CN109360414B - Identification and screening method for frequently congested road sections - Google Patents
Identification and screening method for frequently congested road sections Download PDFInfo
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/01—Detecting movement of traffic to be counted or controlled
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Abstract
The invention provides a method for identifying and screening frequently congested road sections, which comprises the following steps: acquiring historical congestion data, counting the daily accumulated congestion duration of a certain road section, and determining the congestion durationCalculating the reliability of the road section accumulated congestion time length according to the probability distribution of the road section accumulated congestion time length obeying, and when the reliability exceeds a set threshold value1If so, judging the road section to be a frequently congested road section; under the condition of given reliability, calculating the accumulated congestion time of the given reliability of the road section according to the probability distribution obeyed by the accumulated congestion time of the road section, and when the accumulated congestion time of the given reliability exceeds a set threshold value2And judging the road section as the frequently congested road section. The method calculates the accumulated congestion duration of the road section exceeding the expected threshold T0The reliability of the road congestion detection method or the accumulated congestion duration of the given reliability under the given reliability condition are calculated to respectively judge whether the road congestion is a frequently congested road section or not, and an accurate and powerful scientific basis is provided for a traffic management department to screen the frequently congested road section.
Description
Technical Field
The invention relates to the technical field of traffic information processing, in particular to a method for identifying and screening frequently congested road sections.
Background
Traffic congestion is a problem facing each large city, and mainly comprises occasional congestion and frequent congestion. The occasional congestion is caused by an emergency, and the frequent congestion is usually caused by the reasons of uneven traffic supply and demand, defects in a road network and the like. For example, traffic planning is not coordinated with urban development, the designed traffic capacity of interchange ramps is not sufficient, the number of lanes at the junction of a high-grade road and a low-grade road is reduced, the distance between intersections is too large, the turning setting is not reasonable, and the timing of signal lights at the intersections is not good. Identifying and screening frequently congested road sections of a road network, on one hand, providing a reconstruction project library for urban congestion removing engineering, providing early warning information for traffic management departments, taking response measures in time, and dredging congested traffic by using advanced traffic control measures and intelligent induction information; on the other hand, unreasonable parts in road network planning can be found, and scientific basis is provided for traffic and construction departments to re-plan road alignment, improve road infrastructure and the like. The frequently congested road sections have certain regularity and predictability, but a mature and reliable method for quantifying the frequently congested road sections is still lacking at present.
The conventional frequent congestion road section identification method mainly comprises the following steps:
the first method is to divide the frequently congested road sections into daily congested road sections, weekly congested road sections, monthly congested road sections and annual congested road sections. The daily congestion road section refers to a road section with the speed of the 5 th-level average interval within 1 hour (inclusive) in the peak time period, and statistics is respectively carried out according to the early peak time period and the late peak time period; the daily congested road sections mean daily congested road sections within 5 working days of a week, and statistics is respectively carried out according to early peak time periods and late peak time periods in at least 4 days; the monthly frequent congestion road section refers to a weekly frequent congestion road section within 4 weeks of a month, and statistics are respectively carried out according to the early peak time period and the late peak time period, wherein at least 3 weeks are weekly frequent congestion road sections; the frequently congested road sections in the year mean the frequently congested road sections in the month of at least 6 months within 12 months of the year, and statistics is respectively carried out according to the early peak time and the late peak time.
The second statistical method is to divide the congestion accumulated time period number of each road segment by the total peak time period number in the peak time period, and the percentage reflects the normal congestion situation during the peak time period. On the express way, the percentage of the road sections is more than 25 percent of the frequently congested road sections; on the primary trunk road and the secondary trunk road, the percentage of the sections is more than 30 percent of the sections which are frequently jammed.
The existing frequent congestion road section identification method only considers congestion in peak time periods, cannot identify the daily integral congestion degree of a road section, and does not consider the fluctuation of the congestion. Due to the fact that the congestion time lengths of the road sections have difference and fluctuation, the frequently congested road sections with short congestion time and low reliability are difficult to identify and screen in the prior art.
Disclosure of Invention
The embodiment of the invention provides a method for identifying and screening frequently congested road sections, which aims to solve the problems.
In order to achieve the purpose, the invention adopts the following technical scheme.
According to one aspect of the invention, a frequent congestion road segment identification and screening method is provided, and comprises the following steps:
acquiring historical congestion data, and counting the daily accumulated congestion duration of a certain road section;
determining probability distribution obeyed by the accumulated congestion duration of the road section according to the accumulated congestion duration of the certain road section;
under the condition of giving the road section accumulated congestion time length, calculating the road section accumulated congestion time according to the probability distribution obeyed by the road section accumulated congestion time lengthLong reliability, when the reliability of the accumulated congestion time of the road section exceeds a set threshold1And if so, judging the road section to be the frequently congested road section.
Preferably, the obtaining of the historical congestion data and the statistics of the cumulative congestion duration of a certain road segment per day include:
the accumulated congestion time of the certain road section every day is calculated according to the following formula:
wherein, betaiFor the cumulative congestion duration, α, for road section i during the dayi，jA state indicating whether or not the link i is congested at time j · Δ, j being 1,2, …, M indicating a number of cycles in a comparison period, Δ indicating a sampling interval of data, and α if the link i is congested at time j · Δi，j1 is ═ 1; if not, then αi，j＝0。
Preferably, the determining, according to the accumulated congestion time length of the certain road segment, a probability distribution to which the accumulated congestion time length of the road segment is obeyed includes:
determining the probability distribution obeyed by the accumulated congestion duration of the road section as normal distribution according to the accumulated congestion duration of the certain road section,
wherein muiThe average value of the accumulated congestion time length of the road section is obtained,
accumulating the variance of the congestion duration for the road section;
the mean value muiSum variance
Calculated according to the following formula:
wherein (beta)i，1，βi，2，…，βi，n) Is a set of samples of the population, n is the sample size;
calculating the probability distribution obeyed by the accumulated congestion time length of the road section according to the following probability distribution formula:
wherein P { a < beta [ ]iB is less than or equal to b and is the accumulated congestion duration beta of the road section iiIn the interval (a, b)]F (x) is betaiRepresents the distribution function of the standard normal distribution, and a and b are set time period thresholds.
Preferably, the calculating the reliability of the accumulated congestion duration of the road segment according to the probability distribution obeyed by the accumulated congestion duration of the road segment under the condition of the given accumulated congestion duration of the road segment includes:
the reliability of the accumulated congestion duration of the road section is calculated according to the following formula:
P{βi＞T0the accumulated congestion duration beta of the road section iiGreater than a desired threshold T0Probability of (1), will P { beta [)i＞T0And the reliability of the accumulated congestion duration of the road section is used.
According to another aspect of the invention, a frequent congestion road segment identification and screening method is provided, which comprises the following steps:
acquiring historical congestion data, and counting the daily accumulated congestion duration of a certain road section;
determining probability distribution obeyed by the accumulated congestion duration of the road section according to the accumulated congestion duration of the certain road section;
under the condition of given reliability, calculating the accumulated congestion time of the given reliability of the road section according to the probability distribution obeyed by the accumulated congestion time of the road section, and when the calculated accumulated congestion time of the given reliability of the road section exceeds a set threshold value2And judging that the road section is the frequently congested road section.
Preferably, the step S10 of obtaining historical congestion data, and counting the cumulative congestion duration of a certain road segment per day includes:
the accumulated congestion time of the certain road section per day is calculated according to the following formula:
wherein, betaiFor the cumulative congestion duration, α, for road section i during the dayi，jA state indicating whether or not the link i is congested at time j · Δ, j being 1,2, …, M indicating a number of cycles in a comparison period, Δ indicating a sampling interval of data, and α if the link i is congested at time j · Δi，j1 is ═ 1; if not, then αi，j＝0。
Preferably, the determining, according to the accumulated congestion time length of the certain road segment, a probability distribution to which the accumulated congestion time length of the road segment is obeyed includes:
determining the probability distribution obeyed by the accumulated congestion duration of the road section as normal distribution according to the accumulated congestion duration of the certain road section,
wherein muiThe average value of the accumulated congestion time length of the road section is obtained,
accumulating the variance of the congestion duration for the road section;
the mean value muiSum variance
Calculated according to the following formula:
wherein (beta)i，1，βi，2，…，βi，n) Is a set of samples of the population, n is the sample size;
calculating the probability distribution obeyed by the accumulated congestion time length of the road section according to the following probability distribution formula:
wherein P { a < beta [ ]iB is less than or equal to b and is the accumulated congestion duration beta of the road section iiIn the interval (a, b)]F (x) is betaiRepresents the distribution function of a standard normal distribution.
Preferably, the calculating the cumulative congestion duration of the given reliability θ of the road segment according to the probability distribution obeyed by the cumulative congestion duration of the road segment under the given reliability includes:
an accumulated congestion duration β for a given reliability θ of the road sectioniCalculated according to the following formula:
βi＝Φ-1(1-θ)·σi+μi(7)
wherein phi-1(. cndot.) represents the inverse of the cumulative probability density function for a normal distribution with a degree of reliability θ.
According to the technical scheme provided by the embodiment of the invention, the identification and screening method for the frequently congested road sections utilizes the traffic big data to statistically analyze the probability distribution of the accumulated congestion time of the road sections, calculates the probability that the accumulated congestion time of the road sections exceeds the expected threshold value and the congestion time of the road sections under the condition of given reliability, and provides an accurate and powerful scientific basis for the traffic management department to screen the frequently congested road sections.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a processing flow chart of a method for identifying and screening frequently congested road segments according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below, examples of which are illustrated in the accompanying drawings, and the embodiments described below by referring to the drawings are exemplary only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained by taking specific embodiments as examples with reference to the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Examples
Fig. 1 is a processing flow chart of a method for identifying and screening a frequently congested road segment according to an embodiment of the present invention, and referring to fig. 1, a method for identifying and screening a frequently congested road segment includes:
s10, historical congestion data is obtained, and the cumulative congestion time of a certain road section every day is counted.
Calculated according to the following formula:
wherein, betaiFor the cumulative congestion duration, α, for road section i during the dayi，jThe traffic congestion state is represented by j · Δ, where j is 1,2, …, M represents the number of cycles in the comparison period, and Δ represents the sampling interval of the data.
Illustratively, the traffic conditions include three types of clear, slow, and congested. And calculating the accumulated congestion time of each road section every day. With a 5 minute sampling interval, there are 288 sampling periods in a day. If the link i is congested at time j.DELTA.i，j1 is ═ 1; if not in the possession ofBlocking is then alphai，j＝0。
S20, determining the probability distribution obeyed by the road section accumulated congestion duration according to the accumulated congestion duration of the certain road section.
Further, according to the accumulated congestion time of a certain road section, determining the probability distribution obeyed by the accumulated congestion time of the road section as normal distribution,
i.e. betaiSubject to a mathematical expectation of μiVariance of
Is normally distributed. Wherein muiThe average value of the accumulated congestion time length of the road section is obtained,
variance, mean mu, of accumulated congestion duration for a road segmentiSum variance
Calculated according to the following formula:
wherein (beta)i，1，βi，2，…，βi，n) Is a set of samples of the population and n is the sample size.
Calculating the probability distribution obeyed by the accumulated congestion time of the road section according to the following probability distribution formula:
furthermore, the accumulated congestion duration obtained through data statistics obeys normal distribution,
the following formula is thus obtained:
wherein P { a < beta [ ]iB is less than or equal to b and is the accumulated congestion duration beta of the road section iiIn the interval (a, b)]F (x) is betaiThe probability density function is obtained by fitting according to historical accumulated congestion duration, phi (-) represents a distribution function of standard normal distribution, and a and b are set time period thresholds.
S31, under the condition that the road section accumulated congestion time length is given, according to the probability distribution obeyed by the road section accumulated congestion time length, calculating the reliability of the road section accumulated congestion time length exceeding the expected threshold, and when the reliability of the road section accumulated congestion time length exceeds the set threshold1And if so, judging the road section to be the frequently congested road section.
Calculating the reliability of the accumulated congestion duration of the road section according to the following formula:
P{βi＞T0the accumulated congestion duration beta of the road section iiGreater than a desired threshold T0Probability of (1), will P { beta [)i＞T0And the reliability of the accumulated congestion duration of the road section is used.
Fig. 1 is a processing flow chart of a method for identifying and screening a frequently congested road segment according to an embodiment of the present invention, and with reference to fig. 1, another method for identifying and screening a frequently congested road segment is further included, where the method includes:
s10, historical congestion data is obtained, and the cumulative congestion time of a certain road section every day is counted.
Calculated according to the following formula:
wherein, betaiFor the cumulative congestion duration, α, for road section i during the dayi，jThe traffic congestion state is represented by j · Δ, where j is 1,2, …, M represents the number of cycles in the comparison period, and Δ represents the sampling interval of the data.
Illustratively, the traffic conditions include three types of clear, slow, and congested. And calculating the accumulated congestion time of each road section every day. With a 5 minute sampling interval, there are 288 sampling periods in a day. If the link i is congested at time j.DELTA.i，j1 is ═ 1; if not, then αi，j＝0。
S20, determining the probability distribution obeyed by the road section accumulated congestion duration according to the accumulated congestion duration of the certain road section.
Further, according to the accumulated congestion time of a certain road section, determining the probability distribution obeyed by the accumulated congestion time of the road section as normal distribution,
wherein muiThe average value of the accumulated congestion time length of the road section is obtained,
variance, mean mu, of accumulated congestion duration for a road segmentiSum variance
Calculated according to the following formula:
wherein (beta)i，1，βi，2，…，βi，n) Is a set of samples of the population and n is the sample size.
Calculating the probability distribution obeyed by the accumulated congestion time of the road section according to the following probability distribution formula:
furthermore, the accumulated congestion duration obtained through data statistics obeys normal distribution,
the following formula is thus obtained:
wherein P { a < beta [ ]iB is less than or equal to b and is the accumulated congestion duration beta of the road section iiIn the interval (a, b)]F (x) is betaiThe probability density function of (1) is obtained by fitting according to historical accumulated congestion duration, and phi (-) represents a distribution function of standard normal distribution.
S32, under the condition of given reliability, calculating the accumulated congestion time length of the given reliability of the road section according to the probability distribution obeyed by the accumulated congestion time length of the road section, and when the calculated accumulated congestion time length of the given reliability of the road section exceeds a set threshold value2And judging that the road section is the frequently congested road section.
Calculating an accumulated congestion time beta for a given reliability theta of the road segmentiCalculated according to the following formula:
βi＝Φ-1(1-θ)·σi+μi(7)
wherein phi-1(. cndot.) represents the inverse of the cumulative probability density function for a normal distribution with a degree of reliability θ.
In summary, the method for identifying and screening the frequently congested road segments according to the embodiment of the present invention can identify the overall congestion degree of the road segments each day and consider the fluctuation of congestion. The reliability is calculated to identify and screen the frequently jammed road section under the condition of giving the accumulated jam time of the road section, or the accumulated jam time of the given reliability is calculated to identify and screen the frequently jammed road section under the condition of giving the reliability, so that an accurate and effective reconstruction project library can be provided for urban jam clearance engineering, early warning information can be provided for a traffic management department, countermeasures can be taken in time to dredge jammed traffic, unreasonable parts in road network planning can be found, scientific bases are provided for traffic planning and construction departments to re-plan road alignment, improve road infrastructure and the like, and the aim of relieving road jam is fulfilled.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A method for identifying and screening frequently congested road sections is characterized by comprising the following steps:
acquiring historical congestion data, and counting the daily accumulated congestion duration of a certain road section;
determining probability distribution obeyed by the accumulated congestion duration of the road section according to the accumulated congestion duration of the certain road section;
obeying according to the accumulated congestion duration of the road section under the condition of giving the accumulated congestion duration of the road sectionProbability distribution, calculating the reliability of the accumulated congestion time of the road section, and when the reliability of the accumulated congestion time of the road section exceeds a set threshold value1If so, judging the road section to be a frequently congested road section; calculating the reliability of the road section accumulated congestion time according to the probability distribution obeyed by the road section accumulated congestion time, wherein the reliability comprises the following steps:
the reliability of the accumulated congestion duration of the road section is calculated according to the following formula:
P{βi>T0the accumulated congestion duration beta of the road section iiGreater than a desired threshold T0Probability of (1), will P { beta [)i>T0Determining, according to the accumulated congestion duration of a certain road segment, a probability distribution obeying the accumulated congestion duration of the road segment, including:
determining the probability distribution obeyed by the accumulated congestion duration of the road section as normal distribution according to the accumulated congestion duration of the certain road section,
wherein muiThe average value of the accumulated congestion time length of the road section is obtained,
accumulating the variance of the congestion duration for the road section;
the mean value muiSum variance
Calculated according to the following formula:
wherein (beta)i,1,βi,2,…,βi,n) Is a set of samples of the population, n is the sample size;
calculating the probability distribution obeyed by the accumulated congestion time length of the road section according to the following probability distribution formula:
wherein P { a < beta [ ]iB is less than or equal to b and is the accumulated congestion duration beta of the road section iiIn the interval (a, b)]F (x) is betaiRepresents the distribution function of the standard normal distribution, and a and b are set time period thresholds.
2. The method for identifying and screening frequent congestion road segments according to claim 1, wherein the obtaining of historical congestion data and the statistics of the cumulative congestion duration of a certain road segment per day comprise:
the accumulated congestion time of the certain road section every day is calculated according to the following formula:
wherein, betaiFor the cumulative congestion duration, α, for road section i during the dayi,jA state indicating whether or not the link i is congested at time j · Δ, j being 1,2, …, M indicating a number of cycles in a comparison period, Δ indicating a sampling interval of data, and α if the link i is congested at time j · Δi,j1 is ═ 1; if not, then αi,j＝0。
3. A method for identifying and screening frequently congested road sections is characterized by comprising the following steps:
acquiring historical congestion data, and counting the daily accumulated congestion duration of a certain road section;
determining probability distribution obeyed by the accumulated congestion duration of the road section according to the accumulated congestion duration of the certain road section;
under the condition of given reliability, calculating the accumulated congestion time of the given reliability of the road section according to the probability distribution obeyed by the accumulated congestion time of the road section, and when the calculated accumulated congestion time of the given reliability of the road section exceeds a set threshold value2Judging that the road section is a frequently congested road section;
the determining, according to the accumulated congestion time of the certain road segment, a probability distribution obeyed by the accumulated congestion time of the road segment includes:
determining the probability distribution obeyed by the accumulated congestion duration of the road section as normal distribution according to the accumulated congestion duration of the certain road section,
wherein muiThe average value of the accumulated congestion time length of the road section is obtained,
accumulating the variance of the congestion duration for the road section;
the mean value muiSum variance
Calculated according to the following formula:
wherein (beta)i,1,βi,2,…,βi,n) Is a set of samples of the population, n is the sample size;
calculating the probability distribution obeyed by the accumulated congestion time length of the road section according to the following probability distribution formula:
wherein P { a < beta [ ]iB is less than or equal to b and is the accumulated congestion duration beta of the road section iiIn the interval (a, b)]F (x) is betaiPhi (-) represents the distribution function of a standard normal distribution; under the condition of the given reliability, calculating the accumulated congestion time of the given reliability theta of the road section according to the probability distribution obeyed by the accumulated congestion time of the road section, wherein the calculation comprises the following steps:
an accumulated congestion duration β for a given reliability θ of the road sectioniCalculated according to the following formula:
βi＝Φ-1(1-θ)·σi+μi(7)
wherein phi-1(. cndot.) represents the inverse of the cumulative probability density function for a normal distribution with a degree of reliability θ.
4. The method for identifying and screening frequent congestion road segments according to claim 3, wherein the obtaining of historical congestion data and the statistics of the cumulative congestion duration of a certain road segment per day comprise:
the accumulated congestion time of the certain road section per day is calculated according to the following formula:
wherein, betaiFor the cumulative congestion duration, α, for road section i during the dayi,jIndicating the congestion status of the link i at time j · Δ, j being 1,2, …, M indicating the number of cycles in the comparison period, Δ indicating the number of cycles in the comparison periodSampling interval of data, if the link i is congested at time j.DELTA.i,j1 is ═ 1; if not, then αi,j＝0。
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| CN111815941B (en) * | 2019-04-10 | 2021-07-23 | 青岛海信网络科技股份有限公司 | Frequent congestion bottleneck identification method and device based on historical road conditions |
| CN112509326B (en) * | 2020-12-02 | 2021-12-10 | 北京航空航天大学 | Non-periodic congestion detection method based on generalized extreme value T distribution deviation |
| CN114582141B (en) * | 2022-03-04 | 2023-09-22 | 北京交通大学 | Method and system for identifying traffic of common departure on large road section |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288193A (en) * | 2011-07-06 | 2011-12-21 | 东南大学 | Motor vehicle travel route determination method based on historical data |
| CN104112361A (en) * | 2014-04-25 | 2014-10-22 | 浙江大学 | Identification method of common bottleneck and accidental bottleneck in road network |
| CN106408945A (en) * | 2016-11-28 | 2017-02-15 | 北京掌行通信息技术有限公司 | Traffic congestion evaluation method and traffic congestion evaluation system |
| CN106530694A (en) * | 2016-11-07 | 2017-03-22 | 深圳大学 | Traffic congestion prediction method and system based on traffic congestion propagation model |
| CN108320506A (en) * | 2018-02-05 | 2018-07-24 | 青岛大学 | A kind of discovery method of the congestion period based on composite network |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002367077A (en) * | 2001-06-07 | 2002-12-20 | Mitsubishi Electric Corp | Device and method for deciding traffic congestion |
| JP2006330942A (en) * | 2005-05-24 | 2006-12-07 | Sumitomo Electric Ind Ltd | Traffic condition deciding system, image processor, computer program, and traffic condition deciding method |
| JP4910885B2 (en) * | 2007-05-25 | 2012-04-04 | アイシン・エィ・ダブリュ株式会社 | Traffic jam prediction device, traffic jam prediction method, and computer program |
| CN101710449A (en) * | 2009-12-04 | 2010-05-19 | 吉林大学 | Traffic flow running rate recognizing method based on bus GPS data |
| WO2013003504A2 (en) * | 2011-06-27 | 2013-01-03 | Stc, Inc. | Signal light priority system utilizing estimated time of arrival |
| CN106205129B (en) * | 2016-08-17 | 2018-06-19 | 广州市市政工程设计研究总院 | Intersection Controlled drug-release Forecasting Methodology based on stochastic traffic demand and the traffic capacity |
| CN106816018A (en) * | 2017-02-16 | 2017-06-09 | 上海电科智能系统股份有限公司 | A kind of city changeable-message sign traffic above-ground induction section determines method |
-
2018
- 2018-09-18 CN CN201811085797.4A patent/CN109360414B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288193A (en) * | 2011-07-06 | 2011-12-21 | 东南大学 | Motor vehicle travel route determination method based on historical data |
| CN104112361A (en) * | 2014-04-25 | 2014-10-22 | 浙江大学 | Identification method of common bottleneck and accidental bottleneck in road network |
| CN106530694A (en) * | 2016-11-07 | 2017-03-22 | 深圳大学 | Traffic congestion prediction method and system based on traffic congestion propagation model |
| CN106408945A (en) * | 2016-11-28 | 2017-02-15 | 北京掌行通信息技术有限公司 | Traffic congestion evaluation method and traffic congestion evaluation system |
| CN108320506A (en) * | 2018-02-05 | 2018-07-24 | 青岛大学 | A kind of discovery method of the congestion period based on composite network |
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