CN106652434A - Bus dispatching method based on rail transit coordination - Google Patents
Bus dispatching method based on rail transit coordination Download PDFInfo
- Publication number
- CN106652434A CN106652434A CN201611100288.5A CN201611100288A CN106652434A CN 106652434 A CN106652434 A CN 106652434A CN 201611100288 A CN201611100288 A CN 201611100288A CN 106652434 A CN106652434 A CN 106652434A
- Authority
- CN
- China
- Prior art keywords
- bus
- transfer
- time
- passenger
- public
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000012546 transfer Methods 0.000 claims abstract description 127
- 230000002068 genetic effect Effects 0.000 claims abstract description 10
- 238000011160 research Methods 0.000 claims description 26
- 238000011835 investigation Methods 0.000 claims description 13
- 238000009826 distribution Methods 0.000 claims description 11
- 230000008901 benefit Effects 0.000 claims description 10
- 230000035772 mutation Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 5
- 238000013461 design Methods 0.000 claims description 4
- 108090000623 proteins and genes Proteins 0.000 claims description 3
- 238000005457 optimization Methods 0.000 description 7
- 238000003556 assay Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a bus dispatching method based on rail transit coordination, and the method comprises the following steps: determining an optimized rail transit stops and a bus line; collecting the selected bus line and the related data of rail transit traffic; dividing passengers waiting for buses at transfer stops into three classes according to the structure of a conventional bus passenger flow: random passengers, transfer passengers and detained passengers; calculating the waiting time of each class, and building a bus dispatching model with the shortest waiting time of passengers; designing an improved genetic algorithm, solving the model, and obtaining a departure time table of a bus; calculating the parking time and traveling time of the bus, and obtaining the departure time table of the bus. According to the invention, the method can improve the transfer efficiency of passengers, and improves the bus service level of the whole city.
Description
Technical field
The invention belongs to intelligent transport system field, more particularly to a kind of bus dispatching side coordinated based on track traffic
Method.
Background technology
In recent years, the speed of Large Cities ' Building track traffic dramatically speeds up, and track traffic is big as a kind of capacity, safety,
The Passenger Traffic Mode of environmental protection, extremely important and crucial effect has been played with its unique advantage on urban issues is solved.
But endless can not possibly build down because the population size of Rail traffic network always reaches the upper limit, its service range is generally only
Circuit both sides can be covered, now, routine bus system then needs the secondary attraction for doing passenger flow for it, and its coverage is extended to into city
Each corner.Therefore, coordinate optimization to the timetable of routine bus system based on track traffic to be particularly important.
For the theoretical and method of the coordinated scheduling of public transport and track traffic all achieves both at home and abroad certain achievement, but go back
Come with some shortcomings part, is mainly manifested in the following aspects：The less carrying capacity for considering bus, in peak time meeting
There is stayer, cause theoretical research to have certain deviation with actual conditions；Using the fixed departure interval as supposed premise
Or optimization aim, the departure interval of less consideration routine bus system is often with uncertainty；Fail effectively to consider that public transit vehicle exists
The berthing time of transfer website, and in practice, can the berthing time of vehicle be to affect passenger realize that transfer is successful one
Key factor.
Therefore, each influence factor during passenger's transfer is only taken into full account, more rational public affairs can be just made
Hand over scheduling scheme.
The content of the invention
Goal of the invention：A kind of bus dispatching method coordinated based on track traffic is provided, to improve the interchange efficiency of passenger,
Reduce the loss of passenger's connecting time.
Technical scheme：A kind of bus dispatching method coordinated based on track traffic, is comprised the steps：
Step 1：Track traffic website to be optimized is determined, it is determined that the public bus network and search time section of research；
Step 2：Collection public bus network data to be optimized, track traffic data and other data, public bus network packet
Vehicle is included from origin to the free running time of transfer website, moment leaving from station, research vehicle number, maximum departure interval, minimum
Departure interval, and non-transfer passenger averagely reaches rate；Track traffic data include the arrival time of train, the train of arrival
Several, every train reaches number, the transfer travel time of passenger of bus platform, and determines the public affairs of research by sample investigation
The transfer ratio on intersection road；Other data include the origin of public transit vehicle to the link counting between transfer website；
Step 3：The transfer travel time information of collection passenger, and data are fitted, calculate its average and variance；
Step 4：Predict that the public transit vehicle of different order of classes or grades at school reaches remaining carrying capacity when changing to website based on historical data；
Step 5：According to the composition of regular public traffic passenger flow, the passenger of transfer website waiting public transport is divided into into taking advantage of for random arrival
Visitor, transfer passenger and stayer, and transfer passenger flow is divided based on passenger's transfer travel time, all kinds of taking advantage of is calculated respectively
The stand-by period of visitor；With the public transit vehicle time leaving from station as optimized variable, set up based on the bus dispatching of most short passenger waiting time
Model；
Step 6：The genetic algorithm of orderly integer coding is gone out according to the characteristic Design of bus dispatching model, and is solved；
Step 7：Get off number of each order of classes or grades at school public transit vehicle in transfer website is predicted based on historical data；Counted according to step 6
The time leaving from station of the bus for calculating, calculate per order of classes or grades at school bus in the transfer number and number of getting on the bus of the website；According to up and down
Car number calculates vehicle in the down time of website, and then calculates the arrival time of bus；
Step 8：Public transit vehicle is calculated based on BRP functions and reaches the running time before transfer website, generated bus and dispatch a car
Timetable.
The data gathered in the step 2 include：The free running time of public transit vehicle is ts, research vehicle number is m, from
The moment stand for tbdj, 1≤j≤m, the maximum departure interval is IMax, the minimum departure interval is IMin, non-transfer passenger's average arrival rate
For λ；Arrival train number is n, and the arrival time of train is tri, 1≤i≤n, passenger's transfer travel time is te, and every time train is arrived
Number up to bus platform is Qi, transfer ratio α of the public bus network of research is determined by sample investigation, public transit vehicle is originated
Ground arrives the link counting v between transfer website；
The step 5 is further：
Step 51：Make the time that non-transfer passenger reaches bus platform obey to be uniformly distributed, and non-transfer passenger averagely arrives
Up to rate be constant λ, then the stand-by period of non-transfer passenger be：
Wherein：T1For the stand-by period of non-transfer passenger, tbdjFor the time leaving from station of jth bus, λ takes advantage of for non-
The average arrival rate of visitor；
Step 52：Passenger is made to change to travel time te Normal Distributions, its probability density function is：
Wherein：μ is the average of normal distribution, and σ is poor for normal distribution standard, makes te～N (μ, σ2), passenger is most short, and transfer is walked
Row time temin, the most long transfer travel time temax of passenger and
Arrangement can be obtained：
Step 53：After i-th train is reached, the people of transfer public transport can take a bus nearby, this
In the case of, the stand-by period of transfer passenger is：
Wherein：QiIt is the number of every time train arrival bus platform, α is the public bus network that research is determined by sample investigation
Transfer ratio, triFor the arrival time of i-th train；
Step 54：After i-th train is reached, the comparatively faster occupant ride of a walking speed bus nearby,
But the relatively slow passenger of walking then needs to wait next bus to arrive, in this case, the stand-by period of transfer passenger
For：
Wherein, qi,jFor the number that i-th train changes to jth bus；
The total waiting time of so transfer passenger is：
Wherein：T2For the stand-by period of transfer passenger, fi,jFor 0-1 variables, when the transfer passenger of i-th train can be complete
Portion catches up with bus nearby, then fi,jTake 1, otherwise take 0；
Step 55：If the stranded crowd number of jth bus is dj, then the secondary stand-by period of stayer be：
Wherein：T3For the stand-by period of stayer；
Step 56：, the time leaving from station with public transit vehicle, the bus dispatching model of foundation was as follows as optimized variable：
Constraints is：
(1) within the research period, the departure interval of bus should be within the minimum and maximum departure interval, i.e.,：IMax≥
tbdj+1-tbdj≥IMin；
(2) study the period in, first public transport transfer stop set out the moment should within the maximum departure interval of public transport,
I.e.：IMax≥tbd1≥0；
(3) after last train is reached, it shall be guaranteed that all transfer passengers can realize transfer, i.e. tbdm≥trn+
temax；
(4) situation 1 or situation 2 should be met from the situation of orbit traffic transfer public transport, i.e.,：fi,j(1-fi,j)=0, its
In, fi,jThen it is 1 when the passenger of i-th train can take jth public transport for 0-1 variables, is otherwise 0.
The step 6 is further：
Step 61：Setting genetic algorithm parameter, including：Initial population number, mutation probability, crossover probability and iterations；
Step 62：Coding, by the way of real coding, it is assumed that in research period [0, H], has m bus to reach,
Then the variable m of Optimized model is individual, and the moment leaving from station is respectively { x (1), x (2), x (3) ... x (m-2), x (m-1), x (m) }, then
In population each individuality using X={ x (1), x (2), x (3) ... x (m-2), x (m-1), x (m) } coding form；
Step 63：The generation of initial population, because the variable of model is one group of number arranging in order, and without repeat number
According to according to a kind of following method generation initial population：
(1) [0, IMax] in random generate number x (1)；
(2) in [IMin,IMax] between it is random generate number α, then x (2)=x (1)+α；
(3) aforesaid operations are repeated, until generating x (m)；
Step 64：Fitness function is calculated, the target of this model is minimum problems, then it is fitness letter to take its inverse
Number：
Step 65：Selection operation, using roulette method, each individual inheritance to follow-on probability is equal to the suitable of the individuality
The ratio of response and the fitness of whole population, then, each individual inheritance to follow-on probability is：
Wherein, ∑ pi=1
A number r ∈ [0,1] is randomly generated, if p1+p2+p3…+pk-1≤r≤p1+p2+p3…+pk, then k-th individuality will
It is selected into the next generation；
Step 66：Crossover operation, it is specific as follows：
(1) according to two individualities of the probability selection for randomly generating as parent, and two crosspoints, crosspoint are randomly choosed
Interior all variables are used as cross object；
(2) first variable in the intersection region in parent two and each variable in parent one are subtracted each other, and is taken absolutely
To value, such that it is able to obtain one group of data, if containing 0 element in the data, do not intersected, if not containing 0 element, will
This variable in parent two is swapped with variable minimum with its difference in parent one；
Step 67：Mutation operation, it is specific as follows using the method for uniform variation：
(1) a number r ∈ [0,1] is produced immediately, if r chooses the gene of current location less than given mutation probability
Enter row variation；
(2) new variation value is calculated, particularly may be divided into following three kinds of situations：
If 1. the position of variation is not in head and end, new variate-value after making a variation：
X (i) '=x (i-1)+r × (x (i+1)-x (i))
If 2. the position of variation is in first point, new variate-value after making a variation：
X (1) '=x (2)+r × x (2)
If 3. the position of variation is in last point, new variate-value after making a variation：
X (m) '=x (m-1)+r × (R-x (m-1))
Wherein, R is search time length, represents the maximum of each variable.
Step 68：Optimal individual conserving method；Its specific operating process is as follows：
(1) by the value of calculating fitness, fitness value maximum in population in current iteration is found out；
(2) it is current fitness highest is individual compared with individuality best so far, if current fitness is best
Individual its fitness is more than individuality best so far, then using the optimal individuality of current fitness as fitness so far
Optimal individuality；
(3) through selecting, intersect, the worst individuality of current fitness is found out in the operation such as variation, with fitness so far
Highest individuality is replaced；
(4) aforesaid operations are repeated；
Step 69：Output result, the optimal individuality of fitness is the optimal solution of problem till after last time iteration,
Public transport timetable leaving from station after optimizing, result is exported.
The step 7 is further：
Step 71：For ridership of getting off, using grey forecasting model to working day or festivals or holidays difference order of classes or grades at school bus
Transfer website ridership of getting off be predicted；
Step 72：The time leaving from station of the bus calculated according to step 6, the number of getting on the bus for calculating bus is：
Step 73：The berthing time of public transit vehicle is：
Wherein：CjFor the remaining carrying capacity of bus, tdFor the berthing time of bus, tocFor the switch gate of bus
Time, TaFor objective time, T under rear door of busbBus Qianmen pickup time, tbFor the average pick-up time of single passenger, taFor
Single passenger average time getting off, PjaFor lower guest's number, PjbFor upper guest's number；
Step 74：The arrival time of public transit vehicle is:
tbaj=tbdj-td=tbdj-toc-max(Ta,Tb)=tbdj-toc-max(taPja,tbPja)
Wherein：tbajFor the arrival time of jth bus；
The step 8 is further：
Step 81：Public transit vehicle from originating point to transfer website running time be：
Wherein, tsBe public transit vehicle from the starting station to transfer website actual run time, tfFreely travel for the section
Time, v is that, at that time by the volume of traffic in the section, c is the actual capacity in section,β for model undetermined parameter, its value
Respectively 0.15 and 0.4；
Step 82：Calculate the frequency of public transit vehicle：t0j=tbdj-ts；
Wherein, t0jFor the frequency of jth bus.
Beneficial effect：The present invention has considered the transfer travel time of passenger, public transport handling capacity of passengers and vehicle parking time
Etc. factor, regular public traffic passenger flow is divided, it is proposed that a kind of new dispatching method.For playing each of track traffic and routine bus system
From advantage, interchange efficiency is improved, the park-and-ride demand for meeting passenger has great importance.
Description of the drawings
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is present invention transfer behavioural analysis figure.
Fig. 3 is genetic algorithm result figure of the present invention.
Specific embodiment
Technical scheme described in detail below：A kind of bus dispatching method coordinated based on track traffic is mainly wrapped
Include following steps：
Step 1：Track traffic website to be optimized is determined, it is determined that the public bus network and search time section of research；
Step 2：Collection public bus network data to be optimized, track traffic data and other data, public bus network packet
Vehicle is included from origin to the free running time of transfer website, moment leaving from station, research vehicle number, maximum departure interval, minimum
Departure interval, and non-transfer passenger averagely reaches rate；Track traffic data include the arrival time of train, the train of arrival
Several, every train reaches number, the transfer travel time of passenger of bus platform, and determines the public affairs of research by sample investigation
The transfer ratio on intersection road；Other data include the origin of public transit vehicle to the link counting between transfer website；
Step 3：The transfer travel time information of collection passenger, and data are fitted, calculate its average and variance；
Step 4：Predict that the public transit vehicle of different order of classes or grades at school reaches remaining carrying capacity when changing to website based on historical data；
Step 5：According to the composition of regular public traffic passenger flow, the passenger of transfer website waiting public transport is divided into into taking advantage of for random arrival
Visitor, transfer passenger and stayer, and transfer passenger flow is divided based on passenger's transfer travel time, all kinds of taking advantage of is calculated respectively
The stand-by period of visitor；With the public transit vehicle time leaving from station as optimized variable, set up based on the bus dispatching of most short passenger waiting time
Model；
Step 6：The genetic algorithm of orderly integer coding is gone out according to the characteristic Design of bus dispatching model, and is solved；
Step 7：Get off number of each order of classes or grades at school public transit vehicle in transfer website is predicted based on historical data；Counted according to step 6
The time leaving from station of the bus for calculating, calculate per order of classes or grades at school bus in the transfer number and number of getting on the bus of the website；According to up and down
Car number calculates vehicle in the down time of website, and then calculates the arrival time of bus；
Step 8：Public transit vehicle is calculated based on BRP functions and reaches the running time before transfer website, generated bus and dispatch a car
Timetable.
In a further embodiment, the data for gathering in the step 2 include：The free running time of public transit vehicle is
ts, research vehicle number is m, and the moment leaving from station is tbdj, 1≤j≤m, the maximum departure interval is IMax, the minimum departure interval is IMin, it is non-
Transfer passenger's average arrival rate is λ；Arrival train number is n, and the arrival time of train is tri, 1≤i≤n, passenger's transfer traveling
Time is te, and the number of every time train arrival bus platform is Qi, the transfer of the public bus network of research is determined by sample investigation
Ratio α, the origin of public transit vehicle to the link counting v between transfer website；
In a further embodiment, the step 5 is further：
Step 51：Make the time that non-transfer passenger reaches bus platform obey to be uniformly distributed, and non-transfer passenger averagely arrives
Up to rate be constant λ, then the stand-by period of non-transfer passenger be：
Wherein：T1For the stand-by period of non-transfer passenger, tbdjFor the time leaving from station of jth bus, λ takes advantage of for non-
The average arrival rate of visitor；
Step 52：Passenger is made to change to travel time te Normal Distributions, its probability density function is：
Wherein：μ is the average of normal distribution, and σ is poor for normal distribution standard, makes te～N (μ, σ2), passenger is most short, and transfer is walked
Row time temin, the most long transfer travel time temax of passenger and
Arrangement can be obtained：
Step 53：After i-th train is reached, the people of transfer public transport can take a bus nearby, this
In the case of, the stand-by period of transfer passenger is：
Wherein：QiIt is the number of every time train arrival bus platform, α is the public bus network that research is determined by sample investigation
Transfer ratio, triFor the arrival time of i-th train；
Step 54：After i-th train is reached, the comparatively faster occupant ride of a walking speed bus nearby,
But the relatively slow passenger of walking then needs to wait next bus to arrive, in this case, the stand-by period of transfer passenger
For：
Wherein, qi,jFor the number that i-th train changes to jth bus；
The total waiting time of so transfer passenger is：
Wherein：T2For the stand-by period of transfer passenger, fi,jFor 0-1 variables, when the transfer passenger of i-th train can be complete
Portion catches up with bus nearby, then fi,jTake 1, otherwise take 0；
Step 55：If the stranded crowd number of jth bus is dj, then the secondary stand-by period of stayer be：
Wherein：T3For the stand-by period of stayer；
Step 56：, the time leaving from station with public transit vehicle, the bus dispatching model of foundation was as follows as optimized variable：
Constraints is：
(1) within the research period, the departure interval of bus should be within the minimum and maximum departure interval, i.e.,：IMax≥
tbdj+1-tbdj≥IMin；
(2) study the period in, first public transport transfer stop set out the moment should within the maximum departure interval of public transport,
I.e.：IMax≥tbd1≥0；
(3) after last train is reached, it shall be guaranteed that all transfer passengers can realize transfer, i.e. tbdm≥trn+
temax；
(4) situation 1 or situation 2 should be met from the situation of orbit traffic transfer public transport, i.e.,：fi,j(1-fi,j)=0, its
In, fi,jThen it is 1 when the passenger of i-th train can take jth public transport for 0-1 variables, is otherwise 0.
In a further embodiment, the step 6 is further：
Step 61：Setting genetic algorithm parameter, including：Initial population number, mutation probability, crossover probability and iterations；
Step 62：Coding, by the way of real coding, it is assumed that in research period [0, H], has m bus to reach, then optimize mould
Variable m of type, the moment leaving from station is respectively { x (1), x (2), x (3) ... x (m-2), x (m-1), x (m) }, then every in population
An individual is all using the coding form of X={ x (1), x (2), x (3) ... x (m-2), x (m-1), x (m) }；
Step 63：The generation of initial population, because the variable of model is one group of number arranging in order, and without repeat number
According to according to a kind of following method generation initial population：
(1) [0, IMax] in random generate number x (1)；
(2) in [IMin,IMax] between it is random generate number α, then x (2)=x (1)+α；
(3) aforesaid operations are repeated, until generating x (m)；
Step 64：Fitness function is calculated, the target of this model is minimum problems, then it is fitness function to take its inverse：
Step 65：Selection operation, using roulette method, each individual inheritance to follow-on probability is equal to the suitable of the individuality
The ratio of response and the fitness of whole population, then, each individual inheritance to follow-on probability is：
Wherein, ∑ pi=1
A number r ∈ [0,1] is randomly generated, if p1+p2+p3…+pk-1≤r≤p1+p2+p3…+pk, then k-th individuality will
It is selected into the next generation；
Step 66：Crossover operation, it is specific as follows：
(1) according to two individualities of the probability selection for randomly generating as parent, and two crosspoints, crosspoint are randomly choosed
Interior all variables are used as cross object；
(2) first variable in the intersection region in parent two and each variable in parent one are subtracted each other, and is taken absolutely
To value, such that it is able to obtain one group of data, if containing 0 element in the data, do not intersected, if not containing 0 element, will
This variable in parent two is swapped with variable minimum with its difference in parent one；
Step 67：Mutation operation, it is specific as follows using the method for uniform variation：
(1) a number r ∈ [0,1] is produced immediately, if r chooses the gene of current location less than given mutation probability
Enter row variation；
(2) new variation value is calculated, particularly may be divided into following three kinds of situations：
If 1. the position of variation is not in head and end, new variate-value after making a variation：
X (i) '=x (i-1)+r × (x (i+1)-x (i))
If 2. the position of variation is in first point, new variate-value after making a variation：
X (1) '=x (2)+r × x (2)
If 3. the position of variation is in last point, new variate-value after making a variation：
X (m) '=x (m-1)+r × (R-x (m-1))
Wherein, R is search time length, represents the maximum of each variable.
Step 68：Optimal individual conserving method；Its specific operating process is as follows：
(1) by the value of calculating fitness, fitness value maximum in population in current iteration is found out；
(2) it is current fitness highest is individual compared with individuality best so far, if current fitness is best
Individual its fitness is more than individuality best so far, then using the optimal individuality of current fitness as fitness so far
Optimal individuality；
(3) through selecting, intersect, the worst individuality of current fitness is found out in the operation such as variation, with fitness so far
Highest individuality is replaced；
(4) aforesaid operations are repeated；
Step 69：Output result, the optimal individuality of fitness is the optimal solution of problem till after last time iteration,
Public transport timetable leaving from station after optimizing, result is exported.
In a further embodiment, the step 7 is further：
Step 71：For ridership of getting off, using grey forecasting model to working day or festivals or holidays difference order of classes or grades at school bus
Transfer website ridership of getting off be predicted；
Step 72：The time leaving from station of the bus calculated according to step 6, the number of getting on the bus for calculating bus is：
Step 73：The berthing time of public transit vehicle is：
Wherein：CjFor the remaining carrying capacity of bus, tdFor the berthing time of bus, tocFor the switch gate of bus
Time, TaFor objective time, T under rear door of busbBus Qianmen pickup time, tbFor the average pick-up time of single passenger, taFor
Single passenger average time getting off, PjaFor lower guest's number, PjbFor upper guest's number；
Step 74：The arrival time of public transit vehicle is:
tbaj=tbdj-td=tbdj-toc-max(Ta,Tb)
=tbdj-toc-max(taPja,tbPja)
Wherein：tbajFor the arrival time of jth bus；
In a further embodiment, the step 8 is further：
Step 81：Public transit vehicle from originating point to transfer website running time be：
Wherein, tsBe public transit vehicle from the starting station to transfer website actual run time, tfFreely travel for the section
Time, v is that, at that time by the volume of traffic in the section, c is the actual capacity in section,β for model undetermined parameter, its value
Respectively 0.15 and 0.4；
Step 82：Calculate the frequency of public transit vehicle：t0j=tbdj-ts；
Wherein, t0jFor the frequency of jth bus.
Certain case study on implementation is below described.
With reference to Fig. 1 description present invention based on the bus dispatching method coordinated with track traffic, comprise the following steps：
Step 1：No. two line City Movement parks of certain city's subway station is chosen for transfer website, according to investigations, the website public transport 329
Road transfer number is more, therefore selects it to be research object, and its departure time-table is optimized；
Step 2：According to investigations, the tunnel of public transport 329, uplink is the ecology peninsula to the Xi'an north passenger station that makes a clear distinction between good and evil, downlink
Road is Xi'an north passenger station to the entirely different ecological peninsula, and overall length 25400m, speed of operation is about 20km/h, and the whole service time is about
For 1.5h；In the research period, vehicle number is 9, and the maximum departure interval is 21min, and the minimum departure interval is 9min；Collection Xi'an
The arrival time-table of No. two lines of subway, and count the number of every train transfer bus；
Step 3：The transfer travel time of collection passenger, should determine that first the amount of capacity of sample, and its computational methods is as follows：
Wherein：N is the size of sample size；Z is the confidence level of standard error, and it is 0.95 to take confidence level, then z is
1.96；σ is population standard deviation.In the case of unknown, can first be estimated with sample；E is the permission for changing to travel time
Error, can set allowable error as 10%；
First selected part sample, is carried out according to a preliminary estimate, and passenger's transfer travel time variance is 0.55, then Minimal sample size is：
By follow-up investigation, sample data is obtained, and data are fitted with normal distribution, then carry out k-s inspections,
Assay is as shown in table 1：
The K-S assays of table 1
From assay, average is 240s (about 4min), and standard deviation is 33s (about 0.55min), conspicuousness water
Put down as 0.849, more than 0.05, it can be considered that the data are Normal Distributions.
Step 4：Predict the bus of different order of classes or grades at school in the remaining carrying capacity for changing to website using Grey System Model
Detailed process is as follows：
The first step, calculates historical data X(0)=(X(0)(1),X(0)(2),...,X(0)(n)) level ratio
Wherein, X(0)M () represents m-th historical data, k (m) is level ratio；
If all of level of the ordered series of numbers all falls than k (m)It is interior, then can adopt Grey System Model logarithm
According to being predicted.Otherwise, need to process initial data, appropriate constant K can be taken, make translation transformation so as to level ratio
Both falling within can hold the scope of coveringIt is interior；
Second step, 1 accumulated generating sequence is done to historical data ordered series of numbers, and formula is as follows：
3rd step, sets up grey differential equation, and formula is as follows：
X(1)(t)=(X(1)(0)-u/a)e-at+u/a
In formula, undetermined coefficient A that coefficient a and u is constituted can be obtained by least square method, and formula is：
A=(a, u)T
Can setYn=[X(0)(2),X(0)(3),…X(0)(n)]T, then A=
(BTB)-1BTYn, can set up response time equation, and then can try to achieve predicted value；
4th step, checks residual error, and the computing formula of residual error is as follows：
Generally, if ε (m)<0.2, then it is believed that inspection is qualified.
In the same manner, refer in step 71 of the invention GM (1,1) predict process ibid.
Step 5：The parameter calibration wanted needed for modeling process of the present invention is as shown in table 2：
The model part parameter calibration table of table 2
Model program is write using matlab, then above-mentioned parameter is brought into model.
Step 6：The parameter value of algorithm is as shown in table 3：
The genetic algorithm parameter value table of table 3
According to the genetic algorithmic steps described in step 6, algorithm routine is write, as shown in Figure 3, through 1000 iteration, can
Public transport timetable leaving from station after to solve optimization, as shown in table 4.
Step 7：Using Grey System Model Forecasting Methodology described above, each pass bus can be predicted in transfer
The number of getting off of website, its result is as shown in table 4.
According to the bus time leaving from station calculated by step 6, the ridership that each car need to plug into is calculated, it is then pre- with step 4
The bus residue passenger carrying capacity measured is compared, and takes minimum of a value for upper guest's number.
The dwell time of bus is calculated according to boarding and alighting.Wherein, by substantial amounts of data survey, bus is opened
Close the door tocTake 3s, unit passenger loading time tbFor 1.2s, lower objective time taFor 0.9s.The then dwell time meter of every bus
Calculate result as shown in the table：
4 public transport dwell time of table result of calculation table
Then the moment leaving from station of public transit vehicle is deducted into berthing time, it can be deduced that the arrival time-table of public transit vehicle.
Step 8：According to investigations, public transit vehicle is about 1200s from origin to the running time that freely flows of transfer website, actual
Volume of traffic v is 1400pcu/h, and the traffic capacity in track is 1500pcu/h, and number of track-lines is 3, then the actual travel time of vehicle
For：
The departure time-table of the bus for then calculating is as shown in the table：
The bus departure timetable of table 5
The optimum results of the present invention are contrasted with present situation, it is as shown in the table for comparing result, it can be seen that taking advantage of after optimization
The total waiting time of visitor is 1.041 × 105s, and compared with before optimization, total waiting time reduces 0.409 × 105s, optimizes amplitude
For 28.29%, improvement is more obvious, therefore scheduling model involved in the present invention and algorithm optimization effect are more significantly,
With certain real value.
Contrast table before and after the optimization of table 6
To sum up, the present invention is proposed based on the bus dispatching method coordinated with track traffic.The present invention is walked in consideration transfer
The row time, on the basis of the factor such as vehicle parking time and public transport capacity limit, the passenger flow of bus platform is divided, built
The most short bus dispatching model of vertical passenger waiting time, and devise algorithm and solved.In analysis of cases, using the present invention
The passenger's total waiting time for solving can effectively reduce the travel time of passenger compared with present situation, improve interchange efficiency.
The preferred embodiment of the present invention described in detail above, but, the present invention is not limited in above-mentioned embodiment
Detail, the present invention range of the technology design in, various equivalents can be carried out to technical scheme, this
A little equivalents belong to protection scope of the present invention.
It is further to note that each particular technique feature described in above-mentioned specific embodiment, in not lance
In the case of shield, can be combined by any suitable means.In order to avoid unnecessary repetition, the present invention to it is various can
The combination of energy is no longer separately illustrated.
Additionally, can also be combined between a variety of embodiments of the present invention, as long as it is without prejudice to this
The thought of invention, it should equally be considered as content disclosed in this invention.
Claims (6)
1. it is a kind of based on track traffic coordinate bus dispatching method, it is characterised in that comprise the steps：
Step 1：Track traffic website to be optimized is determined, it is determined that the public bus network and search time section of research；
Step 2：Collection public bus network data to be optimized, track traffic data and other data, public bus network data include car
From origin to the transfer free running time of website, moment leaving from station, research vehicle number, maximum departure interval, minimum dispatch a car
Interval, and non-transfer passenger averagely reaches rate；The arrival time of track traffic data including train, the train number for reaching,
Every train reaches number, the transfer travel time of passenger of bus platform, and determines the public transport of research by sample investigation
The transfer ratio of circuit；Other data include the origin of public transit vehicle to the link counting between transfer website；
Step 3：The transfer travel time information of collection passenger, and data are fitted, calculate its average and variance；
Step 4：Predict that the public transit vehicle of different order of classes or grades at school reaches remaining carrying capacity when changing to website based on historical data；
Step 5：According to the composition of regular public traffic passenger flow, by the passenger of transfer website waiting public transport be divided into random arrival passenger,
Transfer passenger and stayer, and transfer passenger flow is divided based on passenger's transfer travel time, all kinds of passengers are calculated respectively
Stand-by period；With the public transit vehicle time leaving from station as optimized variable, set up based on the bus dispatching mould of most short passenger waiting time
Type；
Step 6：The genetic algorithm of orderly integer coding is gone out according to the characteristic Design of bus dispatching model, and is solved；
Step 7：Get off number of each order of classes or grades at school public transit vehicle in transfer website is predicted based on historical data；Calculated according to step 6
Bus time leaving from station, calculate per order of classes or grades at school bus the website transfer number and get on the bus number；According to the people that gets on or off the bus
Number calculates vehicle in the down time of website, and then calculates the arrival time of bus；
Step 8：Public transit vehicle is calculated based on BRP functions and reaches the running time before transfer website, generate bus frequency
Table.
2. it is as claimed in claim 1 to be based on the bus dispatching method that track traffic is coordinated, it is characterised in that in the step 2
The data of collection include：The free running time of public transit vehicle is ts, research vehicle number is m, and the moment leaving from station is tbdj, 1≤j≤
M, the maximum departure interval is IMax, the minimum departure interval is IMin, non-transfer passenger's average arrival rate is λ；Arrival train number is n,
The arrival time of train is tri, 1≤i≤n, passenger's transfer travel time is te, and the number of every time train arrival bus platform is
Qi, transfer ratio α of the public bus network of research is determined by sample investigation, the origin of public transit vehicle is to the road between transfer website
Section volume of traffic v.
3. it is as claimed in claim 2 to be based on the bus dispatching method that track traffic is coordinated, it is characterised in that the step 5 is entered
One step is：
Step 51：Make the time that non-transfer passenger reaches bus platform obey to be uniformly distributed, and non-transfer passenger's average arrival rate
For constant λ, then the stand-by period of non-transfer passenger be：
Wherein：T1For the stand-by period of non-transfer passenger, tbdjFor the time leaving from station of jth bus, λ is non-transfer passenger
Average arrival rate；
Step 52：Passenger is made to change to travel time te Normal Distributions, its probability density function is：
Wherein：μ is the average of normal distribution, and σ is poor for normal distribution standard, makes te～N (μ, σ2), during passenger's most short transfer traveling
Between te min, the most long transfer travel time te max of passenger and
Arrangement can be obtained：
Step 53：After i-th train is reached, the people of transfer public transport can take a bus nearby, such case
Under, the stand-by period of transfer passenger is：
Wherein：QiIt is the number of every time train arrival bus platform, α is to determine changing for the public bus network studied by sample investigation
Take advantage of ratio, triFor the arrival time of i-th train；
Step 54：After i-th train is reached, the comparatively faster occupant ride of a walking speed bus nearby, but walk
The relatively slow passenger of row then needs to wait next bus to arrive, and in this case, the stand-by period of transfer passenger is：
Wherein, qi,jFor the number that i-th train changes to jth bus；
The total waiting time of so transfer passenger is：
Wherein：T2For the stand-by period of transfer passenger, fi,jFor 0-1 variables, when the transfer passenger of i-th train can all catch up with
Bus nearby, then fi,jTake 1, otherwise take 0；
Step 55：If the stranded crowd number of jth bus is dj, then the secondary stand-by period of stayer be：
Wherein：T3For the stand-by period of stayer；
Step 56：, the time leaving from station with public transit vehicle, the bus dispatching model of foundation was as follows as optimized variable：
Constraints is：
(1) within the research period, the departure interval of bus should be within the minimum and maximum departure interval, i.e.,：IMax≥
tbdj+1-tbdj≥IMin；
(2) study in the period, set out moment of first public transport in transfer stop should be within the maximum departure interval of public transport, i.e.,：
IMax≥tbd1≥0；
(3) after last train is reached, it shall be guaranteed that all transfer passengers can realize transfer, i.e. tbdm≥trn+te
max；
(4) situation 1 or situation 2 should be met from the situation of orbit traffic transfer public transport, i.e.,：fi,j(1-fi,j)=0, wherein,
fi,jThen it is 1 when the passenger of i-th train can take jth public transport for 0-1 variables, is otherwise 0.
4. it is as claimed in claim 3 to be based on the bus dispatching method that track traffic is coordinated, it is characterised in that the step 6 is entered
One step is：
Step 61：Setting genetic algorithm parameter, including：Initial population number, mutation probability, crossover probability and iterations；
Step 62：Coding, by the way of real coding, it is assumed that in research period [0, H], has m bus to reach, then excellent
Change model variable m, the moment leaving from station is respectively { x (1), x (2), x (3) ... x (m-2), x (m-1), x (m) }, then population
In each individuality using X={ x (1), x (2), x (3) ... x (m-2), x (m-1), x (m) } coding form；
Step 63：The generation of initial population, because the variable of model is one group of number arranging in order, and without duplicate data,
Initial population is generated according to a kind of following method：
(1) [0, IMax] in random generate number x (1)；
(2) in [IMin,IMax] between it is random generate number α, then x (2)=x (1)+α；
(3) aforesaid operations are repeated, until generating x (m)；
Step 64：Fitness function is calculated, the target of this model is minimum problems, then it is fitness function to take its inverse：
Step 65：Selection operation, using roulette method, each individual inheritance to follow-on probability is equal to the individual fitness
With the ratio of the fitness of whole population, then, each individual inheritance to follow-on probability is：
Wherein, ∑ pi=1
A number r ∈ [0,1] is randomly generated, if p1+p2+p3…+pk-1≤r≤p1+p2+p3…+pk, then k-th individuality will be selected
Enter to the next generation；
Step 66：Crossover operation, it is specific as follows：
(1) according to two individualities of the probability selection for randomly generating as parent, and two crosspoints are randomly choosed, in crosspoint
All variables are used as cross object；
(2) first variable in the intersection region in parent two and each variable in parent one are subtracted each other, and are taken absolute value,
Such that it is able to obtain one group of data, if containing 0 element in the data, do not intersected, if not containing 0 element, by parent
This variable in two is swapped with variable minimum with its difference in parent one；
Step 67：Mutation operation, it is specific as follows using the method for uniform variation：
(1) a number r ∈ [0,1] is produced immediately, if r is less than given mutation probability, choosing the gene of current location is carried out
Variation；
(2) new variation value is calculated, particularly may be divided into following three kinds of situations：
If 1. the position of variation is not in head and end, new variate-value after making a variation：
X (i) '=x (i-1)+r × (x (i+1)-x (i))
If 2. the position of variation is in first point, new variate-value after making a variation：
X (1) '=x (2)+r × x (2)
If 3. the position of variation is in last point, new variate-value after making a variation：
X (m) '=x (m-1)+r × (R-x (m-1))
Wherein, R is search time length, represents the maximum of each variable.
Step 68：Optimal individual conserving method；Its specific operating process is as follows：
(1) by the value of calculating fitness, fitness value maximum in population in current iteration is found out；
(2) it is current fitness highest is individual compared with individuality best so far, if the best individuality of current fitness
Its fitness is more than individuality best so far, then the optimal individuality of current fitness is optimal as fitness so far
Individuality；
(3) through selecting, intersect, the worst individuality of current fitness is found out in the operation such as variation, with fitness highest so far
Individuality be replaced；
(4) aforesaid operations are repeated；
Step 69：Output result, the optimal individuality of fitness is the optimal solution of problem till after last time iteration, i.e., excellent
Public transport timetable leaving from station after change, result is exported.
5. it is as claimed in claim 4 to be based on the bus dispatching method that track traffic is coordinated, it is characterised in that the step 7 is entered
One step is：
Step 71：For ridership of getting off, working day or festivals or holidays difference order of classes or grades at school public transit vehicle are existed using grey forecasting model
The ridership of getting off of transfer website is predicted；
Step 72：The time leaving from station of the bus calculated according to step 6, the number of getting on the bus for calculating bus is：
Step 73：The berthing time of public transit vehicle is：
Wherein：CjFor the remaining carrying capacity of bus, tdFor the berthing time of bus, tocFor the switch gate time of bus,
TaFor objective time, T under rear door of busbBus Qianmen pickup time, tbFor the average pick-up time of single passenger, taFor single
Passenger's average time getting off, PjaFor lower guest's number, PjbFor upper guest's number；
Step 74：The arrival time of public transit vehicle is:
tbaj=tbdj-td=tbdj-toc-max(Ta,Tb)
=tbdj-toc-max(taPja,tbPja)
Wherein：tbajFor the arrival time of jth bus.
6. it is as claimed in claim 5 to be based on the bus dispatching method that track traffic is coordinated, it is characterised in that the step 8 is entered
One step is：
Step 81：Public transit vehicle from originating point to transfer website running time be：
Wherein, tsBe public transit vehicle from the starting station to transfer website actual run time, tfFor the section freely travel when
Between, v is that, at that time by the volume of traffic in the section, c is the actual capacity in section,β is the undetermined parameter of model, and its value is divided
Wei 0.15 and 0.4；
Step 82：Calculate the frequency of public transit vehicle：t0j=tbdj-ts；
Wherein, t0jFor the frequency of jth bus.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611100288.5A CN106652434B (en) | 2016-12-02 | 2016-12-02 | A kind of bus dispatching method coordinated based on rail traffic |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611100288.5A CN106652434B (en) | 2016-12-02 | 2016-12-02 | A kind of bus dispatching method coordinated based on rail traffic |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106652434A true CN106652434A (en) | 2017-05-10 |
| CN106652434B CN106652434B (en) | 2019-04-30 |
Family
ID=58818350
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611100288.5A Active CN106652434B (en) | 2016-12-02 | 2016-12-02 | A kind of bus dispatching method coordinated based on rail traffic |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106652434B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107194612A (en) * | 2017-06-20 | 2017-09-22 | 清华大学 | A kind of train operation dispatching method learnt based on deeply and system |
| CN108515988A (en) * | 2018-01-03 | 2018-09-11 | 中车工业研究院有限公司 | A kind of route map of train optimization method improving passenger's timeliness |
| CN108549953A (en) * | 2018-03-16 | 2018-09-18 | 华南理工大学 | A kind of method of unilateral side enabling urban track traffic dwell time planning |
| CN109615178A (en) * | 2018-11-16 | 2019-04-12 | 浩鲸云计算科技股份有限公司 | A kind of method that Public Transport Transfer track changes to the route shift adjustment of bus trip again |
| CN109765578A (en) * | 2019-02-19 | 2019-05-17 | 武汉元光科技有限公司 | Bus GPS equipment clock calibration method and device |
| CN110188938A (en) * | 2019-05-23 | 2019-08-30 | 东南大学 | A kind of rail traffic networking initial stage routine bus system route screening technique to be adjusted |
| CN111341138A (en) * | 2020-03-24 | 2020-06-26 | 北方工业大学 | Method for optimizing bus running efficiency |
| CN111861279A (en) * | 2020-08-04 | 2020-10-30 | 北京交通大学 | Multi-target high-speed toll station shift car scheduling method considering transfer |
| CN112381472A (en) * | 2021-01-15 | 2021-02-19 | 深圳市城市交通规划设计研究中心股份有限公司 | Subway connection bus route optimization method and device and storage medium |
| CN112562378A (en) * | 2020-12-01 | 2021-03-26 | 平安科技（深圳）有限公司 | Bus scheduling method and device, computer equipment and medium |
| CN113077086A (en) * | 2021-03-30 | 2021-07-06 | 东南大学 | Method for designing bus synchronous transfer timetable for connecting subway hubs |
| CN113537546A (en) * | 2020-04-21 | 2021-10-22 | 上海大众祥云运输管理有限公司 | Path planning method and device for commuter vehicle management |
| CN113988371A (en) * | 2021-09-26 | 2022-01-28 | 北京市智慧交通发展中心(北京市机动车调控管理事务中心) | Urban rail transit cross-station stop-start scheme optimization method based on passenger flow direct |
| CN114141044A (en) * | 2021-11-22 | 2022-03-04 | 东南大学 | Bus schedule coordination optimization method considering passenger selection behaviors |
| WO2023174187A1 (en) * | 2022-03-12 | 2023-09-21 | 南京理工大学 | Coordinated optimization scheduling method for electric bus |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5657010A (en) * | 1993-05-18 | 1997-08-12 | Global Research Systems, Inc. | Advance notification system and method utilizing vehicle progress report generator |
| CN101630440A (en) * | 2009-06-01 | 2010-01-20 | 北京交通大学 | Operation coordination optimizing method of common public transit connecting with urban rail transit and system thereof |
| CN102033932A (en) * | 2010-12-17 | 2011-04-27 | 东南大学 | Integrated design-oriented urban rail transit station sorting method |
| CN102156908A (en) * | 2010-12-17 | 2011-08-17 | 东南大学 | Bus route and station adjusting method based on rail transit and bus integration |
| CN103136933A (en) * | 2013-01-14 | 2013-06-05 | 东南大学 | Transferring coordination control method of conventional buses and subway stations |
| CN104599001A (en) * | 2015-02-12 | 2015-05-06 | 东南大学 | City streetcar, BRT and conventional bus transit network traffic allocation method and transfer junction determining method |
-
2016
- 2016-12-02 CN CN201611100288.5A patent/CN106652434B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5657010A (en) * | 1993-05-18 | 1997-08-12 | Global Research Systems, Inc. | Advance notification system and method utilizing vehicle progress report generator |
| CN101630440A (en) * | 2009-06-01 | 2010-01-20 | 北京交通大学 | Operation coordination optimizing method of common public transit connecting with urban rail transit and system thereof |
| CN102033932A (en) * | 2010-12-17 | 2011-04-27 | 东南大学 | Integrated design-oriented urban rail transit station sorting method |
| CN102156908A (en) * | 2010-12-17 | 2011-08-17 | 东南大学 | Bus route and station adjusting method based on rail transit and bus integration |
| CN103136933A (en) * | 2013-01-14 | 2013-06-05 | 东南大学 | Transferring coordination control method of conventional buses and subway stations |
| CN104599001A (en) * | 2015-02-12 | 2015-05-06 | 东南大学 | City streetcar, BRT and conventional bus transit network traffic allocation method and transfer junction determining method |
Non-Patent Citations (3)
| Title |
|---|
| 滕靖: ""大型活动下配合城轨交通客流集散的公交调度"", 《系统工程理论与实践》 * |
| 赵硕: ""城市轨道交通与地面常规公交站点衔接换乘研究"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 马天山 等: ""城市轨道交通与接运公交换乘优化模型"", 《长安大学学报（自然科学版）》 * |
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107194612A (en) * | 2017-06-20 | 2017-09-22 | 清华大学 | A kind of train operation dispatching method learnt based on deeply and system |
| CN107194612B (en) * | 2017-06-20 | 2020-10-13 | 清华大学 | Train operation scheduling method and system based on deep reinforcement learning |
| CN108515988B (en) * | 2018-01-03 | 2021-04-06 | 中车工业研究院有限公司 | Train operation diagram optimization method for improving passenger timeliness |
| CN108515988A (en) * | 2018-01-03 | 2018-09-11 | 中车工业研究院有限公司 | A kind of route map of train optimization method improving passenger's timeliness |
| CN108549953A (en) * | 2018-03-16 | 2018-09-18 | 华南理工大学 | A kind of method of unilateral side enabling urban track traffic dwell time planning |
| CN108549953B (en) * | 2018-03-16 | 2020-07-28 | 华南理工大学 | Method for planning stop time of urban rail transit with door opened on one side |
| CN109615178A (en) * | 2018-11-16 | 2019-04-12 | 浩鲸云计算科技股份有限公司 | A kind of method that Public Transport Transfer track changes to the route shift adjustment of bus trip again |
| CN109615178B (en) * | 2018-11-16 | 2024-04-09 | 浩鲸云计算科技股份有限公司 | Method for adjusting line shifts of bus transfer track transfer bus trip |
| CN109765578A (en) * | 2019-02-19 | 2019-05-17 | 武汉元光科技有限公司 | Bus GPS equipment clock calibration method and device |
| CN109765578B (en) * | 2019-02-19 | 2020-11-03 | 武汉元光科技有限公司 | Bus GPS equipment clock calibration method and device |
| CN110188938A (en) * | 2019-05-23 | 2019-08-30 | 东南大学 | A kind of rail traffic networking initial stage routine bus system route screening technique to be adjusted |
| CN110188938B (en) * | 2019-05-23 | 2023-08-22 | 东南大学 | Method for screening ground bus lines to be adjusted in early stage of rail transit networking |
| CN111341138A (en) * | 2020-03-24 | 2020-06-26 | 北方工业大学 | Method for optimizing bus running efficiency |
| CN111341138B (en) * | 2020-03-24 | 2021-05-28 | 北方工业大学 | Method for optimizing bus running efficiency |
| CN113537546A (en) * | 2020-04-21 | 2021-10-22 | 上海大众祥云运输管理有限公司 | Path planning method and device for commuter vehicle management |
| CN111861279B (en) * | 2020-08-04 | 2024-05-03 | 北京交通大学 | Multi-target high-speed toll station class car scheduling method considering transfer |
| CN111861279A (en) * | 2020-08-04 | 2020-10-30 | 北京交通大学 | Multi-target high-speed toll station shift car scheduling method considering transfer |
| CN112562378A (en) * | 2020-12-01 | 2021-03-26 | 平安科技（深圳）有限公司 | Bus scheduling method and device, computer equipment and medium |
| CN112381472B (en) * | 2021-01-15 | 2021-07-30 | 深圳市城市交通规划设计研究中心股份有限公司 | Subway connection bus route optimization method and device and storage medium |
| CN112381472A (en) * | 2021-01-15 | 2021-02-19 | 深圳市城市交通规划设计研究中心股份有限公司 | Subway connection bus route optimization method and device and storage medium |
| CN113077086A (en) * | 2021-03-30 | 2021-07-06 | 东南大学 | Method for designing bus synchronous transfer timetable for connecting subway hubs |
| CN113077086B (en) * | 2021-03-30 | 2024-07-12 | 东南大学 | Bus synchronous transfer schedule design method for subway junction |
| CN113988371A (en) * | 2021-09-26 | 2022-01-28 | 北京市智慧交通发展中心(北京市机动车调控管理事务中心) | Urban rail transit cross-station stop-start scheme optimization method based on passenger flow direct |
| CN114141044A (en) * | 2021-11-22 | 2022-03-04 | 东南大学 | Bus schedule coordination optimization method considering passenger selection behaviors |
| CN114141044B (en) * | 2021-11-22 | 2023-02-21 | 东南大学 | Bus schedule coordination optimization method considering passenger selection behaviors |
| WO2023174187A1 (en) * | 2022-03-12 | 2023-09-21 | 南京理工大学 | Coordinated optimization scheduling method for electric bus |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106652434B (en) | 2019-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106652434A (en) | Bus dispatching method based on rail transit coordination | |
| CN104318758B (en) | Based on multi-level multimodal Public transport network planning method | |
| Ying et al. | An actor-critic deep reinforcement learning approach for metro train scheduling with rolling stock circulation under stochastic demand | |
| CN101436345B (en) | System for forecasting harbor district road traffic requirement based on TransCAD macroscopic artificial platform | |
| CN104778834B (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| CN102044149B (en) | City bus operation coordinating method and device based on time variant passenger flows | |
| CN109508751B (en) | Deep neural network model modeling method for high-speed railway train late time prediction | |
| CN105857350A (en) | High-speed rail train driving method based on section profile passenger flow | |
| CN108550098A (en) | A kind of urban rail transit network passenger flow current-limiting method | |
| CN109558985A (en) | A kind of bus passenger flow amount prediction technique based on BP neural network | |
| CN112309119B (en) | Urban traffic system capacity analysis optimization method | |
| CN110909434B (en) | Urban public transport trunk line network design method under low-carbon guidance | |
| CN103049829B (en) | Integrated fusion method of urban and rural passenger line network and hub station | |
| CN112465205B (en) | Rail transit train running density optimization method for single-wire multi-station cooperative current limiting | |
| CN114511143A (en) | Urban rail transit network generation method based on grouping division | |
| CN105427001A (en) | Optimal route of school bus of regional middle and primary school | |
| CN109102114A (en) | A kind of bus trip get-off stop estimation method based on data fusion | |
| CN111063204A (en) | Expressway vehicle speed prediction model training method based on toll station flow | |
| CN113537626B (en) | Method for predicting neural network combined time sequence by aggregating information difference | |
| CN106781464A (en) | A kind of congestion in road situation method of testing | |
| CN105404934A (en) | Urban population mobile data model analysis method based on conditional random field | |
| CN114298510A (en) | Time schedule and speed curve optimization method based on NSPSO algorithm | |
| CN106373384A (en) | Remote area passenger transport regular bus route real-time generation method | |
| CN110443422B (en) | OD attraction degree-based urban rail transit OD passenger flow prediction method | |
| CN110363358A (en) | Public transportation mode share prediction technique based on multi-agent simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |