the POI index is used for measuring the land distribution scale, type and density of a historical block in a traffic cell; the traffic cell is a set of nodes or connecting lines with certain traffic association degree and traffic similarity degree; the POI index of the traffic cell i is calculated by:
Figure BDA0003578209610000032
in the formula: e is the total number of categories of POI; e is the category of POI; omega e The weight of the e-type POI in the traffic cell i; d ie The number of e-type POIs in the traffic cell i;
s200, taking the bus routes which do not reach the accessibility threshold as the bus routes to be optimized, and establishing a target function and constraint conditions; the objective function is:
T i ＝w1 i ×T1 i +w2 i ×T2 i +w3 i ×T3 i
the constraint conditions are as follows:
(1) the length of the line is as follows: l is more than or equal to 5km and less than or equal to 20 km;
(2) average station distance:
Figure BDA0003578209610000033
n is the total number of docking stations;
(3) wire mesh density:
Figure BDA0003578209610000034
s is the area of all traffic districts;
(4) line nonlinear coefficient:
Figure BDA0003578209610000035
d is the straight-line distance between two points;
(5) house with house bodyThe travel time of the people is consumed: t is i Tmax is less than or equal to, and is the maximum time consumption of 95 percent of residents related to the urban scale in one trip;
in the formula:
ti is the travel resistance of the traffic cell i;
T1 i the average walking time from the center position of the traffic cell i to the nearest station;
T2 i calculating the time of 1-time zero transfer by taking a bus by the following formula for the average waiting time from the center position of the traffic cell i to the nearest station:
Figure BDA0003578209610000041
in the formula: f is the departure frequency of the bus, and the unit is one hour; delta is a deviation factor which represents the deviation between the actual departure time and the departure timetable of the bus;
T3 i the average route riding time from the traffic cell i to the nearest station to each traffic cell is calculated by the following formula:
Figure BDA0003578209610000042
in the formula: n is the number of bus lines between bus stops; l ij The distance between a certain bus net stop j and the previous stop, which starts from the stop of the traffic cell i; v1 is the average running speed of the bus;
and S300, obtaining the minimum value of the objective function to obtain the optimized line meeting the constraint condition and the accessibility threshold.
According to the technical scheme, on the basis of accessibility, an objective function is constructed by improving land use and reducing travel resistance, the line length, the average station distance, resident travel time consumption and the like are used as models of constraint conditions, the ant colony algorithm is used for optimally designing the public transport network, the optimized public transport network improves the conventional public transport accessibility of the historical block, the network operation efficiency is high, the resident travel time is short, the conventional public transport accessibility of the historical block is improved on the basis of protecting the current situation of building the historical block, the environmental protection of the historical block is combined with the public transport network layout, the travel convenience of the traffic block of the historical block is met, and the bus travel resistance of the residents is reduced.
As an improvement of the above technical solution, in the method, the optimized route is obtained for the bus route through an improved ant colony algorithm, and the improved ant colony algorithm enables the objective function to be within an iteration range and meet a minimum value under a constraint condition, so that an optimized bus route meeting the reachability measurement of the present invention is quickly obtained, and the travel experience of residents in a historical block is improved without destroying a historical building environment. The improved ant colony algorithm is as follows:
s301, selecting an unoptimized bus line as a current line;
s302, placing M1 ants on the determined starting and ending points, calculating pheromones of all road sections, gradually optimizing according to the transition probability, and determining the moving direction of the ants;
s303, updating the alternative node set once each time the search is completed, and removing the bus stops which have already walked;
s304, searching the next node until the complete path search is completed, and deleting the ant; searching the starting and ending points of bus trips in the community until the bus routes of all the starting and ending point pairs are completed;
s305, after the ant colony completes path optimization, calculating the length of the optimized bus line, comparing the calculation result with the value of the current bus line, updating the value of the current bus line to be the minimum value of the two, and updating the pheromone strength on the path;
s306, judging whether the current optimized bus line meets all constraints and reachability standards, and if so, outputting all nodes of the line as bus stops and the distance between the two nodes; otherwise, returning to S302 for readjustment;
and S307, judging whether an unoptimized bus route exists, if so, selecting the unoptimized bus route as the current route, and returning to S302.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic diagram of distribution of a traffic cell and start and stop points in a tombstone area in the city of west ampere in embodiment 1;
FIG. 2 is a schematic view of the weighted travel time of each stage in embodiment 1;
fig. 3 is a schematic diagram of the reachability measurement result before optimization of public transportation network in tombstone areas in west ampere city in embodiment 1;
fig. 4 is a bus route layout diagram of a bus network after optimization before and after the optimization design in embodiment 1;
fig. 5 is a schematic diagram of the reachability measurement result after optimization of the public transportation network in the tombstone area in west ampere city in embodiment 1;
fig. 6 is a comparison graph of the reachability levels of the traffic zones before and after the optimization design of the public transportation network in embodiment 1.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the embodiment 1, a public transportation network accessibility optimization design object is taken as a public transportation network accessibility optimization design object in a public security public business tombstone area. According to factors such as land utilization property of the tombstone area, road network structure and the like, the research area is divided into 16 traffic districts. The traffic cell is a set of nodes or connecting lines with certain traffic relevance and traffic similarity, changes along with the change of time, relevance and similarity, and reflects the time-space change characteristic of the traffic characteristics of the urban road network. As shown in fig. 1.
The tombstone is in the southeast of the west ampere city center. Nearly 100 bus lines pass through the area, and nearly 100 conventional ground bus stops are arranged. Meanwhile, three subway lines of the subway No. 2 line, the subway No. 3 line and the subway No. 4 line pass through the area at the same time, and the total length is nearly 10 kilometers. Three subway lines are all in the north-south direction and are distributed in parallel basically, and more than 10 rail stations are distributed. And obtaining the accessibility status of the public transportation network in the tombstone area according to the preliminary arrangement condition of the stations and the lines.
Under an Arcgis platform, the center position of a traffic cell is obtained through a gravity center method and is used as a starting point of travel, and a station set closest to the center of the cell is obtained through a neighbor analysis method and is used as a starting station of public transport travel of residents in the cell.
Based on the topological relation between the public transportation network and the stations. And (3) establishing a public transport data set through verification and correction, and generating an OD (start and stop) distance cost matrix among the traffic cells after verification. And (3) performing path analysis by using ArcGIS, and calculating the travel path length and travel time of the buses between stations, as shown in FIG. 2.
And sequentially exporting the divided traffic cells in the ArcMap according to fields through the ArcMap, adding a cutting tool, inputting the POI point data as an input element, and cutting the POI data as traffic cell area data to obtain the POI number of each traffic cell. The method has the advantages that the number of types of POI is large, and the influence degree of different types of POI in the traffic district on the public transport trip is different. Determining the weight of each POI through a structure entropy weight method, wherein the calculation flow of the structure entropy weight method is as follows:
(1) obtaining POI types, setting E evaluation indexes for each type, and setting the index set as U-U 1 ，u 2 ，...，u E Obtaining F importance ranking matrices a ═ of evaluation indexes fe ) FE ，a fe Is to the index u e The importance of (2) is sorted;
(2) for the index u e Order of importance of a fe Calculating the membership degree:
Figure BDA0003578209610000061
e＝1，2...，E，f＝1，2...，F
wherein b is the amount of the transformation parameter, b ═ e 2 . Obtaining a membership matrix R of each index factor, and matrix elements R fe ＝u′(a fe )。
(3) Calculating the average degree of cognition r e
Figure BDA0003578209610000071
(4) Calculation of the degree of blindness J e
Figure BDA0003578209610000072
(5) For the index u e Is u' e ：
Figure BDA0003578209610000073
(6) The overall cognition degree ue' of each index is normalized to obtain the weight we of each index, which is shown as follows:
Figure BDA0003578209610000074
after the POI data are merged and classified, 13 categories of 44057 points are selected in total, and the weight and the number of the POI data are shown in table 1. And calculating the positive utility of each traffic cell according to the POI index.
Figure BDA0003578209610000075
Calculating the POI index of each traffic cell according to the POI index, wherein the calculation result is shown in a table 2:
Figure BDA0003578209610000076
Figure BDA0003578209610000081
according to the reachability formula of the present invention, the reachability of each cell is calculated, as shown in fig. 3. The reachability is calculated by the following formula:
Figure BDA0003578209610000082
in the formula:
di is POI index of the traffic cell i, and Ti is travel resistance of the traffic cell i; the POI index is used for measuring the land distribution scale, type and density of a historical block in a traffic cell; the traffic cell is a set of nodes or connecting lines with certain traffic association degree and traffic similarity degree; the POI index of the traffic cell i is calculated by:
Figure BDA0003578209610000083
in the formula: e is the total number of categories of POI; e is the category of POI; omega e The weight of the e-type POI in the traffic cell i; d ie The number of e-type POIs in the traffic cell i.
According to the set reachability threshold, selecting the bus routes needing optimized design as shown in table 3:
TABLE 3
Figure BDA0003578209610000084
And establishing an objective function and a constraint condition of the bus route to be optimized.
(I) An objective function:
T i ＝w1 i ×T1 i +w2 i ×T2 i +w3 i ×T3 i
in the formula:
ti is the travel resistance of the traffic cell i;
T1 i the average walking time from the center position of the traffic cell i to the nearest station;
T2 i calculating the time of 1-time zero transfer by taking a bus by the following formula for the average waiting time from the center position of the traffic cell i to the nearest station:
Figure BDA0003578209610000091
in the formula: f is the departure frequency of the bus, and the unit is one hour; delta is a deviation factor which represents the deviation between the actual departure time and the departure timetable of the bus;
T3 i the average route riding time from the traffic cell i to the nearest station to each traffic cell is calculated by the following formula:
Figure BDA0003578209610000092
in the formula: n is the number of bus lines between bus stops; l ij The distance between a certain bus net stop j and the previous stop, which starts from the stop of the traffic cell i; v1 is the average running speed of the bus.
(II) the constraint conditions are as follows:
(1) the length of the line is as follows: l is more than or equal to 5km and less than or equal to 20 km;
(2) average station distance:
Figure BDA0003578209610000093
n is the total number of docking stations;
(3) wire mesh density:
Figure BDA0003578209610000094
s is all traffic smallArea of the zone;
(4) line nonlinear coefficient:
Figure BDA0003578209610000095
d is the straight-line distance between two points;
(5) resident travel time consumption: t is i Tmax is less than or equal to, and is the maximum time consumption of 95 percent of residents related to the urban scale in one trip;
the optimized bus route is obtained by adopting the following improved ant colony algorithm, and the steps comprise:
s301, selecting an unoptimized bus line as a current line;
s302, placing M1 ants on the determined starting and ending points, calculating pheromones of all road sections, gradually optimizing according to the transition probability, and determining the moving direction of the ants;
s303, updating the alternative node set once each time the search is finished, and removing the bus stops which have already passed;
s304, searching the next node until the complete path search is completed, and deleting the ant; searching starting and ending points of bus trips in a community until bus routes of all starting and ending point pairs are completed;
s305, after the ant colony completes path optimization, calculating the length of the optimized bus line, comparing the calculation result with the value of the current bus line, updating the value of the current bus line to be the minimum value of the two, and updating the pheromone strength on the path;
s306, judging whether the current optimized bus line meets all constraints and reachability standards, and if so, outputting all nodes of the line as bus stops and the distance between the two nodes; otherwise, returning to S302 for readjustment;
and S307, judging whether an unoptimized bus route exists, if so, selecting an unoptimized bus route as the current route, and returning to S302.
Fig. 4 is a schematic diagram of the wiring layout before and after optimization. The optimized traffic cell reachability is shown in fig. 5, and can be seen by comparing with fig. 2. The area of poor reachability in fig. 2 has improved relative lift in the reachability in fig. 5, which is shown in a comparison graph of reachability before and after optimization in fig. 6.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present disclosure may be implemented by software plus necessary general hardware, and certainly may also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, software program implementation is a more preferred implementation for more of the present disclosure.
Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments and application fields, and the above-described embodiments are illustrative, instructive, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto without departing from the scope of the invention as defined by the appended claims.

Claims (7)

1. A method for measuring and calculating the reachability of a public traffic network in a historical block is characterized in that the method calculates the reachability of the public traffic network in a traffic cell i by using the following reachability formula:
Figure FDA0003578209600000011
in the formula:
di is the POI index of the traffic cell i, and Ti is the travel resistance of the traffic cell i;
the POI index is used for measuring the land distribution scale, type and density of a historical block in a traffic cell;
the traffic cell is a set of nodes or connecting lines with certain traffic relevance and traffic similarity.
2. The method of claim 1, wherein the POI index for the traffic cell i is calculated by:
Figure FDA0003578209600000012
in the formula:
e is the total number of categories of POI;
e is the category of POI;
ω e the weight of the e-type POI in the traffic cell i;
d ie the number of e-type POIs in the traffic cell i.
3. The method of claim 1, wherein the negative effect is calculated by:
T i ＝w1 i ×T1 i +w2 i ×T2 i +w3 i ×T3 i
w1 i 、w2 i 、w3 i is a weight;
T1 i the average walking time from the center position of the traffic cell i to the nearest station;
T2 i calculating the time of 1-time zero transfer by taking a bus by the following formula for the average waiting time from the center position of the traffic cell i to the nearest station:
Figure FDA0003578209600000013
in the formula: f is the departure frequency of the bus, and the unit is one hour; delta is a deviation factor which represents the deviation between the actual departure time and the departure timetable of the bus;
T3 i the average route riding time from the traffic cell i to the station nearest to each traffic cell is calculated by the following formulaAnd (3) calculating:
Figure FDA0003578209600000021
in the formula: n is the number of bus lines between bus stops; l ij The distance between a certain bus net stop j and the previous stop, which starts from the stop of the traffic cell i; v1 is the average running speed of the bus.
4. The method of claim 2, wherein the categories of POIs comprise: catering services, scenic spots, public facilities, shopping services, business housing, living services, financial insurance services, science and education culture services, sports and leisure services, medical services, government agencies and social groups, lodging services, transportation facilities.
5. The method of claim 2, wherein the weight of the POI is determined by a structure entropy method.
6. A historical block public traffic network optimization method is characterized by comprising the following steps:
s100, calculating the accessibility of each bus line according to the following formula, and setting the accessibility threshold value:
Figure FDA0003578209600000022
in the formula:
di is the POI index of the traffic cell i, and Ti is the travel resistance of the traffic cell i; the POI index is used for measuring the land distribution scale, type and density of a historical block in a traffic cell; the traffic cell is a set of nodes or connecting lines with certain traffic association degree and traffic similarity degree; the POI index of the traffic cell i is calculated by:
Figure FDA0003578209600000023
in the formula: e is the total number of categories of POI; e is the category of POI; omega e The weight of the e-type POI in the traffic cell i; d ie The number of e-type POIs in the traffic cell i;
s200, taking the bus routes which do not reach the accessibility threshold as the bus routes to be optimized, and establishing a target function and constraint conditions; the objective function is:
T i ＝w1 i ×T1 i +w2 i ×T2 i +w3 i ×T3 i
the constraint conditions are as follows:
(1) the length of the line is as follows: l is more than or equal to 5km and less than or equal to 20 km;
(2) average station distance:
Figure FDA0003578209600000031
n is the total number of docking stations;
(3) wire mesh density:
Figure FDA0003578209600000032
s is the area of all traffic districts;
(4) line nonlinear coefficient:
Figure FDA0003578209600000033
d is the linear distance between the lattice points;
(5) resident travel time consumption: t is i Tmax is less than or equal to, and is the maximum time consumption of 95 percent of residents related to the urban scale in one trip;
in the formula:
ti is the travel resistance of the traffic cell i;
T1 i the average walking time from the center position of the traffic cell i to the nearest station;
T2 i calculating the time of 1-time zero transfer by taking a bus by the following formula for the average waiting time from the center position of the traffic cell i to the nearest station:
Figure FDA0003578209600000034
in the formula: f is the departure frequency of the bus, and the unit is one hour; delta is a deviation factor representing the deviation between the actual departure time and the departure timetable of the bus;
T3 i the average route riding time from the traffic cell i to the nearest station to each traffic cell is calculated by the following formula:
Figure FDA0003578209600000035
in the formula: n is the number of bus lines between bus stops; l ij The distance between a certain bus net stop j and the previous stop, which starts from the stop of the traffic cell i; v1 is the average running speed of the bus;
and S300, obtaining the minimum value of the objective function to obtain the optimized line meeting the constraint condition and the accessibility threshold.
7. The method of claim 6, wherein the optimization circuit is obtained by a modified ant colony algorithm; the improved ant colony algorithm is as follows:
s301, selecting an unoptimized bus line as a current line;
s302, placing M1 ants on the determined starting and ending points, calculating pheromones of all road sections, gradually optimizing according to the transition probability, and determining the moving direction of the ants;
s303, updating the alternative node set once each time the search is completed, and removing the bus stops which have already walked;
s304, searching the next node until the complete path search is completed, and deleting the ant; searching the starting and ending points of bus trips in the community until the bus routes of all the starting and ending point pairs are completed;
s305, after the ant colony completes path optimization, calculating the length of the optimized bus line, comparing the calculation result with the value of the current bus line, updating the value of the current bus line to be the minimum value of the two, and updating the pheromone strength on the path;
s306, judging whether the current optimized bus line meets all constraints and reachability standards, and if so, outputting all nodes of the line as bus stops and the distance between the two nodes; otherwise, returning to S302 for readjustment;
and S307, judging whether an unoptimized bus route exists, if so, selecting an unoptimized bus route as the current route, and returning to S302.
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* Cited by examiner, † Cited by third party
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CN116127330A (en) * 2022-09-14 2023-05-16 兰州交通大学 Road network semantic similarity measurement model
CN116127330B (en) * 2022-09-14 2023-11-03 兰州交通大学 Road network semantic similarity measurement model

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