CN106709833A - School bus path optimization method based on big data - Google Patents
School bus path optimization method based on big data Download PDFInfo
- Publication number
- CN106709833A CN106709833A CN201710012927.0A CN201710012927A CN106709833A CN 106709833 A CN106709833 A CN 106709833A CN 201710012927 A CN201710012927 A CN 201710012927A CN 106709833 A CN106709833 A CN 106709833A
- Authority
- CN
- China
- Prior art keywords
- congestion
- school bus
- node
- big data
- school
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 title abstract description 5
- 238000013439 planning Methods 0.000 claims abstract description 11
- 230000003068 static effect Effects 0.000 claims description 13
- 208000015181 infectious disease Diseases 0.000 claims description 8
- 230000003466 anti-cipated effect Effects 0.000 claims description 5
- 230000007480 spreading Effects 0.000 claims description 4
- 230000002123 temporal effect Effects 0.000 claims description 4
- 230000003862 health status Effects 0.000 claims description 3
- 238000013316 zoning Methods 0.000 claims description 3
- 241000208340 Araliaceae Species 0.000 claims description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 claims description 2
- 235000008434 ginseng Nutrition 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 210000004709 eyebrow Anatomy 0.000 description 1
- 210000000720 eyelash Anatomy 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010030 laminating Methods 0.000 description 1
- 238000012913 prioritisation Methods 0.000 description 1
- 210000001747 pupil Anatomy 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
-
- G06Q50/40—
Abstract
The invention discloses a school bus path optimization method based on big data, and the method specifically comprises the steps: carrying out the statistics of existing mass data, and obtaining prior information; extracting a feature parameter subset through employing an akaike information model and combining with the prior information; calculating probability density according to the feature parameter subset, and obtaining the traffic congestion degree; carrying out the region dividing, and carrying out the initial stationary path planning of student points in each region; building a dynamic prediction optimal path model based on a stationary path and the traffic congestion degree; sequentially judging an optimal point, and obtaining an optimal path. The method guarantees the safety of students and the traffic convenience, saves the cost and time, and alleviates the social traffic pressure.
Description
Technical field
The invention belongs to vehicle routing optimization field, specifically a kind of school bus method for optimizing route based on big data.
Background technology
As China's urban and rural economies are developed rapidly, living standards of the people are increasingly improved, attention degree of the people to education
Increasing, the Chinese government also increases the input educated year by year so that middle and primary schools of China admission number increases, middle and primary schools' number
Also increased considerably than Initial stages for reform and opening-up.
The development of social economy, the quickening of urbanization process, the congestion of road traffic and safety problem are also following, its
The trip problem of middle school student is even more today's society common concern.And school bus is used as a kind of public transport work of special Student oriented
Tool, that is, disclosure satisfy that the demand for transporting a large amount of students can guarantee that safety again, become the optimal side for solving student's trip problem
Case.For school bus, most important is exactly the selection of travel route, and this is accomplished by considering whether school bus disclosure satisfy that and be distributed in
The demand of the student of each residence.Compel in eyebrow so carrying out scientific and reasonable Optimized Operation to driving of school bus route and just seeming
Eyelash.
Due to riding fee use and the complexity of urban highway traffic, there is existing school bus path planning school district to be distributed not
, number distribution is uneven, the not high limitation of security；Solution analyzes wish of riding just for mass data is plain
And position distribution, do not find out the relevance and potential worth of data between mass data.As data mining technology is ground
The progress and development studied carefully, new method and thinking is brought to school bus path optimization.
With reference to above mentioned problem, along with the last few years, congestion points have turned into major urban traffic blockings improvement in front of the door for school
Key point.It is the supporting means of transportation in school periphery according to its reason is investigated, collecting and distributing place and picks parking space and lack
It is weary, if especially primary school in front of the door road be major urban arterial highway road, between first and second halfof the term once occur traffic congestion, just influence whether
The traffic capacity of neighboring area.It is to alleviate traffic congestion, it is supporting to improve middle and primary schools' periphery means of transportation, safeguards school's periphery traffic
Order, while ensureing the traffic safety between pupil's first and second halfof the term, sets up middle and primary schools' trip big data analysis system very necessary.
The content of the invention
In view of the above problems, in order to improve school bus operational efficiency, it is ensured that the safety of student and the convenience of traffic, save into
Sheet and time, mitigate social traffic pressure, propose a kind of school bus method for optimizing route based on big data.
To achieve the above object, the technical scheme of the application use is：A kind of school bus path optimization side based on big data
Method, specifically includes：
S1：Existing mass data is counted, prior information is drawn；
S2：Using red pond information computation, with reference to prior information, characteristic parameter subset is extracted；
S3：According to characteristic parameter subset, probability density is calculated, obtain traffic congestion degree；
S4：Zoning, initial static path planning is carried out to student's point in each region；
S5：Dynamic prediction optimal route model is set up based on static path and traffic congestion degree；
S6：Optimum point is judged successively, obtains optimal path.
Further, red pond information computation is：
AICH=log σ2+(m/n)logn
Wherein σ2It is model variance, m is the highest parameter of model, and n is number of parameters, chooses the minimum feature ginseng of AIC values
Number subset, as optimal influence factor subset.
Further, probability density refers to the congestion level contacted between each section of network；Contact between network is got over
Closely, the congestion level in section is bigger, and its computing formula is as follows：
P=f (S, T, M)
Wherein, S, T, M are the influence factors that above-mentioned red pond information computation is selected.
Further, congestion status section is considered as health status S, half congestion status are considered as Infection Status I, congestion shape
State is considered as removed state R；Sk(t)、Ik(t) and RkT () is respectively and is in not congestion status, half congestion with the k node on side
State, the relative density of congestion status, and meet normalizing condition：
Sk(t)+Ik(t)+Rk(t)=1.
Further, S (t), I (t), R (t) represent respectively nodes be in not congestion status, half congestion status, gather around
The averag density of stifled state, can be expressed as with the relative density with the k node on side：
Further, the not relative density S of congestion status node, half congestion status node and congestion status nodek
(t)、Ik(t) and RkT the nonlinear differential equation of () Temporal Evolution is：
Wherein, when healthy individuals are in contact with infected individuals, healthy individuals will be infected with the probability of p, meanwhile, infection
Body reverts to healthy individuals with δ probability, and it is λ=p/ δ to define effective spreading rate；And 0≤Θ (t)≤1 represents any one and gives
The probability that fixed side is connected with an infection node, in onrelevant scales-free network, Θ (t) expression formulas are：
WhereinRepresent network node average degree.
Further, nb region is divided according to school bus quantity nb, centered on school, according to geographical position, is made every
Individual region is substantially the same comprising student's quantity：
Each school bus can manned number be n, the school rides total number of persons for N, after student's point determines in each region, with
School is starting point, finds nearest student's point, is in turn connected into initial static path.
Used as further, dynamic prediction optimal route model is：
L=α A+ β D+ δ E
Wherein, A is congestion level, and D is distance, and E is bias；α is congestion coefficient, represents the tune of each section congestion level
Section parameter；β is distance coefficient, represents the regulation parameter of present node and next node distance；δ is coefficient of deviation, direction of advance
With the regulation parameter of anticipated orientation departure degree, wherein congestion coefficient is larger with coefficient of deviation proportion, and three coefficients are full
Sufficient formula：
Alpha+beta+δ=1；
Optimal next point is judged according to above-mentioned model, is chosen successively to n-th point, finally give school bus optimum programming
Route.
Used as further, dynamic prediction optimal route model variable is included：
Congestion level A：Congestion of each section in different time is represented, is divided into not congestion status S (t), half congestion
State I (t), congestion status R (t)；Then：
Apart from D：Represent the distance between present node and next node；
Bias E：The departure degree of direction of advance and anticipated orientation is represented, departure degree value is 0 °~90 °.
The present invention can obtain following technique effect due to using above technical scheme：The application is by big data
Analysis gives laminating actual influence factor subset, is that model foundation more tallies with the actual situation there is provided reference frame；Traffic
Jam situation analysis can effectively reflect dynamic congestion level in real time, improve path planning reliability and safety of student；Together
When the dynamic prediction optimal route model that is proposed using static path and congestion level as reference, choose out optimal route,
And then an optimal school bus path planning scheme is obtained, and operating cost and time are saved, mitigate social traffic pressure, it is ensured that learn
Raw safety and convenient traffic.This provides a kind of prioritization scheme for school bus path planning.
Brief description of the drawings
The total width of accompanying drawing 7 of the present invention：
Fig. 1 is this method flow chart；
Fig. 2 is influence factor figure；
Fig. 3 is the polynomial regression calculating process based on red pond information computation AIC；
Fig. 4 is road congestion state evolution diagram；
Fig. 5 is the student's regional distribution chart for importing ArcGis softwares；
Fig. 6 is a student region static path figure for importing ArcGis softwares；
Fig. 7 is the optimum path planning figure imported after model designed by ArcGis softwares.
Specific embodiment
In order that the object, technical solutions and advantages of the present invention are clearer, below in conjunction with the accompanying drawings with specific embodiment pair
The present invention is described in detail.
Embodiment 1
The present embodiment provides a kind of school bus method for optimizing route based on big data, specifically includes：
S1：Existing mass data is counted, prior information is drawn；Using red pond information computation, with reference to priori letter
Breath, extracts characteristic parameter subset；
Statistical analysis is carried out by existing student data, influence structure structure of each parameter to school bus path planning is found out
Into with reference to big data it can be seen that the factor of influence student's trip mode has a lot, such as：Go to school the time of leaving home, home address, institute
In class of grade, sex, trip number, school position, trip mode of going to school, classes are over travel time etc..These factors are all
Student's trip and the route planning of school bus are had a certain impact, but influence intensity is different, as shown in Fig. 2 by each influence because
The exponent number of son is all set to once and is separate, hence sets up trip influence factor equation X=[x0x1...xm]=[goes to school
Trip mode, home address, sex, the time of leaving home of going to school, trip number, place grade class, school position, classes are over goes out
The row time].Therefore m=8 carries out experience packet to influence factor according to the influence degree of the Different Effects factor in data analysis, and
AIC values are calculated, it is as shown in table 1 below.
Based on upper table result, choose combinations of features that serial number 3 is AIC values minimum (trip mode of going to school, home address,
Go to school the time of leaving home), can obtain best explanation jam situation and comprising the model of minimum factor of influence.Determine in prior information
After factor of influence, we are calculated with reference to calculating process as shown in Figure 3.
Used red pond information content is defined as follows shown in formula：
AIC=2k-2ln (L)
K is independent parameter number in formula, and L is likelihood function.Here it is shown below using red pond information computation AIC：
AICH=log σ2+(m/n)logn
Wherein σ2It is model variance, m is the highest parameter of model, and n is number of parameters.
S2：According to characteristic parameter subset, probability density is calculated, and calculate relative density and averag density, obtained traffic and gather around
Stifled degree；
School bus should select the section of not congestion to be travelled in theory, but by data analysis, congested link need to not
The student for taking school bus is less, is not driving of school bus optimal route, when driving of school bus is in half congestion route, takes one
After dividing student, relatively reduced congestion in road situation is understood, at this moment half congested link develops into not congested link, meanwhile, congested link
Not congested link " will be infected ", itself traffic pressure is dispersed, so that mitigate the traffic pressure of congested link, its evolution
Model is as shown in Figure 4.
When healthy individuals are in contact with infected individuals, healthy individuals will be infected with the probability of p；Meanwhile, infected individuals
Healthy individuals are reverted to δ probability.Effective spreading rate is defined for λ=p/ δ.Healthy node in scales-free network, infection node and
It is removed the relative density S of nodek(t)、Ik(t) and RkT () Temporal Evolution meets nonlinear differential equation.
S3：Zoning, initial static path planning is carried out to student's point in each region；
In the light of actual conditions, according to each car can manned several n, the school rides total number of persons N.Obtain school bus quantity.
Each region is divided according to school bus quantity, centered on school, according to geographical position, each region is included student's quantity substantially phase
Closely；
Student's geographical location information is imported into ArcGis softwares, result as shown in Figure 5 is obtained.Student in each region
After point determines, with school as starting point, nearest student's point is found, be in turn connected into initial static path, resulting result is such as
Shown in Fig. 6.
S4：Dynamic prediction optimal route model is set up based on static path and traffic congestion degree；
The static path and congestion level A obtained with reference to above-mentioned analysis, set up dynamic prediction optimal route model.Model becomes
Bias E of the amount comprising congestion level A, two nodal distance D and enforcement route relative quiescent path, model coefficient then corresponds to each
The coefficient of model variable.Variable and coefficient are finally utilized, following formula dynamic prediction optimal route model is constituted：
L=α A+ β D+ δ E
S5：Optimum point is judged successively, obtains optimal path；
Optimal next point may determine that according to above-mentioned formula, choose successively to n-th point, finally give school bus optimal
Programme path.Model and data are imported, actual optimum path as shown in Figure 7 is finally formed.
Embodiment 2
Explanation is further expalined to the step S2 in embodiment 1, the jam situation of each section of travel route of school bus is seen
Individuality is done, the influence factor according to above-mentioned analysis obtains the congestion level of different sections of highway, can respectively correspond to three kinds of states：Do not gather around
Stifled state, half congestion status, congestion status.Not congestion status section can be considered as health status S, half congestion status are considered as infection
State I, congestion status are considered as removed state R.
Sk(t)、Ik(t) and RkT () is respectively and is in not congestion status, half congestion status, congestion with the k node on side
The relative density of state, and meet normalizing condition：
Sk(t)+Ik(t)+Rk(t)=1
All nodes are in not congestion status, half congestion status, congestion shape during S (t), I (t), R (t) represent network respectively
The averag density of state, and can be represented with the k relative density of the node on side：
Wherein, probability density refers to the congestion level contacted between each section of network.Contact is tightr, and congestion level is just
Bigger, its computing formula is as follows：
P=f (S, T, M)
P is probability density, and S is home address, and to go to school the time of leaving home, M is trip mode of going to school to T.S, T, M are above-mentioned
The influence factor that AIC criterion is selected.
The not relative density S of congestion status node, half congestion status node and congestion status node in scales-free networkk
(t)、Ik(t) and RkT the nonlinear differential equation of () Temporal Evolution is：
Wherein, when healthy individuals are in contact with infected individuals, healthy individuals will be infected with the probability of p, meanwhile, infection
Body reverts to healthy individuals with δ probability, and it is λ=p/ δ to define effective spreading rate；And 0≤Θ (t)≤1 represents any one and gives
The probability that fixed side is connected with an infection node, in onrelevant scales-free network, Θ (t) expression formulas are：
WhereinRepresent network node average degree.
Embodiment 3
Explanation is further expalined to the step S4 in embodiment 1：Dynamic prediction optimal route model is：
L=α A+ β D+ δ E
Wherein, A is congestion level, and D is distance, and E is bias；α is congestion coefficient, and β is distance coefficient, and δ is to deviate
Number.
Model variable is included,
A, congestion level A：Congestion of each section in different time is represented, not congestion status S (t), half can be divided into
Congestion status I (t), congestion status R (t).Then：
B, apart from D：Represent the distance between present node and next node；
C, bias E：The departure degree of direction of advance and anticipated orientation is represented, departure degree value is 0 °~90 °.
Model coefficient is included,
A, congestion factor alpha：Represent the regulation parameter of each section congestion level；
B, distance coefficient β：Represent the regulation parameter of present node and next node distance；
C, coefficient of deviation δ：The regulation parameter of direction of advance and anticipated orientation departure degree.
Wherein congestion coefficient is larger with coefficient of deviation proportion, and three coefficients meet following formula：
Alpha+beta+δ=1
Optimal next point is judged according to above-mentioned model, is chosen successively to n-th point.Finally give school bus optimum programming
Route.
Embodiment 4
The present embodiment is further verified for the school bus method for optimizing route that embodiment 1-3 is proposed, using ArcGis
Software carries out geodata analysis, by school and student's family address import system, and finds optimal path by the above method.
Illustrated by example of a region of a certain primary school in Shenyang, taking A primary schools has the geography of the student for taking school bus wish
Positional information, converts longitude and latitude, imports in ArcGIS platforms, obtains geographic distribution.Student is divided into according to geographical distribution
Different zones, for the particular geographic location design initial static path of regional student.And draw the real-time of each point path
Congestion level, finally using dynamic prediction optimal route model, using school as initial point, carrys out dynamic and selects according to a upper path point
A path point is removed, optimal dynamic route is finally drawn, result as shown in Figure 7 is drawn.
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto,
Any one skilled in the art in the technical scope of present disclosure, technology according to the present invention scheme and its
Inventive concept is subject to equivalent or change, should all be included within the scope of the present invention.
Claims (10)
1. a kind of school bus method for optimizing route based on big data, it is characterised in that specifically include：
S1：Existing mass data is counted, prior information is drawn；
S2：Using red pond information computation, with reference to prior information, characteristic parameter subset is extracted；
S3：According to characteristic parameter subset, probability density is calculated, obtain traffic congestion degree；
S4：Zoning, initial static path planning is carried out to student's point in each region；
S5：Dynamic prediction optimal route model is set up based on static path and traffic congestion degree；
S6：Optimum point is judged successively, obtains optimal path.
2. a kind of school bus method for optimizing route based on big data according to claim 1, it is characterised in that red pond information content
Model is：
AICH=log σ2+(m/n)logn
Wherein σ2It is model variance, m is the highest parameter of model, and n is number of parameters, chooses minimum characteristic parameter of AIC values
Collection, as optimal influence factor subset.
3. a kind of school bus method for optimizing route based on big data according to claim 2, it is characterised in that probability density is
The congestion level contacted between each section of network；Contact is tightr, and the congestion level in section is bigger, and its computing formula is as follows：
P=f (S, T, M)
Wherein, S, T, M are the influence factors that above-mentioned red pond information computation is selected.
4. a kind of school bus method for optimizing route based on big data according to claim 3, it is characterised in that will not congestion shape
State section is considered as health status S, and half congestion status are considered as Infection Status I, and congestion status are considered as removed state R；Sk(t)、Ik
(t) and RkT () is respectively the relative density that not congestion status, half congestion status, congestion status are in the k node on side.
5. a kind of school bus method for optimizing route based on big data according to claim 3 or 4, it is characterised in that S (t), I
T (), R (t) represent that nodes are in not congestion status, half congestion status, the averag density of congestion status respectively, can apparatus
The relative density for having the k node on side is expressed as：
6. a kind of school bus method for optimizing route based on big data according to claim 4, it is characterised in that not congestion status
The relative density S of node, half congestion status node and congestion status nodek(t)、Ik(t) and RkThe non-thread of (t) Temporal Evolution
The property differential equation is：
Wherein, when healthy individuals are in contact with infected individuals, healthy individuals will be infected with the probability of p, meanwhile, infected individuals with
δ probability reverts to healthy individuals, and it is λ=p/ δ to define effective spreading rate；And 0≤Θ (t)≤1 represent any one it is given
The probability that node is connected is infected in side with one, and Θ (t) expression formulas are：
WhereinRepresent network node average degree.
7. a kind of school bus method for optimizing route based on big data according to claim 1, it is characterised in that according to school bus number
Amount nb divides nb region, centered on school, according to geographical position, each region is substantially the same comprising student's quantity：
Each school bus can manned number be n, the school rides total number of persons for N, after student's point determines in each region, with school
It is starting point, finds nearest student's point, is in turn connected into initial static path.
8. a kind of school bus method for optimizing route based on big data according to claim 3 or 7, it is characterised in that dynamic is pre-
Surveying optimal route model is：
L=α A+ β D+ δ E
Wherein, A is congestion level, and D is distance, and E is bias；α is congestion coefficient, represents the regulation ginseng of each section congestion level
Number；β is distance coefficient, represents the regulation parameter of present node and next node distance；δ is coefficient of deviation, direction of advance with it is pre-
The regulation parameter of phase direction departure degree；
Optimal next point is judged according to above-mentioned model, is chosen successively to n-th point, finally give school bus optimum programming road
Line.
9. a kind of school bus method for optimizing route based on big data according to claim 8, it is characterised in that congestion coefficient,
Coefficient of deviation proportion is more than distance coefficient, and three coefficients meet formula：
Alpha+beta+δ=1.
10. a kind of school bus method for optimizing route based on big data according to claim 8, it is characterised in that dynamic prediction
Optimal route model variable is included：
Congestion level A：Congestion of each section in different time is represented, is divided into not congestion status S (t), half congestion status I
(t), congestion status R (t)；Then：
Apart from D：Represent the distance between present node and next node；
Bias E：The departure degree of direction of advance and anticipated orientation is represented, departure degree value is 0 °~90 °.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710012927.0A CN106709833A (en) | 2017-01-09 | 2017-01-09 | School bus path optimization method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710012927.0A CN106709833A (en) | 2017-01-09 | 2017-01-09 | School bus path optimization method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106709833A true CN106709833A (en) | 2017-05-24 |
Family
ID=58908122
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710012927.0A Pending CN106709833A (en) | 2017-01-09 | 2017-01-09 | School bus path optimization method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106709833A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109764884A (en) * | 2019-01-02 | 2019-05-17 | 北京科技大学 | A kind of school bus paths planning method and device for planning |
| CN109918422A (en) * | 2019-03-04 | 2019-06-21 | 山东浪潮云信息技术有限公司 | A method of with Cartographic Technique building classes are over key monitoring route model |
| CN111798039A (en) * | 2020-06-12 | 2020-10-20 | 河海大学 | Auxiliary system for picking up and delivering students |
| CN113077087A (en) * | 2021-03-31 | 2021-07-06 | 华录智达科技股份有限公司 | Route planning method and system based on public transport means |
| CN116311932A (en) * | 2023-03-16 | 2023-06-23 | 东南大学 | Dynamic traffic distribution method considering hybrid equalization in MaaS background |
-
2017
- 2017-01-09 CN CN201710012927.0A patent/CN106709833A/en active Pending
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109764884A (en) * | 2019-01-02 | 2019-05-17 | 北京科技大学 | A kind of school bus paths planning method and device for planning |
| CN109918422A (en) * | 2019-03-04 | 2019-06-21 | 山东浪潮云信息技术有限公司 | A method of with Cartographic Technique building classes are over key monitoring route model |
| CN111798039A (en) * | 2020-06-12 | 2020-10-20 | 河海大学 | Auxiliary system for picking up and delivering students |
| CN111798039B (en) * | 2020-06-12 | 2022-08-16 | 河海大学 | Student pick-up auxiliary system |
| CN113077087A (en) * | 2021-03-31 | 2021-07-06 | 华录智达科技股份有限公司 | Route planning method and system based on public transport means |
| CN113077087B (en) * | 2021-03-31 | 2024-01-26 | 华录智达科技股份有限公司 | Path planning method and system based on public transport means |
| CN116311932A (en) * | 2023-03-16 | 2023-06-23 | 东南大学 | Dynamic traffic distribution method considering hybrid equalization in MaaS background |
| CN116311932B (en) * | 2023-03-16 | 2024-03-01 | 东南大学 | Dynamic traffic distribution method considering hybrid equalization in MaaS background |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106709833A (en) | School bus path optimization method based on big data | |
| CN100547625C (en) | Method for analysis of prototype run route in a kind of urban transportation | |
| Mitra et al. | Built environment correlates of active school transportation: neighborhood and the modifiable areal unit problem | |
| CN104217250B (en) | A kind of urban rail transit new line based on historical data opens passenger flow forecasting | |
| Rahman et al. | Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones | |
| CN105869401A (en) | Road network dynamic partitioning method based on different crowdedness degrees | |
| CN103745089A (en) | Multi-dimensional public transport operation index evaluation method | |
| CN106205156A (en) | A kind of crossing self-healing control method for the sudden change of part lane flow | |
| Ma et al. | Impact of weather conditions on middle school students’ commute mode choices: Empirical findings from Beijing, China | |
| CN106096798A (en) | A kind of city road network optimization method under accessibility optimal conditions | |
| CN109558969A (en) | A kind of VANETs car accident risk forecast model based on AdaBoost-SO | |
| CN105427001A (en) | Optimal route of school bus of regional middle and primary school | |
| CN102867217A (en) | Projection pursuit-based risk evaluation method for meteorological disasters of facility agriculture | |
| CN107919014A (en) | Taxi towards more carrying kilometres takes in efficiency optimization method | |
| CN111060119B (en) | City slow-travel navigation method and system based on street view visual element scoring | |
| CN107680391A (en) | Two pattern fuzzy control methods of crossroad access stream | |
| CN104320789A (en) | Internet of vehicles RSU optimal allocation method based on game theory | |
| Halat et al. | Impact of crime statistics on travel mode choice: Case study of the city of Chicago, Illinois | |
| CN107886720A (en) | One kind closes festivals or holidays urban traffic blocking transfer analysis method based on section type-2 fuzzy sets | |
| CN106960246A (en) | A kind of vehicle guidance method based on evolutionary Game | |
| CN105489009B (en) | A kind of tide lane recognition method and system | |
| Chalumuri et al. | Development and evaluation of an integrated transportation system: A case study of Delhi | |
| Feng et al. | Association of the built environment with motor vehicle emissions in small cities | |
| Ha et al. | Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods | |
| CN104183135B (en) | The method of estimation of vehicle traveling expense and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170524 |
|
| RJ01 | Rejection of invention patent application after publication |