CN112767687A - Intersection congestion prediction method based on commuting path selection behavior analysis - Google Patents
Intersection congestion prediction method based on commuting path selection behavior analysis Download PDFInfo
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Abstract
A method for predicting congestion at an intersection based on analysis of commuting path selection behavior is characterized in that firstly, in a selected area, a concerned commuting path is established by taking a designated origin-destination point as a target analysis range. And secondly, extracting the total traffic flow and unit interval flow of each commuting path in the target analysis range. And thirdly, forming a composite analysis matrix of the intersection, the traffic proportion of the single commuting path and the total traffic in the full analysis range by correlating the occurrence of the intersection congestion state in the time period. Analyzing a preset congestion threshold, determining a maximum possible congestion triggering total flow quantity segmented threshold interval, performing proportional analysis on the path flow, determining a maximum possible congestion triggering path flow proportion segmented threshold interval, and predicting the possibility of occurrence of the intersection congestion. The invention not only can clearly and quantitatively master the traffic of each commuting path, but also can guide the deployment and the dispatching of the preset police strength through the advance prediction of congestion and more scientifically develop traffic management work.
Description
Technical Field
The invention relates to the field of intelligent transportation and data mining, in particular to an intersection congestion prediction method based on commuting path selection behavior analysis.
Background
With the rapid development of economy, the number of motor vehicles in cities is increased year by year, the traffic flow is rapidly increased, the frequency of road congestion conditions is increased day by day, and the motor vehicles are most intensively concentrated in daily traffic commuting periods. The concentrated peak short-term traffic commuting demands put a great explosive pressure on urban road traffic management. According to the traditional manual work mode of putting a large amount of police force to preset road intersection points, key roads to patrol and the like, the passive conditions that congestion occurs first and people find later often exist, so that the congestion dredging labor cost is high, and the disposal timeliness is low. In order to relieve the traffic pressure of the existing roads and meet the urban development requirements, newly building or expanding the roads becomes one of the approaches for solving the problems, but the central urban area has the problems of less available space and high construction difficulty. Simultaneously, newly-built or extension road also can increase the variety of the selection of commuting route, can further promote route complexity and management and control demand to a certain extent, increases the management and control and administers the degree of difficulty.
In recent years, traffic authorities continuously strengthen informatization construction investment, and constantly build abundant outfield facility equipment, including video monitoring, high definition bayonet, electronic police, flow detectors and the like, so that a more comprehensive channel is provided for information data acquisition of vehicles. Meanwhile, with the continuous improvement of big data AI processing capacity, the intelligent traffic application based on large-scale vehicle collected data is rapidly developed, prediction indexes are provided through data mining and research and judgment, decision auxiliary support is provided for traffic management, and the important significance and the realization value are achieved when the post-treatment is changed into pre-prevention.
Disclosure of Invention
In view of the above, the present invention has been made to provide an intersection congestion prediction method based on commuting path selection behavior analysis that overcomes or at least partially solves the above-mentioned problems.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
an intersection congestion prediction method based on commuting path selection behavior analysis comprises the following steps:
s100, selecting a target area to be analyzed and a starting-destination range;
s200, acquiring a regional passing intersection data set, and judging whether crossing passing data to be analyzed meet the regional passing intersection data set;
s300, when the crossing traffic data to be analyzed meet the regional crossing traffic data set, judging whether the crossing traffic data to be analyzed has an impassable road junction;
s400, when the traffic data of the intersection to be analyzed does not have the non-feasible intersection, obtaining a feasible path of a start-to-end point of a target area to be analyzed, and judging whether the non-feasible path exists according to the feasible path of the start-to-end point;
s500, when the feasible path of the origin-destination point has no infeasible path, obtaining a final feasible path data set;
s600, extracting traffic flow and intersection congestion data of each traffic path of the origin-destination point of the area to be analyzed, and judging whether the traffic flow and the intersection congestion data of each traffic path of the origin-destination point of the area to be analyzed meet preset requirements of the traffic flow and the congestion amount of each traffic path of the origin-destination point;
s700, when the traffic of each passing path at the origin-destination point of an area to be analyzed and congestion data of an intersection meet the minimum requirements of the traffic and the congestion amount of each passing path at the origin-destination point, generating a congestion intersection and path unit time traffic proportion incidence matrix;
s800, analyzing a preset congestion threshold value, and determining a maximum possible congestion trigger flow total segmented threshold value interval;
s900, carrying out proportion analysis on the path flow, and determining a maximum possible congestion triggering path flow proportion subsection threshold interval;
s1000, predicting the occurrence of the road junction congestion based on the maximum possible congestion triggering flow total threshold, the maximum possible congestion triggering path flow proportion threshold and the real-time each path flow.
Further, in S300, when the intersection passage data to be analyzed does not satisfy the area passage intersection data group, editing operation may be performed by manual addition, deletion, and the like, and an entry area passage intersection data group is newly generated.
Further, in S400, when there is an infeasible intersection in the intersection traffic data to be analyzed, the infeasible intersection data is cleaned and labeled, and the feasible path of the origin-destination point of the target area to be analyzed is obtained again.
Further, in S500, when there is an infeasible path in the origin-destination feasible path, the infeasible path is cleaned, and the final feasible path data set is obtained again.
Further, in S600, the total traffic flow and the unit interval flow of each commuting path within the target analysis range are extracted by dividing the traffic flow into time interval units and taking days as time periods, so as to generate the data of the congested intersection.
Further, in S600, when the traffic of each passing path at the origin-destination point of the area to be analyzed and the congestion data at the intersection cannot meet the preset requirements of the traffic and the congestion amount of each passing path at the origin-destination point, data resampling may be performed by extending a time interval or a time period, and the traffic and congestion data at the intersection at the origin-destination point of the area to be analyzed are obtained again.
Further, the specific method of S800 is:
s801, calculating the average value of the total path flow when congestion occurs at a single intersection:
s802, removing undersized and oversized sporadic special case data to obtain a traffic total threshold interval when the road junction congestion occurs;
and S803, performing threshold segmentation to obtain a segmented threshold interval in the total threshold, wherein the narrower the segmented interval, the denser the segmented interval, the higher the prediction accuracy.
Further, the specific method of S900 is:
s901, calculating the proportion of each path flow when congestion occurs at a single intersection;
and S902, carrying out segmentation data distribution according to the total amount of the path flow and the total amount threshold interval when congestion occurs, and obtaining proportional threshold intervals in different segments of the total amount threshold interval.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the intersection congestion prediction method based on the commuting path selection behavior analysis has important significance for dredging, preventing and treating the traffic congestion of the peak commuting.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an intersection congestion prediction method based on commuting path selection behavior analysis in embodiment 1 of the present invention;
fig. 2 is a logic diagram of an intersection congestion prediction method based on commuting path selection behavior analysis in embodiment 1 of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to solve the problems in the prior art, the embodiment of the invention provides an intersection congestion prediction method based on commuting path selection behavior analysis.
Example 1
The embodiment discloses an intersection congestion prediction method based on commuting path selection behavior analysis, as shown in fig. 1, including:
s100, selecting a target area to be analyzed and a starting-destination range; in this embodiment, the target area and the origin-destination point (e.g., commuting origin-destination point with obvious area coverage) are reasonably selected, so that the analysis and prediction accuracy can be effectively improved.
S200, obtaining a regional passing intersection data set, and judging whether the crossing passing data to be analyzed meet the regional passing intersection data set.
S300, when the crossing traffic data to be analyzed meet the regional crossing traffic data set, judging whether the crossing traffic data to be analyzed has an illegal crossing.
In some preferred embodiments, as in fig. 2, when the intersection traffic data to be analyzed does not satisfy the regional traffic intersection data set, such as in an actual traffic management application: when some non-key intersections are not in the conditions that the analysis range is negligible, some specific intersections are not automatically generated, and the like, editing operation can be carried out through manual addition, deletion and the like, and the data group of the passing intersection entering the area is generated again.
S400, when the traffic data of the intersection to be analyzed does not have the non-feasible intersection, obtaining a feasible path of a start-to-end point of a target area to be analyzed, and judging whether the non-feasible path exists according to the feasible path of the start-to-end point.
Preferably, when the traffic data of the intersection to be analyzed has an infeasible intersection, such as when the actual traffic management work is performed, and restrictions such as restriction on certain intersections and prohibition of traffic (such as prohibition of left turn) between specific intersections are performed, the data of the infeasible intersection is cleaned and labeled, and the feasible path of the origin and destination of the target area to be analyzed is obtained again. Preferably, the cleaned data may also be stored for later machine learning database.
S500, when the feasible path of the origin-destination point has no infeasible path, obtaining a final feasible path data set; and when the feasible paths at the origin-destination points have the infeasible paths, cleaning the infeasible paths and obtaining the final feasible path data set again. Preferably, the cleaned data may also be stored for later machine learning database.
S600, extracting traffic flow and intersection congestion data of each traffic path of the origin-destination point of the area to be analyzed, and judging whether the traffic flow and the intersection congestion data of each traffic path of the origin-destination point of the area to be analyzed meet preset requirements of the traffic flow and the congestion amount of each traffic path of the origin-destination point.
Preferably, in this embodiment, the total traffic flow and the unit section flow of each commuting route within the target analysis range are extracted in units of time sections and days as time periods, so as to generate the data of the congested intersection. When the flow of each passing path at the origin-destination point of the area to be analyzed and the congestion data at the intersection cannot meet the preset requirements of the flow and the congestion amount of each passing path at the origin-destination point, data resampling can be carried out by prolonging a time interval or a time period, and the flow of each passing path at the origin-destination point of the area to be analyzed and the congestion data at the intersection are obtained again.
S700, when the traffic of each passing path at the origin-destination point of an area to be analyzed and congestion data of an intersection meet the minimum requirements of the traffic and the congestion amount of each passing path at the origin-destination point, a congestion intersection and unit-time traffic proportion incidence matrix of the path is generated.
Preferably, in this embodiment, the congested intersection is taken as an axis Y, the target analyzes the total amount of the total path traffic and the ratio of the single path traffic as an axis X, and a two-dimensional matrix of the congested intersection and the relevance in the target range is generated.
And S800, analyzing a preset congestion threshold value, and determining a maximum possible congestion trigger flow total quantity segmented threshold value interval.
The specific method of S800 is as follows:
s801, calculating the average value of the total amount of the path flow when congestion occurs at a single intersection, wherein x is:
wherein x is1，x2.，。。。。，，xnRepresenting the flow in n time periods of a single intersection.
S802, removing the accidental special case data which are too small and too large to obtain a threshold interval T of the total flow when the road junction congestion occurss-Tb；
S803, carrying out threshold segmentation to obtain a segmented threshold interval T in the total thresholds1-Tb1，…，Tsn-TbnThe more narrow and dense the segment interval, the higher the prediction accuracy.
And S900, carrying out proportion analysis on the path flow, and determining the maximum possible congestion triggering path flow proportion subsection threshold interval.
Preferably, the specific method of S900 is:
s901, calculating the proportion of each path flow when congestion occurs at a single intersection; the calculation formula is as follows:
Psn＝sn/xn
wherein s isnFor single crossing path traffic, xnFor the entire area path traffic.
S902, segmenting data distribution according to the total amount of the path flow and the total amount threshold interval when congestion occurs, and obtaining proportion threshold intervals Ps in different segments of the total amount threshold interval1-Pb1，…，Psn-Pbn. S1000, predicting the occurrence of the road junction congestion based on the maximum possible congestion triggering flow total threshold, the maximum possible congestion triggering path flow proportion threshold and the real-time each path flow.
The invention discloses an intersection congestion prediction method based on commuting path selection behavior analysis. And secondly, extracting the total traffic flow and unit interval flow of each commuting path in the target analysis range by dividing the commuting path into time interval units and taking days as time periods. And thirdly, forming a composite analysis matrix of the intersection, the traffic proportion of the single commuting path and the total traffic in the full analysis range by correlating the occurrence of the intersection congestion state in the time period. Based on the data support, the preset congestion threshold is analyzed, the maximum possible congestion trigger flow total segmented threshold interval is determined, the path flow is subjected to proportion analysis, the maximum possible congestion trigger path flow proportion segmented threshold interval is determined, and the correlation cause of different commuting path flow ratios and intersection congestion can be deduced under different flow total amounts, so that the prediction capability is provided for the intersection congestion possible occurrence in real time. By using the method, traffic management departments can not only clearly and quantitatively master the traffic of each commuting path, but also guide the deployment and the dispatching of preset police strength through the advance prediction of congestion, and more scientifically develop traffic management work.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (9)
1. A method for predicting intersection congestion based on commuting path selection behavior analysis is characterized by comprising the following steps:
s100, selecting a target area to be analyzed and a starting-destination range;
s200, acquiring a regional passing intersection data set, and judging whether crossing passing data to be analyzed meet the regional passing intersection data set;
s300, when the crossing traffic data to be analyzed meet the regional crossing traffic data set, judging whether the crossing traffic data to be analyzed has an impassable road junction;
s400, when the traffic data of the intersection to be analyzed does not have the non-feasible intersection, obtaining a feasible path of a start-to-end point of a target area to be analyzed, and judging whether the non-feasible path exists according to the feasible path of the start-to-end point;
s500, when the feasible path of the origin-destination point has no infeasible path, obtaining a final feasible path data set;
s600, extracting traffic flow and intersection congestion data of each traffic path of the origin-destination point of the area to be analyzed, and judging whether the traffic flow and the intersection congestion data of each traffic path of the origin-destination point of the area to be analyzed meet preset requirements of the traffic flow and the congestion amount of each traffic path of the origin-destination point;
s700, when the traffic of each passing path at the origin-destination point of an area to be analyzed and congestion data of an intersection meet the minimum requirements of the traffic and the congestion amount of each passing path at the origin-destination point, generating a congestion intersection and path unit time traffic proportion incidence matrix;
s800, analyzing a preset congestion threshold value, and determining a maximum possible congestion trigger flow total segmented threshold value interval;
s900, carrying out proportion analysis on the path flow, and determining a maximum possible congestion triggering path flow proportion subsection threshold interval;
s1000, predicting the occurrence of the road junction congestion based on the maximum possible congestion triggering flow total threshold, the maximum possible congestion triggering path flow proportion threshold and the real-time each path flow.
2. The method for predicting intersection congestion based on analysis of commuting path selection behavior as claimed in claim 1, wherein in S300, when the intersection traffic data to be analyzed does not satisfy the regional traffic intersection data set, editing operation can be performed by manual addition, deletion, etc., and the entering regional traffic intersection data set is regenerated.
3. The method for predicting traffic congestion at an intersection based on analysis of commuting path selection behavior as claimed in claim 1, wherein in S400, when the traffic data at the intersection to be analyzed has an infeasible intersection, the data at the infeasible intersection is cleaned and labeled, and the feasible path at the destination area to be analyzed is obtained again.
4. The method for predicting congestion at an intersection based on commute path selection behavior analysis as claimed in claim 1, wherein in S500, when there is an infeasible path at the origin-destination feasible path, the infeasible path is cleaned, and the final feasible path data set is retrieved.
5. The method for predicting the congestion at the intersection based on the analysis of the commuting path selection behavior as claimed in claim 1, wherein in S600, the total traffic flow and the unit interval flow of each commuting path within the target analysis range are extracted in units of time intervals and days as time periods, so as to generate the congestion intersection data.
6. The method for predicting traffic jam at intersection based on analysis of commuting path selection behavior as claimed in claim 1, wherein in S600, when the traffic flow and the traffic jam data at the origin and destination of the area to be analyzed cannot meet the preset requirements of the traffic flow and the traffic jam amount at the origin and destination, data resampling can be performed by extending the time interval or the time period, and the traffic flow and the traffic jam data at the origin and destination of the area to be analyzed can be obtained again.
7. The method for predicting congestion at an intersection based on analysis of commuting path selection behavior as claimed in claim 1, wherein in S700, a congested intersection is taken as a Y-axis, a total amount of full-path traffic and a ratio of single-path traffic are analyzed by a target as an X-axis, and a two-dimensional matrix of congested intersections and correlations in a target range is generated.
8. The intersection congestion prediction method based on the commuting path selection behavior analysis as claimed in claim 1, wherein the specific method of S800 is as follows:
s801, calculating the average value of the total path flow when congestion occurs at a single intersection;
s802, removing undersized and oversized sporadic special case data to obtain a traffic total threshold interval when the road junction congestion occurs;
and S803, performing threshold segmentation to obtain a segmented threshold interval in the total threshold, wherein the narrower the segmented interval, the denser the segmented interval, the higher the prediction accuracy.
9. The intersection congestion prediction method based on the commuting path selection behavior analysis as claimed in claim 1, wherein the specific method of S900 is:
s901, calculating the proportion of each path flow when congestion occurs at a single intersection;
and S902, carrying out segmentation data distribution according to the total amount of the path flow and the total amount threshold interval when congestion occurs, and obtaining proportional threshold intervals in different segments of the total amount threshold interval.
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