CN109753694A - Small and medium-sized cities Transit Network Design method based on overall process trip detecting period - Google Patents
Small and medium-sized cities Transit Network Design method based on overall process trip detecting period Download PDFInfo
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Abstract
The invention discloses a kind of small and medium-sized cities Transit Network Design methods based on overall process trip detecting period, this method is constraint with line length, gauze vehicle fleet, departure interval, number of transfer etc., so that passenger goes on a journey, the minimum target of overall process trip detecting period carries out public bus network design, first, it determines bus station set and first and last website pair, generates initial candidate sets of lines using K shortest path algorithm.Secondly, screening to initial candidate sets of lines by the sum of the weight coefficient of public transport node in line length and every route, second-generation candidate sets of lines is obtained.Again, it is utilized respectively genetic algorithm and the method for exhaustion obtains three generations's public bus network collection, determine main transit line and branch line scheme, finally, public transport network is adjusted in conjunction with actual conditions, it is allowed to more realistic demand, this method can be small and medium-sized cities Transit Network Design providing method foundation according to the design requirement of main transit line and branch line different levels.
Description
Technical Field
The invention relates to an urban public transport planning technology, in particular to a middle and small city public transport network design method based on overall travel sensing time.
Background
According to the novel national township planning (2014-2020), the accelerated development of small and medium cities is taken as a main attack direction for optimizing the scale structure of the town, so that the distribution guidance of industrial and public service resources is enhanced, the quality is improved, and the quantity is increased. Meanwhile, in recent years, the rapid promotion of national highway and high-speed railway network construction also provides good conditions for the development of medium and small cities. The acceleration of the urbanization process enables the level of motorization of medium and small cities to continuously rise, the travel demand of residents is also continuously increased, and more residents select individual motorized transportation means for traveling. In addition, the population gathering capacity of medium and small cities is gradually increasing, and the traveling demands of external floating population such as tourism and public affairs are also urgently needed to be met. This not only brings huge traffic pressure for the urban infrastructure, but also can cause a series of urban diseases simultaneously, is unfavorable for urban sustainable development. The public transportation is preferentially developed, and the green travel of residents is guided, so that the public transportation is a necessary way for promoting the health and sustainable development of medium and small cities. At present, the public transportation service of small and medium-sized cities in China still needs to be further improved.
From the supply side, due to the lack of system planning, the promotion of public transportation service of medium and small cities lags behind the increase of travel demands of residents; meanwhile, the advantages of the public transport service in long-distance travel are difficult to show; under the condition of no road right guarantee, the operation environment of public transport is severe. From the demand side, the daily travel of residents in medium and small cities has the following characteristics: (1) the scale of the urban built-up area is small, and the trip distance is generally concentrated within 3 km; (2) the time sensitivity is strong, and the travel time change has great influence on the traffic mode selection; (3) the proportion of middle-aged and old people of public transport passengers is high, the problem in the public transport service of middle and small cities is solved, the requirement characteristic of resident trip is considered at first, therefore, when the public transport network planning of middle and small cities is underway, the requirement of the resident is used as guidance, the problem which is most concerned by the passenger is solved, namely the trip sensing time in the whole process is taken as a target, factors such as line mileage, passenger groups and the like are comprehensively considered, the line network organization optimization and the schedule arrangement are carried out, the public transport service which is more suitable for the trip characteristic of middle and small cities is provided, and the healthy sustainable development of urban traffic is.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the defects of the prior art and provides a method for designing a public traffic network in a medium and small city based on the whole-process travel sensing time.
The technical scheme is as follows: in order to achieve the purpose, the invention provides a public transportation network design method of a medium and small city based on whole-process travel sensing time, which comprises the following steps:
collecting OD data at a trip peak time period in a line network design area range, wherein the OD data are used as an OD input matrix of a preferred part of a bus trunk line, and all-weather OD data are collected and used as an OD input matrix of a preferred part of a bus branch line; setting related parameters of bus network design, including bus running speed, pedestrian walking speed, bus scale, bus sharing rate, departure interval, unsatisfied demand scale coefficient and perception time coefficient;
step two, determining a bus stop set;
step three, generating an initial candidate line set by utilizing a Dijkstra algorithm and a K-shortest path algorithm, namely deleting a certain arc on the existing shortest path in the directed graph, and searching the next optional shortest path by searching a replaced arc;
step four, screening conditions to generate a second generation candidate line set;
fifthly, optimizing the bus trunk line by the genetic algorithm;
step six, optimizing the bus branch lines by an exhaustion method;
and step seven, integrating the optimal selection results of the bus trunk lines and the bus branches, and adjusting the lines according to the requirements of actual conditions, net coverage rate and the like.
As a further preferred aspect of the present invention, in the step two, the specific steps of determining the bus station set are as follows:
2.1, distributing the passenger flow of each traffic cell to a main passenger flow source point of the traffic cell:
2.2, calculating the weight coefficient W of the bus stop to be selectedv：
2.3, screening and generating a bus stop set S, and initializing a candidate stop set S, wherein S is an empty set; the stations in the original bus station set V are mixed and arranged according to the descending order of the weight coefficient, and the generated list is usedVL' represents; each time, the station with the maximum mixed weight coefficient is selected from VL' as s*If s is*If the bus stop is not within the range of 300m of any confirmed bus stop, the bus stop is classified into a bus stop set S, otherwise, the bus stop set S is deleted; repeating the above operations until VL' is an empty set;
and 2.4, considering the land requirements of the first and last stations, and selecting the first and last station pairs of the bus lines in the existing first and last station facilities.
As a further preferable aspect of the present invention, in step 2.2, the method for calculating the weight coefficient of the bus stop to be selected is as follows:
v is an original bus station set, V is a node in V for screening, and z is a passenger flow source point in a traffic cell; l (v, z) refers to the walking distance of node v to the traffic source point z, Oz and DzRespectively, the passenger flow generation amount and the attraction amount of the passenger flow source point z, and e is a natural constant.
As a further preferable aspect of the present invention, in step 2.3, the method for calculating the hybrid weight coefficient of the bus stop to be selected is as follows:
Wvzong＝0.8*Wv elderly person+0.2*Wv non-elderly
wherein ,Wv elderly person and Wv non-elderlyThe weighting coefficients of the stations for the two types of people are calculated by taking the attraction degree of the appointed bus station v to the travel amount of the elderly and the non-elderly as a reference.
As a further preferred aspect of the present invention, in step four, the specific steps of conditional screening to generate the second generation candidate line set are as follows:
4.1, screening out the line which meets the line length constraint according to the line length requirement,
wherein ,lkThe unit of the line length of the kth line is km, and A is an initial candidate line set;
4.2, calculating the sum sigma W of the weight coefficients of all the bus nodes on each linevzongAnd sorting the lines, and removing the lines with the last 5% of the ranking to generate a second generation candidate line set.
As a further preferred aspect of the present invention, in step five, the specific steps of the genetic algorithm for optimizing the bus trunk line are as follows:
5.1, based on the second generation candidate line set, converting each line into chromosomes by adopting binary coding, wherein each chromosome represents a bus network and comprises a plurality of bus lines; the gene label is 0 to indicate that the bus line does not exist in the current line set, and 1 to indicate that the bus line belongs to the current line set; the length of each chromosome is equal to the number of lines in the second generation candidate line set; when i is the iteration number, and when i is 1, the bus route set of the first iteration is randomly generated;
5.2, calculating the fitness of each chromosome in the set aiming at the set of chromosomes generated by each iteration:
minβi＝min[γ+(WD-αDzong)*100]i
γ＝(InVehTimp,q+ω1*WalkTimp,q+ω2*WaitTimp,q+ω3*TransferTimp,q)*dp,q
where i is the number of iterations βiIs the fitness value corresponding to the ith iteration chromosome, p and q are the numbers of the traffic district passenger flow source points of the corresponding departure point and the destination, gamma is the sum of the total travel sensing time of the passengers from the departure point p to the destination q, WD is the number of unsatisfied demands of the wire network, DzongFor total scale of travel demand, αInVehTim for the size coefficient of the unmet demand at the corresponding net levelp,qThe time spent in the train from the departure point p to the destination point q is in min, WalkTimp,qThe walking time from the departure point p to the getting-on station m and from the getting-off station n to the destination q is min, WaitTimp,qThe waiting time of the station at the station-boarding point m is min, transferTimp,qThe time for waiting for transfer is min, omega, required for transferring the next bus line at the intermediate station r1、ω2、ω3Weight coefficients, ω, of walking time, waiting time, and transfer waiting time, respectively1，ω2，ω3>0，dp,qRepresenting the departure amount from the departure point p to the destination q;
5.3, calculating the selection probability P of each chromosome in the populationj：
wherein ,Pj and hjRespectively the probability of the jth chromosome being selected and the corresponding fitness value, R is the number of chromosomes in the population, g is the algebraic serial number, T is the pre-temperature0Initial temperature, C and D are positive parameters, and the probability of gene mutation is set to be 0.05;
5.4, demand allocation, which comprises the following specific steps:
for passengers who do not transfer, the travel sensing time of the whole process of the passengers is calculated as follows:
wherein ,the line length from the getting-on station m to the getting-off station n in the kth line is km; l (m is,p) and l (n, q) are walking distances from the station m and the station n to the corresponding departure point p and destination q, respectively, and the unit is km; t iskIs the departure interval of the riding route k, and the unit is min;is the trip demand corresponding to the kth line;
the constraint conditions for calculating the travel perception time of the passenger in the whole process are as follows:
(1) vehicle size constraints: n is a radical ofbus≤Nnow，NbusTotal number of vehicles in the net, NnowFor the total number of vehicles currently in existence,vvehicle with wheelsThe unit is km/h for the running speed of the bus, and t is the rest time of a driver in a back-and-forth clearance, which is generally 10 min;
(2) restraint of departure interval: t is more than or equal to 5k≤30；
(3) Constraint of transfer times: wherein ,is the number of transfers;
(4) the constraint of the required scale is that WD is less than or equal to αzongWhere WD is the number of unmet demands, α is the factor of the size of the unmet demands at the corresponding net level, DzongThe number of total travel demands;
for passengers needing one transfer, the method for calculating the travel perception time of the passengers in the whole process is as follows:
wherein ,is the k-th1，k1The line length from station m, n to transfer station r in a line is km, Tk1，Tk2Are respectively k1，k1The departure interval of each line is min; if there is no direct line between sites m, n, but passes through line set A of site mmAnd line A through site nnIf the cross site r exists, all OD requirements are distributed to the corresponding lines; if a plurality of changeable routes exist between the stations m and n, the OD requirements are distributed according to the ratio of the departure frequency of the corresponding route to the departure frequency of the main route;
the constraint conditions for calculating the travel perception time of the passenger in the whole process are as follows:
(1) vehicle size constraints: n is a radical ofbus≤Nnow；
(2) Restraint of departure interval: t is more than or equal to 5k1，Tk2≤30；
(3) Constraint of transfer times:
(4) the constraint of the required scale is that WD is less than or equal to αzong；
As a further preferred aspect of the present invention, in step 5.4, the trip demand corresponding to the k-th lineThe calculation method of (2) is as follows:
if only one line exists between the station m and the station n, all OD requirements are distributed to the line; if a plurality of lines exist between the station m and the station n, the OD requirements are distributed according to the ratio of the departure frequency of the corresponding line to the departure frequency of the total line:
wherein ,dm,nIs the total travel demand between m and n sites, fkIs the departure frequency of the kth line, K ∈ K, PmaxThe maximum passenger flow of the section is represented by the unit of people/h, W is the passenger load of the expected bus and is represented by the unit of people, W is more than or equal to 20 and less than or equal to 40, fk0And K is a direct bus route set between m and n stations as an initial departure frequency.
As a further preferred aspect of the present invention, in the sixth step, the detailed steps of optimizing the bus branch line by the exhaustion method are as follows:
6.1, converting the travel demand between the passenger flow source points of the traffic community into the travel demand between the bus stops according to the stop weight coefficients of the bus stops
6.2, the minimum travel sensing time of the whole process of the passenger is taken as a target:
minγm,n＝minInVehTimm,n
the constraint conditions are as follows: n is a radical ofbus≤Nnow，WD≤α*Dzong；
6.3, screening the preferred bus trunk line in the step 5 from the second generation candidate line set, and dividing the bus trunk line set by the sum sigma W of the mixed weight coefficients of each linevzongTaking the route as a reference, performing descending order arrangement on the rest routes, and sequentially distributing the travel demands among the sites to the routes in the second generation candidate route set; if the same station OD time pair exists in the plurality of lines, the OD requirement is preferentially distributed to the shortest path, and the line direct passenger flow of each line is calculated according to the OD requirement;
6.4, based on the line direct passenger flow of each line, sorting and screening lines with line direct passenger flow ranking front epsilon% between each pair of head and end station pairs according to the bus head and end station pairs, and using the lines as a preferred bus branch line set; and (4) circulating operation until the travel demand quantity which can be met by the bus trunk line and the bus branch line is higher than a certain proportion of the total travel demand.
As a further preferred aspect of the present invention, in step 6.1, the method for converting the travel demand of the passenger flow source point in the traffic community into the travel demand among the bus stops includes:
wherein m, n belongs to V, p, q belongs to Z, l (m, p), l (n, q) is less than or equal to 1.0, b is a balance coefficient for enabling the total travel demand between the converted bus stops to be equal to the total travel demand between the traffic district passenger flow source points before conversion,
has the advantages that: according to the design method of the medium and small city bus net based on the passenger overall process travel sensing time, the time-space accessibility of bus travel is measured according to the passenger overall process travel sensing time, the minimization of the passenger overall process travel sensing time is taken as a target, and the medium and small city bus net is designed and optimized under the constraint that the line length, the vehicle scale, the departure interval, the transfer times and the travel demand do not meet the scale, so that the medium and small city bus net is more suitable for the travel demand characteristics of residents in medium and small cities, more reliable and comfortable high-quality bus service is provided, and the travel experience of the residents in the medium and small cities is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a bus stop layout determined after screening;
FIG. 3 is a schematic view of a bus trunk line;
FIG. 4 is a schematic diagram of a bus branch line;
fig. 5 is a schematic diagram of the complementary spur line run.
Detailed Description
The technical scheme of the invention is further explained in detail with reference to the attached drawings.
The invention relates to a medium and small city bus net design method based on overall travel sensing time, which mainly comprises the following steps as shown in figure 1:
step 1, collecting OD data at a trip peak time period in a line network design area range, wherein the OD data are used as an OD input matrix of a preferred part of a bus trunk line, and collecting all-weather OD data which are used as an OD input matrix of a preferred part of a bus branch line; setting related parameters of bus network design, including bus running speed, pedestrian walking speed, bus scale, bus sharing rate, departure interval, unsatisfied demand scale coefficient and inapplicable coefficient index;
step 2, determining a bus stop set, which comprises the following specific steps:
2.1, distributing the passenger flow of each traffic cell to a main passenger flow source point of the traffic cell:
the method comprises the steps of counting social service facilities (education, medical treatment, cultural and cultural relics, social benefits and the like) in each traffic district, taking the social service facilities as main customer source points, and distributing the passenger flow of the traffic districts according to the proportion;
2.2, calculating the weight coefficient W of the bus stop to be selectedv：
V is an original bus station set, V is a node in V for screening, and z is a passenger flow source point in a traffic cell; l (v, z) refers to the walking distance of node v to the traffic source point z, Oz and DzRespectively the passenger flow generation amount and the attraction amount of the passenger flow source point z, and e is a natural constant;
2.3, screening and generating a bus stop set S:
initializing a candidate site set S, wherein S is an empty set;
arranging stations in the original bus station set V according to the descending order of the weight coefficient, wherein the generated list is represented by VL', and the weight coefficient of the bus station to be selected is as follows:
Wvzong＝0.8*Wv elderly person+0.2*Wv non-elderly
wherein ,Wv elderly person and Wv non-elderlyThe weight coefficients of the stations for the two groups of people are calculated respectively by taking the attraction degree of the appointed bus station v to the travel amount of the elderly and the non-elderly as a reference;
each time, the station with the maximum weight coefficient is selected from VL' as s*If s is*If the bus stop is not within the range of 300m of any confirmed bus stop, the bus stop is classified into a bus stop set S, otherwise, the bus stop set S is deleted;
repeating the above operations until VL' is an empty set;
2.4, considering the land requirements of the first and last stations, and selecting the first and last station pairs of the bus lines in the existing first and last station facilities;
step 3, generating an initial candidate line set by utilizing a Dijkstra algorithm and a K-shortest path algorithm, namely deleting a certain arc on the existing shortest path in the directed graph, and searching the next optional shortest path by searching a replaced arc;
step 4, generating a second-generation candidate line set by condition screening, which comprises the following specific steps:
4.1, screening out the lines meeting the line length constraint according to the line length requirement:
wherein ,lkThe unit of the line length of the kth line is km, and A is an initial candidate line set;
4.2, calculating the sum sigma W of the weight coefficients of all the bus nodes on each linevzongSorting and eliminating the lines with the last 5 percent of the ranking to generate a second generation candidate line set;
and 5, optimizing the bus trunk line by a genetic algorithm, wherein the specific steps are as follows:
5.1, based on the second generation candidate line set, converting each line into chromosomes by adopting binary coding, wherein each chromosome represents a bus network and comprises a plurality of bus lines; the gene label is 0 to indicate that the bus line does not exist in the current line set, and 1 to indicate that the bus line belongs to the current line set; the length of each chromosome is equal to the number of lines in the second generation candidate line set; when i is the iteration number, and when i is 1, the bus route set of the first iteration is randomly generated;
5.2, calculating the fitness of each chromosome in the set aiming at the set of chromosomes generated by each iteration:
minβi＝min[γ+(WD-αDzong)*100]i
γ＝(InVehTimp,q+ω1*WalkTimp,q+ω2*WaitTimp,q+ω3*TransferTimp,q)*dp,q
where i is the number of iterations βiThe fitness value corresponding to the ith iteration chromosome, and the traffic cell passenger flow source points of the corresponding departure point and destination point are p and qThe number is gamma, the total of the travel sensing time of the passengers from the departure point p to the destination q in the whole process, WD is the number of unsatisfied demands of the net, DzongFor total trip demand size, α is the factor that the corresponding net level does not meet the demand size, InVehTimep,qThe time spent in the train from the departure point p to the destination point q is in min, WalkTimp,qThe walking time from the departure point p to the getting-on station m and from the getting-off station n to the destination q is min, WaitTimp,qThe waiting time of the station at the station-boarding point m is min, transferTimp,qThe time for waiting for transfer is min, omega, required for transferring the next bus line at the intermediate station r1、ω2、ω3Weight coefficients, ω, of walking time, waiting time, and transfer waiting time, respectively1，ω2，ω3>0，dp,qRepresenting the departure amount from the departure point p to the destination q;
5.3, calculating the selection probability P of each chromosome in the populationj：
wherein ,Pj and hjRespectively the probability of the jth chromosome being selected and the corresponding fitness value, R is the number of chromosomes in the population, g is the algebraic serial number, T is the pre-temperature0Initial temperature, C and D are positive parameters, and the probability of gene mutation is set to be 0.05;
5.4, demand allocation, which is specifically as follows:
for passengers who do not transfer, the travel sensing time of the whole process of the passengers is calculated as follows:
wherein ,the line length from the getting-on station m to the getting-off station n in the kth line is km; l (m, p) and l (n, q) are walking distances from the station m and the station n to the corresponding departure point p and destination q, respectively, and the unit is km; t iskIs the departure interval of the riding route k, and the unit is min;
if only one line exists between the station m and the station n, all OD requirements are distributed to the line; if a plurality of lines exist between the station m and the station n, the OD requirements are distributed according to the ratio of the departure frequency of the corresponding line to the departure frequency of the total line:
wherein ,dm,nIs the total travel demand between m and n sites, fkIs the departure frequency of the kth line, K ∈ K, PmaxThe maximum passenger flow of the section is represented by the unit of people/h, W is the passenger load of the expected bus and is represented by the unit of people, W is more than or equal to 20 and less than or equal to 40, fk0The method comprises the following steps that (1) K is an initial departure frequency, and is a direct bus route set between m and n stops;
the constraint conditions for calculating the travel perception time of the passenger in the whole process are as follows:
(1) constraint of vehicle size: n is a radical ofbus≤Nnow，NbusTotal number of vehicles in the net, NnowFor the total number of vehicles currently in existence,vvehicle with wheelsThe unit is km/h for the running speed of the bus, and t is the rest time of a driver in a back-and-forth clearance, which is generally 10 min;
(2) restraint of departure interval: t is more than or equal to 5k≤30；
(3) Constraint of transfer times: wherein ,is the number of transfers;
(4) constraint of required scale WD ≦ α × DzongWhere WD is the number of unmet demands, α is the factor of the size of the unmet demands at the corresponding net level, DzongThe number of total travel demands;
for passengers needing one transfer, the method for calculating the travel perception time of the passengers in the whole process is as follows:
wherein ,is the k-th1，k1The line length from station m, n to transfer station r in a line is km, Tk1，Tk2Are respectively k1，k1The departure interval of each line is min; if there is no direct line between sites m, n, but passes through line set A of site mmAnd line A through site nnIf the cross site r exists, all OD requirements are distributed to the corresponding lines; if a plurality of changeable routes exist between the stations m and n, the OD requirements are distributed according to the ratio of the departure frequency of the corresponding route to the departure frequency of the main route;
the constraint conditions for calculating the travel perception time of the passenger in the whole process are as follows:
(1) constraint of vehicle size: n is a radical ofbus≤Nnow；
(2) Restraint of departure interval: t is more than or equal to 5k1，Tk2≤30；
(3) Constraint of transfer times:
(4) constraint of required scale WD ≦ α × Dzong；
Step 6, optimizing the bus branch lines by an exhaustion method, and specifically comprising the following steps:
6.1 calculating the travel demand between sitesThe travel demand among the passenger flow source points of the traffic community is converted into the travel demand among the bus stops according to the stop weight coefficients of the bus stops, and the conversion process is as follows:
wherein m, n belongs to V, p, q belongs to Z, l (m, p), l (n, q) is less than or equal to 1.0, b is a balance coefficient for enabling the total travel demand between the converted bus stops to be equal to the total travel demand between the traffic district passenger flow source points before conversion,
6.2, the minimum travel sensing time of the whole process of the passenger is taken as a target:
minγm,n＝minInVehTimm,n
the constraint conditions are as follows: (1) constraint of vehicle size: n is a radical ofbus≤Nnow，Constraint of required scale WD ≦ α × Dzong；
6.3 selection of step 5The bus trunk line is intensively screened from the second generation candidate line, and the summation sigma W of the mixed weight coefficients of each linevzongTaking the route as a reference, performing descending order arrangement on the rest routes, and sequentially distributing the travel demands among the sites to the routes in the second generation candidate route set; if the same station OD time pair exists in the plurality of lines, the OD requirement is preferentially distributed to the shortest path, and the line direct passenger flow of each line is calculated according to the OD requirement;
6.4, based on the line direct passenger flow of each line, sorting and screening lines with line direct passenger flow ranking front epsilon% between each pair of head and end station pairs according to the bus head and end station pairs, and using the lines as a preferred bus branch line set; circulating operation until the travel demand quantity which can be met by the bus trunk line and the bus branch line is higher than a certain proportion of the total travel demand;
and 7, integrating the optimal selection results of the bus trunk lines and the bus branches, and adjusting the lines according to the requirements of actual conditions, net coverage rate and the like.
The method of the present invention is further described below with reference to an example.
And (3) extracting the traveling quantities of the peak and the flat peak by adopting resident survey data of Deqing county 2016, and obtaining the OD input matrix of the public transport trunk line according to the step 1. Table 1 is a preferred partial OD input matrix (unit: number of people in one hour) for the bus trunk, and table 2 is a preferred partial OD input matrix for the bus branch.
TABLE 1
| O\D | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ``` | Total of |
| 1 | 188 | 41 | 1 | 41 | 42 | 1 | 2 | 2 | ``` | 822 |
| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ``` | 42 |
| 3 | 1 | 1 | 263 | 124 | 93 | 21 | 41 | 1 | ``` | 1554 |
| 4 | 1 | 1 | 1 | 5 | 23 | 21 | 2 | 1 | ``` | 394 |
| 5 | 186 | 52 | 5 | 273 | 796 | 145 | 242 | 10 | ``` | 3723 |
| 6 | 1 | 1 | 23 | 2 | 32 | 1 | 10 | 1 | ``` | 196 |
| 7 | 1 | 1 | 7 | 2 | 31 | 1 | 21 | 1 | ``` | 229 |
| 8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ``` | 42 |
| ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` |
| Total of | 1194 | 890 | 696 | 3480 | 2628 | 634 | 1091 | 201 | ``` | 62367 |
TABLE 2
And setting related parameters of bus network design, wherein the table 3 is a bus network design parameter list.
TABLE 3
According to the step 2, allocating OD among 42 traffic cells to 82 passenger flow source points, calculating the distance between each candidate node and the passenger flow source point within 1km close to each candidate node, and based on the mixed weight coefficient W of the combination of the old and non-old people of the candidate nodevzongScreening and clustering bus stops, and screening bus stops with the spacing larger than that of the bus stops300m bus stops, a bus stop set S is generated, and 77 stops are included. As shown in fig. 2, is a bus stop layout determined after screening. The method is characterized in that the first and last bus stations are arranged on the periphery of a city in consideration of the city land layout, the travel demand distribution and the construction conditions of the first and last bus stations in the Deqing county. Fig. 3 is a schematic diagram of the positions of the first station and the last station of the bus.
And table 4 shows the serial numbers and station names of the first and last bus station pairs.
TABLE 4
And 3, generating an initial candidate line set by using a Dijkstra algorithm and a K-shortest path algorithm, and according to the current urban scale and the resident trip demand of the Germany-Qing county, taking 20 from K, namely, each pair of starting and ending points in the generated initial candidate line set comprises 20 bus lines to obtain 220 bus lines as the initial candidate line set.
And on the basis of the step 3, according to the step 4, the length of the bus lines does not exceed 13km and is not less than 6km, and the total number of the bus lines is 160. And counting the sum of the mixed weight coefficients of the bus nodes on each line of the rest lines according to the line numbers, removing the lines with the last 5% of the ranking, and finally obtaining 152 lines which are used as a second-generation candidate line set. Table 5 is a partial list of the second generation candidate route set.
TABLE 5
According to the step 5, the bus trunk line is preferably selected in the second generation candidate line set obtained in the step 4. In the parameter setting of the genetic algorithm, oneThe second iteration generates 15 chromosomes with a chromosome size of 152, i.e., 152 genes per chromosome, which is consistent with the number of candidate lines in the second generation of candidate line set. There are 5 lines on each chromosome, i.e. the number of the preferred line concentration lines of the final bus trunk line is 5. Cross probability of 0.5, mutation probability of 0.05, initial temperature T010000 is set, and the positive parameter C, D is 100 and 0.05 respectively. The number of iterations was set to 30. And inputting a travel OD matrix, bus network design parameters, a bus stop adjacency matrix and second-generation candidate line set data, wherein the bus network corresponding to the approximate optimal solution can meet 50.3% of travel demand in the total passenger flow scale, and can cover more than 50.3% of travel demand in the early-late travel peak time period. Table 6 shows the bus trunk route plan corresponding to the best chromosome. Fig. 3 is a schematic diagram showing the route of the public transportation trunk line in deqing county.
TABLE 6
According to the step 6, the through and transfer OD demand quantity in one day which can be met by the bus trunk line network under the current departure frequency scheme is removed from the OD total demand, and the OD demand between the passenger flow source points of the remaining traffic cells is converted into the travel demand between the bus stops by using a stop demand conversion formula; screening the line scheme selected as the bus trunk line from the second generation candidate line set, calculating the sum of station mixed weight coefficients of the remaining 147 lines, and distributing station OD requirements in a descending order according to the total weight coefficient; and screening the bus lines each time to meet the line reaching 5% of the rank of the passenger flow, calculating the total amount of the travel demands met by the bus line network after the trunk line is combined with the screening result, finishing the screening operation until the total amount of the met travel demands reaches 80% of the total amount of the urban travel demands, and calculating the passenger flow actually taken based on the expected bus sharing rate of 15% to determine the vehicle scale. Table 7 shows the results of the bus branch line optimization by the exhaustion method. Fig. 4 is a schematic diagram showing the route of a branch bus in deqing county.
TABLE 7
And 5, based on the bus trunk lines and the branch lines obtained in the step 5 and the step 6, the vehicle scale is totally 99 standard stations. The existing bus scale of the Duqing county city bus is 105 benchmarks, on the basis of line optimization, three city bus branches along the out-of-city direction from the west station of a train are added in order to enable the existing bus facilities to fully exert practical effects, the bus scale of each line is 2 benchmarks, and the departure interval is 30 min. Fig. 5 is a schematic diagram of the complementary branch line routing.
Claims (9)
1. A middle and small city bus network design method based on overall travel sensing time is characterized by comprising the following steps:
collecting OD data at a trip peak time period in a line network design area range, wherein the OD data are used as an OD input matrix of a preferred part of a bus trunk line, and all-weather OD data are collected and used as an OD input matrix of a preferred part of a bus branch line; setting related parameters of bus network design, including bus running speed, pedestrian walking speed, bus scale, bus sharing rate, departure interval, unsatisfied demand scale coefficient and perception time coefficient;
step two, determining a bus stop set;
step three, generating an initial candidate line set by utilizing a Dijkstra algorithm and a K-shortest path algorithm, namely deleting a certain arc on the existing shortest path in the directed graph, and searching the next optional shortest path by searching a replaced arc;
step four, screening conditions to generate a second generation candidate line set;
fifthly, optimizing the bus trunk line by the genetic algorithm;
step six, optimizing the bus branch lines by an exhaustion method;
and step seven, integrating the optimal selection results of the bus trunk lines and the bus branches, and adjusting the lines according to the requirements of actual conditions, net coverage rate and the like.
2. The design method of the medium and small city bus net based on the whole-process travel perception time as claimed in claim 1, wherein the design method comprises the following steps: in the second step, the specific steps for determining the bus stop set are as follows:
2.1, distributing the passenger flow of each traffic cell to a main passenger flow source point of the traffic cell;
2.2, calculating the weight coefficient W of the bus stop to be selectedv；
2.3, screening and generating a bus stop set S, and initializing a candidate stop set S, wherein S is an empty set; arranging the station mixtures in the original bus station set V according to the descending order of the weight coefficient, and representing the generated list by VL'; each time, the station with the maximum mixed weight coefficient is selected from VL' as s*If s is*If the bus stop is not within the range of 300m of any confirmed bus stop, the bus stop is classified into a bus stop set S, otherwise, the bus stop set S is deleted; repeating the above operations until VL' is an empty set;
and 2.4, considering the land requirements of the first and last stations, and selecting the first and last station pairs of the bus lines in the existing first and last station facilities.
3. The design method of the medium and small city bus net based on the whole-process travel perception time as claimed in claim 2, characterized in that: in step 2.2, the method for calculating the weight coefficient of the bus stop to be selected comprises the following steps:
v is an original bus station set, V is a node in V for screening, and z is a passenger flow source point in a traffic cell; l (v, z) refers to the walking distance of node v to the traffic source point z, Oz and DzRespectively, the passenger flow generation amount and the attraction amount of the passenger flow source point z, and e is a natural constant.
4. The design method of the medium and small city bus net based on the whole-process travel perception time as claimed in claim 2, characterized in that: in step 2.3, the method for calculating the mixed weight coefficient of the bus stop to be selected comprises the following steps:
Wvzong＝0.8*Wv elderly person+0.2*Wv non-elderly
wherein ,Wv elderly person and Wv non-elderlyThe weighting coefficients of the stations for the two types of people are calculated by taking the attraction degree of the appointed bus station v to the travel amount of the elderly and the non-elderly as a reference.
5. The design method of the medium and small city bus net based on the whole-process travel perception time as claimed in claim 1, wherein the design method comprises the following steps: in the fourth step, the concrete steps of generating the second generation candidate line set by condition screening are as follows:
4.1, screening out the line which meets the line length constraint according to the line length requirement,
wherein ,lkThe unit of the line length of the kth line is km, and A is an initial candidate line set;
4.2, calculating the weight coefficients of all the bus nodes on each lineSum Σ WvzongAnd sorting the lines, and removing the lines with the last 5% of the ranking to generate a second generation candidate line set.
6. The design method of the medium and small city bus net based on the whole-process travel perception time as claimed in claim 1, wherein the design method comprises the following steps: in the fifth step, the specific steps of the genetic algorithm for optimizing the bus trunk line are as follows:
5.1, based on the second generation candidate line set, converting each line into chromosomes by adopting binary coding, wherein each chromosome represents a bus network and comprises a plurality of bus lines; the gene label is 0 to indicate that the bus line does not exist in the current line set, and 1 to indicate that the bus line belongs to the current line set; the length of each chromosome is equal to the number of lines in the second generation candidate line set; when i is the iteration number, and when i is 1, the bus route set of the first iteration is randomly generated;
5.2, calculating the fitness of each chromosome in the set aiming at the set of chromosomes generated by each iteration:
minβi＝min[γ+(WD-αDzong)*100]i
γ＝(InVehTimp,q+ω1*WalkTimp,q+ω2*WaitTimp,q+ω3*TransferTimp,q)*dp,q
where i is the number of iterations βiIs the fitness value corresponding to the ith iteration chromosome, p and q are the numbers of the traffic district passenger flow source points of the corresponding departure point and the destination, gamma is the sum of the total travel sensing time of the passengers from the departure point p to the destination q, WD is the number of unsatisfied demands of the wire network, DzongFor total trip demand size, α is the factor that the corresponding net level does not meet the demand size, InVehTimep,qThe time spent in the train from the departure point p to the destination point q is in min, WalkTimp,qThe walking time from the departure point p to the getting-on station m and from the getting-off station n to the destination q is min, WaitTimp,qThe waiting time of the station at the station-boarding point m is min, transferTimp,qTo be at an intermediate siter the time for waiting for transfer required for transferring the next bus line, the unit is min, omega1、ω2、ω3Weight coefficients, ω, of walking time, waiting time, and transfer waiting time, respectively1，ω2，ω3>0，dp,qRepresenting the departure amount from the departure point p to the destination q;
5.3, calculating the selection probability P of each chromosome in the populationj：
wherein ,Pj and hjRespectively the probability of the jth chromosome being selected and the corresponding fitness value, R is the number of chromosomes in the population, g is the algebraic serial number, T is the pre-temperature0Initial temperature, C and D are positive parameters, and the probability of gene mutation is set to be 0.05;
5.4, demand allocation, which comprises the following specific steps:
for passengers who do not transfer, the travel sensing time of the whole process of the passengers is calculated as follows:
wherein ,the line length from the getting-on station m to the getting-off station n in the kth line is km; l (m, p) and l (n, q) are walking distances from the station m and the station n to the corresponding departure point p and destination q, respectively, and the unit is km; t iskIs the departure interval of the riding route k, and the unit is min;is the trip demand corresponding to the kth line;
the constraint conditions for calculating the travel perception time of the passenger in the whole process are as follows:
(1) vehicle size constraints: n is a radical ofbus≤Nnow，NbusTotal number of vehicles in the net, NnowFor the total number of vehicles currently in existence,vvehicle with wheelsThe unit is km/h for the running speed of the bus, and t is the rest time of a driver in a back-and-forth clearance, which is generally 10 min;
(2) restraint of departure interval: t is more than or equal to 5k≤30；
(3) Constraint of transfer times: wherein ,is the number of transfers;
(4) the constraint of the required scale is that WD is less than or equal to αzongWhere WD is the number of unmet demands, α is the factor of the size of the unmet demands at the corresponding net level, DzongThe number of total travel demands;
for passengers needing one transfer, the method for calculating the travel perception time of the passengers in the whole process is as follows:
wherein ,is the k-th1，k1The line length from station m, n to transfer station r in a line is km, Tk1，Tk2Are respectively k1，k1The departure interval of each line is min; if there is no direct line between sites m, n, but passes through line set A of site mmAnd line A through site nnIf the cross site r exists, all OD requirements are distributed to the corresponding lines; if there are multiple changeable routes between sites m, n, the OD demand is according to the corresponding routeThe dispatching frequency of the bus route is distributed in proportion to the dispatching frequency of the bus route;
the constraint conditions for calculating the travel perception time of the passenger in the whole process are as follows:
(1) vehicle size constraints: n is a radical ofbus≤Nnow；
(2) Restraint of departure interval: t is more than or equal to 5k1，Tk2≤30；
(3) Constraint of transfer times:
(4) the constraint of the required scale is that WD is less than or equal to αzong；
7. The design method of the medium and small city bus net based on the whole-process travel perception time as claimed in claim 6, wherein the design method comprises the following steps: in step 5.4, the trip demand corresponding to the kth lineThe calculation method of (2) is as follows:
if only one line exists between the station m and the station n, all OD requirements are distributed to the line; if a plurality of lines exist between the station m and the station n, the OD requirements are distributed according to the ratio of the departure frequency of the corresponding line to the departure frequency of the total line:
wherein ,dm,nIs the total travel demand between m and n sites, fkIs the departure frequency of the kth line, K ∈ K, PmaxThe maximum passenger flow of the section is represented by the unit of people/h, W is the passenger load of the expected bus and is represented by the unit of people, W is more than or equal to 20 and less than or equal to 40, fk0And K is a direct bus route set between m and n stations as an initial departure frequency.
8. The design method of the medium and small city bus net based on the whole-process travel perception time as claimed in claim 1, wherein the design method comprises the following steps: in the sixth step, the concrete steps of optimizing the bus branch line by an exhaustion method are as follows:
6.1, converting the travel demand between the passenger flow source points of the traffic community into the travel demand between the bus stops according to the stop weight coefficients of the bus stops
6.2, the minimum travel sensing time of the whole process of the passenger is taken as a target:
minγm,n＝minIn VehTimm,n
the constraint conditions are as follows: n is a radical ofbus≤Nnow，WD≤α*Dzong；
6.3, screening the preferred bus trunk line in the step 5 from the second generation candidate line set, and dividing the bus trunk line set by the sum sigma W of the mixed weight coefficients of each linevzongTaking the route as a reference, performing descending order arrangement on the rest routes, and sequentially distributing the travel demands among the sites to the routes in the second generation candidate route set; if the same station OD time pair exists in the plurality of lines, the OD requirement is preferentially distributed to the shortest path, and the line direct passenger flow of each line is calculated according to the OD requirement;
6.4, based on the line direct passenger flow of each line, sorting and screening lines with line direct passenger flow ranking front epsilon% between each pair of head and end station pairs according to the bus head and end station pairs, and using the lines as a preferred bus branch line set; and (4) circulating operation until the travel demand quantity which can be met by the bus trunk line and the bus branch line is higher than a certain proportion of the total travel demand.
9. The medium and small city bus net design method based on overall travel sensing time as claimed in claim 8, wherein: in step 6.1, the method for converting the travel demand of the passenger flow source point of the traffic community into the travel demand among the bus stops comprises the following steps:
wherein m, n belongs to V, p, q belongs to Z, l (m, p), l (n, q) is less than or equal to 1.0, b is a balance coefficient for enabling the total travel demand between the converted bus stops to be equal to the total travel demand between the traffic district passenger flow source points before conversion,
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