CN112150800A - Method for maximizing road passing efficiency under multi-source data perception - Google Patents
Method for maximizing road passing efficiency under multi-source data perception Download PDFInfo
- Publication number
- CN112150800A CN112150800A CN202010835781.1A CN202010835781A CN112150800A CN 112150800 A CN112150800 A CN 112150800A CN 202010835781 A CN202010835781 A CN 202010835781A CN 112150800 A CN112150800 A CN 112150800A
- Authority
- CN
- China
- Prior art keywords
- time
- data
- phase
- module
- real
- 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
Images
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
- G08G1/065—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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method for maximizing road traffic efficiency under multi-source data perception, which comprises the following steps: s1, collecting all data sources of road vehicle data into an information module; s2, the first data summarization processing module processes the data in the information module; s3, the calculation processing module outputs data such as real-time traffic flow, real-time vehicle speed, green light utilization rate of each phase, green light utilization rate of relevant lanes under the phase, congestion index and the like; s4, the second data summarization processing module performs data summarization and reprocessing on the data generated in the step S3; and S5, the prediction processing module outputs phase time optimization data, lane direction optimization data, tidal traffic flow prediction data and congestion road section prediction data. According to the invention, through the combination processing of various data of the intersection, the time optimization of each phase of the signal lamp is realized, so that the maximization of the lane utilization efficiency is realized.
Description
Technical Field
The invention relates to the technical field of traffic, in particular to a method for maximizing road traffic efficiency under multi-source data perception.
Background
With the rapid development of economy and the rapid construction of urban traffic, the traffic problem increasingly becomes a focus problem in how to manage cities more efficiently and orderly. It is known that in many traffic problems the road traffic efficiency is low due to unreasonable settings of the signal lamp period and the respective phase time, and the overall traffic time of the road is long. Therefore, it is necessary to provide a method for predicting the time to be set for the cycle phase in some time periods by analyzing multiple data sources on the road, so as to achieve the effect of maximizing the road passing efficiency.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for maximizing road traffic efficiency under multi-source data perception, and time optimization of each phase of a signal lamp is realized by combining and processing multiple data of a road junction so as to maximize lane utilization efficiency. To achieve the above objects and other advantages in accordance with the present invention, there is provided a method for maximizing road passing efficiency under multi-source data perception, comprising:
the system comprises an information module, a first data summarizing processing module in signal connection with the information module, a calculating processing module in signal connection with the first data summarizing processing module, a second data summarizing processing module in signal connection with the calculating processing module and a predicting processing module in signal connection with the second data summarizing processing module;
further comprising the steps of:
s1, collecting all data sources of road vehicle data into an information module;
s2, the first data summarization processing module processes the data in the information module;
s3, the calculation processing module outputs data such as real-time traffic flow, real-time vehicle speed, green light utilization rate of each phase, green light utilization rate of relevant lanes under the phase, congestion index and the like;
s4, the second data summarization processing module performs data summarization and reprocessing on the data generated in the step S3;
and S5, the prediction processing module outputs phase time optimization data, lane direction optimization data, tidal traffic flow prediction data and congestion road section prediction data.
Preferably, the information module includes a signal machine, a real-time video unit, a radar and a gate database, the signal machine is used for providing time of each phase of the intersection, the real-time video unit is used for performing simulation identification, positions of vehicles in a video stream in a road are identified and displayed in the system, the radar is used for providing a current intersection speed and a congestion index, and the gate database is used for providing vehicle information.
Preferably, the calculation processing module comprises a real-time traffic flow calculation module, a real-time average speed calculation module, an inter-phase green light utilization rate calculation module, an intra-phase inter-lane green light utilization rate calculation module and a real-time congestion index calculation module;
the real-time traffic flow calculation module is used for calculating the real-time traffic flow of the intersection;
the real-time average vehicle speed calculating module is used for calculating the real-time average vehicle speed of the intersection;
the phase-to-phase green light utilization rate calculation module is used for calculating the phase-to-phase green light utilization rate;
the phase inner lane green light utilization rate calculation module is used for calculating the phase inner lane green light utilization rate;
and the real-time congestion index calculation module is used for calculating the real-time congestion index.
Preferably, the real-time traffic flow calculation module takes data in the card port database every 5 minutes, performs traffic flow statistics on each lane under each intersection, and records the statistical result into the database.
Preferably, the real-time average vehicle speed calculation module is used for acquiring radar data every 5 minutes, averaging the running speed of the vehicle in the time period under each lane under each intersection, and recording the statistical result into the database.
Preferably, the real-time inter-phase green light utilization rate calculation module acquires signal 1 data every 5 minutes, acquires green light time of each phase, acquires interface data, acquires traffic flow of a related lane at each phase in the time period, and divides the traffic flow by the time to obtain the green light utilization rate of each phase.
Preferably, the module for calculating the utilization rate of green lights between lanes in the real-time phase acquires data of the signal machine 1 every 5 minutes, acquires green light time of each phase, acquires interface data, acquires traffic flow of each lane in the time period, and divides the traffic flow by the time to obtain the utilization rate of the green lights of each lane.
Preferably, the prediction processing module comprises a phase time optimization prediction module, a lane direction optimization prediction module, a tidal traffic flow prediction module and a congestion road section prediction module;
the phase time optimization prediction module is used for calculating the real-time traffic flow of the intersection;
the lane direction optimization prediction module is used for calculating the real-time average speed of the intersection;
the tidal traffic flow prediction module is used for calculating the utilization rate of green lights between phases;
and the jammed road section prediction module is used for calculating the utilization rate of green lights between lanes in the phase.
Preferably, the phase time optimization prediction module compares the utilization rate of the green light of each phase at one intersection, and the higher the utilization rate of the green light is, the longer the phase time occupies in the whole period time; the lane direction optimization prediction module compares the green light utilization rate of each lane under the phase, and prompts a user to consider that the lane direction can be changed into the lane direction with higher green light utilization rate for the lane with extremely low green light utilization rate.
Preferably, the tidal traffic flow prediction module is used for counting whether an obvious tidal phenomenon exists or not and giving a prediction by analyzing and comparing the traffic flow of the same road section at the high peak in the morning and at the night in different directions; the congestion road section prediction module predicts that the congestion index of a road section is higher in certain time periods through analysis of the historical radar congestion index of the road section, and can prolong the whole cycle time of a signal lamp in the time periods so as to reduce time waste caused by frequent phase change and lower road utilization rate.
Compared with the prior art, the invention has the beneficial effects that: the method is suitable for all urban road traffic intersections, and the cycle time of the signals suggested in different time periods and the time of all phases are given out through analysis and processing of all data sources. The method solves the problems that the utilization rate of green lights in an idle time period is low, the phase time configuration is unreasonable, the cycle time of a congestion time period is short, and the phase change is frequent to cause time waste and the like in the traditional road traffic due to the fixed period and phase time.
Drawings
Fig. 1 is a schematic structural diagram of a method for maximizing road traffic efficiency under multi-source data perception according to the invention.
In the figure: 1. a signal machine; 2. a real-time video unit; 3. a radar; 4. a card port database; 5. a first data summarization processing module; 6. a real-time traffic flow calculation module; 7. a real-time average vehicle speed calculation module; 8. a phase-to-phase green light utilization rate calculation module; 9. a phase inner lane green light utilization rate calculation module; 10. a real-time congestion index calculation module; 11. a second data summarization processing module; 12. a phase time optimization prediction module; 13. a lane direction optimization prediction module; 14. a tidal traffic flow prediction module; 15. and a congested road section prediction module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a method for maximizing road traffic efficiency under multi-source data perception includes: the system comprises an information module, a first data summarizing processing module 5 in signal connection with the information module, a calculating processing module in signal connection with the first data summarizing processing module 5, a second data summarizing processing module 11 in signal connection with the calculating processing module, and a predicting processing module in signal connection with the second data summarizing processing module 11;
further comprising the steps of:
s1, collecting all data sources of road vehicle data into an information module;
s2, the first data summarization processing module 5 processes the data in the information module;
s3, the calculation processing module outputs data such as real-time traffic flow, real-time vehicle speed, green light utilization rate of each phase, green light utilization rate of relevant lanes under the phase, congestion index and the like;
s4, the second data summarization processing module 11 performs data summarization and reprocessing on the data generated in the step S3;
and S5, the prediction processing module outputs phase time optimization data, lane direction optimization data, tidal traffic flow prediction data and congestion road section prediction data.
Further, the information module comprises a signal machine 1, a real-time video unit 2, a radar 3 and a gate database 4, wherein the signal machine 1 is used for providing time of each phase of the intersection, the real-time video unit 2 is used for carrying out simulation identification, identifying the position of a vehicle in a video stream in a road and displaying the position in the system, the radar 3 is used for providing the current intersection speed and the congestion index, and the gate database 4 is used for providing vehicle information.
Further, the calculation processing module comprises a real-time traffic flow calculation module 6, a real-time average speed calculation module 7, a phase-to-phase green light utilization ratio calculation module 8, a phase-to-lane green light utilization ratio calculation module 9 and a real-time congestion index calculation module 10;
the real-time traffic flow calculation module 6 is used for calculating the real-time traffic flow of the intersection;
the real-time average vehicle speed calculating module 7 is used for calculating the real-time average vehicle speed of the intersection;
the phase-to-phase green light utilization rate calculation module 8 is used for calculating the phase-to-phase green light utilization rate;
the phase inner lane green light utilization rate calculation module 9 is used for calculating the phase inner lane green light utilization rate;
the real-time congestion index calculation module 10 is configured to calculate a real-time congestion index.
Further, the real-time traffic flow calculation module 6 takes data in the card port database 4 every 5 minutes, performs traffic flow statistics on each lane under each intersection, and records the statistical result into the database.
Further, the real-time average vehicle speed calculation module 7 is used for acquiring data of the radar 3 every 5 minutes, averaging the driving speeds of the vehicles in the time period under each lane under each intersection, and recording the statistical result into the database.
Further, the real-time inter-phase green light utilization rate calculation module 8 acquires data of the signal machine 1 every 5 minutes, acquires green light time of each phase, acquires bayonet data, acquires traffic flow of a relevant lane at each phase in the time period, and divides the traffic flow by the time to obtain the green light utilization rate of each phase.
Further, the module 9 for calculating the utilization rate of green lights between lanes in the real-time phase acquires data of the signal machine 1 every 5 minutes, acquires green light time of each phase, acquires data of a card port, acquires traffic flow of each lane in the time period, and divides the traffic flow by the time to obtain the utilization rate of green lights of each lane.
Further, the prediction processing module comprises a phase time optimization prediction module 12, a lane direction optimization prediction module 13, a tidal traffic flow prediction module 14 and a congestion road section prediction module 15;
the phase time optimization prediction module 12 is used for calculating the real-time traffic flow of the intersection;
the lane direction optimization prediction module 13 is used for calculating the real-time average speed of the intersection;
the tidal traffic flow prediction module 14 is used for calculating the utilization rate of green lights between phases;
and the congested road section prediction module 15 is used for calculating the utilization rate of green lights between lanes in the phase.
Further, the phase time optimization prediction module 12 compares the green light utilization rate of each phase at an intersection, and takes the green light utilization rate as a weight, so that the higher the green light utilization rate is, the longer the phase time occupies in the whole period; the lane direction optimization prediction module 13 compares the green light utilization rates of the lanes in the phase, and prompts the user that the lane direction is changed into the lane direction with the higher green light utilization rate for the lane with the particularly low green light utilization rate.
Further, the tidal traffic flow prediction module 14 is used for counting whether an obvious tidal phenomenon exists or not and giving a prediction by analyzing and comparing the traffic flow of the same road section in the morning and evening peak in different directions; the congestion road section prediction module 15 predicts that the congestion index of a certain road section is higher in certain time periods through analysis of the congestion index of the historical radar 3 of the certain road section, and can prolong the whole period time of the signal lamp in the time periods so as to reduce time waste caused by frequent phase change and lower road utilization rate.
The working principle is as follows: the method comprises a real-time traffic flow calculation module 6, a real-time average speed calculation module 7, a phase-to-phase green light utilization rate calculation module 8, a phase-to-phase lane green light utilization rate calculation module 9, a real-time congestion index calculation module 10, a phase time optimization prediction module 12, a lane direction optimization prediction module 13, a tide traffic flow prediction module 14 and a congestion road section prediction module 15. The proposed cycle times of the signals in the different time periods and the times of the individual phases are given by the evaluation of the individual data sources. The method solves the problems that the utilization rate of green lights in an idle time period is low, the phase time configuration is unreasonable, the cycle time of a congestion time period is short, and the phase change is frequent to cause time waste and the like in the traditional road traffic due to the fixed period and phase time.
The number of devices and the scale of the processes described herein are intended to simplify the description of the invention, and applications, modifications and variations of the invention will be apparent to those skilled in the art.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (10)
1. A method for maximizing road traffic efficiency under multi-source data perception is characterized by comprising the following steps: the system comprises an information module, a first data summarizing processing module in signal connection with the information module, a calculating processing module in signal connection with the first data summarizing processing module, a second data summarizing processing module in signal connection with the calculating processing module and a predicting processing module in signal connection with the second data summarizing processing module;
further comprising the steps of:
s1, collecting all data sources of road vehicle data into an information module;
s2, the first data summarization processing module processes the data in the information module;
s3, the calculation processing module outputs data such as real-time traffic flow, real-time vehicle speed, green light utilization rate of each phase, green light utilization rate of relevant lanes under the phase, congestion index and the like;
s4, the second data summarization processing module performs data summarization and reprocessing on the data generated in the step S3;
and S5, the prediction processing module outputs phase time optimization data, lane direction optimization data, tidal traffic flow prediction data and congestion road section prediction data.
2. The method as claimed in claim 1, wherein the information module includes a traffic signal for providing time of each phase of the intersection, a real-time video unit for performing simulation recognition, recognizing and displaying the position of the vehicle in the video stream in the road, a radar for providing the current intersection speed and the congestion index, and a checkpoint database for providing vehicle information.
3. The method for maximizing road traffic efficiency under multi-source data perception according to claim 1, wherein the calculation processing module comprises a real-time traffic flow calculation module, a real-time average speed calculation module, an inter-phase green light utilization ratio calculation module, an intra-phase inter-lane green light utilization ratio calculation module and a real-time congestion index calculation module;
the real-time traffic flow calculation module is used for calculating the real-time traffic flow of the intersection;
the real-time average vehicle speed calculating module is used for calculating the real-time average vehicle speed of the intersection;
the phase-to-phase green light utilization rate calculation module is used for calculating the phase-to-phase green light utilization rate;
the phase inner lane green light utilization rate calculation module is used for calculating the phase inner lane green light utilization rate;
and the real-time congestion index calculation module is used for calculating the real-time congestion index.
4. The method for maximizing road traffic efficiency under multi-source data perception according to claim 3, wherein the real-time traffic flow calculation module takes data in the card port database every 5 minutes, performs traffic flow statistics on each lane under each intersection, and records the statistical results into the database.
5. The method for maximizing road traffic efficiency under multi-source data perception according to claim 3, wherein the real-time average vehicle speed calculation module takes radar data every 5 minutes, averages the driving speeds of vehicles in the time period under each lane under each intersection, and records the statistical results into a database.
6. The method for maximizing road traffic efficiency under multi-source data perception according to claim 3, wherein the real-time inter-phase green light utilization rate calculation module acquires signal machine data every 5 minutes, acquires green light time of each phase, acquires card port data, acquires traffic flow of a related lane under each phase in the time period, and divides the traffic flow by the time to obtain the green light utilization rate of each phase.
7. The method for maximizing road traffic efficiency under multi-source data perception according to claim 3, wherein the module for calculating the utilization rate of green lights between lanes in the real-time phase acquires signal machine data every 5 minutes, acquires green light time of each phase, acquires card port data, acquires traffic flow of each lane in the time period, and divides the traffic flow by the time to obtain the utilization rate of the green lights of each lane.
8. The method for maximizing road traffic efficiency under multi-source data perception according to claim 1, wherein the prediction processing module comprises a phase time optimization prediction module, a lane direction optimization prediction module, a tidal traffic flow prediction module and a congestion section prediction module;
the phase time optimization prediction module is used for calculating the real-time traffic flow of the intersection;
the lane direction optimization prediction module is used for calculating the real-time average speed of the intersection;
the tidal traffic flow prediction module is used for calculating the utilization rate of green lights between phases;
and the jammed road section prediction module is used for calculating the utilization rate of green lights between lanes in the phase.
9. The method for maximizing road traffic efficiency under multi-source data perception according to claim 8, wherein the phase time optimization prediction module compares the green light utilization rate of each phase under an intersection, and takes the green light utilization rate as a weight, and the higher the green light utilization rate is, the longer the phase time occupies the whole period time; the lane direction optimization prediction module compares the green light utilization rate of each lane under the phase, and prompts a user to consider that the lane direction can be changed into the lane direction with higher green light utilization rate for the lane with extremely low green light utilization rate.
10. The method for maximizing road traffic efficiency under multi-source data perception according to claim 8, wherein the tidal traffic flow prediction module is used for counting whether obvious tidal phenomena exist or not and giving a prediction by analyzing and comparing the traffic flow of the same road section in the early and late peak directions; the congestion road section prediction module predicts that the congestion index of a road section is higher in certain time periods through analysis of the historical radar congestion index of the road section, and can prolong the whole cycle time of a signal lamp in the time periods so as to reduce time waste caused by frequent phase change and lower road utilization rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010835781.1A CN112150800A (en) | 2020-08-19 | 2020-08-19 | Method for maximizing road passing efficiency under multi-source data perception |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010835781.1A CN112150800A (en) | 2020-08-19 | 2020-08-19 | Method for maximizing road passing efficiency under multi-source data perception |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112150800A true CN112150800A (en) | 2020-12-29 |
Family
ID=73888787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010835781.1A Pending CN112150800A (en) | 2020-08-19 | 2020-08-19 | Method for maximizing road passing efficiency under multi-source data perception |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112150800A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408948A (en) * | 2014-10-30 | 2015-03-11 | 生茂光电科技股份有限公司 | Vehicle-mounted-GPS-based public transport priority signal control method of urban road traffic |
| EP3349200A1 (en) * | 2015-09-11 | 2018-07-18 | Hangzhou Hikvision Digital Technology Co., Ltd. | Method and device for processing traffic road information |
| CN109448385A (en) * | 2019-01-04 | 2019-03-08 | 北京钛星科技有限公司 | Dispatch system and method in automatic driving vehicle intersection based on bus or train route collaboration |
| CN109615887A (en) * | 2018-12-24 | 2019-04-12 | 张鹏 | Wisdom traffic network system signal guidance method |
| CN110111592A (en) * | 2019-06-25 | 2019-08-09 | 浪潮软件集团有限公司 | Method based on traffic signal controlling machine Dynamic Matching Optimal Signals timing scheme |
-
2020
- 2020-08-19 CN CN202010835781.1A patent/CN112150800A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408948A (en) * | 2014-10-30 | 2015-03-11 | 生茂光电科技股份有限公司 | Vehicle-mounted-GPS-based public transport priority signal control method of urban road traffic |
| EP3349200A1 (en) * | 2015-09-11 | 2018-07-18 | Hangzhou Hikvision Digital Technology Co., Ltd. | Method and device for processing traffic road information |
| CN109615887A (en) * | 2018-12-24 | 2019-04-12 | 张鹏 | Wisdom traffic network system signal guidance method |
| CN109448385A (en) * | 2019-01-04 | 2019-03-08 | 北京钛星科技有限公司 | Dispatch system and method in automatic driving vehicle intersection based on bus or train route collaboration |
| CN110111592A (en) * | 2019-06-25 | 2019-08-09 | 浪潮软件集团有限公司 | Method based on traffic signal controlling machine Dynamic Matching Optimal Signals timing scheme |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109493620B (en) | Traffic road condition analysis system, method and device | |
| CN103050005B (en) | Method and system for space and time analysis of urban road traffic states | |
| CN108665715B (en) | Intelligent traffic studying and judging and signal optimizing method for intersection | |
| CN109410577B (en) | Self-adaptive traffic control subarea division method based on space data mining | |
| CN108399740B (en) | Method for predicting collision probability of motor vehicles at signalized intersection | |
| CN102855760B (en) | On-line queuing length detection method based on floating vehicle data | |
| CN109191872B (en) | Intersection traffic flow characteristic parameter extraction method based on number plate data | |
| CN103177576B (en) | The determination methods of a kind of charge station upstream and downstream traffic information and device | |
| CN105046990A (en) | Pavement signal lamp control method between adjacent intersections based on particle swarm algorithm | |
| CN111915885B (en) | Urban road traffic simulation experiment method and system | |
| CN114267173B (en) | Multisource data fusion method, device and equipment for space-time characteristics of expressway | |
| CN108648445A (en) | Dynamic traffic Tendency Prediction method based on traffic big data | |
| CN105869398A (en) | Road traffic open degree judging method based on K-means cluster | |
| CN111192457A (en) | Method and device for controlling urban-level integral traffic signals | |
| CN110827537B (en) | Method, device and equipment for setting tidal lane | |
| CN106683406B (en) | A kind of current bottleneck detection method of the public transportation lane based on public transport vehicle-mounted GPS data | |
| CN114299720A (en) | Public service traffic management method and system based on Internet of things | |
| CN114360264A (en) | Intelligent city traffic management method based on traffic flow regulation | |
| CN110459067B (en) | Traffic green road signal coordination control evaluation method and system based on vehicle individuals | |
| CN112150800A (en) | Method for maximizing road passing efficiency under multi-source data perception | |
| CN116704750B (en) | Traffic state identification method based on clustering algorithm, electronic equipment and medium | |
| CN110399771B (en) | Traffic environment field maintenance system | |
| CN109345825B (en) | Signal intersection one-way traffic flow control system and method under cooperative vehicle and road environment | |
| CN110264725A (en) | The determination method and device of link flow | |
| CN114387778B (en) | Urban expressway congestion cause analysis method |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201229 |