CN104217579A - Method for searching critical road sections of traffic networks on basis of redundancy of road section sub-networks - Google Patents
Method for searching critical road sections of traffic networks on basis of redundancy of road section sub-networks Download PDFInfo
- Publication number
- CN104217579A CN104217579A CN201310312172.8A CN201310312172A CN104217579A CN 104217579 A CN104217579 A CN 104217579A CN 201310312172 A CN201310312172 A CN 201310312172A CN 104217579 A CN104217579 A CN 104217579A
- Authority
- CN
- China
- Prior art keywords
- network
- section
- sub
- networks
- transportation network
- 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
Abstract
The invention discloses a 'method for searching critical road sections of traffic networks on the basis of the redundancy of road section sub-networks'. Road section redundancy indexes which reflect topological characteristics of the road section sub-networks are selected to be used as selection indexes of candidate critical road sections by the aid of the method. The method includes building a sub-network of each directional road section, simultaneously counting the numbers of nodes of the sub-networks and the numbers of the directional road sections and computing the corresponding redundancy indexes; preliminarily selecting the candidate critical road sections according to a principle that the road sections with the small redundancy indexes are optimally selected, computing an influence degree of each candidate critical road section on the traffic networks and selecting the road sections with the maximum influence degrees as the critical road sections. The method has the advantages that the searched critical road sections of the traffic networks are high in search accuracy, search efficiency and operability, and the problem of difficulty in determining selection indexes of candidate road sections with good search effects and high operability can be solved.
Description
Technical field
The present invention relates to technical field of intelligent traffic, relate generally to the identification of transportation network key road segment and search aspect, more particularly, be a kind of meet certain search accuracy rate condition under there is the key road segment searching method of higher searching efficiency.
Background technology
Transportation network is the important component part of City Traffic Transport System, is spatially connected in city, is the basis that the every function in city is run well.And the key road segment of transportation network refers to that damage over a period to come, degradation or inefficacy can produce the section of great negative effect to whole transportation network.The key road segment of accurate Fast Identification transportation network strategic planning aspect, tactical planning aspect and traffic operation and control plane all significant, the fragile position of accurate quick position transportation network, take traffic correcting measure pointedly, contribute to the antijamming capability improving network, there is higher ratio for input and output.
The more important in transportation network key road segment identification process searches for the key road segment of transportation network efficiently and accurately, and conventional key road segment searching method can be divided into force search method and candidate road section method two class.
Traditional force search method is key road segment searching algorithm comparatively conventional in theoretical research, every bar section is calculated to the influence degree of transportation network performance by all sections disconnecting transportation network successively, can accurately search out the maximum key road segment of transportation network performance impact degree with this, be the simplest searching method.The advantage of force search method can compare all sections of transportation network to the influence degree of network performance, therefore can the key road segment of accurate search network, and search accuracy rate is 100%.But owing to needing the every bar section disconnecting network successively to carry out a traffic assignation, calculated amount is comparatively large, and search efficiency is lower, feasible poor in Large-scale Traffic Network application.Such as, for Chicago local traffic network of 39018 section compositions, carry out a traffic assignation about 0.5 hour (reaching 0.0001 precision), force search method can be calculated consuming time more than 2 years in the traffic assignation stage.
Candidate road section method first chooses according to certain rule the alternatively section, section most possibly becoming key road segment in advance, then disconnect candidate road section successively and obtain every bar candidate road section to the influence degree of transportation network performance, search for from candidate road section the maximum section of transportation network performance impact degree as key road segment using this.General candidate road section number is much smaller than total section number of transportation network, and therefore the traffic assignation execution number of times of candidate road section method is significantly less than force search method, and search efficiency is higher, applies more in actual Large-scale Traffic Network.What its key was to determine candidate road section chooses rule, can be the composition section of water channel principium between OD pair, or have the section of higher select probability in Stochastic Traffic Assignment.
Have searching method to have chosen 10 kinds of candidate road section index for selection based on section (such as V/C ratio), but result show that these indexs more adequately can not search for the key road segment of transportation network.Also there is searching method to introduce a kind of candidate road section based on range of influence, section and choose rule, its basic thought is the section sub-network of the fixed step size radius generated centered by certain section, by the OD matrix of the anti-hairclipper network of sub-network each road section traffic volume flow, the traffic assignation of sub-network before and after disconnecting for this section, the index for selection in the increase degree of sub-network journey time alternatively section after finally disconnecting with section, the large section of preferential index for selection value is as the candidate road section of key road segment.Result shows, under suitable Parameter Conditions, the key road segment searching method based on range of influence, section has good search accuracy rate and searches plain efficiency.But this candidate road section is chosen rule and needed instead to push away OD matrix, choosing of obvious OD Backstepping has material impact to the search accuracy rate of key road segment and search efficiency, and operation is comparatively complicated.
To sum up, existing transportation network key road segment searching method is difficult to accomplish higher search accuracy rate and search efficiency and stronger operability simultaneously.
Reference
[1]Dial R B.A path-based user-equilibrium traffic assignment algorithm that obviates path storage and enumeration[J].Transportation Research Part B:Methodological,2006,40(10):917-936.
[2]Knoop V L,Snelder M,Van Zuylen H J.Comparison of link-level robustness indicators[C]//3rd International Symposium on Transportation Network Reliability,Delft. 2007.
[3]Chen B Y,Lam W H K,Sumalee A,et al.Vulnerability analysis for large-scale and congested road networks with demand uncertainty[J].Transportation Research Part A:Policy and Practice,2012,46(3):501-516.
Summary of the invention
The object of the invention is the defect and the deficiency that solve prior art existence, the index for selection of the section redundance index choosing reflection section sub-network topological property alternatively key road segment, propose the transportation network key road segment searching method based on section sub-network redundance, there is higher search accuracy rate and search efficiency, and owing to not relating to the anti-complicated approach such as to push away of OD, can operate stronger.
The technical scheme that the present invention provides come for:
Based on a transportation network key road segment searching method for section sub-network redundance, it is characterized in that, comprise the steps:
(1), set up the topological model of transportation network by original method, construct the sub-network in the oriented section of every bar, add up nodes and the Oriented Path from Its Maximal Subtrees hop count of sub-network simultaneously, calculate the redundance index of respective stretch place sub-network;
(2) principle, with sub-network redundance index little person in place, section in step (1) preferentially chosen, first chooses some sections alternatively key road segment from all sections of transportation network;
(3), the damage in every bar candidate key section in calculation procedure (2), degradation or the influence degree that lost efficacy to transportation network one by one, choose the key road segment of some larger sections of influence degree as transportation network.
In the present invention, step (1) method original in Complex Networks Theory sets up the topological model of transportation network, that intersection and road are looked closely to the greatest extent for node, the section of connection two intersection is considered as limit, transportation network is the most simply mapped, directly can reflect the connectedness of transportation network.Geometric distance between each connected node can be mapped on two internodal limits simply again, the metric system distance on geography can be used, compare the cognition custom meeting people, easy understand and utilization.
In step (1), construct with oriented section <i, centered by j>, step-length radius is the sub-network of η
step-length radius η represents sub-network
in the ultimate range of all nodes and section <i, j>.
In step (1), described sub-network
redundance index
account form be
wherein,
represent sub-network
in nodes,
represent sub-network
in directed edge number or 2 times of nonoriented edge number, construct in sub-network and count simultaneously.
In the present invention, it is with section <i, j> place sub-network redundance index that step (2) first chooses candidate key section from all sections of transportation network
little person is preferentially chosen for principle.Section redundance index
less, then replaceable path is fewer, and section <i, j> lost efficacy to sub-network
influence degree larger, section <i, j> more may become the key road segment of transportation network, are therefore preferentially chosen for candidate road section.
In step (2), candidate key section number affects search efficiency and the search accuracy rate of key road segment searching algorithm simultaneously, and candidate key section number is larger, and search accuracy rate is higher, but search duration is longer, and search efficiency is lower.
In the present invention, in step (3) candidate key section the influence degree of transportation network is damaged by section, degradation or the measure of variation of network performance after losing efficacy, and network performance describe transportation network within given period can by the possibility used.Conventional transportation network performance index can be divided into reliability index, vulnerability inder and risk indicator.Reliability index stresses the degree of reliability that transportation network meets pre-provisioning request, comprises connected sets index, Travel Time Reliability index and capacity reliability index.Vulnerability inder comprises the vulnerability inder (distribution of characteristic path length, cluster coefficients, degree, efficiency and centrality index etc.) only considering network topology structure and the vulnerability inder (overall accessibility exponential sum ARIA (the Accessibility/Remoteness Index of Australia) index of trip integrated cost, Hansen) measured with network reachability.Wherein, comparatively conventional is that the increment of the comprehensive travel cost of transportation network after damaging with section, demote or losing efficacy is for index.
In step (3), after section damage, degradation or inefficacy, network performance recession level is larger, and represent that the influence degree of this section to transportation network is larger, section is more crucial.Search for the transportation network key road segment obtained, according to the large minispread of transportation network influence degree, former the key road segments that required influence degree is maximum can be obtained, and quantitatively can export the key indices of the key road segment of needs, the key road segment that quantitative comparison obtains is to the influence degree of transportation network.
With existing transportation network key road segment searching method still based on traditional force search method, and the candidate key section of candidate road section method is chosen rule in search efficiency, search accuracy rate and ease-to-operate and still had deficiency to compare, the present invention has carried out the optimization of following several respects to transportation network key road segment searching method:
(1) choose key road segment candidate road section in advance, from candidate road section, search for the key road segment of transportation network, improve search efficiency;
(2) the section redundance index of the sub-network of constructing using section, as the index for selection of key road segment candidate road section, under rational Parameter Conditions, has higher search standard and removes rate and search efficiency;
(3) construct section sub-network comparatively simple to calculate sub-network redundance index method, operability is stronger.
Therefore, when ensureing certain key road segment search accuracy rate, effectively can improve search efficiency, and there is stronger operability.
Accompanying drawing explanation
Fig. 1 is the transportation network key road segment searching method process flow diagram based on section sub-network redundance.
Fig. 2 is that the example calculating section sub-network redundance index cores method implements basic logic figure.
Fig. 3 is with section <9 in example, and centered by 10>, step-length radius η is the sub-network of 1
Fig. 4 is with section <9 in example, and centered by 10>, step-length radius η is the sub-network of 2
embodiment
Below in conjunction with accompanying drawing, technical solution of the present invention is described further.
Core concept of the present invention is, a kind of transportation network key road segment searching method based on section sub-network redundance is provided, by introducing the index for selection of section sub-network redundance index alternatively key road segment, overcome existing transportation network key road segment searching method at search efficiency, search deficiency in plain accuracy rate and operability.
Figure 1 shows that the transportation network key road segment search routine based on section sub-network redundance:
First, urban road traffic network comprises through street (overhead), major trunk roads, subsidiary road and communicating branch road, but for simplified network can according to practical study demand selected part type road, transportation network topological model G=(V is set up by the original method of Complex Networks Theory, E, Ω), wherein V represents the non-NULL set of node of transportation network, E represents the directed edge collection of transportation network, and Ω represents the property set of transportation network.
The non-NULL set of node V={u of N number of element
1, u
2..., u
nin element u
irepresent the numbering (1≤i≤N) of i-th node.With natural number 1...N to each node serial number, make u
i=i, then set of node can be expressed as V={1 again, and 2 ..., N}.The number of the node be connected with i ∈ V node is represented with one-dimension array R (i), two-dimensional array RV (i, j), the node number (arranging from small to large by number) of jth (1≤j≤R (the i)) node be connected with i node is represented.Therefore the directed edge be connected with i node can represent with <i .RV (i, j) > ∈ E or <RV (i, j), i> ∈ E.In property set Ω, element is mainly road section length attribute L, section free flow speed V
free, section actual capacity attribute Ca and link flow attribute F, i.e. Ω={ L, V
free, C α, F}.
Road section length attribute L is the base attribute of road, available two-dimensional array L={l (i, j) } describe, represent from node i to Oriented Path from Its Maximal Subtrees segment length corresponding to the directed edge being attached thereto the jth node (node R V (i, j)) connect, that is:
When there is directed edge <i, during RV (i, j) >, l (i, j) value is the physical length in corresponding oriented section, and unit is rice (m); When there is not this directed edge, represent with just infinite.
Section free flow speed V
freereflect the speed restriction in section, available two-dimensional array V
free={ v
free(i, j) } describe, represent from node i to oriented section free flow speed corresponding to the directed edge being attached thereto the jth node (RV (i, j)) connect, that is:
When there is directed edge <i, during RV (i, j) >, v
free(i, j) value is the free flow speed FreeVelocity in corresponding oriented section, and unit is meter per second (m/s), can determine according to design speed; When there is not this directed edge, can null representation.By road section length attribute L and free flow speed V
freethe journey time under the freestream conditions of each section can be calculated.
Section actual capacity attribute Ca reflects category of roads to a certain extent, available two-dimensional array Ca={ca (i, j) } describe, represent from node i to being attached thereto a jth node (RV (i connect, j) the oriented road section capacity that directed edge) is corresponding, that is:
When there is directed edge <i, RV (i, j) during >, ca (i, j) value is the actual capacity Capacity in corresponding oriented section, unit be standard car/time (PCU/h) or hour vehicle flowrate (veh/h) by investigation or with reference to HCM(Highway Capacity Manual) etc. specification determine; When there is not this directed edge, can null representation.
Link flow attribute F reflects the routing result of traveler, available two-dimensional array F={f (i, j) } describe, represent from node i to being attached thereto a jth node (RV (i connect, j) the oriented section Real-Time Traffic Volume that directed edge) is corresponding, that is:
When there is directed edge <i, during RV (i, j) >, f (i; J) value is the real-time traffic amount Flow in corresponding oriented section, unit be standard car/time (PCU/h) or hour vehicle flowrate (veh/h); When there is not this directed edge, can null representation.
Then, for the oriented section <i of transportation network, j> ∈ E, wherein i, j represent the start, end (i in section respectively, j ∈ V), can generate from network G with section <i, centered by j>, step-length radius is the sub-network of η, is designated as
the distance of note node k and section <i, j> is Dist (k, <i, j>), then the definition of step-length η is:
The value major effect key road segment searching algorithm of the step-length radius η of sub-network search plain accuracy rate, general exist an optimum value.Can η=3 ~ 5 be trial value, specifically should determine according to network size and topological structure, but should not be excessive and make sub-network scale close to the scale of whole transportation network.
Represent sub-network
in the ultimate range of all nodes and section <i, j>.The distance of note node i, j is || i, j||, and represent the minimum nonoriented edge number of connected node i, j, then the definition of the distance Dist (k, <i, j>) in node and section is:
Dist(k,<i,j>)=Min(||k,i||,||k,j||) (6)
As shown in Figure 2, with section <i, centered by j>, sub-network is constructed
meanwhile, sub-network is counted
in nodes
with directed edge number
calculate corresponding redundance index
core methed, embodiment is as follows:
Input:
Transportation network topological model G=(V, E, Ω)
Step-length radius η
Section <i, j> ∈ E
Export:
Redundance index
1. initialization:
The distance of all nodes of transportation network and section <i, j> is set
Dist (i, < i, j >)=O and Dist (j, < i, j >)=0 is set;
Point set I={i is played in research section, j};
Sub-network node counter N is set
sub=0 and Oriented Path from Its Maximal Subtrees segment counter M
sub=0.
2. construct sub-network and add up sub-network nodes and Oriented Path from Its Maximal Subtrees hop count simultaneously:
From choose node k in point set I, make Dist (k, <i, j >)=mIN Dist (a, <i, j>), a ∈ I, until playing a point set I be sky;
From remove node k in point set I;
Statistics sub-network nodes, arranges node counter N
sub=N
sub+ 1;
Research road segment end collection J={a} (a ∈ V), wherein a is for meeting <a, all nodes of k> ∈ E or <k, a> ∈ E;
For each node l in terminal collection J, if meet following two formulas (7) (8) simultaneously, Dist (l is then set, <i, j>)=Dist (k, <i, j>)+1, and added node l to point set I.If there is section <k, l> ∈ E, meets following formula (9) simultaneously, then add up sub-network Oriented Path from Its Maximal Subtrees hop count, be provided with to section counter M
usb=M
usb+ 1;
Dist(k,<i,j>)+1≤η (7)
Dist(l,<i,j>)≥Dist(k,<i,j>)+1 (8)
Dist(l,＜i,j＞)≤η (9)
3. sub-network redundance index is calculated:
Calculate with section <i, centered by j>, step-length radius is the sub-network of η
redundance index be
Fig. 3 and Fig. 4 is respectively with section <9, and centered by 10>, step-length radius η is respectively the sub-network of 1,2
with
calculate corresponding redundance index to be respectively
With
Secondly, for all section <i, j> ∈ E of transportation network G, the redundance index of respective subnet network can all be calculated
less, then replaceable path is fewer, and section <i, j> lost efficacy to sub-network
influence degree larger, section <i, j> more may become the key road segment of transportation network, should preferentially be chosen for candidate key section, can obtain λ bar candidate key section thus.
Obviously, number λ is larger in candidate key section, and search accuracy rate is higher, but search duration is longer, and search efficiency is lower.Candidate key section number tentatively can be chosen for 2 ~ 5 times of critical path hop count to be identified, specifically should test estimation according to network size and obtain, but generally no more than the half of whole transportation network section number.
Finally, the influence degree calculating the damage in every bar candidate key section, degradation one by one or lost efficacy to transportation network, chooses the key road segment of some larger sections of influence degree as transportation network.After section can adopt section complete failure to the influence degree of transportation network, the relative increment Measure Indexes of transportation network comprehensive travel cost.
For the transportation network G=(V under normal condition, E, Ω), utilize the OD matrix of the method for traffic assignment distribute traffic networks such as Stochastic User Equilibrium algorithm, the volume of traffic f on each section < i, j> ∈ E can be obtained
0(i, j) and journey time c
0(i, j); For candidate key section <a, the transportation network G after b> complete failure
ab=(V, E
ab, Ω), wherein E
ab=E { <a, b>}, the OD matrix of same distribute traffic network correspondingly can obtain the volume of traffic f on each section <i, j> ∈ E
ab(i, j) and journey time c
ab(i, j).Then candidate road section <a, b> are to the Intrusion Index CI of transportation network
abcalculating formula is:
CI
abexponential quantity is larger, represents that section <a, the b> influence degree to transportation network G is larger, namely more crucial.
The key indices in each section is sorted from big to small, that is:
In formula, N
erepresent the section number of transportation network G=(V, E, Ω).Therefrom choose CI
abremarkable several the larger sections of index are as the key road segment of transportation network.
Claims (3)
1., based on a transportation network key road segment searching method for section sub-network redundance, it is characterized in that, comprise the steps:
(1), set up the topological model of transportation network by original method, construct the sub-network in the oriented section of every bar, add up nodes and the Oriented Path from Its Maximal Subtrees hop count of sub-network simultaneously, calculate the redundance index of respective stretch place sub-network;
(2) principle, with sub-network redundance index little person in place, section in step (1) preferentially chosen, first chooses some sections alternatively key road segment from all sections of transportation network;
(3), the damage in every bar candidate key section in calculation procedure (2), degradation or the influence degree that lost efficacy to transportation network one by one, choose the key road segment of some larger sections of influence degree as transportation network.
2. the method for claim 1, is characterized in that, in step (1), structure is with oriented section <i, and centered by j>, step-length radius is the sub-network of η
step-length radius η represents sub-network
in the ultimate range of all nodes and section <i, j>.
3. the method for claim 1, is characterized in that, in step (1), and described sub-network
redundance index
account form be
wherein,
represent sub-network
in nodes,
represent sub-network
in directed edge number or 2 times of nonoriented edge number, construct in sub-network and count simultaneously.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310312172.8A CN104217579B (en) | 2013-07-23 | 2013-07-23 | Transportation network key road segment searching method based on section sub-network redundancy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310312172.8A CN104217579B (en) | 2013-07-23 | 2013-07-23 | Transportation network key road segment searching method based on section sub-network redundancy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104217579A true CN104217579A (en) | 2014-12-17 |
| CN104217579B CN104217579B (en) | 2016-08-24 |
Family
ID=52099014
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310312172.8A Expired - Fee Related CN104217579B (en) | 2013-07-23 | 2013-07-23 | Transportation network key road segment searching method based on section sub-network redundancy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104217579B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105548067A (en) * | 2015-12-16 | 2016-05-04 | 西南大学 | Method for minimum-redundancy wavelength selection through amoeboid network |
| CN106652440A (en) * | 2015-10-30 | 2017-05-10 | 杭州海康威视数字技术股份有限公司 | Method and apparatus for determining frequent activity area of vehicle |
| CN106981194A (en) * | 2017-05-02 | 2017-07-25 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN109377048A (en) * | 2018-10-23 | 2019-02-22 | 北京航空航天大学 | A kind of comprehensive traffic network hub node selection method |
| CN111028511A (en) * | 2019-12-25 | 2020-04-17 | 亚信科技（中国）有限公司 | Traffic jam early warning method and device |
| CN111862595A (en) * | 2020-06-08 | 2020-10-30 | 同济大学 | Speed prediction method, system, medium and device based on road network topological relation |
| CN112598896A (en) * | 2020-12-07 | 2021-04-02 | 吉林大学 | Industrial park logistics key road section identification method based on GPS data |
| CN114399124A (en) * | 2022-03-24 | 2022-04-26 | 腾讯科技（深圳）有限公司 | Path data processing method, path planning method, path data processing device, path planning device and computer equipment |
| CN115691126A (en) * | 2022-10-26 | 2023-02-03 | 长安大学 | Traffic network redundancy measure method based on depth-first search algorithm |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0902406A2 (en) * | 1997-09-12 | 1999-03-17 | MANNESMANN Aktiengesellschaft | Method for transmitting traffic data and for analysing the road traffic network, traffic-evaluating central unit and terminal therefor |
| CN101488158A (en) * | 2009-02-13 | 2009-07-22 | 同济大学 | Road network modeling method based on road element |
| CN101650191A (en) * | 2009-09-11 | 2010-02-17 | 北京四维图新科技股份有限公司 | Abstract method and device of road network topology |
| CN102175256A (en) * | 2010-12-27 | 2011-09-07 | 浙江工业大学 | Path planning determining method based on cladogram topological road network construction |
| CN102194259A (en) * | 2010-03-16 | 2011-09-21 | 上海交技发展股份有限公司 | Method for computing toll by modeling networked tolling lane of highway based on graph theory |
| CN202584124U (en) * | 2011-12-23 | 2012-12-05 | 北京易华录信息技术股份有限公司 | Road data processing system based on dynamic segmentation technology |
| KR20130012608A (en) * | 2011-07-26 | 2013-02-05 | 한국철도기술연구원 | Transit network information management system and method using smart card data |
| CN103150680A (en) * | 2013-03-25 | 2013-06-12 | 东南大学 | Automatic layout method for urban development land with best road accessibility |
-
2013
- 2013-07-23 CN CN201310312172.8A patent/CN104217579B/en not_active Expired - Fee Related
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0902406A2 (en) * | 1997-09-12 | 1999-03-17 | MANNESMANN Aktiengesellschaft | Method for transmitting traffic data and for analysing the road traffic network, traffic-evaluating central unit and terminal therefor |
| CN101488158A (en) * | 2009-02-13 | 2009-07-22 | 同济大学 | Road network modeling method based on road element |
| CN101650191A (en) * | 2009-09-11 | 2010-02-17 | 北京四维图新科技股份有限公司 | Abstract method and device of road network topology |
| CN102194259A (en) * | 2010-03-16 | 2011-09-21 | 上海交技发展股份有限公司 | Method for computing toll by modeling networked tolling lane of highway based on graph theory |
| CN102175256A (en) * | 2010-12-27 | 2011-09-07 | 浙江工业大学 | Path planning determining method based on cladogram topological road network construction |
| KR20130012608A (en) * | 2011-07-26 | 2013-02-05 | 한국철도기술연구원 | Transit network information management system and method using smart card data |
| CN202584124U (en) * | 2011-12-23 | 2012-12-05 | 北京易华录信息技术股份有限公司 | Road data processing system based on dynamic segmentation technology |
| CN103150680A (en) * | 2013-03-25 | 2013-06-12 | 东南大学 | Automatic layout method for urban development land with best road accessibility |
Non-Patent Citations (1)
| Title |
|---|
| 云美萍 等: "基于减灾的城市交通系统规划射击与管理", 《城市交通》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106652440A (en) * | 2015-10-30 | 2017-05-10 | 杭州海康威视数字技术股份有限公司 | Method and apparatus for determining frequent activity area of vehicle |
| CN106652440B (en) * | 2015-10-30 | 2019-05-21 | 杭州海康威视数字技术股份有限公司 | A kind of determination method and device in the frequent activities region of vehicle |
| CN105548067B (en) * | 2015-12-16 | 2018-07-03 | 西南大学 | A kind of Wavelength selecting method using amoeba real-time performance minimum redundancy |
| CN105548067A (en) * | 2015-12-16 | 2016-05-04 | 西南大学 | Method for minimum-redundancy wavelength selection through amoeboid network |
| CN106981194B (en) * | 2017-05-02 | 2019-06-07 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN106981194A (en) * | 2017-05-02 | 2017-07-25 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN109377048B (en) * | 2018-10-23 | 2020-06-12 | 北京航空航天大学 | Comprehensive transportation network hub node selection method |
| CN109377048A (en) * | 2018-10-23 | 2019-02-22 | 北京航空航天大学 | A kind of comprehensive traffic network hub node selection method |
| CN111028511A (en) * | 2019-12-25 | 2020-04-17 | 亚信科技（中国）有限公司 | Traffic jam early warning method and device |
| CN111028511B (en) * | 2019-12-25 | 2021-10-15 | 亚信科技（中国）有限公司 | Traffic jam early warning method and device |
| CN111862595A (en) * | 2020-06-08 | 2020-10-30 | 同济大学 | Speed prediction method, system, medium and device based on road network topological relation |
| CN111862595B (en) * | 2020-06-08 | 2021-12-31 | 同济大学 | Speed prediction method, system, medium and device based on road network topological relation |
| CN112598896A (en) * | 2020-12-07 | 2021-04-02 | 吉林大学 | Industrial park logistics key road section identification method based on GPS data |
| CN112598896B (en) * | 2020-12-07 | 2021-11-12 | 吉林大学 | Industrial park logistics key road section identification method based on GPS data |
| CN114399124A (en) * | 2022-03-24 | 2022-04-26 | 腾讯科技（深圳）有限公司 | Path data processing method, path planning method, path data processing device, path planning device and computer equipment |
| CN115691126A (en) * | 2022-10-26 | 2023-02-03 | 长安大学 | Traffic network redundancy measure method based on depth-first search algorithm |
| CN115691126B (en) * | 2022-10-26 | 2023-08-22 | 长安大学 | Traffic network redundancy measurement method based on depth-first search algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104217579B (en) | 2016-08-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104217579B (en) | Transportation network key road segment searching method based on section sub-network redundancy | |
| Liu et al. | Approximating betweenness centrality to identify key nodes in a weighted urban complex transportation network | |
| CN100491918C (en) | Two-step multi-path optimization method for central controlled vehicle information system | |
| CN106789190B (en) | Vulnerability assessment and route optimization method for power communication network | |
| CN103971184B (en) | Transmission line of electricity path generating method based on GIS Spatial GISs | |
| CN105091889B (en) | A kind of determination method and apparatus of hotspot path | |
| CN110135092A (en) | Complicated weighting network of communication lines key node recognition methods based on half local center | |
| CN103471603B (en) | The matching process of a kind of public bus network and road network and device | |
| CN107123260A (en) | Method of traffic assignment based on traveler budget limit | |
| CN109474023B (en) | Intelligent power distribution network section real-time updating method and system, storage medium and terminal | |
| CN103020267B (en) | Based on the complex network community structure method for digging of triangular cluster multi-label | |
| CN101650191A (en) | Abstract method and device of road network topology | |
| CN105976048A (en) | Power transmission network extension planning method based on improved artificial bee colony algorithm | |
| CN101187996A (en) | A quick bus line selection algorithm based on self-adapted backbone network | |
| CN107730113A (en) | A kind of quantitative evaluation method of the urban road network planning based on function | |
| CN104391907A (en) | Variable resolution rapid path searching method | |
| CN104462714A (en) | Calculation method for optimizing municipal drainage network plane layout design | |
| CN105740964A (en) | Urban road network data organization and shortest path rapid calculation method | |
| CN105447595A (en) | Scenic spot route recommending method based on spectral clustering algorithm | |
| CN105162654A (en) | Link prediction method based on local community information | |
| CN106289287A (en) | A kind of vehicle-mounted end Multiobjective Shortest Path computational methods based on route selection experience | |
| CN103823951A (en) | Method for quantifying characteristics of road network and effect of characteristics of road network on land utilization | |
| CN105160429B (en) | It is a kind of that there is the multi-mode Public Transport Transfer method for virtually changing to micro- hinge | |
| CN105403812A (en) | Triangular ring network line single-end fault distance measuring method based on fault traveling wave line decomposition and distance calibration | |
| CN107588779B (en) | Intelligent vehicle navigation method based on travel time between any two nodes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160824 Termination date: 20190723 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |