CN109753694B - Method for designing medium and small city public transportation network based on whole-process travel sensing time - Google Patents
Method for designing medium and small city public transportation network based on whole-process travel sensing time Download PDFInfo
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Abstract
The invention discloses a method for designing a medium and small city bus network based on whole-process travel sensing time, which takes the length of a line, the total number of network vehicles, the distance between departure, the number of transfer times and the like as constraints, and aims to minimize the passenger travel whole-process travel sensing time to carry out bus line design. And secondly, screening the initial candidate line set according to the sum of the line length and the weight coefficient of the bus node on each line to obtain a second-generation candidate line set. Thirdly, a third generation public transportation line set is obtained by using a genetic algorithm and an exhaustion method respectively, a public transportation trunk line and a branch line scheme are determined, and finally, the public transportation line network is adjusted according to actual conditions so as to meet actual requirements.
Description
Technical Field
The invention relates to a city public transportation planning technology, in particular to a medium and small city public transportation network design method based on whole-process travel sensing time.
Background
According to the national new town planning (2014-2020), the accelerating middle and small cities are taken as the main attack direction for optimizing the town scale structure, the industrial and public service resource layout guidance is enhanced, the quality is improved, and the quantity is increased. Meanwhile, in recent years, the rapid promotion of national expressway and high-speed railway network construction also provides good conditions for the development of medium and small cities. The acceleration of the urban process enables the motorized level of the middle and small cities to be continuously increased, the travel demands of residents are also continuously increased, and more residents select individual motorized vehicles to travel. In addition, the population gathering capacity of small and medium cities is gradually increasing, and the travel demands of external floating population such as travel, public service and the like are also urgently needed to be met. The method brings huge traffic pressure to urban infrastructure, and also causes a series of urban diseases, which is unfavorable for urban sustainable development. Public transportation is preferentially developed, and green traveling of residents is a necessary way for promoting the health and sustainable development of middle and small cities. At present, public transportation services in small and medium cities in China still need to be further improved.
From the supply side, due to lack of system planning, the improvement of public transportation service in medium and small cities lags behind the increase of resident travel demands; meanwhile, the advantage of the public transport service in long-distance travel is difficult to develop; under the condition of no road right guarantee, the public transportation has a bad operation environment. From the demand side, the daily travel of the residents in the middle and small cities has the following characteristics: (1) The scale of the urban built-up area is small, and the travel distance is generally concentrated within 3 km; (2) The time sensitivity is strong, and travel time change has a great influence on traffic mode selection; (3) The proportion of middle-aged and elderly people of public transport passengers is higher, the problem in the middle-small city public transport service is solved, firstly, the demand characteristics of resident travel are considered, therefore, when the middle-small city public transport network planning is conducted, the demands of resident are used as guidance to solve the most concerned problem of passengers, namely, the whole process travel sensing time is used as a target, the factors such as line mileage, passenger groups and the like are comprehensively considered, the network organization optimization and the schedule arrangement are conducted, the public transport service which is more fit with the travel characteristics of the middle-small city is provided, and the healthy and sustainable development of urban traffic is guided.
Disclosure of Invention
The invention aims to: the invention aims to solve the defects of the prior art and provides a method for designing a public transportation network in a medium and small city based on overall travel sensing time.
The technical scheme is as follows: in order to achieve the above purpose, the invention provides a bus network design method of medium and small cities based on overall process travel sensing time, which comprises the following steps:
step one, in the range of an online network design area, acquiring OD data of a peak period as an OD input matrix of a preferable part of a bus trunk line, and acquiring all-weather OD data as an OD input matrix of the preferable part of the bus branch line; setting relevant 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;
generating an initial candidate line set by using a Dijkstra algorithm and a K-shortest algorithm, namely deleting a certain arc on the existing shortest path in the directed graph, and searching for the next optional shortest path by searching for an alternative arc;
step four, condition screening to generate a second generation candidate line set;
step five, optimizing a public transportation trunk line by using a genetic algorithm;
step six, optimizing bus branch lines by an exhaustion method;
and step seven, integrating the preferable results of the public transportation trunk line and the public transportation branch line, and carrying out line adjustment according to the actual conditions and the requirements of network coverage rate and the like.
As a further preferred aspect of the present invention, in the second step, the specific step of determining the bus stop set is as follows:
2.1, distributing the passenger flow of each traffic cell to main passenger flow source points of the traffic cells:
2.2, calculating the weight coefficient W of the bus station to be selected v ：
2.3, screening and generating a bus station set S, and initializing a candidate station set S, wherein S is an empty set; mixing stations in an original bus station set V, arranging the stations in descending order according to weight coefficients, and representing a generated list by VL'; each time a site with the largest mixed weight coefficient is selected from VL' as s * If s * If the bus station is not within 300m of any confirmed bus station, the station is marked into a bus station set S, otherwise, the station is deleted; repeating the above operation until VL' is an empty set;
and 2.4, considering the land requirements of the first station and the last station, and selecting a first station and last station pair of the bus route in the existing first station and last station facilities.
As a further preferred embodiment of the present invention, in step 2.2, the method for calculating the weight coefficient of the bus stop to be selected includes:
v is an original public transportation 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 the node v to the passenger flow source point z, O z and Dz The passenger flow generation amount and the attraction amount are respectively the passenger flow source point z, and e is a natural constant.
As a further preferred embodiment of the present invention, in step 2.3, the method for calculating the mixing weight coefficient of the bus stop to be selected includes:
W vzong ＝0.8*W v elderly people +0.2*W v non-elderly people
wherein ,Wv elderly people and Wv non-elderly people The weight coefficients of the stations for the two types of people are calculated based on the attraction degree of the appointed bus station v to the travel amounts of the old and the non-old.
As a further preferred aspect of the present invention, in the fourth step, the specific step of generating the second generation candidate line set by conditional screening is as follows:
4.1, screening out the lines meeting the line length constraint according to the line length requirement,
wherein ,lk 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 bus nodes on each line vzong And sorting, namely removing the last 5% of lines in the ranking, and generating a second generation candidate line set.
As a further preferred aspect of the present invention, in step five, the genetic algorithm preferably uses the bus trunk as follows:
5.1, converting each line into a chromosome by adopting binary codes based on a second generation candidate line set, wherein each chromosome represents a public transportation line network and comprises a plurality of public transportation 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; i is the iteration number, and when i=1, i.e. the bus route set of the first iteration is randomly generated;
5.2, for each iteration of the generated set of chromosomes, computing fitness of each chromosome in the set:
minβ i ＝min[γ+(WD-αD zong )*100] i
γ＝(InVehTim p,q +ω 1 *WalkTim p,q +ω 2 *WaitTim p,q +ω 3 *TransferTim p,q )*d p,q
where i is the number of iterations, β i For the fitness value corresponding to the ith iteration chromosome, p and q are the corresponding traffic district passenger flow source point numbers of the departure point and the destination, gamma is the sum of the passenger overall process travel sensing time from the departure point p to the destination q, WD is the number of unsatisfied demands of the network, D zong For the total traveling demand scale, alpha is the coefficient of the demand scale which is not met by the corresponding wire mesh layer, inVehTim p,q In-vehicle time in min, walkTim, taken from departure point p to destination q p,q For walking time from departure point p to boarding point m and from alighting point n to destination q, the unit is min, waitTim p,q For waiting time of the boarding station m, the unit is min, and the unit is TransferTim p,q In order to transfer the next bus line at the intermediate station r, the unit is min and omega 1 、ω 2 、ω 3 Weight coefficients, ω, of walking time, waiting time and transfer waiting time, respectively 1 ，ω 2 ，ω 3 >0，d p,q Representing the travel amount from the departure point p to the destination q;
5.3 calculation ofProbability of selection P for each chromosome in a population j ：
wherein ,Pj and hj Respectively the probability of the j-th chromosome being selected and the corresponding fitness value, R is the number of chromosomes in the population, g is algebraic sequence number, T is the pre-temperature, T 0 The initial temperature, C, D is a positive parameter, and the probability of gene mutation is set to be 0.05;
5.4, demand distribution, which comprises the following specific steps:
for a passenger who does not transfer, the passenger overall travel sensing time is calculated as follows:
wherein ,the line length from the boarding station m to the alighting station n in the kth line is km; l (m, p) and l (n, q) are walking distances from a station m and a station n to a corresponding departure point p and a destination q, respectively, and the unit is km; t (T) k The distance between departure of the riding line k is in min;Is the travel requirement corresponding to the kth line;
the constraint condition of the travel sensing time of the whole process of the passenger is calculated as follows:
(1) Constraint on vehicle scale: n (N) bus ≤N now ，N bus N is the total number of vehicles in the net now As a total number of vehicles currently existing,v vehicle with a frame The unit is km/h, t is the rest time of the back and forth clearance of the driver,typically 10min;
(2) Constraint of departure interval: t is not less than 5 k ≤30；
(3) Constraint of transfer times: wherein ,Is the transfer times;
(4) Constraint on demand scale: WD is less than or equal to alpha D zong Where WD is the number of unmet demands, α is the scale factor of the demand for the corresponding net level, D zong The number of overall travel demands;
for the passengers needing one transfer, the calculation method of the whole-process travel sensing time of the passengers is as follows:
wherein ,is the kth 1 ，k 1 The line length from the stations m and n in the line to the transfer station r is km and T k1 ，T k2 Respectively the kth 1 ，k 1 The departure interval of the lines is in min; if there is no direct line between stations m, n, but the line set A passing station m m With line A passing through station n n If a cross site r exists, the OD requirements are all distributed to the corresponding lines; if a plurality of changeable lines exist among the stations m and n, the OD requirements are distributed according to the proportion of the departure frequency of the corresponding line to the departure frequency of the main line;
the constraint condition of the travel sensing time of the whole process of the passenger is calculated as follows:
(1) Constraint on vehicle scale: n (N) bus ≤N now ；
(2) Constraint of departure interval: t is not less than 5 k1 ，T k2 ≤30；
(3) Constraint of transfer times:
(4) Constraint on demand scale: WD is less than or equal to alpha D zong ；
As a further preferred aspect of the present invention, in step 5.4, the travel requirement 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, the OD requirements are distributed to the line entirely; if a plurality of lines exist between the station m and the station n, the OD demand is allocated according to the proportion of the departure frequency of the corresponding line to the departure frequency of the main line:
wherein ,dm,n Is the total travel demand between stations m and n, f k Is the departure frequency of the kth line, K is K, P max The unit of the maximum passenger flow of the section is person/h, W is the passenger capacity of the expected bus, W is person and is more than or equal to 20 and less than or equal to 40, and f k0 For initial departure frequency, K is the direct public transport line set between stations m and n.
As a further preferred aspect of the present invention, in step six, the specific steps for exhaustively selecting bus branches are as follows:
6.1, converting travel demands among passenger flow source points of the traffic district into travel demands among bus stops according to stop weight coefficients of the bus stops
6.2, taking the minimum travel sensing time of the whole process of the passengers as a target:
minγ m,n ＝minInVehTim m,n
the constraint conditions are as follows: n (N) bus ≤N now ，WD≤α*D zong ；
6.3, screening the bus trunk line selected in the step 5 from the second generation candidate line set, and adding the sum sigma W of the mixed weight coefficients of each line vzong According to the method, the remaining lines are arranged in a descending order, and travel demands among stations are sequentially distributed to lines in the second generation candidate line set; if the same station OD pairs exist in the multiple lines, the OD requirements are preferentially distributed to the shortest lines, and the line direct passenger flow of each line is calculated according to the OD requirements;
6.4, based on the direct passenger flow of each line, classifying and screening lines with the direct passenger flow of the lines between each pair of head and tail stations according to the head and tail station pairs of the bus, and using the lines with the direct passenger flow of the lines with the epsilon% as a best selected bus branch line set; and (3) circularly operating 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 embodiment of the present invention, in step 6.1, the method for converting the travel demand of the traffic cell passenger flow source point into the travel demand between each bus stop is as follows:
wherein m, n epsilon V, p, q epsilon Z, l (m, p), l (n, q) is less than or equal to 1.0, b is a balance coefficient for making the total travel demand between converted bus stops equal to the total travel demand between traffic cell passenger flow source points before conversion,
the beneficial effects are that: according to the method for designing the medium and small city public transportation network based on the passenger whole-process travel sensing time, which is provided by the invention, the passenger whole-process travel sensing time is used for measuring the space-time accessibility of public transportation travel, the passenger whole-process travel sensing time is used as a target, and the medium and small city public transportation network is designed and optimized under the constraint that the line length, the vehicle scale, the departure interval, the transfer times and the travel demands do not meet the scale, so that the medium and small city public transportation network is more suitable for the characteristics of the resident travel demands of medium and small cities, more reliable and comfortable high-quality public transportation service is provided, and the travel experience of resident 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 diagram of a bus stop layout determined after screening;
FIG. 3 is a schematic diagram of a bus trunk line trend;
FIG. 4 is a schematic diagram of a bus branch line trend;
fig. 5 is a schematic diagram of the make-up feeder line routing.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings.
The invention relates to a method for designing a medium and small city bus network based on whole-process travel sensing time, which mainly comprises the following steps as shown in figure 1:
step 1, in the range of an online network design area, acquiring OD data of a peak period as an OD input matrix of a preferable part of a bus trunk line, and acquiring all-weather OD data as an OD input matrix of the preferable part of the bus branch line; setting relevant parameters of bus network design, including bus running speed, pedestrian walking speed, bus scale, bus sharing rate, departure interval, unsatisfied demand scale coefficient and discomfort coefficient index;
step 2, determining a bus stop set, which specifically comprises the following steps:
2.1, distributing the passenger flow of each traffic cell to main passenger flow source points of the traffic cells:
counting social service facilities (education, medical treatment, literature, social welfare and the like) in each traffic cell, taking the social service facilities as main source points, and distributing the passenger flow of the traffic cell according to a proportion;
2.2, calculating the weight coefficient W of the bus station to be selected v ：
V is an original public transportation 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 the node v to the passenger flow source point z, O z and Dz The passenger flow generation amount and the attraction amount are respectively the passenger flow source point z, and e is a natural constant;
2.3, screening and generating a bus station set S:
initializing a candidate site set S, wherein S is an empty set;
the stations in the original bus station set V are arranged in descending order according to weight coefficients, a generated list is represented by VL', and the weight coefficients of the bus stations to be selected are as follows:
W vzong ＝0.8*W v elderly people +0.2*W v non-elderly people
wherein ,Wv elderly people and Wv non-elderly people The weight coefficients of the stations for two groups of people are calculated based on the attraction degree of the appointed bus station v to the travel amounts of the old and the non-old;
each time a site with the largest weight coefficient is selected from VL' as s * If s * If the bus station is not within 300m of any confirmed bus station, the station is marked into a bus station set S, otherwise, the station is deleted;
repeating the above operation until VL' is an empty set;
2.4, considering the land requirements of the first station and the last station, and selecting a first station and the last station pair of the bus route in the existing first station and the last station facilities;
step 3, generating an initial candidate line set by using a Dijkstra algorithm and a K-shortest algorithm, namely deleting a certain arc on the existing shortest path in the directed graph, and searching for the next optional shortest path by searching for an alternative arc;
step 4, condition screening to generate a second generation candidate line set, which comprises the following specific steps:
4.1, screening out lines meeting the line length constraint according to the line length requirement:
wherein ,lk 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 bus nodes on each line vzong Sorting is carried out, and 5% of lines at the last rank are removed to generate a second generation candidate line set;
and 5, optimizing a public transportation trunk line by using a genetic algorithm, wherein the specific steps are as follows:
5.1, converting each line into a chromosome by adopting binary codes based on a second generation candidate line set, wherein each chromosome represents a public transportation line network and comprises a plurality of public transportation 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; i is the iteration number, and when i=1, i.e. the bus route set of the first iteration is randomly generated;
5.2, for each iteration of the generated set of chromosomes, computing fitness of each chromosome in the set:
minβ i ＝min[γ+(WD-αD zong )*100] i
γ＝(InVehTim p,q +ω 1 *WalkTim p,q +ω 2 *WaitTim p,q +ω 3 *TransferTim p,q )*d p,q
where i is the number of iterations, β i For the fitness value corresponding to the ith iteration chromosome, p and q are the corresponding traffic district passenger flow source point numbers of the departure point and the destination, gamma is the sum of the passenger overall process travel sensing time from the departure point p to the destination q, WD is the number of unsatisfied demands of the network, D zong For the total traveling demand scale, alpha is the coefficient of the demand scale which is not met by the corresponding wire mesh layer, inVehTim p,q In-vehicle time in min, walkTim, taken from departure point p to destination q p,q For walking time from departure point p to boarding point m and from alighting point n to destination q, the unit is min, waitTim p,q For waiting time of the boarding station m, the unit is min, and the unit is TransferTim p,q In order to transfer the next bus line at the intermediate station r, the unit is min and omega 1 、ω 2 、ω 3 Weight coefficients, ω, of walking time, waiting time and transfer waiting time, respectively 1 ，ω 2 ，ω 3 >0，d p,q Representing the travel amount from the departure point p to the destination q;
5.3, calculating the selection probability P of each chromosome in the population j ：
wherein ,Pj and hj Respectively the probability of the j-th chromosome being selected and the corresponding fitness value, R is the number of chromosomes in the population, g is algebraic sequence number, T is the pre-temperature, T 0 The initial temperature, C, D is a positive parameter, and the probability of gene mutation is set to be 0.05;
5.4, demand distribution, specifically as follows:
for a passenger who does not transfer, the passenger overall travel sensing time is calculated as follows:
wherein ,the line length from the boarding station m to the alighting station n in the kth line is km; l (m, p) and l (n, q) are walking distances from a station m and a station n to a corresponding departure point p and a destination q, respectively, and the unit is km; t (T) k The distance between departure of the riding line k is in min;
if only one line exists between the station m and the station n, OD requirements are distributed to the line; if a plurality of lines exist between the station m and the station n, the OD demand is allocated according to the proportion of the departure frequency of the corresponding line to the departure frequency of the main line:
wherein ,dm,n Is the total travel demand between stations m and n, f k Is the departure frequency of the kth line, K is K, P max The unit of the maximum passenger flow of the section is person/h, W is the passenger capacity of the expected bus, W is person and is more than or equal to 20 and less than or equal to 40, and f k0 K is a direct public transport line set among stations m and n for initial departure frequency;
the constraint condition of the travel sensing time of the whole process of the passenger is calculated as follows:
(1) Constraint on vehicle scale: n (N) bus ≤N now ，N bus N is the total number of vehicles in the net now As a total number of vehicles currently existing,v vehicle with a frame The unit is km/h, t is the rest time of the back and forth clearance of a driver, and is generally 10min;
(2) Constraint of departure interval: t is not less than 5 k ≤30；
(3) Constraint of transfer times: wherein ,Is the transfer times;
(4) Constraint on demand scale: WD is less than or equal to alpha D zong Where WD is less than desiredThe quantity of the solution is that alpha is the coefficient of the scale of the corresponding net layer which does not meet the requirement, D zong The number of overall travel demands;
for the passengers needing one transfer, the calculation method of the whole-process travel sensing time of the passengers is as follows:
wherein ,is the kth 1 ，k 1 The line length from the stations m and n in the line to the transfer station r is km and T k1 ，T k2 Respectively the kth 1 ，k 1 The departure interval of the lines is in min; if there is no direct line between stations m, n, but the line set A passing station m m With line A passing through station n n If a cross site r exists, the OD requirements are all distributed to the corresponding lines; if a plurality of changeable lines exist among the stations m and n, the OD requirements are distributed according to the proportion of the departure frequency of the corresponding line to the departure frequency of the main line;
the constraint condition of the travel sensing time of the whole process of the passenger is calculated as follows:
(1) Constraint on vehicle scale: n (N) bus ≤N now ；
(2) Constraint of departure interval: t is not less than 5 k1 ，T k2 ≤30；
(3) Constraint of transfer times:
(4) Constraint on demand scale: WD is less than or equal to alpha D zong ；
Step 6, optimizing bus branch lines by an exhaustion method, wherein the specific steps are as follows:
6.1, calculating travel demands between sitesAccording to each publicThe station weight coefficient of the traffic station converts travel demands among passenger flow source points of the traffic district into travel demands among bus stations, and the conversion process is as follows:
wherein m, n epsilon V, p, q epsilon Z, l (m, p), l (n, q) is less than or equal to 1.0, b is a balance coefficient for making the total travel demand between converted bus stops equal to the total travel demand between traffic cell passenger flow source points before conversion,
6.2, taking the minimum travel sensing time of the whole process of the passengers as a target:
minγ m,n ＝minInVehTim m,n
the constraint conditions are as follows: (1) constraint of vehicle size: n (N) bus ≤N now ，Constraint on demand scale: WD is less than or equal to alpha D zong ；
6.3, screening the bus trunk line selected in the step 5 from the second generation candidate line set, and adding the sum sigma W of the mixed weight coefficients of each line vzong According to the method, the remaining lines are arranged in a descending order, and travel demands among stations are sequentially distributed to lines in the second generation candidate line set; if the same station OD pairs exist in the multiple lines, the OD requirements are preferentially distributed to the shortest lines, and the line direct passenger flow of each line is calculated according to the OD requirements;
6.4, based on the direct passenger flow of each line, classifying and screening lines with the direct passenger flow of the lines between each pair of head and tail stations according to the head and tail station pairs of the bus, and using the lines with the direct passenger flow of the lines with the epsilon% as a best selected bus branch line set; circularly operating 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, synthesizing preferable results of the public transportation trunk line and the public transportation branch line, and carrying out line adjustment according to actual conditions, network coverage rate and other requirements.
The method according to the invention will be further described with reference to an example.
And (3) extracting the travel amounts of the peak and the flat peak by adopting resident survey data of Deqing county in 2016, and obtaining an OD input matrix of the public transportation trunk line according to the step (1). Table 1 shows the preferred partial OD input matrix (one hour, unit: number of persons) of the bus trunk, and Table 2 shows the preferred partial OD input matrix of the bus branch.
TABLE 1
| O\D | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ``` | Totals to |
| 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 |
| ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` | ``` |
| Totals to | 1194 | 890 | 696 | 3480 | 2628 | 634 | 1091 | 201 | ``` | 62367 |
TABLE 2
Setting bus network design related parameters, and table 3 is a bus network design parameter list.
TABLE 3 Table 3
According to step 2, OD among 42 traffic cells is distributed to 82 passenger flow source points, distance between the OD and the passenger flow source point within 1km from each candidate node is calculated, and mixed weight coefficient W based on combination of the old and non-old people of the candidate node vzong And screening and clustering bus stops, wherein the screening distance of the bus stops is more than 300m, and a bus stop set S is generated and contains 77 stops. As shown in fig. 2, a map of the bus stop is determined after screening. And the arrangement of the bus first and last stops at the periphery of the city is considered by combining the land layout of the city in the Deqing county, the travel demand distribution and the construction conditions of the bus first and last stops. Fig. 3 is a schematic view of the head and tail station positions of a bus.
Table 4 is the number and station name of the bus head-end station pair.
TABLE 4 Table 4
According to step 3, an initial candidate line set is generated by using a Dijkstra algorithm and a K-shortest algorithm, and according to the current city scale and resident trip requirements of Deqing county, K is taken to 20, namely 20 public lines are respectively contained between each pair of starting and ending points in the generated initial candidate line set, and 220 public lines are obtained and used as the initial candidate line set.
Based on the step 3, according to the step 4, the length of the bus route is not more than 13km and not less than 6km, and 160 bus routes are used. And counting the sum of the mixed weight coefficients of the bus nodes on each line according to the line numbers of the remaining lines, removing the last 5% of the lines, and finally obtaining 152 lines as a second generation candidate line set. Table 5 is a partial list of second generation candidate route sets.
TABLE 5
According to step 5, the public transportation trunk line is preferably selected in the second generation candidate line set obtained in step 4. In the parameter setting of the genetic algorithm, 15 chromosomes are generated in one iteration, the chromosome size is 152, that is, 152 genes are arranged on each chromosome, and the number of candidate lines in the second-generation candidate line set is consistent with that of the candidate lines. There are 5 lines on each chromosome, i.e. the final bus trunk line is 5 in the preferred line concentration trunk line number. Crossover probability of 0.5, mutation probability of 0.05, initial temperature T 0 10000, the positive number parameter C, D is 100 and 0.05, respectively. The number of iterations is set to 30. The travel OD matrix, the public transportation network design parameters, the public transportation station adjacency matrix and the second generation candidate line set data are input, and the public transportation network corresponding to the approximate optimal solution can meet the travel demand of 50.3% in the total passenger flow scale, namely, the travel demand exceeding 50.3% in the travel early and late peak time period can be covered. Table 6 shows the bus route protocol for the best chromosome. Fig. 3 shows a schematic diagram of the trend of the trunk line of the de qing county.
TABLE 6
According to step 6, removing the direct and transfer OD demand quantity in one day which can be met by the public transportation trunk line network under the current departure frequency scheme from the OD total demand, and converting the OD demand between the passenger flow source points of the rest traffic cells into the travel demand between the public transportation stations by using a station demand conversion formula; screening out the line scheme which is selected as the bus trunk line from the second generation candidate line set, calculating the sum of the station mixing weight coefficients of the rest 147 lines, and distributing the station OD requirements according to the descending order of the total weight coefficients; and screening the lines with the bus lines meeting the line ranking of 5% of the direct passenger flow rate each time, calculating the total quantity of travel demands met by the bus network after the combination of the trunk lines and the screening result, finishing the screening operation after the total quantity of the travel demands met reaches 80% of the total quantity of the urban travel demands, and calculating the passenger flow rate of actual taking based on the expected bus sharing rate of 15%, thereby determining the vehicle scale. Table 7 is an exhaustive list of preferred bus branch line results. Fig. 4 shows a schematic diagram of the directions of the branch lines of the buses in de qing county.
TABLE 7
Based on the bus trunk and branch obtained in the step 5 and the step 6, the vehicle scale amounts to 99 benches. The existing buses in Deqing county city are 105 standard buses in scale, and three city bus branch lines along the city outlet direction from a train west station are added on the basis of line preference by considering the travel demands of city passenger flows in order to make the existing buses fully exert actual effects, the vehicle scale of each line is 2 standard buses, and the departure interval is 30min. Fig. 5 shows a schematic diagram of the supplementary feeder line.
Claims (5)
1. The method for designing the medium and small city public transportation network based on the whole travel sensing time is characterized by comprising the following steps of:
step one, in the range of an online network design area, acquiring OD data in a peak period as an OD input matrix of a bus trunk line, and acquiring all-weather OD data as an OD input matrix of a bus branch line; setting relevant 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; the method specifically comprises the following steps:
2.1, distributing the passenger flow of each traffic cell to the passenger flow source point of the traffic cell;
2.2, calculating the weight coefficient W of the bus station to be selected v ；
2.3, screening to generate a bus stop set S, and initializing the bus stop set S, wherein S is an empty set; the stations in the original public transportation station set V are arranged in descending order according to the mixed weight coefficient, and the generated list is expressed by VL'; each time a site with the largest mixed weight coefficient is selected from VL' as s * If s * If the bus station is not within 300m of any confirmed bus station, the station is marked into a bus station set S, otherwise, the station is deleted; repeating the above operation until VL' is an empty set;
2.4, considering the land requirements of the first station and the last station, and selecting a first station and the last station pair of the bus route in the existing first station and the last station facilities;
generating an initial candidate line set by using a Dijkstra algorithm and a K-shortest algorithm, namely deleting a certain arc on the existing shortest path in the directed graph, and searching for the next optional shortest path by searching for an alternative arc;
step four, condition screening to generate a second generation candidate line set; the method comprises the following specific steps:
4.1, screening out the lines meeting the line length constraint according to the line length requirement,
wherein ,lk 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 bus nodes on each line vzong Sorting is carried out, and 5% of lines at the last rank are removed to generate a second generation candidate line set;
step five, screening a public transportation trunk line by a genetic algorithm; the method comprises the following specific steps:
5.1, converting each line into a chromosome by adopting binary codes based on a second generation candidate line set, wherein each chromosome represents a public transportation line network and comprises a plurality of public transportation 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; i is the iteration number, and when i=1, i.e. the bus route set of the first iteration is randomly generated;
5.2, for each iteration of the generated set of chromosomes, computing fitness of each chromosome in the set:
minβ i ＝min[γ+(WD-α·D zong )*100] i
γ＝(InVehTim p,q +ω 1 *WalkTim p,q +ω 2 *WaitTim p,q +ω 3 *TransferTim p,q )*d p,q
where i is the number of iterations, β i For the fitness value corresponding to the ith iteration chromosome, p and q are the corresponding traffic district passenger flow source point numbers of the departure point and the destination, gamma is the sum of the passenger overall process travel sensing time from the departure point p to the destination q, WD is the number of unsatisfied demands of the network, D zong For the total traveling demand scale, alpha is the coefficient of the demand scale which is not met by the corresponding wire mesh layer, inVehTim p,q In-vehicle time in min, walkTim, taken from departure point p to destination q p,q For walking time from departure point p to boarding point m and from alighting point n to destination q, the unit is min, waitTim p,q For waiting time of the boarding station m, the unit is min, and the unit is TransferTim p,q In order to transfer the next bus line at the intermediate station r, the unit is min and omega 1 、ω 2 、ω 3 Weight coefficients, ω, of walking time, waiting time and transfer waiting time, respectively 1 ，ω 2 ，ω 3 And all are greater than 0, d p,q Representing the travel amount from the departure point p to the destination q;
5.3, calculating the selection probability P of each chromosome in the population j ：
T＝T 0 *0.9 g-1
wherein ,Pj and hj Respectively the probability of the j-th chromosome being selected and the corresponding fitness value, R is the number of chromosomes in the population, g is algebraic sequence number, T is the pre-temperature, T 0 The initial temperature, C, D is positive, and the probability of gene mutation is set to 0.05; e is a natural constant;
5.4, demand distribution, which comprises the following specific steps:
for a passenger who does not transfer, the passenger overall travel sensing time is calculated as follows:
wherein ,the line length from the boarding station m to the alighting station n in the kth line is km; l (m, p) and l (n, q) are walking distances from a station m and a station n to a corresponding departure point p and a destination q, respectively, and the unit is km; t (T) k The distance between departure of the riding line k is in min;Is the travel requirement corresponding to the kth line;
the constraint condition of the travel sensing time of the whole process of the passenger is calculated as follows:
(1) Constraint on vehicle scale: n (N) bus ≤N now ，N bus N is the total number of vehicles in the net now As a total number of vehicles currently existing,v vehicle with a frame The unit is km/h, t is the rest time of the back and forth clearance of a driver, and the unit is 10min;
(2) Constraint of departure interval: t is not less than 5 k ≤30；
(3) Constraint of transfer times: wherein ,Is the transfer times;
(4) Constraint on demand scale: WD is less than or equal to alpha D zong Where WD is the number of unmet demands, α is the scale factor of the demand for the corresponding net level, D zong The number of overall travel demands;
for the passengers needing one transfer, the calculation method of the whole-process travel sensing time of the passengers is as follows:
wherein ,is the kth 1 ，k 2 The line length from the stations m and n in the line to the transfer station r is km and T k1 ，T k2 Respectively the kth 1 ，k 2 The departure interval of the lines is in min; if no line can realize 0 transfer between stations m and n, but the line set A passing through station m m With line A passing through station n n If a cross site r exists, the OD requirements are all distributed to the corresponding lines; if a plurality of changeable lines exist among the stations m and n, the OD requirements are distributed according to the proportion of the departure frequency of the corresponding line to the departure frequency of the main line; d, d m,n Is the total bus travel demand between stations m and n;
the constraint condition of the travel sensing time of the whole process of the passenger is calculated as follows:
(1) Constraint on vehicle scale: n (N) bus ≤N now ；
(2) Constraint of departure interval: t (T) k1 ≥5，T k2 ≤30；
(3) Constraint of transfer times:
(4) Constraint on demand scale: WD is less than or equal to alpha D zong ；
Step six, optimizing bus branch lines by an exhaustion method; the method comprises the following specific steps:
6.1, converting travel demands among passenger flow source points of the traffic district into travel demands among bus stops according to stop weight coefficients of the bus stops
6.2, taking the minimum travel sensing time of the whole process of the passengers as a target:
minγ m,n ＝minInVehTim m,n
the constraint conditions are as follows: n (N) bus ≤N now ，WD≤α*D zong ；
6.3, selecting the bus trunk line screened in the fifth step from the second generation candidatesLine concentration screening is performed by the sum sigma W of the mixed weight coefficient of each line vzong According to the method, the remaining lines are arranged in a descending order, and travel demands among stations are sequentially distributed to lines in the second generation candidate line set; if the same station OD pairs exist in the multiple lines, the OD requirements are distributed to the shortest lines, and the line direct passenger flow of each line is calculated according to the OD requirements;
6.4, based on the direct passenger flow of each line, classifying and screening lines with the direct passenger flow of the lines between each pair of head and tail stations according to the head and tail station pairs of the bus, and using the lines with the direct passenger flow of the lines with the epsilon% as a best selected bus branch line set; circularly operating 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 step seven, integrating the preferable results of the bus trunk line in the step five and the bus branch line in the step six, and carrying out line adjustment according to actual conditions and network coverage rate requirements.
2. The method for designing the medium and small city bus network based on the whole process travel sensing time according to claim 1, wherein the method comprises the following steps: in step 2.2, the method for calculating the weight coefficient of the bus stop to be selected comprises the following steps:
v∈V，l(v,z)≤1.0
v is an original public transportation 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 the node v to the passenger flow source point z, O z and Dz The passenger flow generation amount and the attraction amount are respectively the passenger flow source point z, and e is a natural constant.
3. The method for designing the medium and small city bus network based on the whole process travel sensing time according to claim 1, wherein the method comprises the following steps: in step 2.3, the method for calculating the mixing weight coefficient of the bus stop to be selected comprises the following steps:
W vzong ＝0.8*W v elderly people +0.2*W v non-elderly people
wherein ,Wv elderly people and Wv non-elderly people The weight coefficients of the stations for the two types of people are calculated based on the attraction degree of the appointed bus station v to the travel amounts of the old and the non-old.
4. The method for designing the medium and small city bus network based on the whole process travel sensing time according to claim 1, wherein the method comprises the following steps: in step 5.4, the travel requirement 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, the OD requirements are distributed to the line entirely; if a plurality of lines exist between the station m and the station n, the OD demand is allocated according to the proportion of the departure frequency of the corresponding line to the departure frequency of the main line:
wherein ,dm,n Is the total bus travel demand between stations m and n, f k Is the departure frequency of the kth line, K is K, P max The unit of the maximum passenger flow of the section is person/h, W is the passenger capacity of the expected bus, W is person and is more than or equal to 20 and less than or equal to 40, and f k0 For initial departure frequency, K is the set of bus routes directly through stops m, n.
5. The method for designing the medium and small city bus network based on the whole process travel sensing time according to claim 1, wherein the method comprises the following steps: in step 6.1, the travel demand of the traffic district passenger flow source point is converted into the travel demand among the bus stops by the following method:
wherein m, n epsilon V, p, q epsilon Z, l (m, p), l (n, q) is less than or equal to 1.0, b is a balance coefficient for making the total travel demand between converted bus stops equal to the total travel demand between traffic cell passenger flow source points before conversion,v is the original set of bus stops.
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