CN115116217B - Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane - Google Patents
Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane Download PDFInfo
- Publication number
- CN115116217B CN115116217B CN202210585725.6A CN202210585725A CN115116217B CN 115116217 B CN115116217 B CN 115116217B CN 202210585725 A CN202210585725 A CN 202210585725A CN 115116217 B CN115116217 B CN 115116217B
- Authority
- CN
- China
- Prior art keywords
- lane
- cav
- flow rate
- permeability
- time
- 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.)
- Active
Links
Classifications
-
- 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
-
- 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
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/085—Controlling traffic signals using a free-running cyclic timer
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a dynamic measuring and calculating method and a system for traffic lane saturation flow rate and starting loss time, wherein the method comprises the following steps: step one, constructing a vehicle information background database; measuring and calculating the saturation flow rate and the starting loss time of each lane; step three, measuring and calculating the saturation flow rate and the phase starting loss time of the lane group; and step four, checking the measuring and calculating result and establishing a dynamic updating mechanism. The invention is suitable for the whole network road traffic environment, namely whether the vehicle is driven manually or automatically, has the function of network communication, can exchange information with a traffic signal controller, and has wide application.
Description
Technical Field
The invention belongs to the technical field of traffic transportation engineering, and particularly relates to a dynamic measuring and calculating method and a system for traffic lane saturation flow rate and starting loss time.
Background
The saturated flow rate and the starting loss time are basic core parameters for optimizing the intersection signal timing scheme and evaluating the intersection traffic capacity, and have very important roles in a road traffic control system. The current intersection entrance lane start loss time and saturation flow rate are established mainly by traffic investigation and correction methods, and are generally static parameters. In the fully manual driving era, the traffic flow operation characteristics of a certain area in one city within a period of time have statistics, and although the road traffic changes in real time, the parameters such as saturated flow rate, starting loss time and the like are not obviously changed, so that the static parameters are used for optimally controlling the dynamic road traffic to basically meet the actual control requirements.
With the advent of the co-driving age, autopilot vehicles will gradually penetrate into one of the road traffic systems. In view of the high safety, high maneuverability and high controllability of the automatic driving vehicle, the traffic flow running characteristics of the automatic driving vehicle have essential differences with those of the manual driving vehicle; the mixed running of the two can cause some traffic flow parameters in the signal control system to change in quality; the same traffic scale, different autopilot penetration, these traffic flow parameters may exhibit significant differences. Therefore, under the man-machine hybrid driving environment, the real-time detection and dynamic measurement and calculation of the traffic flow parameters are related to the optimization effect of the road traffic signal control system.
Based on the above, a dynamic measuring and calculating method for the saturation flow rate and the starting loss of the entrance lane of the intersection in the man-machine hybrid driving environment is needed, and the road traffic signal in the man-machine hybrid driving environment is optimized.
Disclosure of Invention
The invention provides a dynamic measuring and calculating method and a system for traffic lane saturation flow rate and starting loss time to solve the problems.
The invention relates to a dynamic measuring and calculating method for traffic lane saturation flow rate and starting loss time, which comprises the following steps:
step one, constructing a vehicle information background database;
measuring and calculating the saturation flow rate and the starting loss time of each lane;
step three, measuring and calculating the saturation flow rate and the phase starting loss time of the lane group;
and step four, checking the measuring and calculating result and establishing a dynamic updating mechanism.
Further, optimizing signal control parameters before starting a signal period, and collecting initial queuing lengths of all phase lanes of an intersection, permeability of CAV in the initial queuing lengths and CAV permeability of all vehicles on the lanes in real time; taking all the CAV permeability grades of vehicles on each lane as constraints, extracting the historical data of the lane in a historical background library, and establishing the following regression model set:
g ijk,m ＝l ijk,m +h ijk,m ×x,p ijk,cav ∈([(m-1)/N]×100％,(m/N)×100％]
wherein: g ijk,m -green time, s, required for initial queuing and emptying of the kth lane of the ith phase jth lane group at mth CAV permeability; l (L) ijk,m -a start-up loss time, s, of a kth lane vehicle of the ith phase jth lane group at mth CAV permeability; h is a ijk,m -saturated headway, s for the kth lane vehicle of the jth lane group at the mth CAV permeability; x-the number of vehicles in the initial queuing, veh; p is p ijk,cav -CAV permeability of the kth lane of the ith phase jth lane group; m-the number of stages to which the current CAV permeability belongs; total number of N-CAV permeability classifications;
starting loss time and saturated headway of the kth lane of the jth lane group of the ith phase under the mth CAV permeability, and corresponding saturated flow rate are converted as follows
S ijk,m ＝3600/h ijk,m 。
Further, in the third step, the lane group saturation flow rate is calculated as follows:
the phase start loss time is determined by the start loss of each lane group in the phase, and the establishment method is as follows:
n j -total number of lanes in the j-th lane group.
In the fourth step, firstly, the fitting degree of the constructed regression model is checked, the fitting degree of the regression equation to the sample data is checked, and R 2 Not less than 0.95; next, variance is performedSignificance test, checking whether a significant linear relation exists in a constructed model, sig<0.05; finally, carrying out variable significance test on the regression model to test whether the green light time and the number of queuing vehicles have significant positive correlation relation or not, and requiring sig<0.05; if the requirements are met, directly adopting; if the data does not meet the requirement, adopting the calculated data which passes the check last time.
Further, in the fifth step, in the signal period running process, the time spent by the tail car from the start of the phase green light to the departure of the parking line in the initial queuing length of each lane is recorded in real time; when each signal period is finished, updating a background database by using the queuing length of each lane, the dissipation time of the queuing length and the CAV permeability of the queuing vehicles in each phase of the signal period; when updating data each time, firstly checking whether the number of the historical data of the same lane under the same CAV permeability classification reaches the upper limit, if not, directly adding the data; if so, the data with the earliest occurrence time is replaced by the data, so that a dynamic measuring and calculating mechanism of the saturated flow rate and the starting loss time is established.
The invention also relates to a system for implementing the dynamic measuring and calculating method of the saturation flow rate and the starting loss time of the lane, which comprises a vehicle information background database acquisition subsystem, a data transmission subsystem and a data management and analysis subsystem.
Advantageous effects
The invention provides a dynamic measuring and calculating method for the saturation flow rate and the starting loss of an entrance lane of an intersection under a man-machine hybrid driving environment. The invention is suitable for the whole network road traffic environment, namely whether the vehicle is driven manually or automatically, has the function of network communication, can exchange information with a traffic signal controller, and has wide application.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present embodiment will be specifically described with reference to fig. 1.
The invention provides a dynamic measuring and calculating method for saturation flow rate and starting loss of an entrance lane of an intersection under a man-machine hybrid driving environment, which comprises the following steps of:
firstly, collecting historical queuing lengths of each lane of each phase of an intersection in each signal period and dissipation time of the queuing lengths, and dividing the historical queuing lengths into a plurality of stages according to the permeability (the proportion of CAV to all vehicles on the same lane) of a networked automatic driving vehicle (CAV), so as to form a background database;
secondly, extracting a corresponding phase, a corresponding lane and historical data under the corresponding CAV permeability classification based on the current CAV permeability classification detected on the inlet lane, and obtaining the saturated flow rate and the starting loss time of the lane through parameter regression calculation;
and finally, updating a background database by using the queuing length of each lane of each phase of the signal period and the dissipation time of the queuing length, and establishing a dynamic measuring and calculating mechanism of the saturation flow rate and the starting loss time.
The invention relates to a dynamic measuring and calculating method for saturation flow rate and starting loss of an entrance lane of an intersection under a man-machine hybrid driving environment, which comprises the following specific steps:
step one, establishing a background database
Under the man-machine hybrid driving environment, the same traffic demand and different automatic driving vehicle permeabilities can be obviously different from the saturation flow rate and the starting loss time of an inlet lane, wherein CAV permeabilities are key influencing factors. In view of this, the CAV permeability is divided into N levels, if it is divided into 10 levels, it is [0,10% ], (10%, 20% ], …, (90%, 100% ], in the course of running the traffic signal control system, the initial queuing length (expressed by queuing number of vehicles) when the green light of each lane is turned on in every signal period, the time period for the last queuing vehicle to drive off the parking line, the permeability of CAV in the queuing vehicle are collected, and stored in the historical background database according to the following table format.
Assume that the saturation flow rates of one entrance lane with an intersection phase number of 1, one lane group number of 1, and one lane with a lane group number of 2 are to be determined. The method comprises the steps of collecting initial queuing length (expressed by queuing vehicle number) of each phase of each traffic lane when a green light of each signal period is started, time length of the last queuing vehicle when the last queuing vehicle leaves a parking line, and permeability of CAV in the queuing vehicle, wherein the number of data pieces is 300, and storing the data pieces in a historical background database according to a table format after collection.
Table 1 historical background database (Lane group number 1)
Table 2 historical background database (Lane group number 2)
Step two, measuring and calculating regression to establish the saturation flow rate and the starting loss time of each lane
The signal control parameters are optimized before being started every signal period. During optimization, the traffic signal controller acquires the initial queuing length of each lane of each phase of the intersection, the CAV permeability in the initial queuing length and the CAV permeability of all vehicles on the lanes in real time; sequentially extracting historical data of all the lanes in a historical background library by taking all the CAV permeability belonging grades of all the vehicles on each lane as constraint, and establishing the following regression model set based on the historical data:
g ijk,m ＝l ijk,m +h ijk,m ×x,p ijk,cav ∈([(m-1)/N]×100％,(m/N)×100％]
wherein: g ijk,m -green time, s, required for initial queuing and emptying of the kth lane of the ith phase jth lane group at mth CAV permeability; l (L) ijk,m -a start-up loss time, s, of a kth lane vehicle of the ith phase jth lane group at mth CAV permeability; h is a ijk,m -saturated headway, s for the kth lane vehicle of the jth lane group at the mth CAV permeability; x-the number of vehicles in the initial queuing, veh; p is p ijk,cav -CAV permeability of the kth lane of the ith phase jth lane group; m-the number of stages to which the current CAV permeability belongs; total number of N-CAV permeability classifications;
starting loss time and saturated headway of the kth lane of the jth lane group of the ith phase under the mth CAV permeability, and corresponding saturated flow rate are converted as follows
S ijk,m ＝3600/h ijk,m 。
The saturation flow rate already contains corrections to the longitudinal correction factors of lane width, gradient, large vehicle proportion, etc.
Assuming that the initial queuing lengths (expressed by the number of queuing vehicles) of one entrance lane with the phase number of 1 and the lane group number of 1 and two entrance lanes with the lane group number of 2 of a certain intersection acquired in real time by a traffic signal controller are respectively 11 and 12, and the CAV permeability of all vehicles on the lanes is 25%; and extracting the historical data of the same type in the historical background database to establish a regression model. The starting loss time of a vehicle of one entrance lane of the lane group 1 under the current CAV permeability grading is 2.122s, the saturated headway is 1.655s, and the corresponding saturated flow rate is 2175; one entrance lane of lane group 2 had a vehicle launch loss time of 2.688 at the current CAV permeability rating, a saturated headway of 1.633, and a corresponding saturation flow rate of 2204.
Step three, measuring and calculating the saturation flow rate and the phase starting loss time of the lane group
The lane group saturation flow rate is calculated as follows:
the phase start loss time is determined by the start loss of each lane group in the phase, and the establishment method is as follows:
n j -total number of lanes in the j-th lane group.
In this assumption, lane group 1 has only one lane, so its saturation flow rate is 2175, phase start loss time is 2.122s, and lane group 2 has only one lane, so its saturation flow rate is 2204, phase start loss time is 2.688s.
Step four, checking the measuring and calculating result
First, the constructed regression model is subjected to fitness test (R 2 Test), test the fitting degree of regression equation to sample data, require R 2 More than or equal to 0.90; secondly, performing variance saliency test (F test) to test whether the constructed model has a significant linear relation, requiring sig<0.05; finally, carrying out variable significance test (t test) on the regression model to test whether the green light time and the number of the queuing vehicles have significant positive correlation relation or not, and requiring sig<0.05. If the requirements are met, directly adopting; if the data does not meet the requirement, adopting the calculated data which passes the check last time.
Firstly, carrying out fitting degree test on a regression model to obtain R 2 0.907 and 0.948 respectively, which show that the model constructed by the invention has very good fitting degree to sample data;
secondly, carrying out variance significance test, wherein Sig values are all approximately 0, which shows that regression effects are obvious and obvious linear correlation is presented;
finally, the variable significance test is carried out, the Sig values of the number of the queuing vehicles are all approximately 0, the Sig values of the starting loss time are respectively 0.024 and 0.033, and the regression effect is obvious.
Step five, establishing a dynamic updating mechanism
And in the signal period running process, the time spent by the tail car of the initial queuing length of each lane from the start of the green light of the phase to the departure of the stop line is recorded in real time. At the end of each signal period, the queuing length, the dissipation time of the queuing length and the CAV permeability of the queuing vehicles of each phase of the signal period are used for updating the background database. When updating data each time, firstly checking whether the number of the historical data of the same lane under the same CAV permeability classification reaches the upper limit, if not, directly adding the data; if the data is reached, the data with the earliest occurrence time is replaced by the data, a dynamic measuring and calculating mechanism of the saturated flow rate and the starting loss time is established, the real-time performance of the measured and calculated result is ensured, the dynamic change of the traffic condition can be adapted, and the aging is avoided.
The foregoing is merely illustrative of the present invention and is not intended to limit the embodiments of the present invention, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present invention, so that the protection scope of the present invention shall be defined by the claims.
Claims (5)
1. A dynamic measuring and calculating method for the saturation flow rate and the starting loss time of a lane is characterized by comprising the following steps:
step one, constructing a vehicle information background database;
measuring and calculating the saturation flow rate and the starting loss time of each lane;
step three, measuring and calculating the saturation flow rate and the phase starting loss time of the lane group;
step four, checking and calculating results;
step five, establishing a dynamic updating mechanism;
step two, collecting initial queuing lengths of all phase lanes of the intersection, the CAV permeability in the initial queuing lengths and the CAV permeability of all vehicles on the lanes in real time; taking all the CAV permeability grades of vehicles on each lane as constraints, extracting the historical data of the lane in a historical background library, and establishing the following regression model set:
g ijk,m =l ijk,m + h ijk,m ×x, p ijk,cav ∈([(m-1)/N]×100%,(m/N)×100%]
wherein:g ijk,m -the firstmLevel CAV permeability of the first layeriPhase numberjLane group IkThe green light time required by the initial queuing and emptying of the lane, s;l ijk,m -the firstmLevel CAV permeability of the first layeriPhase numberjLane group IkThe starting loss time of the lane vehicle, s;h ijk,m -the firstmLevel CAV permeability of the first layeriPhase numberjLane group IkSaturated headway of the lane vehicle, s;x-initial number of vehicles queued, veh;p ijk,cav -the firstiPhase numberjLane group IkCAV permeability of the lane;m-the number of stages to which the current CAV permeability belongs; total number of N-CAV permeability classifications;
first, themLevel CAV permeability of the first layeriPhase numberjLane group IkThe saturation flow rate of the lane is scaled as follows:
S ijk,m =3600/ h ijk,m 。
2. the method for dynamically measuring and calculating the saturation flow rate and the start-up loss time of a lane according to claim 1, wherein in the third step, the saturation flow rate of the lane group is calculated as follows:
the phase start loss time is determined by the start loss time of each lane group in the phase, and the establishment method is as follows: /> ，n j -the firstjTotal number of lanes in the lane group.
3. The lane saturation flow rate and start loss of claim 1A dynamic measurement and calculation method of dead time is characterized in that in the fourth step, firstly, fitting degree test is carried out on a constructed regression model, fitting degree of a regression equation on sample data is tested, and R is 2 Not less than 0.95; secondly, carrying out variance significance test to test whether a significant linear relation exists in the constructed model, sig<0.05; finally, carrying out variable significance test on the regression model to test whether the green light time and the number of queuing vehicles have significant positive correlation relation or not, and requiring sig<0.05; if the requirements are met, directly adopting; if the data does not meet the requirement, adopting the calculated data which passes the check last time.
4. The method for dynamically measuring and calculating the saturation flow rate and the start loss time of a lane according to claim 1, wherein in the fifth step, the time spent by the tail car from the start of the green light of the phase to the departure of the tail car from the stop line is recorded in real time in the initial queuing length of each lane during the signal period running process; when each signal period is finished, updating a background database by using the queuing length of each lane of each phase of the signal period, the dissipation time of the queuing length and the CAV permeability of the queuing vehicles; when updating data each time, firstly checking whether the number of the historical data of the same lane under the same CAV permeability classification reaches the upper limit, if not, directly adding the data; if so, the data with the earliest occurrence time is replaced by the data, so that a dynamic measuring and calculating mechanism of the saturated flow rate and the starting loss time is established.
5. A system for implementing the dynamic measurement and calculation method of lane saturation flow rate and start-up loss time according to any one of claims 1 to 4, characterized in that it comprises a vehicle information background database acquisition subsystem, a data transmission subsystem and a data management and analysis subsystem.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210585725.6A CN115116217B (en) | 2022-05-26 | 2022-05-26 | Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210585725.6A CN115116217B (en) | 2022-05-26 | 2022-05-26 | Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115116217A CN115116217A (en) | 2022-09-27 |
| CN115116217B true CN115116217B (en) | 2023-09-26 |
Family
ID=83325927
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210585725.6A Active CN115116217B (en) | 2022-05-26 | 2022-05-26 | Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115116217B (en) |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1080446A (en) * | 1964-01-23 | 1967-08-23 | Atomic Energy Authority Uk | Improvements in or relating to control apparatus for a remotely operated vehicle |
| CN103136941A (en) * | 2013-01-23 | 2013-06-05 | 东南大学 | Coordination control method of dissymmetric artery |
| CN104282162A (en) * | 2014-09-29 | 2015-01-14 | 同济大学 | Adaptive intersection signal control method based on real-time vehicle track |
| CN104299431A (en) * | 2014-10-15 | 2015-01-21 | 东南大学 | Method for controlling traffic signals under bi-direction and two-lane road maintenance construction situation |
| CN104318788A (en) * | 2014-10-05 | 2015-01-28 | 哈尔滨工业大学 | Method for improving space resource utilization efficiency of intersection entrance lane |
| CN106875700A (en) * | 2017-04-21 | 2017-06-20 | 山东交通学院 | A kind of entrance driveway design for eliminating start-up lost time and signal optimizing method |
| CN107730886A (en) * | 2017-10-16 | 2018-02-23 | 北方工业大学 | Dynamic optimization method for traffic signals at urban intersections in Internet of vehicles environment |
| CN109035786A (en) * | 2018-10-10 | 2018-12-18 | 南京宁昱通交通科技有限公司 | A kind of traffic slot control method improving trunk roads Adjacent Intersections traffic efficiency |
| CN109389826A (en) * | 2018-12-14 | 2019-02-26 | 武汉理工大学 | A kind of real-time computing technique of signalized intersections saturation volume rate |
| CN110162003A (en) * | 2019-04-16 | 2019-08-23 | 江苏大学 | A kind of driving Active Service System test and verification platform and its test method |
| CN110503823A (en) * | 2019-07-11 | 2019-11-26 | 江苏大学 | A kind of automatic driving vehicle dedicated Lanes control system and method |
| CN110992676A (en) * | 2019-10-15 | 2020-04-10 | 同济大学 | Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method |
| CN111508225A (en) * | 2019-01-30 | 2020-08-07 | 阿里巴巴集团控股有限公司 | Information processing method, traffic control method, information processing device, traffic control equipment and storage medium |
| CN111581854A (en) * | 2020-06-02 | 2020-08-25 | 中国地质大学（北京） | Oil reservoir state prediction method considering non-equilibrium anisotropy relative permeability |
| CN112116822A (en) * | 2020-09-21 | 2020-12-22 | 长沙理工大学 | Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation |
| CN112614344A (en) * | 2020-12-14 | 2021-04-06 | 中汽研汽车试验场股份有限公司 | Hybrid traffic system efficiency evaluation method for automatic driving automobile participation |
| CN113129594A (en) * | 2021-04-23 | 2021-07-16 | 武汉科技大学 | Intersection saturation flow rate calculation method based on bayonet data |
| CN113439046A (en) * | 2018-12-20 | 2021-09-24 | 爱皮加特股份公司 | Redundant braking system with pressure supply for electric vehicles and vehicles with 3-stage (HAD) to 4-stage (FAD) autonomous driving |
| CN113781774A (en) * | 2021-08-18 | 2021-12-10 | 华中科技大学 | Signal control intersection traffic capacity determination method considering man-machine mixed driving traffic flow |
| CN113887037A (en) * | 2021-09-28 | 2022-01-04 | 中国科学技术大学先进技术研究院 | Automatic driving system evaluation method under network connection environment with different permeability |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210319691A1 (en) * | 2021-06-25 | 2021-10-14 | Arvind Merwaday | Collaborative detection and avoidance of phantom traffic jams |
-
2022
- 2022-05-26 CN CN202210585725.6A patent/CN115116217B/en active Active
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1080446A (en) * | 1964-01-23 | 1967-08-23 | Atomic Energy Authority Uk | Improvements in or relating to control apparatus for a remotely operated vehicle |
| CN103136941A (en) * | 2013-01-23 | 2013-06-05 | 东南大学 | Coordination control method of dissymmetric artery |
| CN104282162A (en) * | 2014-09-29 | 2015-01-14 | 同济大学 | Adaptive intersection signal control method based on real-time vehicle track |
| CN104318788A (en) * | 2014-10-05 | 2015-01-28 | 哈尔滨工业大学 | Method for improving space resource utilization efficiency of intersection entrance lane |
| CN104299431A (en) * | 2014-10-15 | 2015-01-21 | 东南大学 | Method for controlling traffic signals under bi-direction and two-lane road maintenance construction situation |
| CN106875700A (en) * | 2017-04-21 | 2017-06-20 | 山东交通学院 | A kind of entrance driveway design for eliminating start-up lost time and signal optimizing method |
| CN107730886A (en) * | 2017-10-16 | 2018-02-23 | 北方工业大学 | Dynamic optimization method for traffic signals at urban intersections in Internet of vehicles environment |
| CN109035786A (en) * | 2018-10-10 | 2018-12-18 | 南京宁昱通交通科技有限公司 | A kind of traffic slot control method improving trunk roads Adjacent Intersections traffic efficiency |
| CN109389826A (en) * | 2018-12-14 | 2019-02-26 | 武汉理工大学 | A kind of real-time computing technique of signalized intersections saturation volume rate |
| CN113439046A (en) * | 2018-12-20 | 2021-09-24 | 爱皮加特股份公司 | Redundant braking system with pressure supply for electric vehicles and vehicles with 3-stage (HAD) to 4-stage (FAD) autonomous driving |
| CN111508225A (en) * | 2019-01-30 | 2020-08-07 | 阿里巴巴集团控股有限公司 | Information processing method, traffic control method, information processing device, traffic control equipment and storage medium |
| CN110162003A (en) * | 2019-04-16 | 2019-08-23 | 江苏大学 | A kind of driving Active Service System test and verification platform and its test method |
| CN110503823A (en) * | 2019-07-11 | 2019-11-26 | 江苏大学 | A kind of automatic driving vehicle dedicated Lanes control system and method |
| CN110992676A (en) * | 2019-10-15 | 2020-04-10 | 同济大学 | Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method |
| CN111581854A (en) * | 2020-06-02 | 2020-08-25 | 中国地质大学（北京） | Oil reservoir state prediction method considering non-equilibrium anisotropy relative permeability |
| CN112116822A (en) * | 2020-09-21 | 2020-12-22 | 长沙理工大学 | Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation |
| CN112614344A (en) * | 2020-12-14 | 2021-04-06 | 中汽研汽车试验场股份有限公司 | Hybrid traffic system efficiency evaluation method for automatic driving automobile participation |
| CN113129594A (en) * | 2021-04-23 | 2021-07-16 | 武汉科技大学 | Intersection saturation flow rate calculation method based on bayonet data |
| CN113781774A (en) * | 2021-08-18 | 2021-12-10 | 华中科技大学 | Signal control intersection traffic capacity determination method considering man-machine mixed driving traffic flow |
| CN113887037A (en) * | 2021-09-28 | 2022-01-04 | 中国科学技术大学先进技术研究院 | Automatic driving system evaluation method under network connection environment with different permeability |
Non-Patent Citations (2)
| Title |
|---|
| 倒计时装置对交叉口饱和流率和损失时间影响研究;叶文伟，刘虹;《建筑工程技术与设计》(第33期);全文 * |
| 基于车流到达模式的城市干线信号配时参数优化方法;安成川;《中国知网 博士学位论文》;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115116217A (en) | 2022-09-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110610260B (en) | Driving energy consumption prediction system, method, storage medium and equipment | |
| CN109697854B (en) | Multi-dimensional urban road traffic state evaluation method | |
| CN109558988B (en) | Electric vehicle energy consumption prediction method and system based on big data fusion | |
| CN107564290B (en) | Urban road intersection saturated flow rate calculation method | |
| CN110349427B (en) | Intelligent traffic management system based on big data | |
| CN113159435B (en) | Method and system for predicting remaining driving mileage of new energy vehicle | |
| CN112950128A (en) | Intelligent logistics transportation system and method based on big data fusion | |
| CN109543874A (en) | A kind of aerodrome air qualitative forecasting method that combination meteorological condition influences | |
| CN107886188A (en) | Liquefied natural gas public transport exhaust emissions Forecasting Methodology | |
| CN114899459A (en) | Operation and maintenance control method and device for fuel cell engine | |
| CN114463868A (en) | Toll station traffic flow combination prediction method and system for traffic flow management and control | |
| CN109960889B (en) | Method for constructing typical speed-time running condition of track vehicle line | |
| CN115291110A (en) | Electric pile aging prediction method based on characteristic parameter extraction and aging experience base construction | |
| CN115860214A (en) | Early warning method and device for PM2.5 emission concentration | |
| CN115116217B (en) | Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane | |
| CN112562311B (en) | Method and device for obtaining working condition weight factor based on GIS big data | |
| CN112750304B (en) | Intersection data acquisition interval determining method and device based on traffic simulation | |
| CN116415756B (en) | Urban virtual scene experience management system based on VR technology | |
| CN108596381B (en) | Urban parking demand prediction method based on OD data | |
| CN115100847B (en) | Queuing service time estimation method for low-permeability network-connected track data | |
| CN116205530A (en) | Urban intelligent parking planning method and system | |
| CN115795833A (en) | Traffic flow model simulation method considering driver reaction time | |
| CN117313437B (en) | Method, device, equipment and storage medium for testing influence of traffic flow on energy consumption emission | |
| CN112381560B (en) | Shared equipment product market prediction system and method | |
| CN113781771B (en) | Online operation method of IVE model |
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 |