CN113506439A - Road network traffic bottleneck identification method and device and electronic equipment - Google Patents
Road network traffic bottleneck identification method and device and electronic equipment Download PDFInfo
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Abstract
The disclosure provides a road network traffic bottleneck identification method, a road network traffic bottleneck identification device and electronic equipment, and relates to the technical field of data processing, in particular to the technical field of intelligent traffic. The specific implementation scheme is as follows: taking any congested road section in the multiple congested road sections as a congestion starting point road section, and determining a congestion propagation relation caused by the congestion starting point road section based on congestion relevance among the congested road sections; determining a congestion propagation state value of a congestion starting point road section based on congestion condition values of the congestion propagation road sections contained in the congestion propagation relation and historical congestion relevance between adjacent congestion propagation road sections; determining a congestion comprehensive state value of the congestion starting point road section based on the congestion condition value and the congestion propagation state value of the congestion starting point road section; and determining a bottleneck road section from the congestion starting point road section based on the congestion comprehensive state value. In the scheme, the congestion propagation effect is comprehensively considered, the accuracy of traffic bottleneck identification is improved, and a better basis is provided for improving the operation efficiency of a traffic network.
Description
Technical Field
The disclosure relates to the technical field of data processing, particularly to the technical field of intelligent traffic, and particularly relates to a road network traffic bottleneck identification method and device and electronic equipment.
Background
Traffic jam as an urban 'chronic disease' seriously affects the production and life quality of residents, causes resource waste and economic loss, and is a long-term problem along with urban development.
The traffic bottleneck is a main reason for causing traffic jam, and whether the traffic bottleneck can be effectively distinguished and reasonably debugged is a key for improving the operation efficiency of a traffic network. Therefore, how to effectively identify the traffic bottleneck becomes an important problem in the field of intelligent transportation.
Disclosure of Invention
In order to solve at least one of the above drawbacks, the present disclosure provides a road network traffic bottleneck identification method, device and electronic device.
According to a first aspect of the present disclosure, there is provided a road network traffic bottleneck identification method, including:
taking any congested road section in the multiple congested road sections as a congestion starting point road section, and determining a congestion propagation relation caused by the congestion starting point road section based on congestion relevance among the congested road sections;
determining a congestion propagation state value of a congestion starting point road section based on congestion condition values of the congestion propagation road sections contained in the congestion propagation relation and historical congestion relevance between adjacent congestion propagation road sections;
determining a congestion comprehensive state value of the congestion starting point road section based on the congestion condition value and the congestion propagation state value of the congestion starting point road section;
and determining a bottleneck road section from the congestion starting point road section based on the congestion comprehensive state value.
According to a second aspect of the present disclosure, there is provided a road network traffic bottleneck identification device, comprising:
the congestion propagation relation determining module is used for determining a congestion propagation relation caused by a congestion starting point road section based on congestion relevance among the congestion road sections by taking any congestion road section in the multiple congestion road sections as the congestion starting point road section;
the congestion propagation state value determining module is used for determining the congestion propagation state value of the congestion starting point road section based on the congestion condition value of each congestion propagation road section contained in the congestion propagation relation and the historical congestion relevance between the adjacent congestion propagation road sections;
the congestion comprehensive state value determining module is used for determining a congestion comprehensive state value of the congestion starting point road section based on the congestion condition value and the congestion propagation state value of the congestion starting point road section;
and the bottleneck road section identification module is used for determining a bottleneck road section from the congestion starting point road section based on the congestion comprehensive state value.
According to a third aspect of the present disclosure, there is provided an electronic apparatus comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor, and the instructions are executable by the at least one processor to enable the at least one processor to execute the road network traffic bottleneck identification method.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the above road network traffic bottleneck identification method.
According to a fifth aspect of the present disclosure, a computer program product is provided, which comprises a computer program, and the computer program realizes the above road network traffic bottleneck identification method when being executed by a processor.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
The technical scheme provided by the disclosure has the following beneficial effects:
according to the scheme provided by the embodiment of the disclosure, any congested road section is used as a congestion starting point road section, a congestion propagation relation caused by the congestion starting point road section is determined based on congestion relevance among the congested road sections, a congestion propagation state value of the congestion starting point road section is determined based on a congestion condition value of each congestion propagation road section contained in the congestion propagation relation and historical congestion relevance among adjacent congestion propagation road sections, and a congestion comprehensive state value of the congestion starting point road section is determined based on the congestion condition value and the congestion propagation state value of the congestion starting point road section, so that a bottleneck road section is determined from the congestion starting point road section based on the congestion comprehensive state value. Based on the scheme, the congestion propagation effect can be taken into comprehensive consideration of traffic bottleneck identification, the accuracy of the traffic bottleneck identification is improved, and a better basis is provided for improving the traffic network operation efficiency by combing the traffic bottleneck.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flow chart of a road network traffic bottleneck identification method according to an embodiment of the present disclosure;
fig. 2 is a schematic view of congestion association between CRS1 and congested road segments in an example provided by the embodiment of the present disclosure;
fig. 3 is a schematic view of congestion association between CRS2 and congested road segments in an example provided by the embodiment of the present disclosure;
fig. 4 is a schematic view of congestion association between CRS3 and congested road segments in an example provided by the embodiment of the present disclosure;
fig. 5 is a schematic view of congestion association between CRS4 and congested road segments in an example provided by the embodiment of the present disclosure;
fig. 6 is a constructed congestion multiple propagation map in an example provided by an embodiment of the present disclosure;
fig. 7 is a constructed congestion propagation map in one example provided by an embodiment of the present disclosure;
FIG. 8 is a schematic illustration of a manner of determining congestion relevance for congested road segments as provided in an embodiment of the present disclosure;
fig. 9 is a flowchart illustrating a specific implementation of a road network traffic bottleneck identification method according to an embodiment of the present disclosure.
Fig. 10 is a schematic structural diagram of an identification device for a road network traffic bottleneck provided by the present disclosure;
fig. 11 is a block diagram of an electronic device for implementing the road network traffic bottleneck identification method according to the embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
At present, the method for identifying the traffic bottleneck of the urban road network is generally based on the congestion degree of road sections per se, the congestion condition is evaluated according to the average travel time, the travel speed and the like, the most congested road section is considered to be the bottleneck in the urban road network and is the root cause of the traffic congestion of the road network, but the congestion propagation effect is not considered in the current traffic bottleneck identification mode, if the congestion propagation effect can be considered to identify the traffic bottleneck, the accuracy of the traffic bottleneck identification can be improved, and a better basis is provided for improving the operation efficiency of the traffic network by combing the traffic bottleneck.
The embodiment of the application provides a road network traffic bottleneck identification method, a road network traffic bottleneck identification device and electronic equipment, and aims to solve at least one of the above technical problems in the prior art.
Fig. 1 shows a schematic flow diagram of a road network traffic bottleneck identification method provided by an embodiment of the present disclosure, and as shown in fig. 1, the method mainly includes:
step S110: and determining a congestion propagation relation caused by the congestion starting point road section based on the congestion relevance between the congestion road sections by taking any congestion road section in the multiple congestion road sections as the congestion starting point road section.
The congested road section can be a road section with congestion in a road network, and the congested road section can be determined by observing congestion conditions of the road section.
The congested road section may affect the operation of the traffic flow of the upstream and downstream road sections, and the congestion of the congested road section may be spread to other road sections. Congestion correlations may be used to characterize whether there is a spread of congestion between congested road segments.
Because each congested road section is likely to become a traffic bottleneck and cause congestion to propagate to other road sections, each congested road section can be respectively used as a congestion starting point road section, and a congestion propagation relation caused by the congestion starting point road section can be determined.
Step S120: and determining the congestion propagation state value of the congestion starting point road section based on the congestion condition value of each congestion propagation road section contained in the congestion propagation relation and the historical congestion relevance between the adjacent congestion propagation road sections.
The congestion propagation relationship may include a plurality of congestion propagation road segments, and the congestion condition value may be used to represent an actual congestion condition of the congestion propagation road segment in the current observation period.
The historical congestion association is a congestion association between congestion propagation sections in a historical observation period before the current observation period.
The congestion propagation state value of the congestion starting point road section can be used for representing the congestion propagation effect caused by the congestion starting point road section.
Step S130: and determining a congestion comprehensive state value of the congestion starting point road section based on the congestion condition value and the congestion propagation state value of the congestion starting point road section.
Step S140: and determining a bottleneck road section from the congestion starting point road section based on the congestion comprehensive state value.
In the embodiment of the disclosure, the congestion comprehensive state value determined by the congestion propagation state value of the congestion starting point road segment and the congestion situation value of the congestion starting point road segment comprehensively considers the congestion propagation effect and the current congestion situation, and the bottleneck road segment is determined by the congestion comprehensive state value, so that the accuracy of traffic bottleneck identification can be improved.
The method provided by the embodiment of the disclosure determines a congestion propagation relationship caused by a congestion start point road section based on congestion relevance among the congestion road sections by taking any congestion road section as the congestion start point road section, determines a congestion propagation state value of the congestion start point road section based on a congestion condition value of each congestion propagation road section included in the congestion propagation relationship and historical congestion relevance among adjacent congestion propagation road sections, and determines a congestion comprehensive state value of the congestion start point road section based on the congestion condition value and the congestion propagation state value of the congestion start point road section, thereby determining a bottleneck road section from the congestion start point road section based on the congestion comprehensive state value. Based on the scheme, the congestion propagation effect can be taken into comprehensive consideration of traffic bottleneck identification, the accuracy of the traffic bottleneck identification is improved, and a better basis is provided for improving the traffic network operation efficiency by combing the traffic bottleneck.
In an alternative mode of the disclosure, a congestion propagation relationship caused by a congestion starting point road segment is determined based on congestion relevance between congestion road segments, including;
constructing a congestion propagation map corresponding to a congestion starting point road section based on congestion relevance among all congestion road sections;
determining a target congestion propagation path containing the most congested road sections from a congestion propagation map;
and determining a congestion propagation relation caused by the congestion starting point road section based on the target congestion propagation path.
In the embodiment of the disclosure, the constructed congestion propagation map includes all congestion sections having congestion relevance.
The target congestion propagation path is a path with the most congestion sections in the congestion propagation map, and the maximum propagation range of congestion can be ensured, so that the congestion propagation relationship can be determined according to the target congestion propagation path, namely the congestion propagation relationship exists among all the congestion sections in the target congestion propagation path.
In an optional manner of the present disclosure, constructing a congestion propagation map corresponding to a congestion starting point road segment includes:
determining a congestion road section having congestion relevance with a congestion starting point road section as a first congestion propagation road section;
determining a congestion road segment having congestion relevance with the first congestion propagation road segment as a second congestion propagation road segment;
taking the second congestion propagation road segment as a first congestion propagation road segment, and repeatedly executing the step of determining the congestion road segment having congestion association with the first congestion propagation road segment as the second congestion propagation road segment until no congestion road segment having congestion association with the first congestion propagation road segment exists;
and constructing a congestion propagation map by taking the congestion starting point road section as a root node and the first congestion propagation road section and the second congestion propagation road section as leaf nodes.
In the embodiment of the disclosure, when a congestion propagation map is constructed, a congestion starting point road segment may be used as a root node, a first congestion propagation road segment having congestion relevance with the congestion starting point road segment is searched as a first-stage leaf node, then a second congestion propagation road segment having congestion relevance with the first-stage leaf node is searched as a second-stage leaf node, and the next-stage leaf node is repeatedly searched until all leaf nodes are searched, so that the construction of the congestion propagation map is completed.
As an example, fig. 2, 3, 4, 5, 6, and 7 illustrate a specific process of constructing a congestion propagation map.
Fig. 2 is a schematic view of congestion association between the CRS1 and a congested link, where as shown in fig. 2, the CRS1 is a congestion starting point link, and the CRS2, CRS3, and CRS4 are first congestion propagation links having congestion association with the CRS 1.
Fig. 3 is a schematic view of congestion association between the CRS2 and a congested link, where as shown in fig. 3, the CRS2 is a first congestion propagation link, and the CRS5, CRS6, and CRS7 are second congestion propagation links having congestion association with the CRS 2.
Fig. 4 is a schematic view of congestion association between the CRS3 and a congested link, where as shown in fig. 4, the CRS3 is a first congestion propagation link, and the CRS1, CRS9, and CRS8 are second congestion propagation links having congestion association with the CRS 3.
Fig. 5 is a schematic view of congestion association between the CRS4 and a congestion section, and as shown in fig. 5, the CRS4 is a first congestion propagation section, and the CRS1, CRS10, CRS11, and CRS12 are second congestion propagation sections having congestion association with the CRS 4.
The congestion association schematic diagram (namely, fig. 2) of the CRS1 and the congested road section, the congestion association schematic diagram (namely, fig. 3) of the CRS2 and the congested road section, the congestion association schematic diagram (namely, fig. 4) of the CRS3 and the congestion association schematic diagram (namely, fig. 5) of the CRS4 and the congested road section are superposed and combined by the same node, so that the congestion multi-propagation diagram is constructed. In the congestion multi-propagation graph, a congestion starting point road section is used as a root node, a first congestion propagation road section and a second congestion propagation road section are used as leaf nodes, and the nodes are connected through directed edges.
Fig. 6 is a constructed congestion multiple propagation map. The congestion multi-propagation map may have a ring structure, for example, a ring structure is formed between the CRS1 and the CRS3, and between the CRS1 and the CRS4, and since the congestion propagation from the CRS1 to the outside is considered in this example, the directional edge pointing to the CRS1 may be deleted, so that the congestion propagation map is obtained. Fig. 7 shows a congestion propagation map in this example.
In an alternative aspect of the present disclosure, determining a congestion propagation state value of a congestion start point link based on a congestion situation value of each congestion propagation link included in a congestion propagation relationship and a historical congestion correlation between adjacent congestion propagation links includes:
and determining the congestion propagation state value of the congestion propagation road section from the congestion propagation road section corresponding to the outermost leaf node in the target congestion propagation path in sequence based on the historical congestion relevance between the congestion propagation road section and the next level congestion propagation road section in the target congestion propagation path and the congestion condition value of the next level congestion propagation road section until the congestion propagation state value of the congestion starting point road section is determined.
In the embodiment of the disclosure, the congestion propagation state value of the congestion propagation road segment may be determined based on the historical congestion relevance of the next congestion propagation road segment and the congestion condition value of the next congestion propagation road segment, so that the congestion condition value of each congestion propagation road segment may be calculated sequentially from the congestion propagation road segment corresponding to the outermost leaf node in the target congestion propagation path until the congestion propagation state value of the congestion starting point road segment is calculated.
In actual use, because the congestion propagation section corresponding to the outermost leaf node in the target congestion propagation path does not have the next-level congestion propagation section, that is, the congestion propagation state value is zero, the congestion condition value can be directly used as the congestion propagation state value.
In an optional manner of the present disclosure, determining a congestion propagation state value of a congestion propagation road segment based on historical congestion relevance between the congestion propagation road segment and a next level congestion propagation road segment in a target congestion propagation path and a congestion situation value of the next level congestion propagation road segment includes:
determining historical congestion propagation probabilities of the congestion propagation road section and a next-level congestion propagation road section in the target congestion propagation path based on historical congestion relevance of the congestion propagation road section and the next-level congestion propagation road section in the target congestion propagation path;
and determining the congestion propagation state value of the congestion propagation road section based on the historical propagation probability and the congestion condition value of the next level congestion propagation road section.
In the embodiment of the disclosure, the historical propagation probability may be determined according to historical congestion relevance, and the historical propagation probability may reflect the probability that congestion propagation exists between adjacent congestion propagation road sections.
Specifically, the method for determining the historical congestion propagation probability of the congestion propagation road segment and the next level congestion propagation road segment in the target congestion propagation path based on the historical congestion relevance of the congestion propagation road segment and the next level congestion propagation road segment in the target congestion propagation path comprises the following steps:
determining a historical observation period with congestion relevance between a congestion propagation road section in a plurality of historical observation periods and a next-level congestion propagation road section in a target congestion propagation path as a target period;
and determining the historical propagation probability of the congestion propagation road section and the next level congestion propagation road section in the target congestion propagation path based on the ratio of the target period to the historical observation period.
Wherein the historical observation period may include a plurality of observation periods prior to the current observation period. And congestion relevance between congestion propagation road sections in each historical observation period is historical congestion relevance.
In the embodiment of the disclosure, a historical observation period in which congestion relevance exists between a congestion propagation road section and a next-level congestion propagation road section in a target congestion propagation path may be determined as a target period, and then a ratio of the number of the target periods to the total number of the historical observation periods is determined as a historical propagation probability.
As an example, the historical propagation probability may be determined by equation 1 as follows:
equation 1:
wherein k and i are congestion propagation sections with congestion relevance, and PkiThe historical propagation probabilities of a congestion propagation section k and a congestion propagation section i are shown, N represents the total number of historical observation periods, d represents any one of the historical observation periods, and X represents the historical propagation probability of the congestion propagation section k and the congestion propagation section i in a certain historical observation period when congestion correlation exists between the congestion propagation section k and the congestion propagation section i in the certain historical observation periodsWhen congestion related information does not exist in the congestion propagation link k and the congestion propagation link i in a certain historical observation period, X is 1s＝0。
As an example, the congestion comprehensive status value may be determined by the following formula 2:
equation 2:
wherein k and i are congestion propagation sections with congestion relevance, and CkA congestion integrated state value, C, representing a congestion propagation link kiA congestion general state value, P, representing a congestion propagation link ikiTable congestion propagation probability k and historical propagation probability, S, of congestion propagation link ikA congestion status value representing a congestion propagation link k.
In an optional mode of the present disclosure, the method further includes:
and determining a congestion condition value of the congestion propagation road section based on the congestion duration, the traffic delay and the congestion queue length of the congestion propagation road section.
The congestion duration, the traffic delay and the congestion queue length are parameters representing congestion conditions, and the congestion condition value in this example can be determined by the following formula 3:
equation 3: sk＝ω1CT+ω2TT+ω3QL
Wherein S iskA value representing the congestion situation of the congestion propagation section k, CT represents the congestion duration, omega1Weight coefficient representing congestion duration, TT represents traffic delay, ω2Weight coefficient representing traffic delay, QL represents congestion queue length, ω3The weight coefficient representing the length of the congestion queue can be configured as omega1、ω2、ω3Specific value of (a), such that ω1+ω2+ω3＝1。
In an optional mode of the disclosure, a bottleneck road section is determined from a congestion starting point road section based on a congestion comprehensive state value, and the method includes any one of the following steps;
determining a congestion starting point road section with a congestion comprehensive state value higher than a preset value as a bottleneck road section;
and sequencing the congestion starting point road sections in a sequence from high to low based on the congestion comprehensive state value, and determining the congestion starting point road sections with the preset number at the front in the sequence as bottleneck road sections.
In the embodiment of the disclosure, the bottleneck road section can be determined from the congestion starting point road section based on the congestion comprehensive state value. Specifically, a preset value may be set, and a congestion start road segment having a congestion integrated state value higher than the preset value may be determined as the bottleneck road segment.
In actual use, the congestion starting point road segments can be sorted based on the congestion comprehensive state values, the congestion starting point road segments with the preset number in the sorting process are determined as bottleneck road segments, and for example, the congestion starting point road segment with the highest congestion comprehensive state value is determined as the bottleneck road segment.
In an optional mode of the present disclosure, the method further includes:
and determining the congestion relevance among the congested road sections based on the number of the same vehicles passing through each congested road section in the current observation period.
In the embodiment of the application, the vehicles passing through each congested road section in the current observation period can be counted, and the congestion relevance is calculated based on the number of the same vehicles passing through two congested road sections in the current observation period.
Specifically, the congestion related value may be calculated by the following formula 4:
formula (II)
Wherein A, B denotes a congested link, NABIndicates the number of vehicles passing through both the congested section A and the congested section B in the current observation period, NAIndicating the number of vehicles, DOR, passing through the congested road section A in the current observation periodABThe congestion related value indicates a congestion state when the congestion is propagated from the congestion link a to the congestion link B, and corresponds to a directed edge in the congestion propagation map.
Accordingly, in determining the congestion association of congestion propagating from congested link B to congested link a, the following equation 5 may be employed:
equation 5:
wherein, DORBAA congestion related value indicating a time when congestion propagates from the congested link B to the congested link A, A, B indicating a congested link, NABIndicates the number of vehicles passing through both the congested section A and the congested section B in the current observation period, NBIndicating the number of vehicles passing through the congested road segment B in the current observation period.
In actual use, the total number of vehicles passing through the congested road section can be determined according to the reported track number of the floating vehicles and the average permeability of the floating vehicles.
After the congestion association value is calculated, whether congestion association exists may be determined based on the congestion association value. Specifically, a threshold α, α ∈ [0, 1] may be set, and a congested link having a congestion association value higher than the threshold α may be determined as having congestion association.
Fig. 8 is a schematic diagram illustrating a manner of determining congestion relevance of a congested road segment provided in the embodiment of the present disclosure. As shown in fig. 8, CRS1, CRS2, CRS3, and CRS4 are congestion links, where the congestion occurrence time of CRS1 is 07:00, the congestion occurrence time of CRS2 is 07:15, the congestion occurrence time of CRS3 is 07:20, and the congestion occurrence time of CRS4 is 07: 30. The congestion occurrence time of the CRS1, the CRS2, the CRS3 and the CRS4 belong to one observation period, the CRS1 is in congestion association with the CRS2, and the CRS1 is in congestion association with the CRS4, so that a congestion association schematic diagram of the CRS1 and other congested road sections can be constructed.
In an optional mode of the present disclosure, the method further includes:
the congested road segment is determined from the observed road segments based on an average speed of the vehicle through the observed road segments.
The congestion index of the observation road section can be calculated based on the average speed of the vehicle passing through the observation road section, so that the congestion road section can be determined from the observation road section according to the congestion index.
As an example, the congestion index may be calculated by equation 6 as follows:
equation 6:
wherein TPI represents a congestion index, vactualRepresenting the actual average speed, v, of the vehicle in the observed sectionfreeAnd representing the free flow speed of the road section, wherein the historical average speed of the collected road section can be taken, and 85% of quantiles in the historical average speed are taken as the corresponding free flow speed.
After the congestion index is calculated, the congestion index can be compared with a preset congestion index threshold, and an observation road section with the congestion index larger than the congestion index threshold is determined as a congestion road section.
As an example, fig. 9 is a flowchart illustrating a specific implementation manner of a road network traffic bottleneck identification method provided by the embodiment of the present disclosure.
As shown in fig. 9, the original trajectory cleaning is to perform error processing on the trajectory reported by the vehicle, and to process possible data errors, such as speed error, update time interval error, and position error, so as to improve the accuracy of trajectory data.
And (4) detecting the congested road section, namely, calculating a congestion index (TP I) to determine whether the observed road section is the congested road section.
And constructing a congestion propagation map, wherein congestion association constraint conditions comprise time constraint and demand overlap constraint, the time constraint is that relevance analysis is carried out on track data collected in the current observation period, and the demand overlap constraint is that congestion association values among congested road sections are calculated. And determining congestion relevance among the congested road sections according to the congestion relevance value so as to construct a congestion propagation map, and determining a congestion propagation relation from the congestion propagation map.
And identifying the bottleneck of the road network, namely identifying the bottleneck road section. The congestion cost, i.e., the congestion condition value, may be calculated based on the congestion duration, traffic delays, and congestion queue length. The total congestion cost, namely the comprehensive congestion state value, can be obtained by calculation based on the congestion condition value and the congestion propagation probability. And determining a road network bottleneck identification result based on the congestion comprehensive state value.
Based on the same principle as the method shown in fig. 1, fig. 10 is a schematic structural diagram of a road network traffic bottleneck identification device provided by an embodiment of the present disclosure, and as shown in fig. 10, the road network traffic bottleneck identification device 1000 may include:
a congestion propagation relation determining module 1010, configured to determine a congestion propagation relation caused by a congestion start point road segment based on congestion relevance between congestion road segments by using any one of a plurality of congestion road segments as a congestion start point road segment;
the congestion propagation state value determining module 1020 determines a congestion propagation state value of a congestion starting point road segment based on the congestion condition values of the congestion propagation road segments included in the congestion propagation relationship and historical congestion correlations between adjacent congestion propagation road segments;
the congestion comprehensive state value determining module 1030 is configured to determine a congestion comprehensive state value of the congestion starting point road section based on the congestion condition value and the congestion propagation state value of the congestion starting point road section;
and the bottleneck road section identification module 1040 is configured to determine a bottleneck road section from the congestion starting point road section based on the congestion integrated state value.
The device provided by the embodiment of the disclosure determines a bottleneck section from a congestion starting point section based on a congestion comprehensive state value by taking any congestion section as the congestion starting point section, determining a congestion propagation relation caused by the congestion starting point section based on the congestion relevance between the congestion sections, determining the congestion propagation state value of the congestion starting point section based on the congestion condition value of each congestion propagation section contained in the congestion propagation relation and the historical congestion relevance between adjacent congestion propagation sections, and determining the congestion comprehensive state value of the congestion starting point section based on the congestion condition value and the congestion propagation state value of the congestion starting point section. Based on the scheme, the congestion propagation effect can be taken into comprehensive consideration of traffic bottleneck identification, the accuracy of the traffic bottleneck identification is improved, and a better basis is provided for improving the traffic network operation efficiency by combing the traffic bottleneck.
Optionally, the congestion propagation relationship determining module is specifically configured to determine a congestion propagation relationship caused by the congestion starting point road segment based on the congestion relevance between the congestion road segments;
constructing a congestion propagation map corresponding to a congestion starting point road section based on congestion relevance among all congestion road sections;
determining a target congestion propagation path containing the most congested road sections from a congestion propagation map;
and determining a congestion propagation relation caused by the congestion starting point road section based on the target congestion propagation path.
Optionally, the congestion propagation relation determining module is specifically configured to construct a congestion propagation map corresponding to the congestion starting point road segment;
determining a congestion road section having congestion relevance with a congestion starting point road section as a first congestion propagation road section;
determining a congestion road segment having congestion relevance with the first congestion propagation road segment as a second congestion propagation road segment;
taking the second congestion propagation road segment as a first congestion propagation road segment, and repeatedly executing the step of determining the congestion road segment having congestion association with the first congestion propagation road segment as the second congestion propagation road segment until no congestion road segment having congestion association with the first congestion propagation road segment exists;
and constructing a congestion propagation map by taking the congestion starting point road section as a root node and the first congestion propagation road section and the second congestion propagation road section as leaf nodes.
Optionally, the congestion propagation state value determining module is specifically configured to:
and determining the congestion propagation state value of the congestion propagation road section from the congestion propagation road section corresponding to the outermost leaf node in the target congestion propagation path in sequence based on the historical congestion relevance between the congestion propagation road section and the next level congestion propagation road section in the target congestion propagation path and the congestion condition value of the next level congestion propagation road section until the congestion propagation state value of the congestion starting point road section is determined.
Optionally, the congestion propagation state value determining module is specifically configured to, when determining the congestion propagation state value of the congestion propagation road segment based on the historical congestion relevance between the congestion propagation road segment and the next level congestion propagation road segment in the target congestion propagation path and the congestion condition value of the next level congestion propagation road segment:
determining historical congestion propagation probabilities of the congestion propagation road section and a next-level congestion propagation road section in the target congestion propagation path based on historical congestion relevance of the congestion propagation road section and the next-level congestion propagation road section in the target congestion propagation path;
and determining the congestion propagation state value of the congestion propagation road section based on the historical propagation probability and the congestion condition value of the next level congestion propagation road section.
Optionally, the congestion propagation state value determining module is specifically configured to, when determining the historical propagation probability of the congestion propagation road segment and the next congestion propagation road segment in the target congestion propagation path based on the historical congestion relevance between the congestion propagation road segment and the next congestion propagation road segment in the target congestion propagation path:
determining a historical observation period with congestion relevance between a congestion propagation road section in a plurality of historical observation periods and a next-level congestion propagation road section in a target congestion propagation path as a target period;
and determining the historical propagation probability of the congestion propagation road section and the next level congestion propagation road section in the target congestion propagation path based on the ratio of the target period to the historical observation period.
Optionally, the apparatus further comprises:
and the congestion condition value determining module is used for determining the congestion condition value of the congestion propagation road section based on the congestion duration, the traffic delay and the congestion queue length of the congestion propagation road section.
Optionally, the bottleneck section identification module is specifically configured to any one of:
determining a congestion starting point road section with a congestion comprehensive state value higher than a preset value as a bottleneck road section;
and sequencing the congestion starting point road sections in a sequence from high to low based on the congestion comprehensive state value, and determining the congestion starting point road sections with the preset number at the front in the sequence as bottleneck road sections.
Optionally, the apparatus further comprises:
and the congestion association determining module is used for determining congestion association among the congestion road sections based on the number of the same vehicles which pass through each congestion road section in the current observation period.
Optionally, the apparatus further comprises:
and the congested road section determining module is used for determining the congested road section from the observed road sections on the basis of the average speed of the vehicles passing through the observed road sections.
It can be understood that the modules of the road network traffic bottleneck identification device in the embodiment of the present disclosure have functions of implementing the corresponding steps of the road network traffic bottleneck identification method in the embodiment shown in fig. 1. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules can be software and/or hardware, and each module can be implemented independently or by integrating a plurality of modules. For the functional description of each module of the road network traffic bottleneck identification device, reference may be made to the corresponding description of the road network traffic bottleneck identification method in the embodiment shown in fig. 1, and details are not repeated here.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
The electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for identifying road network traffic bottlenecks according to the embodiment of the disclosure.
Compared with the prior art, the electronic equipment determines a bottleneck section from a congestion starting point section based on a congestion comprehensive state value by taking any congestion section as the congestion starting point section, determining a congestion propagation relation caused by the congestion starting point section based on the congestion relevance between the congestion sections, determining the congestion propagation state value of the congestion starting point section based on the congestion condition value of each congestion propagation section contained in the congestion propagation relation and the historical congestion relevance between adjacent congestion propagation sections, and determining the congestion comprehensive state value of the congestion starting point section based on the congestion condition value and the congestion propagation state value of the congestion starting point section. Based on the scheme, the congestion propagation effect can be taken into comprehensive consideration of traffic bottleneck identification, the accuracy of the traffic bottleneck identification is improved, and a better basis is provided for improving the traffic network operation efficiency by combing the traffic bottleneck.
The readable storage medium is a non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions are used for causing a computer to execute the method for identifying a road network traffic bottleneck provided by the embodiment of the disclosure.
The readable storage medium determines a bottleneck section from the congestion start section based on the congestion comprehensive state value by using any congestion section as the congestion start section, determining a congestion propagation relation caused by the congestion start section based on the congestion association between congestion sections, determining the congestion propagation state value of the congestion start section based on the congestion situation value of each congestion propagation section included in the congestion propagation relation and the historical congestion association between adjacent congestion propagation sections, and determining the congestion comprehensive state value of the congestion start section based on the congestion situation value of the congestion start section and the congestion propagation state value. Based on the scheme, the congestion propagation effect can be taken into comprehensive consideration of traffic bottleneck identification, the accuracy of the traffic bottleneck identification is improved, and a better basis is provided for improving the traffic network operation efficiency by combing the traffic bottleneck.
The computer program product includes a computer program, and the computer program, when executed by a processor, implements the method for identifying road network traffic bottlenecks according to the embodiments of the present disclosure.
Compared with the prior art, the computer program product determines a bottleneck section from a congestion starting point section based on a congestion comprehensive state value by taking any congestion section as the congestion starting point section, determining a congestion propagation relation caused by the congestion starting point section based on the congestion relevance between the congestion sections, determining the congestion propagation state value of the congestion starting point section based on the congestion condition value of each congestion propagation section contained in the congestion propagation relation and the historical congestion relevance between adjacent congestion propagation sections, and determining the congestion comprehensive state value of the congestion starting point section based on the congestion condition value and the congestion propagation state value of the congestion starting point section. Based on the scheme, the congestion propagation effect can be taken into comprehensive consideration of traffic bottleneck identification, the accuracy of the traffic bottleneck identification is improved, and a better basis is provided for improving the traffic network operation efficiency by combing the traffic bottleneck.
FIG. 11 illustrates a schematic block diagram of an example electronic device 2000, which can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 11, the device 2000 includes a computing unit 2010, which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)2020 or a computer program loaded from a storage unit 2080 into a Random Access Memory (RAM) 2030. In the RAM 2030, various programs and data required for the operation of the device 2000 can also be stored. The computing unit 2010, ROM 2020, and RAM 2030 are coupled to each other via bus 2040. An input/output (I/O) interface 2050 is also connected to bus 2040.
Various components in device 2000 are connected to I/O interface 2050, including: an input unit 2060 such as a keyboard, a mouse, or the like; an output unit 2070 such as various types of displays, speakers, and the like; a storage unit 2080 such as a magnetic disk, an optical disk, and the like; and a communication unit 2090, such as a network card, modem, wireless communication transceiver, etc. The communication unit 2090 allows the device 2000 to exchange information/data with other devices over a computer network, such as the internet, and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), application specific integrated circuits (AS ics), Application Specific Standard Products (ASSPs), System On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (14)
1. A road network traffic bottleneck identification method comprises the following steps:
taking any one of a plurality of congested road sections as a congestion starting point road section, and determining a congestion propagation relation caused by the congestion starting point road section based on congestion relevance between the congested road sections;
determining a congestion propagation state value of the congestion starting point road section based on the congestion condition value of each congestion propagation road section included in the congestion propagation relation and historical congestion relevance between adjacent congestion propagation road sections;
determining a congestion comprehensive state value of the congestion starting point road section based on the congestion condition value of the congestion starting point road section and the congestion propagation state value;
determining a bottleneck road section from the congestion starting point road section based on the congestion comprehensive state value.
2. The method of claim 1, wherein the determining a congestion propagation relationship caused by the congestion origin segment based on congestion correlations between the congestion segments comprises;
constructing a congestion propagation map corresponding to the congestion starting point road section based on congestion relevance among the congestion road sections;
determining a target congestion propagation path containing the most congested road sections from the congestion propagation map;
and determining a congestion propagation relation caused by the congestion starting point road section based on the target congestion propagation path.
3. The method of claim 2, wherein the constructing of the congestion propagation map corresponding to the congestion starting point road segment comprises:
determining a congestion road segment having congestion relevance with the congestion starting point road segment as a first congestion propagation road segment;
determining a congestion road segment having congestion relevance with the first congestion propagation road segment as a second congestion propagation road segment;
taking the second congestion propagation road segment as the first congestion propagation road segment, and repeatedly executing the step of determining the congestion road segment having congestion association with the first congestion propagation road segment as the second congestion propagation road segment until no congestion road segment having congestion association with the first congestion propagation road segment exists;
and constructing a congestion propagation map by taking the congestion starting point road section as a root node and the first congestion propagation road section and the second congestion propagation road section as leaf nodes.
4. The method according to claim 3, wherein the determining the congestion propagation state value of the congestion start point link based on the congestion situation value of each congestion propagation link included in the congestion propagation relationship and the historical congestion correlation between the adjacent congestion propagation links comprises:
and determining a congestion propagation state value of the congestion propagation road section from the congestion propagation road section corresponding to the outermost leaf node in the target congestion propagation path in sequence based on the historical congestion relevance between the congestion propagation road section and the next-level congestion propagation road section in the target congestion propagation path and the congestion condition value of the next-level congestion propagation road section until the congestion propagation state value of the congestion starting point road section is determined.
5. The method as claimed in claim 4, wherein said determining a congestion propagation status value for said congestion propagation segment based on said historical congestion association of said congestion propagation segment with a next level congestion propagation segment in a target congestion propagation path and a congestion situation value for said next level congestion propagation segment comprises:
determining historical congestion propagation probabilities of the congestion propagation road sections and a next-level congestion propagation road section in the target congestion propagation path based on historical congestion relevance of the congestion propagation road sections and the next-level congestion propagation road section in the target congestion propagation path;
and determining a congestion propagation state value of the congestion propagation road section based on the historical propagation probability and the congestion condition value of the next level congestion propagation road section.
6. The method of claim 5, wherein determining the historical congestion propagation probability for the congestion propagation segment and the next congestion propagation segment in the target congestion propagation path based on the historical congestion correlations of the congestion propagation segment and the next congestion propagation segment in the target congestion propagation path comprises:
determining a historical observation period with congestion relevance between the congestion propagation road section in the multiple historical observation periods and a next-level congestion propagation road section in a target congestion propagation path as a target period;
and determining the historical propagation probability of the congestion propagation road section and the next level of congestion propagation road section in the target congestion propagation path based on the ratio of the target period to the historical observation period.
7. The method of any of claims 1-6, further comprising:
and determining a congestion condition value of the congestion propagation road section based on the congestion duration, the traffic delay and the congestion queue length of the congestion propagation road section.
8. The method according to any one of claims 1-7, wherein the determining a bottleneck segment from the congestion onset segment based on the congestion general status value comprises any one of;
determining the congestion starting point road section with the congestion comprehensive state value higher than a preset value as a bottleneck road section;
and sequencing the congestion starting point road sections according to the sequence of the congestion comprehensive state values from high to low, and determining the congestion starting point road sections with the preset number at the top in the sequencing as bottleneck road sections.
9. The method according to any one of claims 1-8, further comprising:
and determining congestion relevance among the congested road sections based on the number of the same vehicles passing through the congested road sections in the current observation period.
10. The method of claims 1-9, further comprising:
determining a congested road segment from the observed road segments based on an average speed of the vehicle through the observed road segments.
11. An identification device for road network traffic bottlenecks, comprising:
the congestion propagation relation determining module is used for determining a congestion propagation relation caused by a congestion starting point road section based on congestion relevance among the congestion road sections by taking any congestion road section in a plurality of congestion road sections as the congestion starting point road section;
a congestion propagation state value determination module, configured to determine a congestion propagation state value of the congestion start point link based on a congestion condition value of each congestion propagation link included in the congestion propagation relationship and a historical congestion correlation between adjacent congestion propagation links;
the congestion comprehensive state value determining module is used for determining a congestion comprehensive state value of the congestion starting point road section based on the congestion condition value of the congestion starting point road section and the congestion propagation state value;
and the bottleneck road section identification module is used for determining a bottleneck road section from the congestion starting point road section based on the congestion comprehensive state value.
12. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
13. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.
14. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-10.
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| CN202110780338.3A CN113506439B (en) | 2021-07-09 | 2021-07-09 | Road network traffic bottleneck identification method and device and electronic equipment |
| PCT/CN2021/128433 WO2023279603A1 (en) | 2021-07-09 | 2021-11-03 | Method and apparatus for identifying road network traffic bottleneck, and electronic device |
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| CN113506439B (en) | 2022-12-09 |
| WO2023279603A1 (en) | 2023-01-12 |
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