CN105225502A - A kind of intersection signal control method based on multiple agent - Google Patents
A kind of intersection signal control method based on multiple agent Download PDFInfo
- Publication number
- CN105225502A CN105225502A CN201510733992.3A CN201510733992A CN105225502A CN 105225502 A CN105225502 A CN 105225502A CN 201510733992 A CN201510733992 A CN 201510733992A CN 105225502 A CN105225502 A CN 105225502A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- crossing
- intelligent body
- traffic
- agent
- 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
Landscapes
- Traffic Control Systems (AREA)
Abstract
The present invention relates to a kind of intersection signal control method based on multiple agent, belong to control technology field, intelligent transportation crossing.The method by setting up agent model respectively to traffic entity such as vehicle, section, crossing, signal lamps, utilizes the interactivity between multiple agent, coordination property realizes transport information and dynamically share in real time; Specifically comprise signal period Optimum Regulation, conflict phase place green light signals controls and the speed of a motor vehicle guides auxiliary control three links.This method can the individual running status of each Vehicle Agent of Real-time Obtaining, and Negotiation speed guides the dynamic interaction realized between Vehicle Agent and traffic control system; Simultaneously, for multiple agent traffic environment, if consider current phase place without the waste of vehicle by causing green time while being optimized whole phase cycling, be switched to conflict phase place not only to have avoided collision and to occur but also the vehicle fleet that whole crossing is passed through increases, reduce the queue waiting time of conflict phase place vehicle, effectively can reduce mean delay and the stop frequency of crossing.
Description
Technical field
The invention belongs to control technology field, intelligent transportation crossing, relate to a kind of intersection signal control method based on multiple agent.
Background technology
Along with the continuous quickening of Urbanization in China, vehicle guaranteeding organic quantity rapidly increases, and road traffic demand sharply increases.Although government continues to increase urban transportation infrastructure construction investment in recent years, traffic jam issue is still sharp-pointed.Traffic congestion not only causes huge economic loss and environmental quality to worsen, and even causes the paralysis of city function.But the development of economy certainly will impel vehicle possess amount still can continue to increase at a high speed in one long-time, only relies on the construction strengthening traffic infrastructure, cannot deal with problems from root.
Signal timing dial method is the effective means of transport solution congestion problems.Crossing, as the important component part in urban traffic network, is the node realizing each road section traffic volume stream translation, is the main spot of urban traffic congestion.Better at the track place of intersecting can more reasonably distribute vehicle pass-through power by choosing suitable intersection traffic signal control method, make all kinds of, each orderly to traffic flow, pass through expeditiously.
Signal controls can be divided into Off-line control and On-line Control according to control method, and Off-line control is generally timing controlled, fixing green time, phase place and phase sequence; On-line Control comprises induction and controls and adaptive control.Induction controls to be measured by wagon detector to arrive the transport need of entrance driveway, within preset time interval, if arrive without subsequent vehicle, then and i.e. replaceable phase place; As detected, subsequent vehicle arrives, often record a car, green light extends one preset " unit green extension ", as long as within the time interval that this is preset, vehicle interrupts with regard to commutation, have car continuously, then green light extends continuously, extends to preset " limit time expand " always, after this, send a car even if still have after detecting, also interrupt the power that is open to traffic of this phase place, be converted to another phase place; Adaptive control regards a uncertain system as traffic system, by measuring state amount, as vehicle flowrate, stop frequency, delay time at stop and queue length etc., feed back, realize the dynamic optimization adjustment of signal timing dial, with the random character problem of transport solution network.
Wherein: Off-line control and time control method, although timing controlled is simply easy to operation, it is not demand response formula, as long as signal timing parameter is once determine, would not adjust along with the change of traffic flow, therefore it can not meet actual traffic demand; Induction control overcomes time-controlled deficiency, can adapt to the random variation of transport need to a certain extent, but in traditional induction control method, green time particularly green light still may not be fully utilized time delay; Although adaptive control can change cycle duration, phase sequence etc. to adapt to the demand of traffic system by adjustment signal controling parameters, algorithm complex is high, realizes difficulty large, is unsuitable for popularity application.Therefore, a kind of flexible adaptation traffic flow fluctuation can control section right-of-way in real time and be easy to the intersection traffic signal control method of realization is needed.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of intersection signal control method based on multiple agent, the method effectively can solve intersection vehicles collision, alleviate traffic jam issue, while enhancing crossing regional traffic safe operation, ensure the best travel speed of vehicle, strengthen region, the crossing traffic capacity to greatest extent, alleviate traffic pressure.
For achieving the above object, the invention provides following technical scheme:
A kind of intersection signal control method based on multiple agent, the method by setting up agent model respectively to traffic entity such as vehicle, section, crossing, signal lamps, utilizes the interactivity between multiple agent, coordination property realizes transport information and dynamically share in real time; Specifically comprise signal period Optimum Regulation, conflict phase place green light signals controls and the speed of a motor vehicle guides auxiliary control three links.
Further, described signal period Optimum Regulation link, by introducing time window forecasting techniques, utilizes Vehicle Agent and section intelligent body dynamic interaction information, with whole intersection delay and stop frequency minimum for optimization aim, regulation and control are optimized to the green time of signal lamp intelligent body.
Further, described conflict phase place green light signals controlling unit, the information such as the vehicle program time of arrival utilizing crossing intelligent body and Vehicle Agent to obtain alternately and Vehicular turn intention, are optimized control to the steering signal of vehicle time, avoid traffic flow conflict phenomenon occurs.
Further, guide in auxiliary controlling unit in the described speed of a motor vehicle, Vehicle Agent is by mutual with crossing intelligent body, acquisition can effectively by the effective velocity interval range of this crossing, and induction vehicle keeps maximum travelling speed under the traffic flow in other directions of discord clashes the prerequisite of collision.
Further, described Vehicle Agent is the basic undertaker and the executor of traffic system control strategy, mainly comprises vehicle feature, vehicle behavioral trait, communication interface and mode; Vehicle feature mainly comprise vehicle heading, vehicle acceleration ability, vehicle driver the base attribute parameter such as receptible speed limit value, the position of vehicle in road network; The behavioral trait of Vehicle Agent formed primarily of the mode of vehicle acceleration, deceleration, set direction, inquiry current traffic condition, speed regulative mode, the implementation method that embodies vehicle dynamic behavioral characteristic with car strategy etc.; When Vehicle Agent arrives on the section intelligent body that certain crossing intelligent body manages, Vehicle Agent sends the report the traffic state information of inquiring region, current crossing that comprise current vehicle condition information to crossing, place intelligent body and section intelligent body by communication unit.
Further, described section intelligent body is the abstract of section and surface conditions, mainly comprise the function such as communication of the static attribute (length in the geography information attribute of section in whole traffic network and section, the number in track, section load bearing ability and the link identification with other sections) in section, section traffic information collection, road section traffic volume data prediction and section intelligent body, carry out, after information interaction obtains current vehicle flow state information, information is supplied to crossing intelligent body with Vehicle Agent.
Further, described crossing intelligent body contains the configuring condition etc. of the position of this crossing in whole road network, the traffic capacity of crossing, each current direction, crossing, traffic lights, after crossing intelligent body signal controlling functions is mainly to carry out information interaction with section intelligent body and Vehicle Agent, the signal period of signal lamp intelligent body on crossing, traffic light signal ratio are set, namely transmit corresponding control information to signal lamp intelligent body.
Further, the Signalized control information that described signal lamp intelligent body transmits for receiving described crossing intelligent body, and perform corresponding action, carry out the mutual of information with Vehicle Agent, Induction Control is carried out to its road speed and direction.
Further, described speed of a motor vehicle bootup process is applicable to following two kinds of situations: when (1) vehicle enters guidance field, signal condition is red light, or signal condition is green light but crossing queuing is not yet dissipated, show that the safe velocity of queuing up when travelling is interval by the distance calculating vehicle stand-by period and vehicle and crossing, give the suitable speed of a motor vehicle of vehicle and guide; Under this situation, the object that the speed of a motor vehicle guides is the parking waiting shortest time as far as possible making vehicle; Parking waiting time and vehicle arrive the difference in crossing moment and crossing queue clearance moment, and when it is not more than 0, vehicle does not need to stop namely by crossing; (2) when vehicle enters guidance field, signal condition is green light, and crossing queuing is dissipated, and show that suitable travel speed is interval by the distance calculating residue green time and vehicle distance crossing; Under this situation, the object that the speed of a motor vehicle guides makes vehicle pass through crossing before green light terminates as far as possible; Namely for different traffic behaviors, best travel speed suggestion is provided to each Vehicle Agent, guide it safe and efficiently to pass through crossing.
Beneficial effect of the present invention is: the individual running status of each Vehicle Agent of the method for the invention energy Real-time Obtaining, and guide the dynamic interaction realized between Vehicle Agent and traffic control system by speed, for traffic signalization provides new data source and technological means; Simultaneously, for multiple agent traffic environment, intersection signal control and optimize flow process is proposed, if consider current phase place without the waste of vehicle by causing green time while being optimized whole phase cycling, be switched to conflict phase place not only to have avoided collision and to occur but also the vehicle fleet that whole crossing is passed through increases, reduce the queue waiting time of conflict phase place vehicle, effectively can reduce mean delay and the stop frequency of crossing.
Accompanying drawing explanation
In order to make object of the present invention, technical scheme and beneficial effect clearly, the invention provides following accompanying drawing and being described:
Fig. 1 is information interactive architecture figure;
Fig. 2 is signal control and optimize process flow diagram;
Fig. 3 is travel condition of vehicle figure under multiple agent environment (predicted time window);
Fig. 4 is that turn signal controls schematic diagram.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
As the information interactive architecture figure that Fig. 1 is multiple agent integrative design intersection system, the basic undertaker of system and executor are Vehicle Agents, carry out the mutual of the information such as car speed, position, direction between Vehicle Agent and section intelligent body.Crossing intelligent body receives the current operating conditions information of Vehicle Agent transmission by communication interaction, also need the section current vehicle flow information receiving section intelligent body transmission simultaneously, according to the transport information that these obtain alternately, crossing intelligent body self can calculate the traffic grade signal that generation one is optimized; By the communication interaction with crossing intelligent body, the signal lamp intelligent body in crossing obtains the traffic grade information after optimizing; Information interaction between signal lamp intelligent body and Vehicle Agent then plays effect vehicle being carried out to speed of a motor vehicle guiding and course changing control, thus realizes the control to intersection traffic state.
Be illustrated in figure 2 the particular flow sheet that integrative design intersection is optimized.The key problem of intersection signal optimization is optimized each phase place initial green light time, because current phase place long green light time can impact the traffic flow conditions of this phase place and subsequent phase, introduce the concept of time prediction window (as shown in Figure 3) for this reason, Vehicle Agent status data each in predicted time window is analyzed and short-term prediction, on this basis, with the vehicle of whole crossing incur loss through delay and stop frequency minimum for optimization aim, determine the optimization duration of current phase place.
In order to ensure precision of prediction, predicted time T is unsuitable long, is taken as 20s.Simultaneously by predicted time window, in units of 4s, (be designated as Δ t), be divided into 5 time intervals, green extension g
eshould be k Δ t (k=0,1,2,3,4,5).For green extension optimization problem, using saturation degree as the index weighing intersection efficiency, on entrance driveway travel condition of vehicle analysis foundation, predict the saturation degree of current phase place under often kind of green extension scheme, thus determine preferred plan.For reducing the impact of forecasting traffic flow error, when green light time delay reaches 2 Δ t, predicted time window is upgraded (by predicted time window translation 2 Δ t, the starting point using current time as predicted time window T), and recalculates green extension according to up-to-date traffic flow data.Use the speed of a motor vehicle to guide measure to improve intersection rate, and consider that when green extension is optimized speed guides the impact on current phase place saturation degree.
Whole traffic control signal Optimization Steps is as follows:
Step one: to all Vehicle Agents in current green light phase place, according to itself and section intelligent body information interaction, obtains the information such as the Distance geometry vehicle current driving speed of its distance stop line, can expect the running time t that vehicle is about to arrive stop line.
Step 2: as t < g
etime, this vehicle at the uniform velocity will pass through crossing in this phase place green time; As t>T, show that vehicle is away from crossing, can not stop line be arrived in current time prediction window T, wouldn't be considered.If this phase place does not have vehicle to arrive stop line in green time, and the time that its conflict phase place vehicle arrives stop line is less than its residue green time (as shown in Figure 4), then remained green time be switched to conflict phase place and guide assist its safety crossing within this time period by carrying out the speed of a motor vehicle to the vehicle of conflict phase place, green time terminates rear switching letter in reply signal lamp former running period; As T > t > g
etime, vehicle will arrive crossing within green extension, now be guided by the speed of a motor vehicle and make its safety crossing.
Step 3: add up the sum by intersection vehicles in current phase place green time, the computing formula according to phase place saturation degree:
(wherein N
gfor the vehicle fleet size by crossing during green light, Q
sfor wall scroll access mouth saturation volume rate, g is this phase place green time, and m is the entrance driveway quantity sum of this phase place all directions), calculate the saturation degree of phase place under 5 kinds of value schemes of green extension respectively.
Step 4: compare the phase place saturation degree S under 5 kinds of schemes
i(i=1,2,3,4,5) are chosen it and are worth maximum scheme
Step 5: judge whether maximum phase saturation degree S is less than the lower limit S of setting
s(phase place saturation degree is advisable between 0.8 ~ 0.9).If S≤S
s, then corresponding with S green extension is preferred plan; If S<S
s, switch to next phase place (i.e. g immediately
e=0).
What finally illustrate is, above preferred embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although by above preferred embodiment to invention has been detailed description, but those skilled in the art are to be understood that, various change can be made to it in the form and details, and not depart from claims of the present invention limited range.
Claims (9)
1. the intersection signal control method based on multiple agent, it is characterized in that: the method by setting up agent model respectively to traffic entity such as vehicle, section, crossing, signal lamps, utilizes the interactivity between multiple agent, coordination property realizes transport information and dynamically share in real time; Specifically comprise signal period Optimum Regulation, conflict phase place green light signals controls and the speed of a motor vehicle guides auxiliary control three links.
2. a kind of intersection signal control method based on multiple agent according to claim 1, it is characterized in that: described signal period Optimum Regulation link, by introducing time window forecasting techniques, utilize Vehicle Agent and section intelligent body dynamic interaction information, with whole intersection delay and stop frequency minimum for optimization aim, regulation and control are optimized to the green time of signal lamp intelligent body.
3. a kind of intersection signal control method based on multiple agent according to claim 1, it is characterized in that: described conflict phase place green light signals controlling unit, the information such as the vehicle program time of arrival utilizing crossing intelligent body and Vehicle Agent to obtain alternately and Vehicular turn intention, control is optimized to the steering signal of vehicle time, avoids traffic flow conflict phenomenon occurs.
4. a kind of intersection signal control method based on multiple agent according to claim 1, it is characterized in that: guide in auxiliary controlling unit in the described speed of a motor vehicle, Vehicle Agent is by mutual with crossing intelligent body, acquisition can effectively by the effective velocity interval range of this crossing, and induction vehicle keeps maximum travelling speed under the traffic flow in other directions of discord clashes the prerequisite of collision.
5. a kind of intersection signal control method based on multiple agent according to claim 1, it is characterized in that: described Vehicle Agent is the basic undertaker and the executor of traffic system control strategy, mainly comprises vehicle feature, vehicle behavioral trait, communication interface and mode; Vehicle feature mainly comprise vehicle heading, vehicle acceleration ability, vehicle driver the base attribute parameter such as receptible speed limit value, the position of vehicle in road network; The behavioral trait of Vehicle Agent formed primarily of the mode of vehicle acceleration, deceleration, set direction, inquiry current traffic condition, speed regulative mode, the implementation method that embodies vehicle dynamic behavioral characteristic with car strategy etc.; When Vehicle Agent arrives on the section intelligent body that certain crossing intelligent body manages, Vehicle Agent sends the report the traffic state information of inquiring region, current crossing that comprise current vehicle condition information to crossing, place intelligent body and section intelligent body by communication unit.
6. a kind of intersection signal control method based on multiple agent according to claim 1, it is characterized in that: described section intelligent body is the abstract of section and surface conditions, mainly comprise the function such as communication of the static attribute in section, section traffic information collection, road section traffic volume data prediction and section intelligent body, carry out, after information interaction obtains current vehicle flow state information, information is supplied to crossing intelligent body with Vehicle Agent.
7. a kind of intersection signal control method based on multiple agent according to claim 1, it is characterized in that: described crossing intelligent body contains the configuring condition etc. of the position of this crossing in whole road network, the traffic capacity of crossing, each current direction, crossing, traffic lights, after crossing intelligent body signal controlling functions is mainly to carry out information interaction with section intelligent body and Vehicle Agent, the signal period of signal lamp intelligent body on crossing, traffic light signal ratio are set, namely transmit corresponding control information to signal lamp intelligent body.
8. a kind of intersection signal control method based on multiple agent according to claim 1, it is characterized in that: the Signalized control information that described signal lamp intelligent body transmits for receiving described crossing intelligent body, and perform corresponding action, carry out the mutual of information with Vehicle Agent, Induction Control is carried out to its road speed and direction.
9. a kind of intersection signal control method based on multiple agent according to claim 4, it is characterized in that: described speed of a motor vehicle bootup process is applicable to following two kinds of situations: when (1) vehicle enters guidance field, signal condition is red light, or signal condition is green light but crossing queuing is not yet dissipated, show that the safe velocity of queuing up when travelling is interval by the distance calculating vehicle stand-by period and vehicle and crossing, give the suitable speed of a motor vehicle of vehicle and guide; Under this situation, the object that the speed of a motor vehicle guides is the parking waiting shortest time as far as possible making vehicle; Parking waiting time and vehicle arrive the difference in crossing moment and crossing queue clearance moment, and when it is not more than 0, vehicle does not need to stop namely by crossing; (2) when vehicle enters guidance field, signal condition is green light, and crossing queuing is dissipated, and show that suitable travel speed is interval by the distance calculating residue green time and vehicle distance crossing; Under this situation, the object that the speed of a motor vehicle guides makes vehicle pass through crossing before green light terminates as far as possible; Namely for different traffic behaviors, best travel speed suggestion is provided to each Vehicle Agent, guide it safe and efficiently to pass through crossing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510733992.3A CN105225502A (en) | 2015-11-02 | 2015-11-02 | A kind of intersection signal control method based on multiple agent |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510733992.3A CN105225502A (en) | 2015-11-02 | 2015-11-02 | A kind of intersection signal control method based on multiple agent |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105225502A true CN105225502A (en) | 2016-01-06 |
Family
ID=54994432
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510733992.3A Pending CN105225502A (en) | 2015-11-02 | 2015-11-02 | A kind of intersection signal control method based on multiple agent |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105225502A (en) |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809987A (en) * | 2016-05-17 | 2016-07-27 | 三峡大学 | Multi-agent-based wind-solar complementary type intelligent traffic light system |
| CN106408975A (en) * | 2016-06-17 | 2017-02-15 | 京东方科技集团股份有限公司 | Vehicle driving predication method and device, and vehicle intelligent system |
| CN106448194A (en) * | 2016-10-28 | 2017-02-22 | 清华大学 | Traffic signal in crossroad and vehicle coordinated control method, device and vehicle |
| CN106706342A (en) * | 2017-02-17 | 2017-05-24 | 特路（北京）科技有限公司 | Testing method for continuous crossroad passing ability of automatic vehicle and testing field |
| CN107065888A (en) * | 2017-05-26 | 2017-08-18 | 金陵科技学院 | One kind is based on magnetic navigation robot and navigation scheduling system and method |
| CN107084733A (en) * | 2017-04-10 | 2017-08-22 | 广东数相智能科技有限公司 | A kind of method based on unpiloted path planning, apparatus and system |
| CN108053660A (en) * | 2018-01-11 | 2018-05-18 | 江苏中路工程技术研究院有限公司 | A kind of control method of reduction vehicle exhaust CO2 emission towards traffic flow |
| CN108682168A (en) * | 2018-05-15 | 2018-10-19 | 哈尔滨工业大学 | A kind of speed guiding system and method reducing signal intersection parking |
| CN108693869A (en) * | 2017-03-31 | 2018-10-23 | 本田技研工业株式会社 | Controller of vehicle |
| CN108898839A (en) * | 2018-09-13 | 2018-11-27 | 武汉摩尔数据技术有限公司 | A kind of real-time dynamic information data system and its update method |
| CN109345825A (en) * | 2018-10-18 | 2019-02-15 | 东南大学 | Signalized intersections one-way traffic flow control system and method under bus or train route cooperative surroundings |
| CN109598953A (en) * | 2018-12-21 | 2019-04-09 | 深圳市元征科技股份有限公司 | Method for guiding vehicles and device |
| CN110176138A (en) * | 2018-09-21 | 2019-08-27 | 华南理工大学 | A kind of active traffic induction method of crossing grade |
| CN110264717A (en) * | 2019-06-25 | 2019-09-20 | 牡丹江师范学院 | A kind of municipal intelligent traffic regulator control system |
| CN110853376A (en) * | 2019-09-30 | 2020-02-28 | 重庆中信科信息技术有限公司 | Traffic signal lamp of intelligent network |
| CN111325995A (en) * | 2020-02-17 | 2020-06-23 | 昆明理工大学 | Intelligent control method for intersection signal lamp |
| CN111354184A (en) * | 2018-12-24 | 2020-06-30 | 北京嘀嘀无限科技发展有限公司 | Traffic signal period calculation method and apparatus, and computer-readable storage medium |
| CN111739313A (en) * | 2020-07-17 | 2020-10-02 | 安徽达尔智能控制系统股份有限公司 | Signal lamp management and control system based on road traffic flow big data |
| CN111951571A (en) * | 2020-07-06 | 2020-11-17 | 江苏大学 | System and method for dredging congested vehicles on road section under traffic accident |
| CN112133109A (en) * | 2020-08-10 | 2020-12-25 | 北方工业大学 | Method for establishing single-cross-port multidirectional space occupancy balance control model |
| CN112233434A (en) * | 2020-10-10 | 2021-01-15 | 扬州大学 | Urban intersection traffic signal coordination control system and method based on intelligent agent |
| CN112258856A (en) * | 2020-08-10 | 2021-01-22 | 北方工业大学 | Method for establishing regional traffic signal data drive control model |
| CN112289062A (en) * | 2020-10-26 | 2021-01-29 | 公安部交通管理科学研究所 | Vehicle driving path dynamic navigation method based on green light moment diagram |
| CN112396847A (en) * | 2020-11-16 | 2021-02-23 | 张鹏 | Traffic network signal linkage guiding method based on 5G |
| WO2021051930A1 (en) * | 2019-09-18 | 2021-03-25 | 平安科技（深圳）有限公司 | Signal adjustment method and apparatus based on action prediction model, and computer device |
| CN112967510A (en) * | 2021-02-26 | 2021-06-15 | 安徽达尔智能控制系统股份有限公司 | Bus intersection traffic control method and system based on green wave coordination |
| CN113311829A (en) * | 2021-05-11 | 2021-08-27 | 北京理工大学 | Multi-robot path planning method based on dynamic time window conflict search |
| CN113362618A (en) * | 2021-06-03 | 2021-09-07 | 东南大学 | Multi-mode traffic adaptive signal control method and device based on strategy gradient |
| CN113380036A (en) * | 2021-06-16 | 2021-09-10 | 上海应用技术大学 | Queuing length calculation method based on electronic police data |
| CN113470382A (en) * | 2021-08-19 | 2021-10-01 | 星禾环保科技（上海）有限公司 | Comprehensive multivariable active traffic control method |
| CN114743388A (en) * | 2022-03-22 | 2022-07-12 | 中山大学·深圳 | Multi-intersection signal self-adaptive control method based on reinforcement learning |
| CN118553103A (en) * | 2024-07-29 | 2024-08-27 | 成都交投信息科技有限公司 | Traffic jam dissipating method based on multi-agent reinforcement learning |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102044148A (en) * | 2010-12-30 | 2011-05-04 | 上海市城市建设设计研究院 | Multi-agent-based traffic control method and integrated system thereof |
| CN102097011A (en) * | 2011-02-21 | 2011-06-15 | 昆明理工大学 | Cluster intelligent traffic flow control system |
| CN102768798A (en) * | 2012-05-10 | 2012-11-07 | 吴宗华 | Real-time mobile communication type intelligent traffic |
| CN103559795A (en) * | 2013-11-07 | 2014-02-05 | 青岛海信网络科技股份有限公司 | Multi-strategy and multi-object self-adaptation traffic control method |
| CN103646556A (en) * | 2013-12-16 | 2014-03-19 | 江苏大学 | Control system and control method for dynamic adjusting of traffic light time |
| US8928493B2 (en) * | 2009-09-16 | 2015-01-06 | Road Safety Management Ltd. | Traffic signal control system and method |
| CN104575039A (en) * | 2015-01-19 | 2015-04-29 | 武汉理工大学 | New emergency vehicle preferential pass method and system based on vehicle-road cooperation |
| CN104637315A (en) * | 2015-02-06 | 2015-05-20 | 北京交通大学 | Non-signalized crossing optimization control method and system in cooperative vehicle infrastructure environment |
| CN104916152A (en) * | 2015-05-19 | 2015-09-16 | 苏州大学 | Cooperative vehicle infrastructure system-based intersection vehicle right turning guidance system and guidance method thereof |
-
2015
- 2015-11-02 CN CN201510733992.3A patent/CN105225502A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8928493B2 (en) * | 2009-09-16 | 2015-01-06 | Road Safety Management Ltd. | Traffic signal control system and method |
| CN102044148A (en) * | 2010-12-30 | 2011-05-04 | 上海市城市建设设计研究院 | Multi-agent-based traffic control method and integrated system thereof |
| CN102097011A (en) * | 2011-02-21 | 2011-06-15 | 昆明理工大学 | Cluster intelligent traffic flow control system |
| CN102768798A (en) * | 2012-05-10 | 2012-11-07 | 吴宗华 | Real-time mobile communication type intelligent traffic |
| CN103559795A (en) * | 2013-11-07 | 2014-02-05 | 青岛海信网络科技股份有限公司 | Multi-strategy and multi-object self-adaptation traffic control method |
| CN103646556A (en) * | 2013-12-16 | 2014-03-19 | 江苏大学 | Control system and control method for dynamic adjusting of traffic light time |
| CN104575039A (en) * | 2015-01-19 | 2015-04-29 | 武汉理工大学 | New emergency vehicle preferential pass method and system based on vehicle-road cooperation |
| CN104637315A (en) * | 2015-02-06 | 2015-05-20 | 北京交通大学 | Non-signalized crossing optimization control method and system in cooperative vehicle infrastructure environment |
| CN104916152A (en) * | 2015-05-19 | 2015-09-16 | 苏州大学 | Cooperative vehicle infrastructure system-based intersection vehicle right turning guidance system and guidance method thereof |
Non-Patent Citations (6)
| Title |
|---|
| PATRICK A.M.EHLERT等: "Microscopic traffic simulation with reactive driving agents", 《2001 IEEE INTELLIGNET TRANSPORTATION SYSTEMS CONFERENCE PROCEEDINGS》 * |
| ZHI HAN等: "An urban traffic simulation system based on multi-agent modeling", 《2015 27TH CHINESE CONTROL AND DECISION CONFERENCE》 * |
| 张存保: "基于车路协同的单点信号控制优化方法和模型", 《武汉理工大学学报》 * |
| 张存保: "车路协同下道路交叉口信号控制优化方法", 《交通运输系统工程与信息》 * |
| 李鹏凯: "车路协同环境下信号交叉口车速引导建模与仿真", 《交通信息与安全》 * |
| 邝先验: "基于Agent的城市交通仿真系统研究", 《江西理工大学学报》 * |
Cited By (48)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809987B (en) * | 2016-05-17 | 2019-07-09 | 三峡大学 | It is a kind of based on the wind light mutual complementing formula intelligent traffic light control system more acted on behalf of |
| CN105809987A (en) * | 2016-05-17 | 2016-07-27 | 三峡大学 | Multi-agent-based wind-solar complementary type intelligent traffic light system |
| CN106408975A (en) * | 2016-06-17 | 2017-02-15 | 京东方科技集团股份有限公司 | Vehicle driving predication method and device, and vehicle intelligent system |
| WO2017215497A1 (en) * | 2016-06-17 | 2017-12-21 | 京东方科技集团股份有限公司 | Vehicle travel prediction method, apparatus, and vehicle-mounted smart system |
| CN106408975B (en) * | 2016-06-17 | 2018-10-19 | 京东方科技集团股份有限公司 | Vehicle travels prediction technique, device and vehicle intelligent system |
| CN106448194A (en) * | 2016-10-28 | 2017-02-22 | 清华大学 | Traffic signal in crossroad and vehicle coordinated control method, device and vehicle |
| CN106448194B (en) * | 2016-10-28 | 2019-03-19 | 清华大学 | Intersection traffic signal and vehicle cooperative control method and device, vehicle |
| CN106706342A (en) * | 2017-02-17 | 2017-05-24 | 特路（北京）科技有限公司 | Testing method for continuous crossroad passing ability of automatic vehicle and testing field |
| CN108693869A (en) * | 2017-03-31 | 2018-10-23 | 本田技研工业株式会社 | Controller of vehicle |
| CN107084733A (en) * | 2017-04-10 | 2017-08-22 | 广东数相智能科技有限公司 | A kind of method based on unpiloted path planning, apparatus and system |
| WO2018188177A1 (en) * | 2017-04-10 | 2018-10-18 | 广东数相智能科技有限公司 | Method, device, and system for route planning based on unmanned driving |
| CN107065888A (en) * | 2017-05-26 | 2017-08-18 | 金陵科技学院 | One kind is based on magnetic navigation robot and navigation scheduling system and method |
| CN108053660B (en) * | 2018-01-11 | 2020-11-03 | 江苏中路工程技术研究院有限公司 | Control method for reducing emission of carbon dioxide in automobile exhaust facing traffic flow |
| CN108053660A (en) * | 2018-01-11 | 2018-05-18 | 江苏中路工程技术研究院有限公司 | A kind of control method of reduction vehicle exhaust CO2 emission towards traffic flow |
| CN108682168A (en) * | 2018-05-15 | 2018-10-19 | 哈尔滨工业大学 | A kind of speed guiding system and method reducing signal intersection parking |
| CN108682168B (en) * | 2018-05-15 | 2020-09-22 | 哈尔滨工业大学 | Vehicle speed guiding system and method for reducing stopping at signalized intersection |
| CN108898839B (en) * | 2018-09-13 | 2020-10-09 | 武汉泰坦智慧科技有限公司 | Real-time dynamic traffic information data system and updating method thereof |
| CN108898839A (en) * | 2018-09-13 | 2018-11-27 | 武汉摩尔数据技术有限公司 | A kind of real-time dynamic information data system and its update method |
| CN110176138A (en) * | 2018-09-21 | 2019-08-27 | 华南理工大学 | A kind of active traffic induction method of crossing grade |
| CN109345825A (en) * | 2018-10-18 | 2019-02-15 | 东南大学 | Signalized intersections one-way traffic flow control system and method under bus or train route cooperative surroundings |
| CN109598953B (en) * | 2018-12-21 | 2022-04-12 | 深圳市元征科技股份有限公司 | Vehicle guiding method and device |
| CN109598953A (en) * | 2018-12-21 | 2019-04-09 | 深圳市元征科技股份有限公司 | Method for guiding vehicles and device |
| CN111354184A (en) * | 2018-12-24 | 2020-06-30 | 北京嘀嘀无限科技发展有限公司 | Traffic signal period calculation method and apparatus, and computer-readable storage medium |
| CN110264717A (en) * | 2019-06-25 | 2019-09-20 | 牡丹江师范学院 | A kind of municipal intelligent traffic regulator control system |
| CN110264717B (en) * | 2019-06-25 | 2021-04-20 | 牡丹江师范学院 | Urban intelligent traffic regulation and control system |
| WO2021051930A1 (en) * | 2019-09-18 | 2021-03-25 | 平安科技（深圳）有限公司 | Signal adjustment method and apparatus based on action prediction model, and computer device |
| CN110853376A (en) * | 2019-09-30 | 2020-02-28 | 重庆中信科信息技术有限公司 | Traffic signal lamp of intelligent network |
| CN110853376B (en) * | 2019-09-30 | 2021-12-03 | 重庆中信科信息技术有限公司 | Traffic signal lamp of intelligent network |
| CN111325995A (en) * | 2020-02-17 | 2020-06-23 | 昆明理工大学 | Intelligent control method for intersection signal lamp |
| CN111325995B (en) * | 2020-02-17 | 2021-12-21 | 昆明理工大学 | Intelligent control method for intersection signal lamp |
| CN111951571A (en) * | 2020-07-06 | 2020-11-17 | 江苏大学 | System and method for dredging congested vehicles on road section under traffic accident |
| CN111739313A (en) * | 2020-07-17 | 2020-10-02 | 安徽达尔智能控制系统股份有限公司 | Signal lamp management and control system based on road traffic flow big data |
| CN112133109A (en) * | 2020-08-10 | 2020-12-25 | 北方工业大学 | Method for establishing single-cross-port multidirectional space occupancy balance control model |
| CN112258856A (en) * | 2020-08-10 | 2021-01-22 | 北方工业大学 | Method for establishing regional traffic signal data drive control model |
| CN112233434A (en) * | 2020-10-10 | 2021-01-15 | 扬州大学 | Urban intersection traffic signal coordination control system and method based on intelligent agent |
| CN112289062A (en) * | 2020-10-26 | 2021-01-29 | 公安部交通管理科学研究所 | Vehicle driving path dynamic navigation method based on green light moment diagram |
| CN112289062B (en) * | 2020-10-26 | 2021-10-08 | 公安部交通管理科学研究所 | Vehicle driving path dynamic navigation method based on green light moment diagram |
| CN112396847A (en) * | 2020-11-16 | 2021-02-23 | 张鹏 | Traffic network signal linkage guiding method based on 5G |
| CN112967510A (en) * | 2021-02-26 | 2021-06-15 | 安徽达尔智能控制系统股份有限公司 | Bus intersection traffic control method and system based on green wave coordination |
| CN113311829A (en) * | 2021-05-11 | 2021-08-27 | 北京理工大学 | Multi-robot path planning method based on dynamic time window conflict search |
| CN113311829B (en) * | 2021-05-11 | 2022-04-08 | 北京理工大学 | Multi-robot path planning method based on dynamic time window conflict search |
| CN113362618A (en) * | 2021-06-03 | 2021-09-07 | 东南大学 | Multi-mode traffic adaptive signal control method and device based on strategy gradient |
| CN113362618B (en) * | 2021-06-03 | 2022-08-09 | 东南大学 | Multi-mode traffic adaptive signal control method and device based on strategy gradient |
| CN113380036A (en) * | 2021-06-16 | 2021-09-10 | 上海应用技术大学 | Queuing length calculation method based on electronic police data |
| CN113470382A (en) * | 2021-08-19 | 2021-10-01 | 星禾环保科技（上海）有限公司 | Comprehensive multivariable active traffic control method |
| CN114743388A (en) * | 2022-03-22 | 2022-07-12 | 中山大学·深圳 | Multi-intersection signal self-adaptive control method based on reinforcement learning |
| CN118553103A (en) * | 2024-07-29 | 2024-08-27 | 成都交投信息科技有限公司 | Traffic jam dissipating method based on multi-agent reinforcement learning |
| CN118553103B (en) * | 2024-07-29 | 2024-10-15 | 成都交投信息科技有限公司 | Traffic jam dissipating method based on multi-agent reinforcement learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105225502A (en) | A kind of intersection signal control method based on multiple agent | |
| Xin et al. | Predictive intelligent driver model for eco-driving using upcoming traffic signal information | |
| CN111768637B (en) | Signal intersection traffic signal lamp and vehicle track control method | |
| Yu et al. | Consensus and optimal speed advisory model for mixed traffic at an isolated signalized intersection | |
| CN103021191B (en) | Intelligent traffic control device and control method | |
| CN102514602B (en) | Method and system for planning and controlling train travelling speed | |
| CN108831143A (en) | Signalized intersections fleet speed guiding method based on bus or train route coordination technique | |
| CN106448194A (en) | Traffic signal in crossroad and vehicle coordinated control method, device and vehicle | |
| Kamal et al. | Intersection vehicle cooperative eco-driving in the context of partially connected vehicle environment | |
| CN105551337A (en) | Driving auxiliary method and system for train driver | |
| CN103863364A (en) | Freight locomotive automatic-control real-time optimization control system based on dispatch signal | |
| CN105976621A (en) | Device and method for guiding vehicle to pass across intersection without stopping based on vehicle speed induction strategy | |
| CN108364486B (en) | Multi-scene vehicle priority self-adaptive traffic signal control system and working method thereof | |
| CN205644972U (en) | Intelligent urban traffic control system | |
| CN106128125B (en) | A kind of speed bootstrap technique that control intersection is preferentially sensed for main road | |
| CN109760721A (en) | A kind of train interval operation real-time regulating system and method | |
| Cai et al. | Unsignalized cooperative optimization control method based on vehicle speed guidance and information interaction | |
| CN109398426B (en) | Energy-saving driving strategy optimization method based on discrete ant colony algorithm under timing condition | |
| CN116895163A (en) | Multi-vehicle cooperative control method and system based on signal lamp active communication | |
| Tong et al. | Speed planning for connected electric buses based on battery capacity loss | |
| He et al. | Multi-objective optimization for online train trajectory planning with moving window method | |
| Zhao et al. | The impact of truck platoons on the traffic dynamics around off-ramp regions | |
| Amirgholy et al. | Optimal traffic operation for maximum energy efficiency in signal-free urban networks: A macroscopic analytical approach | |
| Dong et al. | Cooperative eco-driving control of connected multi-vehicles with spatio-temporal constraints | |
| Zhang et al. | Eco-driving strategy for connected electric buses at the signalized intersection with a station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160106 |
|
| RJ01 | Rejection of invention patent application after publication |