CN116311892A - Urban road section traffic key bottleneck identification method based on congestion propagation - Google Patents
Urban road section traffic key bottleneck identification method based on congestion propagation Download PDFInfo
- Publication number
- CN116311892A CN116311892A CN202211711358.6A CN202211711358A CN116311892A CN 116311892 A CN116311892 A CN 116311892A CN 202211711358 A CN202211711358 A CN 202211711358A CN 116311892 A CN116311892 A CN 116311892A
- Authority
- CN
- China
- Prior art keywords
- congestion
- road section
- road
- event
- propagation
- 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
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000004913 activation Effects 0.000 claims abstract description 13
- 238000004590 computer program Methods 0.000 claims description 12
- 238000011144 upstream manufacturing Methods 0.000 claims description 9
- 238000010586 diagram Methods 0.000 claims description 7
- 230000004927 fusion Effects 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000029305 taxis Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- 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
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- 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
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method for identifying a traffic key bottleneck of an urban road section based on congestion propagation, which is used for integrating taxi GPS track data and network taxi GPS track data and finding a congestion propagation link by extracting a congestion event and a congestion activation event; and (3) quantitatively evaluating the influence caused by road section congestion by defining a congestion cost function, and finding out a key congestion bottleneck according to the influence degree. The whole method has clear flow steps, convenient operation and good application value.
Description
Technical Field
The invention relates to the field of urban traffic management, in particular to a method for identifying a traffic key bottleneck of an urban road section based on congestion propagation.
Background
With the continuous construction and development of cities, the load of urban roads is more serious, and the problem of congestion needs to be solved in order to improve the road running efficiency. The smoothness of the road network is directly limited by road section traffic bottlenecks, the road network traffic efficiency is rapidly reduced due to severe traffic bottlenecks, and the identification of key bottleneck road sections is very important for supporting traffic jam management decision making and improving the road running efficiency of the whole city. Different from the traditional research thought of only considering the part of the congestion road section, the method extracts and analyzes the complete propagation link by defining the congestion event and the congestion activation event; in addition, the existing research method lacks a quantitative evaluation method for the road network operation loss cost caused by the congestion link, the method defines a congestion cost function, and the total cost formed by the congestion link is quantized and used as an important basis for judging the criticality of the congestion link; and finally, analyzing the extracted congestion propagation link result, finding out the starting road section of the most critical congestion link, and identifying the starting road section as the road section traffic bottleneck.
Disclosure of Invention
Therefore, one of the purposes of the present invention is to provide a method for identifying a traffic key bottleneck of a city road based on congestion propagation.
The invention aims at realizing the following technical scheme: .
A city road section traffic key bottleneck identification method based on congestion propagation comprises the following steps:
step S1: and (3) data acquisition: collecting GPS track data of vehicles, including taxi track data and network taxi track data, which are used for representing road conditions and collecting road network data;
step S2: data fusion: the acquired network taxi-contracted GPS track and taxi GPS track data are fused, and the data dimension is unified;
step S3: map matching: matching the GPS track to the road section;
step S4: judging the road section congestion state: calculating the traffic speed of the road section, analyzing the speed-time distribution rule of the road section, and determining the saturation critical speed of the road section, namely the speed judgment threshold value when the road section is congested;
step S5: extracting congestion events: calculating the average speed of each road section in each time slice, and calling a set of N continuous time slices from congestion formation to congestion dissipation of one road section as a congestion event;
step S6: extracting congestion activation events: if two adjacent road sections enter the congestion state successively and the congestion state of the downstream road section is continued until the congestion of the upstream road section is formed, judging that the congestion is transmitted from the downstream road section to the upstream road section;
step S7: forming a congestion propagation link: combining the congestion event with the congestion activation event, wherein the complete propagation link is formed by continuously connecting the congestion event with the congestion activation event;
step S8, quantitatively evaluating the congestion cost of the congestion propagation link:
step S9: identifying a road section traffic key bottleneck: analyzing the obtained congestion link-congestion cost distribution diagram, extracting congestion links larger than a congestion cost threshold, analyzing the time distribution of the congestion links, and extracting key bottleneck sections.
Further, in step S2, the hidden markov model map matching method is adopted to process the original GPS track points.
Further, in the step S8, in order to quantitatively represent the loss degree of the congestion propagation link to the road network operation, the length of the congestion road section, the number of lanes of the congestion road section and the duration of congestion are taken as consideration, and the total cost calculation formula of the congestion link is as follows:
wherein Cost is k Is the congestion cost of the kth congestion propagation link, D k Is the congestion event corresponding to the kth congestion propagation linkIs set of Len r Is the length of the road segment r corresponding to the congestion event p, CDS r Is the number of lanes of the road segment r corresponding to the congestion event p, T p Is the duration of congestion for the road segment r corresponding to the congestion event p.
It is a further object of the invention to provide a computer device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, which processor implements the method as described above when executing the computer program.
It is a further object of the invention to provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
The beneficial effects of the invention are as follows: the invention provides a method for identifying a traffic key bottleneck, which is easy to operate, integrates taxi GPS track data and network taxi GPS track data, and finds out a congestion propagation link by extracting a congestion event and a congestion activation event; and (3) quantitatively evaluating the influence caused by road section congestion by defining a congestion cost function, and finding out a key congestion bottleneck according to the influence degree. The whole method has clear flow steps, convenient operation and good application value.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of the present invention for identifying a critical bottleneck in a road segment traffic
Fig. 2 shows road network data of a road section of a partial area selected by an embodiment of the present invention;
FIG. 3 is a road operation level classification of "urban road traffic operation evaluation Standard" to which the present invention refers;
FIG. 4 is a congestion cost profile of a congested link in accordance with an embodiment of the present invention;
FIG. 5 is a frequency histogram of 2 typical period congestion bottlenecks over a week for an embodiment of the invention;
fig. 6 is a schematic diagram of the distribution of 6 to 10 point critical bottleneck segments in an embodiment of the present invention.
Fig. 7 is a schematic diagram of the distribution of the 14 to 17 point critical bottleneck section in the embodiment of the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.
As shown in fig. 1, the method for identifying the traffic key bottleneck of the urban road section based on congestion propagation comprises the following steps:
step S1: and (3) data acquisition: collecting GPS track data of vehicles, including taxi track data and network taxi track data, which are used for representing road conditions and collecting road network data; considering that taxis and network taxi serving as operating vehicles have long running time, regular running time and wide position distribution, and can reflect urban road conditions, the method collects the taxi GPS track and the network taxi GPS track as research data;
step S2: data fusion: the acquired network taxi-contracted GPS track and taxi GPS track data are fused, and the data dimension is unified;
step S3: map matching: matching the GPS track to the road section;
step S4: judging the road section congestion state: calculating the traffic speed of the road section, analyzing the speed-time distribution rule of the road section, and determining the saturation critical speed of the road section, namely the speed judgment threshold value when the road section is congested;
step S5: extracting congestion events: calculating the average speed of each road section in each time slice, and calling a set of N continuous time slices from congestion formation to congestion dissipation of one road section as a congestion event;
step S6: extracting congestion activation events: if two adjacent road sections enter the congestion state successively and the congestion state of the downstream road section is continued until the congestion of the upstream road section is formed, judging that the congestion is transmitted from the downstream road section to the upstream road section;
step S7: forming a congestion propagation link: combining the congestion event with the congestion activation event, wherein the complete propagation link is formed by continuously connecting the congestion event with the congestion activation event;
step S8, quantitatively evaluating the congestion cost of the congestion propagation link:
step S9: identifying a road section traffic key bottleneck: analyzing the obtained congestion link-congestion cost distribution diagram, extracting congestion links larger than a congestion cost threshold, analyzing the time distribution of the congestion links, and extracting key bottleneck sections.
Examples
In this embodiment, the data sources collected in step S1 include taxi GPS track data and network taxi GPS track data of Chongqing city, the data records include a vehicle ID, a position, a time, an instantaneous speed, an azimuth angle, a positioning identifier, and the like, the time includes year, month, date, hour, minute, second, and the position includes longitude and latitude. In addition, network division data of a certain urban area of Chongqing city is acquired, and referring to fig. 2, the network division data comprise the length of a road, the number of lanes, the road level and the upstream and downstream topological relation.
In step S2, the collected network taxi-contracted GPS track and taxi GPS track data are fused, the data dimensions are unified, and only the vehicle ID, time, longitude and latitude, instantaneous speed and azimuth angle fields are reserved. In addition, because the road network data are collected and generally the road sections obtained by dividing the road network nodes, the length of the road sections is generally 100 meters to 200 meters, but the expressway can have a plurality of kilometers, belongs to uneven distribution, and needs to be further segmented, so that the length of each road section is about 100 meters, and congestion which cannot be found in the middle part of the road section due to overlarge length is avoided.
In step S3, an improved hidden markov model map matching method is adopted for the track data after fusion, i.e. the vehicle ID, time, longitude and latitude, instantaneous speed, azimuth angle field data, and road network data after re-segmentation, so as to match the track data to the road segments after segmentation.
Firstly, abnormal track data needs to be removed, including the following cases: (1) removing track points with the instantaneous speed exceeding 28 m/s; (2) removing track points with azimuth angles outside [0,360 ]; (3) the instantaneous speed and azimuth angle of the adjacent track points excluding the same vehicle ID are 0, but the longitude and latitude and the time are at the changed track points. And converting the coordinates into a WGS-84 coordinate system.
And then setting a 50-meter buffer area for road network data, and obtaining candidate state points of the track points in the buffer area. And taking the candidate state points and the road network data as the input of the improved hidden Markov model, and calculating the observation probability and the transition probability of all the candidate state points. And then taking the observation probability as a node and the transition probability as an edge, constructing a directed weight graph, and acquiring a candidate state point set with the largest cumulative sum of the observation probability and the transition probability. And backtracking the point set to obtain a matching point set as an optimal matching track.
In step S4, 5 minutes is taken as a time slice, the track points matched to the same road section and belonging to the same time slice are screened out, and the harmonic mean speed is calculated and is taken as the running speed of the road section in the time slice. The calculation formula is as follows:
in step S5, the road congestion status is determined, and referring to fig. 3, it is easy to find that the congestion determination threshold value of the road of different levels is 40% of the free flow speed and the congestion determination threshold value is 25% of the free flow speed by the road operation level classification table in the urban road traffic operation evaluation criterion. Generally, the free flow speed is mainly related to the grade, the number of lanes, the length of the road and the like, but has no standard correspondence relation, so that a speed-time distribution diagram needs to be drawn for each road section, and 40% and 25% of the average value of the highest 4 instantaneous speeds in one day are taken as the more congestion critical speed and the congestion critical speed of the road section, namely, the speed judgment threshold value when the road section is congested.
And according to the obtained congestion critical speed of each road section, carrying out congestion judgment on the harmonic average speed of all road sections in each time slice.
In step S6, according to the congestion status determination result in step S5, extracting a congestion event for each road segment, starting from the first time slice, recording as the start time of the current congestion event when the congestion determination result is 1, continuing to traverse the subsequent time slices, if the congestion determination result of the subsequent time slice road segment is also 1, then recording the last time slice with the traversed congestion status of 1 as the end time of the current congestion event, otherwise, recording the last time slice with the traversed congestion status of 1 as the end time of the current congestion event. Repeating the steps until all congestion events of all road sections are extracted, and forming a triplet record (road section, congestion start time and congestion end time). The algorithm process is as follows:
in step S7, congestion propagation of the road segment needs to satisfy both the rules of spatial adjacency and temporal intersection. Assuming that road_1 and road_2 are adjacent road segments and that road_1 is a downstream road segment of road_2, then the vehicle will flow from road_2 to road_1 and congestion propagates from road_1 to road_2. Assuming that the congestion state of the downstream link road_1 continues until congestion of the upstream link road_2 is formed, it is determined that congestion is propagated from the downstream link road_1 to the upstream link road_2.
In step 6, congestion events for all road segments are extracted. In step 7, congestion propagation events between two paths of segments are extracted. Next, step S8 is entered, i.e. combining congestion events with congestion activation events, connecting individual congestion events to form a complete propagation link. By way of illustration of the head_1 and the head_2 in step 7, if there are congestion propagation events of the head_1 to the head_2, the end time of a certain congestion event of the head_1 is later than the start time of a certain congestion event of the head_2, and the start time of the congestion event of the head_1 is earlier than the start time of the congestion event of the head_2, the two congestion events are connected, and the above steps are repeated until a new congestion event cannot be connected.
Finally, quantitatively evaluating the congestion cost of the congestion propagation link through the step S9: in order to quantitatively reflect the influence degree of the congestion propagation link on the road network, taking the length of the congestion road section, the number of lanes of the congestion road section and the duration of congestion as consideration factors, summing the costs of all congestion events contained in the link, and calculating the total cost of the congestion link by the following formula:
wherein Cost is k Is the congestion cost of the kth congestion propagation link, D k Is the set of congestion events corresponding to the kth congestion propagation link, len r Is the length of the road segment r corresponding to the congestion event p, CDS r Is the number of lanes of the road segment r corresponding to the congestion event p, T p Is the duration of congestion for the road segment r corresponding to the congestion event p.
For each propagation link, calculating the total cost of the congestion, drawing a congestion cost-propagation link distribution diagram, referring to fig. 4, eliminating outliers, and selecting 100 as a key bottleneck congestion cost threshold according to experience.
And (3) analyzing the time distribution of the congested link in a week on the basis of the congested link result obtained in the step (9). Firstly, dividing a day into a plurality of time periods, namely [ 00:00-06:00 ], [ 06:00-10:00 ], [ 10:00-12:00 ], [ 12:00-14:00 ], [ 14:00-17:00 ], [ 17:00-19:00 ], [ 19:00-21:00 ], [ 21:00-24:00 ]. Drawing frequency histograms of the congestion links with the congestion cost exceeding 100 in each period within a week, and fig. 5 is a frequency histogram of the congestion links with the peak of a section of a certain urban area of Chongqing city in the morning and evening of working days. If at least 3 days of a period of one week becomes a bottleneck, it is referred to as a frequently critical congested link during that period, see fig. 6 and 7.
It should be appreciated that embodiments of the invention may be implemented or realized by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer readable storage medium configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, in accordance with the methods and drawings described in the specific embodiments. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes (or variations and/or combinations thereof) described herein may be performed under control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications), by hardware, or combinations thereof, collectively executing on one or more processors. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable computing platform, including, but not limited to, a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and so forth. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and/or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, which when read by a computer, is operable to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention.
The computer program can be applied to the input data to perform the functions described herein, thereby converting the input data to generate output data that is stored to the non-volatile memory. The output information may also be applied to one or more output devices such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on a display.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (5)
1. A city road section traffic key bottleneck identification method based on congestion propagation is characterized by comprising the following steps of: the method comprises the following steps:
step S1: and (3) data acquisition: collecting GPS track data of vehicles, including taxi track data and network taxi track data, which are used for representing road conditions and collecting road network data;
step S2: data fusion: the acquired network taxi-contracted GPS track and taxi GPS track data are fused, and the data dimension is unified;
step S3: map matching: matching the GPS track to the road section;
step S4: judging the road section congestion state: calculating the traffic speed of the road section, analyzing the speed-time distribution rule of the road section, and determining the saturation critical speed of the road section, namely the speed judgment threshold value when the road section is congested;
step S5: extracting congestion events: calculating the average speed of each road section in each time slice, and calling a set of N continuous time slices from congestion formation to congestion dissipation of one road section as a congestion event;
step S6: extracting congestion activation events: if two adjacent road sections enter the congestion state successively and the congestion state of the downstream road section is continued until the congestion of the upstream road section is formed, judging that the congestion is transmitted from the downstream road section to the upstream road section;
step S7: forming a congestion propagation link: combining the congestion event with the congestion activation event, wherein the complete propagation link is formed by continuously connecting the congestion event with the congestion activation event;
step S8, quantitatively evaluating the congestion cost of the congestion propagation link:
step S9: identifying a road section traffic key bottleneck: analyzing the obtained congestion link-congestion cost distribution diagram, extracting congestion links larger than a congestion cost threshold, analyzing the time distribution of the congestion links, and extracting key bottleneck sections.
2. The urban road section traffic key bottleneck identification method based on congestion propagation according to claim 1, wherein the method comprises the following steps: in step S2, the original GPS track points are processed by adopting a hidden Markov model map matching method.
3. The urban road section traffic key bottleneck identification method based on congestion propagation according to claim 1 or 2, wherein the method comprises the following steps: in step S8, in order to quantitatively represent the loss degree of the congestion propagation link to the running of the road network, the length of the congestion road section, the number of lanes of the congestion road section and the duration of congestion are taken as consideration, and the total cost calculation formula of the congestion link is as follows:
wherein Cost is k Is the congestion cost of the kth congestion propagation link, D k Is the set of congestion events corresponding to the kth congestion propagation link, len r Is the length of the road segment r corresponding to the congestion event p, CDS r Is the number of lanes of the road segment r corresponding to the congestion event p, T p Is the duration of congestion for the road segment r corresponding to the congestion event p.
4. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when executing the computer program, implements the method of any of claims 1-3.
5. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program implementing the method according to any of claims 1-3 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211711358.6A CN116311892A (en) | 2022-12-29 | 2022-12-29 | Urban road section traffic key bottleneck identification method based on congestion propagation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211711358.6A CN116311892A (en) | 2022-12-29 | 2022-12-29 | Urban road section traffic key bottleneck identification method based on congestion propagation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116311892A true CN116311892A (en) | 2023-06-23 |
Family
ID=86776884
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211711358.6A Pending CN116311892A (en) | 2022-12-29 | 2022-12-29 | Urban road section traffic key bottleneck identification method based on congestion propagation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116311892A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117392850A (en) * | 2023-11-29 | 2024-01-12 | 哈尔滨航天恒星数据系统科技有限公司 | SMO-based traffic congestion real-time prediction and release method, electronic equipment and storage medium |
-
2022
- 2022-12-29 CN CN202211711358.6A patent/CN116311892A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117392850A (en) * | 2023-11-29 | 2024-01-12 | 哈尔滨航天恒星数据系统科技有限公司 | SMO-based traffic congestion real-time prediction and release method, electronic equipment and storage medium |
| CN117392850B (en) * | 2023-11-29 | 2024-05-28 | 哈尔滨航天恒星数据系统科技有限公司 | SMO-based traffic congestion real-time prediction and release method, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200211374A1 (en) | System, method, and apparatus for analyzing a traffic road condition | |
| CN109410577B (en) | Self-adaptive traffic control subarea division method based on space data mining | |
| CN102087788B (en) | Method for estimating traffic state parameter based on confidence of speed of float car | |
| CN108415975B (en) | BDCH-DBSCAN-based taxi passenger carrying hot spot identification method | |
| CN107798876B (en) | Road traffic abnormal jam judging method based on event | |
| CN108596202B (en) | Method for calculating personal commuting time based on mobile terminal GPS positioning data | |
| US9418545B2 (en) | Method and system for collecting traffic data | |
| CN112489426B (en) | Urban traffic flow space-time prediction scheme based on graph convolution neural network | |
| CN109686091B (en) | Traffic flow filling algorithm based on multi-source data fusion | |
| CN107657637A (en) | A kind of agricultural machinery working area acquisition methods | |
| CN113553482B (en) | Stay point identification and trip chain construction system, algorithm, equipment and storage medium | |
| Yao et al. | Sampled trajectory data-driven method of cycle-based volume estimation for signalized intersections by hybridizing shockwave theory and probability distribution | |
| CN113379099B (en) | Machine learning and copula model-based highway traffic flow self-adaptive prediction method | |
| CN110021161B (en) | Traffic flow direction prediction method and system | |
| CN116311892A (en) | Urban road section traffic key bottleneck identification method based on congestion propagation | |
| CN110827537B (en) | Method, device and equipment for setting tidal lane | |
| CN117664168A (en) | Park vehicle navigation path planning method, medium and system | |
| Kurte et al. | Regional-scale spatio-temporal analysis of impacts of weather on traffic speed in Chicago using probe data | |
| CN111310340B (en) | Urban area interaction abnormal relation identification method and equipment based on human movement | |
| CN108665723A (en) | A kind of information acquisition method and device | |
| CN109448379A (en) | A kind of identification of sporadic traffic events of social media data and localization method | |
| CN111640303B (en) | City commuting path identification method and equipment | |
| CN114519500A (en) | Method, device and equipment for determining road safety risk level and storage medium | |
| KR20150072470A (en) | System for analyzing dependence of spatiotemporal domain of traffic flow on the city and highway | |
| Hussain et al. | Visualizing Impact of Weather on Traffic Congestion Prediction: A Quantitative Study |
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 |