CN112466126A - Road network expandable area control method based on MFD - Google Patents
Road network expandable area control method based on MFD Download PDFInfo
- Publication number
- CN112466126A CN112466126A CN202011271153.1A CN202011271153A CN112466126A CN 112466126 A CN112466126 A CN 112466126A CN 202011271153 A CN202011271153 A CN 202011271153A CN 112466126 A CN112466126 A CN 112466126A
- Authority
- CN
- China
- Prior art keywords
- traffic
- area
- vehicles
- core area
- road 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
- 238000000034 method Methods 0.000 title claims abstract description 10
- 230000006835 compression Effects 0.000 claims abstract description 13
- 238000007906 compression Methods 0.000 claims abstract description 13
- 238000011217 control strategy Methods 0.000 claims abstract description 13
- 238000013459 approach Methods 0.000 abstract description 3
- 238000004364 calculation method Methods 0.000 description 12
- 230000004308 accommodation Effects 0.000 description 8
- 230000007423 decrease Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a road network expandable area control method based on MFD, which comprises the steps of dividing traffic sub-areas into a core area and an adjacent area to obtain MFD properties of different areas, and obtaining the road network accommodating capacity which can be increased in the adjacent area when the traffic conditions of the whole road network tend to be consistent by utilizing the MFD properties of the traffic networks of the core area and the adjacent area; if the core area adopts traffic control, judging the number of overflowing vehicles in the core area; the number of vehicles in the core area can rush into the adjacent area, and then the traffic control measures when the number of the vehicles which can be accommodated in the adjacent area is increased and approaches the number of the vehicles which overflow from the core area can be obtained, namely the green light compression time adopted by the core area; performing traffic control under green light compression on a core area to enable the traffic states of the core area and an adjacent area to be consistent; a new expanded area is formed, so that the traffic performance of the whole road network is improved through the expandable area control strategy of the traffic road network.
Description
Technical Field
The invention relates to the field of urban road traffic signal control, in particular to a road network expandable area control method based on a macroscopic basic graph.
Background
With the rapid development of urban traffic, the phenomenon of traffic congestion is becoming more and more intense, that is, it is becoming a problem to be solved urgently in traffic research. From the primary and secondary contradictions, the urban traffic core area with large congestion range and strong congestion intensity and wide congestion influence is a key part of the whole traffic congestion phenomenon. From the cause of traffic jam, the traffic jam is related to traffic demand, and a jam phenomenon occurs when the traffic capacity of a road network reaches an upper limit, and the jam in the road network is more serious as more and more vehicles flow into the road network, which directly leads to rapid reduction of traffic flow in the road network. This phenomenon of the correlation between the traffic flow and the number of vehicles that have been flooded, which is directly due to the nature of the underlying road network, is referred to by researchers as a Macroscopic Fundamental Map (MFD). In solving the traffic jam, managers often take management measures such as restriction, jam charging and the like from microscopic and specific vehicles. These measures usually involve a part of the traffic participants, and once implemented, the traffic aspects are affected, and it is difficult to make a macroscopic depiction of the overall traffic state. To provide macro control of traffic conditions, researchers have employed MFDs as a vehicle to assist in the study of traffic networks.
Disclosure of Invention
Considering that traffic control strategies are mostly deterministic control strategies and mostly control flow at border intersections of controlled areas, the invention provides a traffic control strategy for forming a variable area, and the traffic efficiency of the whole road network is improved. If the congestion core area adopts traffic control, traffic overflow is caused, and the overflowed vehicles flow into an adjacent traffic area; if the adjacent area does not have enough storage space, the traffic efficiency of the adjacent area is reduced, and the adjacent area is matched with the measures for taking traffic control in the congested area. Based on the method, constraint conditions of boundary control are considered, the MFD is used as a tool for researching traffic states of the road network, the traffic states of the adjacent areas in the core area are judged according to basic attributes of the road network, a traffic control strategy for forming the variable area is provided, and the traffic efficiency of the whole road network is improved.
The invention specifically adopts the following technical scheme:
a road network expandable area control method based on MFD includes the following steps:
(1) calculating the number of vehicles overflowing at the boundary road section
Assuming that the cycle duration of a boundary intersection i is CiGreen light loss time of LiThe primary phase saturation flow rate is SiThe arrival rate of the phase vehicle is qiPhase flow rate yiTotal flow rate Y to intersectioniThe ratio of wi＝yi/Yi；
When the core area does not adopt the traffic control strategy
Qin＝Ciqi-(Ci-Li)wiSi
Qib＝[Ciqi-(Ci-Li)wiSi]n
Wherein Q isinNumber of vehicles remaining after the end of green light per cycle for the main phase, QibThe number of vehicles remaining in n cycles;
after the core area adopts the traffic control strategy, the green light compression time is delta t in n periods in totaliThen the number of queued vehicles per cycle for that phase is increased to
The number of vehicles in line is increased in the n periods
At the end of the nth cycle of taking traffic control measures in the core area, the number of vehicles overflowing from the boundary link i is as follows:
considering that there are a plurality of important boundary road segments between two adjacent sub-zones S1, S2, assuming that S1 takes traffic control, the number of vehicles overflowing from the N boundary road segments to S2 is:
QS1S2＝NQij；
(2) calculating the proximity may increase vehicle capacity:
the capacity of sub-bay S2 for overflow vehicles of sub-bay S1 is
Wherein D1 and D2 are the key traffic density of the sub-area S1 and the sub-area S2 respectively,
is the average link length of sub-zone S2;
(3) computing compressed green time for core regions
When the traffic control measure is taken in the sub-zone S1, vehicles overflow to the adjacent sub-zone S2, and the capacity of the sub-zone S2 when the traffic density is increased to the core area traffic density can be made equal to the number of vehicles overflowing from the core area:
QS1S2＝ΔQ
and substituting the relevant data into the data to calculate the compressed green time delta t of the intersection of the core area, and then applying the compressed green time delta t to the core area to enable the traffic states of the core area and the adjacent area to be consistent, thereby forming a new expanded area.
The invention has the following beneficial effects:
(1) the method has certain balance in consideration of the vehicle overflow condition of a core area and the vehicle storage capacity of an adjacent cell;
(2) the capacity of accommodating vehicles can be increased by enabling the number of overflowing vehicles in the core area to be equal to that of the vehicles in the adjacent area, and the traffic control measures to be taken by the core area are calculated, so that the overflowing vehicles in the core area are shunted to the adjacent area, the two areas are assimilated, the distribution of the whole road network vehicles is maintained in a reasonable and feasible range, and the running efficiency of the road network is further improved;
(3) when the strategy is applied, the structural characteristics of the whole road network are fully considered, rather than only a single intersection or adjacent intersections are considered;
(4) when the strategy is applied, the situation that the vehicle starts from the microcosmic state and the specific vehicle starts can be avoided, traffic control measures related to part of traffic participants are avoided, and otherwise, macroscopic control can be carried out to a certain extent.
Description of the drawings:
FIG. 1 is a main flow chart of the method of the present invention.
FIG. 2 is a flow chart of a calculation of the number of accommodated vehicles whose proximity zone can be increased.
FIG. 3 is a flowchart of the compressed green time calculation.
Fig. 4 is a macroscopic basic diagram of an urban road traffic network.
The specific implementation mode is as follows:
the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a road network expandable area control strategy based on MFD mainly includes three levels: the system comprises a road network sub-area MFD module 103, an overflow vehicle calculation module 102 of a core area, and a neighboring area capacity increasing vehicle accommodation capacity and green light compression time calculation module 101; the three parts have a step-by-step supporting relationship, namely, the road network sub-area MFD module provides a platform for calculating the vehicle accommodating capacity for overflow vehicles in the core area and the adjacent area, and the calculation result provides a platform for calculating the green light compression time.
As shown in fig. 2, a network structure of a road network, important road segments in the road network, and intersections 205 divide traffic sub-areas 204, loading traffic flow 205 on the divided traffic sub-areas can obtain self MFD attributes 204 of different traffic sub-areas, and the traffic flow data and the key density 204 in the self MFD attributes of different sub-areas provide data support for the key density difference 203 between different areas when the traffic flow is maximum; after calculating the key density difference 203 between different areas when the traffic flow is maximum, calculating 202 the average density of the road network of the traffic cell which contains the number of vehicles overflowing from the core area; and further determines the vehicle accommodation capacity 201 that can be increased in the neighborhood when the traffic density of the core area and the neighborhood tends to be uniform.
As shown in fig. 3, 301 is to calculate the number of queued vehicles that should be provided when no traffic control measures are taken for a certain road segment in a traffic subarea; 302 is the number of vehicles overflowing from the road network after the traffic control is assumed to be adopted in the road network core area, and the numerical value is formed by multiplying the intersection flow rate by the green light compression time; 303 represents the total number of queued vehicles at an intersection without traffic control measures for a certain number of cycles; 304 represents the number of vehicles queued under total assumptions made at an intersection for a certain number of cycles; 305 represents the number of overflowing vehicles at the intersection after the intersection assumes traffic control in a certain number of cycles, and the part is composed of the number of queued vehicles at the intersection and the number of new queued vehicles due to the compression of the green time; 306, the number of vehicles overflowing from all boundary road sections of a road network after a certain road network takes traffic control currently;
307 represent the difference in key density due to the nature of the Macroscopic Fundamental Diagram (MFD) of the two traffic sub-zones; 308 represents the average length of the road segment of the neighboring area itself; 309, the vehicle accommodation capacity of the neighboring area can be increased when the traffic density of the neighboring area approaches to the core area, that is, the traffic density of the whole road network tends to be consistent; 310 represents a vehicle accommodation capacity that can be increased by the core area in case the number of overflowing vehicles after taking traffic control measures is equal to the traffic density of the neighboring area approaching the traffic density of the core area; reference numeral 311 denotes the calculation of the green light compression time that the core region should have taken.
And then, the control mode of green light compression time is applied to the core area, so that overflowing vehicles in the core area are shunted to adjacent areas, the two areas are assimilated, the distribution of the whole road network vehicles is maintained in a reasonable and feasible range, and the running efficiency of the whole road network is improved.
The system comprises a road network sub-area MFD module, an overflow vehicle calculation module of a core area, and a calculation module for increasing vehicle accommodation capacity and compressing green light time in an adjacent area; road network subregion MFD module: dividing each traffic subarea according to the road network topological data and the traffic flow data of the traffic subareas, and obtaining the MFD attribute of each traffic subarea according to the traffic flow data; an overflow vehicle calculation module of the core zone: establishing a method for calculating the number of overflowing vehicles in a core area under the control of traffic measures; the neighborhood may add vehicle capacity and compress the green time calculation module: according to the MFD attribute difference between different traffic subareas, the vehicle accommodation capacity which can be increased in the adjacent area when the traffic density of the whole road network tends to be consistent is obtained, and the vehicle accommodation capacity is equal to the number of overflowing vehicles in the core area when the core area is controlled by traffic measures, and the green light compression time when the core area is controlled is obtained.
The macroscopic basic graph (MFD) for dividing the road network subareas is described as follows: dividing the whole road network into a plurality of traffic subareas, obtaining the relation between the traffic density and the traffic flow according to the traffic flow data, and obtaining the critical density (critical density), wherein the macroscopic basic graph (MFD) of any urban road traffic network can be described as shown in the form of FIG. 4, wherein the abscissa is the traffic density on the road network, the ordinate is the traffic flow on the road network, and the relation between the traffic flow Q and the road network weighted density D in the urban road network is as follows:
when the road network weighting density D is small, the vehicle traffic flow on the whole road network runs freely.
When the road network weighting density D is gradually increased, the vehicle traffic flow of the entire road network gradually steps into a critical region, in which the traffic flow gradually increases to a peak value and then gradually decreases.
When the road network weight density D increases to a very large value, the road network is caused to fall into a severe congestion state, so that the traffic flow almost disappears.
In summary, in order to ensure that the road network inside any MFD runs smoothly, during real-time control, the traffic flow of the road network should be kept below the critical traffic flow as much as possible, and the traffic density on the road network should be kept around the critical density (critical density), so that the maximum running efficiency of the road network can be ensured.
(1) Calculating the number of vehicles overflowing at the boundary road section
The calculation method of the number of overflowing vehicles in the core area under the control of the traffic measure is described as follows:
when traffic control measures are taken based on a traffic core area, traffic vehicles overflow from the core area, and the number of the vehicles overflowing from the core area is calculated according to a traffic theory:
assuming that the cycle duration of a boundary intersection i is CiGreen light loss time of LiThe primary phase saturation flow rate is SiThe arrival rate of the phase vehicle is qiPhase flow rate yiTotal flow rate Y to intersectioniThe ratio of wi＝yi/Yi；
When the core area does not adopt the traffic control strategy
Qin＝Ciqi-(Ci-Li)wiSi
Qib＝[Ciqi-(Ci-Li)wiSi]n
Wherein Q isinNumber of vehicles remaining after the end of green light per cycle for the main phase, QibThe number of vehicles remaining in n cycles;
after the core area adopts the traffic control strategy, the green light compression time is delta t in n periods in totaliThen the number of queued vehicles per cycle for that phase is increased to
The number of vehicles in line is increased in the n periods
In summary, at the end of the nth cycle of the traffic control measures taken in the core area, the number of vehicles overflowing from the boundary link i is:
considering that a plurality of important boundary road sections exist between two adjacent sub-areas S1 and S2, assuming that S1 adopts traffic control, the number of vehicles overflowing from the N-side boundary road to S2 is:
QS1S2＝NQij。
(2) calculating proximity increases vehicle capacity
The neighborhood increased vehicle capacity calculation method is described as follows:
from the nature of the Macroscopic Fundamental Map (MFD), each traffic subarea must maintain the traffic density in the road network within a certain interval to maximize the traffic flow in the traffic network, and when the road network density is higher than the critical density, the traffic flow in the road network will start to decrease.
The macro basic graph has no relation with the traffic flow loaded on the traffic network, and the basic attribute of the traffic network is related, so that the MFD attribute of the traffic network in the core area and the adjacent area is utilized to obtain the increasable capacity of the road network in the adjacent area when the traffic condition of the whole road network tends to be consistent:
the capacity of sub-bay S2 for overflow vehicles in the S1 sub-bay is therefore:
d1, D2 are the MFD sub-zone S1, critical traffic density of sub-zone S2, respectively, and L2 is the average link length of sub-zone 2.
(3) Compressed green time of core region
The number of overflowing vehicles for traffic control in the core area is equal to the increasable vehicle accommodation capacity when the traffic density of the adjacent area approaches to the core area, and the compression green light time of the core area is calculated, namely the traffic control measures to be taken in the core area.
When traffic control measures are taken in the core sub-zone S1, vehicles overflow to the adjacent sub-zone S2, and the capacity of the sub-zone S2 to increase to the core area traffic density can be made equal to the number of vehicles overflowing the core area:
QS1S2＝ΔQ
substituting the relevant data into the data to calculate the compressed green time of the intersection of the core area, and then applying the compressed green time to the core area to enable the traffic states of the core area and the adjacent area to be consistent; a new expanded area is formed, so that the traffic performance of the whole road network is improved through the expandable area control strategy of the traffic road network.
Claims (1)
1. A road network expandable area control method based on MFD is characterized by sequentially comprising the following steps:
(1) calculating the number of vehicles overflowing at the boundary road section
Assuming that the cycle duration of a boundary intersection i is CiGreen light loss time of LiThe primary phase saturation flow rate is SiThe arrival rate of the phase vehicle is qiPhase flow rate yiTotal flow rate Y to intersectioniThe ratio of wi＝yi/Yi；
When the core area does not adopt the traffic control strategy
Qin＝Ciqi-(Ci-Li)wiSi
Qib＝[Ciqi-(Ci-Li)wiSi]n
Wherein Q isinNumber of vehicles remaining after the end of green light per cycle for the main phase, QibThe number of vehicles remaining in n cycles;
after the core area adopts the traffic control strategy, the green light compression time is delta t in n periods in totaliThen the number of queued vehicles per cycle for that phase is increased to
The number of vehicles in line is increased in the n periods
At the end of the nth cycle of taking traffic control measures in the core area, the number of vehicles overflowing from the boundary link i is as follows:
considering that there are a plurality of important boundary road segments between two adjacent sub-zones S1, S2, assuming that S1 takes traffic control, the number of vehicles overflowing from the N boundary road segments to S2 is:
QS1S2＝NQij；
(2) calculating the proximity may increase vehicle capacity:
the capacity of sub-bay S2 for overflow vehicles of sub-bay S1 is
Wherein D1 and D2 are the key traffic density of the sub-area S1 and the sub-area S2 respectively,
is the average link length of sub-zone S2;
(3) computing compressed green time for core regions
When the traffic control measure is taken in the sub-zone S1, vehicles overflow to the adjacent sub-zone S2, and the capacity of the sub-zone S2 when the traffic density is increased to the core area traffic density can be made equal to the number of vehicles overflowing from the core area:
QS1S2＝ΔQ
and substituting the relevant data into the data to calculate the compressed green time delta t of the intersection of the core area, and then applying the compressed green time delta t to the core area to enable the traffic states of the core area and the adjacent area to be consistent, thereby forming a new expanded area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011271153.1A CN112466126B (en) | 2020-11-13 | 2020-11-13 | Road network expandable area control method based on MFD |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011271153.1A CN112466126B (en) | 2020-11-13 | 2020-11-13 | Road network expandable area control method based on MFD |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112466126A true CN112466126A (en) | 2021-03-09 |
| CN112466126B CN112466126B (en) | 2022-02-22 |
Family
ID=74837321
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011271153.1A Active CN112466126B (en) | 2020-11-13 | 2020-11-13 | Road network expandable area control method based on MFD |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112466126B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113537555A (en) * | 2021-06-03 | 2021-10-22 | 太原理工大学 | Traffic sub-region model prediction sliding mode boundary control method considering disturbance |
| CN115331426A (en) * | 2022-06-30 | 2022-11-11 | 同济大学 | Method for calculating traffic bearing capacity of urban district road network |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105023445A (en) * | 2014-07-04 | 2015-11-04 | 吴建平 | Regional traffic dynamic regulation-control method and system |
| CN106952484A (en) * | 2017-03-28 | 2017-07-14 | 广东工业大学 | Road network thresholding control based on macroscopical parent map |
| CN108364465A (en) * | 2018-02-09 | 2018-08-03 | 太原理工大学 | The method for dynamically partitioning of city road network control work zone based on macroscopical parent map |
| CN109559510A (en) * | 2018-11-22 | 2019-04-02 | 太原理工大学 | Multiple MFD sub-district border coordination control methods based on stochastic distribution control algorithm |
| WO2019109561A1 (en) * | 2017-12-06 | 2019-06-13 | 广东交通职业技术学院 | Mfd-based method for dynamically creating multiple control boundaries in over-saturated street network in internet of vehicles environment |
| CN111091295A (en) * | 2019-12-20 | 2020-05-01 | 银江股份有限公司 | Urban area boundary control system |
| CN111429737A (en) * | 2020-03-30 | 2020-07-17 | 银江股份有限公司 | Agent-based urban area boundary control method and system |
-
2020
- 2020-11-13 CN CN202011271153.1A patent/CN112466126B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105023445A (en) * | 2014-07-04 | 2015-11-04 | 吴建平 | Regional traffic dynamic regulation-control method and system |
| CN106952484A (en) * | 2017-03-28 | 2017-07-14 | 广东工业大学 | Road network thresholding control based on macroscopical parent map |
| WO2019109561A1 (en) * | 2017-12-06 | 2019-06-13 | 广东交通职业技术学院 | Mfd-based method for dynamically creating multiple control boundaries in over-saturated street network in internet of vehicles environment |
| CN108364465A (en) * | 2018-02-09 | 2018-08-03 | 太原理工大学 | The method for dynamically partitioning of city road network control work zone based on macroscopical parent map |
| CN109559510A (en) * | 2018-11-22 | 2019-04-02 | 太原理工大学 | Multiple MFD sub-district border coordination control methods based on stochastic distribution control algorithm |
| CN111091295A (en) * | 2019-12-20 | 2020-05-01 | 银江股份有限公司 | Urban area boundary control system |
| CN111429737A (en) * | 2020-03-30 | 2020-07-17 | 银江股份有限公司 | Agent-based urban area boundary control method and system |
Non-Patent Citations (4)
| Title |
|---|
| 刘澜等: "基于MFD的路网可扩展边界控制策略", 《公路交通科技》 * |
| 张逊逊等: "基于MFD的城市区域路网多子区协调控制策略", 《交通运输系统工程与信息》 * |
| 徐建闽等: "基于宏观基本图的路网边界信号控制策略（英文）", 《JOURNAL OF SOUTHEAST UNIVERSITY(ENGLISH EDITION)》 * |
| 赵靖等: "基于宏观基本图的相邻子区协调控制方法", 《交通运输系统工程与信息》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113537555A (en) * | 2021-06-03 | 2021-10-22 | 太原理工大学 | Traffic sub-region model prediction sliding mode boundary control method considering disturbance |
| CN113537555B (en) * | 2021-06-03 | 2023-04-11 | 太原理工大学 | Traffic sub-region model prediction sliding mode boundary control method considering disturbance |
| CN115331426A (en) * | 2022-06-30 | 2022-11-11 | 同济大学 | Method for calculating traffic bearing capacity of urban district road network |
| CN115331426B (en) * | 2022-06-30 | 2023-12-01 | 同济大学 | Urban area road network traffic bearing capacity calculation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112466126B (en) | 2022-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112466126B (en) | Road network expandable area control method based on MFD | |
| CN111445111B (en) | Electric power Internet of things task allocation method based on edge cooperation | |
| CN112995289B (en) | Internet of vehicles multi-target computing task unloading scheduling method based on non-dominated sorting genetic strategy | |
| CN109146173B (en) | Method for modeling and optimizing cascade failure survivability of airspace sector network | |
| CN110580814B (en) | Timing method and device for traffic signal lamp | |
| CN111083733B (en) | Congestion control method and system for wireless sensor network | |
| CN112188627B (en) | Dynamic resource allocation strategy based on state prediction | |
| CN113382059B (en) | Collaborative caching method based on federal reinforcement learning in fog wireless access network | |
| CN115297171B (en) | Edge computing and unloading method and system for hierarchical decision of cellular Internet of vehicles | |
| CN111008450A (en) | Traffic flow simulation method and device, storage medium and electronic equipment | |
| CN111405634B (en) | Method and device for self-adaptive clustering of wireless sensor network | |
| CN115134242A (en) | Vehicle-mounted computing task unloading method based on deep reinforcement learning strategy | |
| CN116962176B (en) | Data processing method, device and system of distributed cluster and storage medium | |
| CN109165718A (en) | Network reconstruction method and system based on paralleling ant cluster algorithm | |
| CN116489668A (en) | Edge computing task unloading method based on high-altitude communication platform assistance | |
| CN116302578A (en) | QoS (quality of service) constraint stream application delay ensuring method and system | |
| Bahreini et al. | Energy-aware resource management in vehicular edge computing systems | |
| CN115665869A (en) | Multi-user collaboration platform and method based on edge calculation and directed acyclic graph | |
| CN109982296A (en) | Overload controlling method, access side device, system and the storage medium of MTC access net | |
| CN107148034A (en) | Radio sensing network management method and system based on geographic grid and mobile agent | |
| Adak et al. | Fuzzy Logic-based adaptive traffic light control of an intersection: A case study | |
| CN115131962B (en) | Urban road network dynamic boundary control method and device | |
| Zhu et al. | Real-Time parking guidance model based on stackelberg game | |
| CN102149117B (en) | Service life predicating method of wireless ribbon sensing network based on cut point set | |
| Cao et al. | Research on Edge Resource Allocation Method Based on Vehicle Trajectories Prediction |
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 |