WO2023279603A1 - Procédé et appareil pour identifier un étranglement de trafic de réseau routier, et dispositif électronique - Google Patents

Procédé et appareil pour identifier un étranglement de trafic de réseau routier, et dispositif électronique Download PDF

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WO2023279603A1
WO2023279603A1 PCT/CN2021/128433 CN2021128433W WO2023279603A1 WO 2023279603 A1 WO2023279603 A1 WO 2023279603A1 CN 2021128433 W CN2021128433 W CN 2021128433W WO 2023279603 A1 WO2023279603 A1 WO 2023279603A1
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congestion
section
propagation
road
starting
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PCT/CN2021/128433
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English (en)
Chinese (zh)
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魏磊
梅雨
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阿波罗智联(北京)科技有限公司
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications

Definitions

  • the present disclosure relates to the technical field of data processing, and in particular to the technical field of intelligent transportation. Specifically, the present disclosure relates to a road network traffic bottleneck identification method, device and electronic equipment.
  • Traffic bottlenecks are the main cause of traffic congestion. Whether the traffic bottlenecks can be effectively identified and reasonably relieved is the key to improving the efficiency of the traffic network. Therefore, how to effectively identify traffic bottlenecks has become an important issue in the field of intelligent transportation.
  • the disclosure provides a road network traffic bottleneck identification method, device and electronic equipment.
  • a method for identifying traffic bottlenecks in a road network comprising:
  • the bottleneck road section is determined from the congestion starting road section based on the congestion comprehensive state value.
  • a device for identifying a road network traffic bottleneck comprising:
  • Congestion propagation relationship determination module is configured to use any congested road section in a plurality of congested road sections as the congested starting road section, and determines the congestion propagation relationship caused by the congested starting road section based on the congestion correlation between each congested road section;
  • the congestion propagation state value determination module is configured to determine the congestion propagation state value of the congestion starting point road section based on the congestion situation value of each congestion propagation section included in the congestion propagation relationship, and the historical congestion correlation between adjacent congestion propagation sections ;
  • the congestion comprehensive state value determination module is configured to determine the congestion comprehensive state value of the congestion starting road section based on the congestion situation value and the congestion propagation state value of the congestion starting road section;
  • the bottleneck road section identification module is configured to determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value.
  • an electronic device includes:
  • a memory communicatively coupled to at least one of the processors; wherein,
  • 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 traffic bottlenecks in the road network.
  • a non-transitory computer-readable storage medium storing computer instructions, wherein, when the computer instructions are executed by a computer, the aforementioned method for identifying traffic bottlenecks in a road network is implemented.
  • a computer program product including a computer program.
  • the computer program When the computer program is executed by a processor, the above method for identifying traffic bottlenecks in a road network is implemented.
  • FIG. 1 is a schematic flowchart of a method for identifying road network traffic bottlenecks provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of the congestion correlation between CRS1 and a congested road section in an example provided by an embodiment of the present disclosure
  • Fig. 3 is a schematic diagram of the congestion correlation between CRS2 and the congested road section in an example provided by the embodiments of the present disclosure
  • FIG. 4 is a schematic diagram of the congestion correlation between CRS3 and a congested road section in an example provided by an embodiment of the present disclosure
  • FIG. 5 is a schematic diagram of the congestion correlation between CRS4 and a congested road section in an example provided by an embodiment of the present disclosure
  • FIG. 6 is a congestion multi-propagation diagram constructed in an example provided by an embodiment of the present disclosure.
  • FIG. 7 is a congestion propagation diagram constructed in an example provided by an embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of a method for determining the congestion correlation of a congested section provided in an embodiment of the present disclosure
  • FIG. 9 is a schematic flowchart of a specific implementation manner of a method for identifying road network traffic bottlenecks provided by an embodiment of the present disclosure.
  • Fig. 10 is a schematic structural diagram of a road network traffic bottleneck identification device provided according to the present disclosure.
  • Fig. 11 is a block diagram of an electronic device used to implement the method for identifying a traffic bottleneck in a road network according to an embodiment of the present disclosure.
  • the traffic bottleneck identification methods for urban road networks are generally only based on the congestion degree of the road section itself, and the congestion situation is evaluated according to the average travel time, travel speed, etc., and the most congested road section is considered to be the bottleneck in the urban road network.
  • the root cause of traffic congestion but the current traffic bottleneck identification method does not consider the congestion propagation effect. If the congestion propagation effect can be taken into consideration to identify traffic bottlenecks, the accuracy of traffic bottleneck identification can be improved. Improve the operational efficiency of the transportation network to provide a better foundation.
  • the method, device and electronic device for identifying road network traffic bottlenecks provided in the embodiments of the present application aim to solve at least one of the above technical problems in the prior art.
  • Fig. 1 shows a schematic flowchart of a method for identifying road network traffic bottlenecks provided by an embodiment of the present disclosure. As shown in Fig. 1 , the method may mainly include:
  • Step S110 Taking any congested road section among the plurality of congested road sections as the congestion starting road section, and determining the congestion propagation relationship caused by the congestion starting road section based on the congestion correlation between each congested road section.
  • the congested road section may be a congested road section in the road network, and the congested road section may be determined by observing the congestion situation of the road section.
  • the congested road section may affect the operation of the traffic flow in the upstream and downstream sections, and the congestion in the congested road section may spread to other road sections.
  • Congestion correlation can be used to characterize whether there is congestion spread among congested sections.
  • each congested road section may become a traffic bottleneck and cause congestion to spread to other road sections, each congested road section can be used as the starting point of congestion, and the congestion propagation relationship caused by it can be determined.
  • Step S120 Based on the congestion situation value of each congestion propagation section included in the congestion propagation relationship and the historical congestion correlation between adjacent congestion propagation sections, determine the congestion propagation state value of the congestion starting section.
  • the congestion propagation relationship may include multiple congestion propagation sections, and the congestion situation value may be used to represent the actual congestion situation of the congestion propagation section in the current observation period.
  • the historical congestion correlation is the congestion correlation between the congestion propagation sections in the historical observation period before the current observation period.
  • the congestion propagation state value of the congestion starting road section can be used to characterize the congestion propagation effect caused by the congestion starting road section.
  • Step S130 Based on the congestion situation value and the congestion propagation state value of the congestion starting road section, determine the congestion comprehensive state value of the congestion starting road section.
  • Step S140 Determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value.
  • the congestion comprehensive state value determined by the congestion propagation state value of the congestion starting road section and the congestion situation value of the congestion starting road section is to take the congestion propagation effect and the current congestion situation into comprehensive consideration, and pass the congestion comprehensive state value To determine the bottleneck section, it can improve the accuracy of traffic bottleneck identification.
  • the congestion propagation relationship caused by the congestion starting road section is determined based on the congestion correlation between each congested road section, and based on the congestion propagation relationship contained in each congestion propagation relationship.
  • the congestion situation value of the propagation road section, and the historical congestion correlation between adjacent congestion propagation road sections determine the congestion propagation state value of the congestion starting road section, and determine the congestion starting road section based on the congestion situation value of the congestion starting road section and the congestion propagation state value.
  • the congestion comprehensive state value of so as to determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value. Based on this scheme, the effect of congestion propagation can be included in the comprehensive consideration of traffic bottleneck identification, which improves the accuracy of traffic bottleneck identification and provides a better basis for improving the efficiency of traffic network operation by sorting out traffic bottlenecks.
  • the congestion propagation relationship caused by the congestion start section is determined based on the congestion correlation between each congestion section, including;
  • the congestion propagation relationship caused by the congestion starting section is determined.
  • the constructed congestion propagation map includes all the congestion road sections with congestion correlation.
  • the target congestion propagation path is the path that contains the most congested road sections in the congestion propagation graph, which can guarantee the maximum propagation range of congestion, so the congestion propagation relationship can be determined according to the target congestion propagation path, that is, all congested road sections in the target congestion propagation path are congested Spread the relationship.
  • constructing a congestion propagation map corresponding to the congestion starting section including:
  • the congestion propagation graph is constructed by using the congestion starting section as the root node, and the first congestion propagation section and the second congestion propagation section as leaf nodes.
  • the congestion starting road section when constructing the congestion propagation graph, can be used as the root node, and the first congestion propagation road section that has congestion correlation with the congestion starting road section can be found as the first-level leaf node, and then the first-level leaf node can be searched for.
  • the second congestion propagation section where the leaf nodes have congestion correlation is used as the second-level leaf node, and the next-level leaf nodes are searched repeatedly until all the leaf nodes are found, and the construction of the congestion propagation graph is completed.
  • Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6 and Fig. 7 show the specific process of constructing the congestion propagation map.
  • FIG. 2 is a schematic diagram of the congestion correlation between CRS1 and the congested road section.
  • CRS1 is the congestion origin road section
  • CRS2, CRS3, and CRS4 are the first congestion propagation sections that have congestion correlation with CRS1.
  • FIG. 3 is a schematic diagram of the congestion correlation between CRS2 and the congested section. As shown in FIG. 3 , CRS2 is the first congestion transmission section, and CRS5, CRS6, and CRS7 are the second congestion transmission sections associated with CRS2.
  • FIG. 4 is a schematic diagram of the congestion correlation between CRS3 and the congested section. As shown in FIG. 4 , CRS3 is the first congestion transmission section, and CRS1, CRS9, and CRS8 are the second congestion transmission sections associated with CRS3.
  • FIG. 5 is a schematic diagram of the congestion correlation between CRS4 and the congested section. As shown in FIG. 5 , CRS4 is the first congestion transmission section, and CRS1, CRS10, CRS11, and CRS12 are the second congestion transmission section associated with CRS4.
  • a schematic diagram of the congestion correlation between CRS1 and the congested road section that is, Figure 2
  • a schematic diagram of the congestion correlation between CRS2 and the congested road section that is, Figure 3
  • a schematic diagram of the congestion correlation between CRS3 and the congested road section that is, Figure 4
  • a schematic diagram of the congestion correlation between CRS4 and the congested road section The schematic diagram of the congestion correlation (ie, Figure 5), the same nodes are superimposed and combined to construct a congestion multi-propagation graph.
  • the congestion starting section is used as the root node
  • the first congestion propagation section and the second congestion propagation section are used as leaf nodes
  • the nodes are connected by directed edges.
  • Figure 6 is the constructed congestion multi-propagation map.
  • the congestion propagation map in this example is shown in FIG. 7 .
  • the congestion propagation state value of the congestion starting section is determined ,include:
  • the congestion propagation state value of the congestion propagation section can be determined based on the historical congestion correlation of the next-level congestion propagation section and the congestion situation value of the next-level congestion propagation section, so it can be determined from the target congestion propagation path. From the congestion propagation section corresponding to the outer leaf node, the congestion situation value of each congestion propagation section is calculated sequentially until the congestion propagation state value of the congestion starting section is calculated.
  • the congestion propagation section corresponding to the outermost leaf node in the target congestion propagation path does not have a next-level congestion propagation section, that is, its congestion propagation state value is zero, the congestion situation value can be directly used as the congestion propagation state value .
  • the congestion propagation of the congestion propagation section is determined based on the historical congestion correlation between the congestion propagation section and the next-level congestion propagation section in the target congestion propagation path and the congestion situation value of the next-level congestion transmission section Status values, including:
  • the congestion propagation state value of the congestion propagation section is determined.
  • the historical propagation probability can be determined according to the historical congestion correlation, and the historical propagation probability can reflect the probability of congestion propagation between adjacent congestion propagation sections.
  • the historical propagation probability of the congestion propagation segment and the next-level congestion propagation segment in the target congestion propagation path is determined, including:
  • the historical propagation probability of the congestion propagation section and the next level of congestion propagation section in the target congestion propagation path is determined.
  • the historical observation period may include multiple observation periods before the current observation period.
  • the congestion correlation between the congestion propagation sections in each historical observation cycle is the historical congestion correlation.
  • the historical observation period in which there is a congestion correlation between the congestion propagation section and the next-level congestion transmission section in the target congestion propagation path can be determined as the target period, and then the number of the target period in the total number of historical observation periods can be determined as the target period.
  • the proportion is determined as the historical spread probability.
  • the historical propagation probability can be determined by Equation 1 as follows:
  • k and i are congestion propagation sections with congestion correlation
  • P ki represents the historical propagation probability of congestion transmission section k and congestion transmission section i
  • N represents the total number of historical observation periods
  • s represents any one of the historical observation periods
  • the congestion comprehensive state value can be determined by the following formula 2:
  • k and i are congestion transmission sections with congestion correlation
  • C k represents the congestion comprehensive state value of congestion transmission section k
  • C i represents the congestion comprehensive state value of congestion transmission section i
  • P ki represents the congestion transmission section k
  • S k represents the congestion situation value of the congestion transmission section k.
  • the above method also includes:
  • the congestion situation value of the congestion propagation path is determined.
  • the congestion duration, traffic delay, and congestion queue length are parameters that characterize the congestion situation.
  • the congestion situation value can be determined by the following formula 3:
  • S k represents the congestion situation value of the congestion propagation section k
  • CT represents the congestion duration
  • ⁇ 1 represents the weight coefficient of the congestion duration
  • TT represents traffic delay
  • ⁇ 2 represents the weight coefficient of traffic delay
  • QL represents the length of the congestion queue
  • ⁇ 3 represents the weight coefficient of the congestion queue length
  • the bottleneck road section is determined from the congestion starting road section based on the congestion comprehensive state value, including any of the following;
  • the congestion starting road sections are sorted, and the congestion starting road sections with a preset number before the sorting are determined as bottleneck road sections.
  • the bottleneck road section may be determined from the congestion starting road section based on the congestion comprehensive state value. Specifically, a preset value may be set, and the congestion starting road section whose congestion comprehensive state value is higher than the preset value is determined as the bottleneck road section.
  • the congestion starting road sections based on the congestion comprehensive state value, and determine the congestion starting road sections with a preset number before the sorting as the bottleneck road section, such as determining the congestion starting road section with the highest congestion comprehensive state value as the bottleneck road section.
  • the above method also includes:
  • the congestion correlation between each congested road segment is determined.
  • the vehicles passing through each congested road section in the current observation period can be counted, and the congestion correlation can be calculated based on the number of identical vehicles passing through two congested road sections in the current observation period.
  • the congestion correlation value can be calculated by the following formula 4:
  • a and B represent congested road sections
  • N AB represents the number of vehicles passing through both congested road section A and congested road section B in the current observation period
  • N A represents the number of vehicles passing through congested road section A in the current observation period
  • DOR AB represents The congestion correlation value when the congestion propagates from the congested section A to the congested section B corresponds to the directed edge in the congestion propagation graph.
  • DOR BA indicates the congestion correlation value when the congestion spreads from the congested road section B to the congested road section A
  • a and B represent the congested road sections
  • N AB represents the vehicles that pass through both the congested road section A and the congested road section B in the current observation period
  • N B represents the number of vehicles passing through the congested road section B in the current observation period.
  • the total number of vehicles passing through the congested road section can be determined according to the number of reported trajectories of floating vehicles and the average penetration rate of floating vehicles.
  • a threshold ⁇ , ⁇ [0,1] may be set, and a congested section whose congestion correlation value is higher than the threshold ⁇ is determined to have a congestion correlation.
  • FIG. 8 shows a schematic diagram of a manner of determining the congestion correlation of a congested road section provided in an embodiment of the present disclosure.
  • CRS1, CRS2, CRS3 and CRS4 are all congested road sections, wherein the congestion occurrence time of CRS1 is 07:00, the congestion occurrence time of CRS2 is 07:15, and the congestion occurrence time of CRS3 is 07:20 , the congestion of CRS4 occurs at 07:30.
  • the congestion occurrence times of CRS1, CRS2, CRS3, and CRS4 belong to the same observation period.
  • the above method also includes:
  • the congested road section is determined from the observed road section.
  • the congestion index of the observed road section can be calculated based on the average speed of vehicles passing through the observed road section, so as to determine the congested road section from the observed road section according to the congestion index.
  • the congestion index can be calculated by the following formula 6:
  • TPI represents the congestion index
  • v actual represents the actual average speed of vehicles in the observed road section
  • v free represents the free flow speed of the road section.
  • the historical average speed of the collected road section can be taken, and the 85% quantile value of the historical average speed can be used as the corresponding free flow speed.
  • the congestion index can be compared with a preset congestion index threshold, and the observed road section whose congestion index is greater than the congestion index threshold is determined as a congested road section.
  • FIG. 9 shows a schematic flowchart of a specific implementation of a method for identifying road network traffic bottlenecks provided by an embodiment of the present disclosure.
  • the original trajectory cleaning is to perform error processing on the trajectory reported by the vehicle, and process possible data errors, such as speed errors, update time interval errors, and position errors, to improve the accuracy of trajectory data.
  • Congested section detection that is, to determine whether the observed section is a congested section by calculating the congestion index (TPI).
  • the congestion correlation constraints include time constraints and demand overlap constraints.
  • the time constraints are to analyze the correlation of the trajectory data collected in the current observation period, and the demand overlap constraints are to calculate the congestion correlation value between congested sections.
  • the congestion correlation value the congestion correlation between the congested road sections can be determined, so as to construct the congestion propagation map, and the congestion propagation relationship can be determined from the congestion propagation map.
  • the congestion charge is the value of the congestion situation, which can be calculated based on the duration of the congestion, traffic delays, and the length of the congestion queue.
  • the total congestion charge is the comprehensive congestion state value, which can be calculated based on the congestion situation value and the congestion propagation probability. Based on the comprehensive state value of congestion, the identification result of the road network bottleneck can be determined.
  • FIG. 10 shows a schematic structural diagram of a road network traffic bottleneck identification device provided by an embodiment of the present disclosure.
  • the road network traffic bottleneck identification Apparatus 1000 may include:
  • Congestion propagation relation determination module 1010 is configured to use any congested road section in the plurality of congested road sections as the congested starting road section, and determine the congestion propagation relationship caused by the congested starting road section based on the congestion correlation between each congested road section;
  • the congestion propagation state value determination module 1020 is configured to determine the congestion propagation state of the congestion starting point road section based on the congestion situation value of each congestion propagation section included in the congestion propagation relationship, and the historical congestion correlation between adjacent congestion propagation sections value;
  • the congestion comprehensive state value determination module 1030 is configured to determine the congestion comprehensive state value of the congestion starting road section based on the congestion situation value and the congestion propagation state value of the congestion starting road section;
  • the bottleneck road section identification module 1040 is configured to determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value.
  • the device by using any congested road section as the congestion starting road section, determines the congestion propagation relationship caused by the congestion starting road section based on the congestion correlation between each congested road section, and based on each congestion propagation relationship contained in the congestion propagation relationship.
  • the congestion situation value of the propagation road section, and the historical congestion correlation between adjacent congestion propagation road sections determine the congestion propagation state value of the congestion starting road section, and determine the congestion starting road section based on the congestion situation value of the congestion starting road section and the congestion propagation state value
  • the congestion comprehensive state value of so as to determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value. Based on this scheme, the effect of congestion propagation can be included in the comprehensive consideration of traffic bottleneck identification, which improves the accuracy of traffic bottleneck identification and provides a better basis for improving the efficiency of traffic network operation by sorting out traffic bottlenecks.
  • the congestion propagation relationship determination module determines the congestion propagation relationship caused by the congestion start road section based on the congestion correlation between each congestion road section, it is configured to;
  • the congestion propagation relationship caused by the congestion starting section is determined.
  • the congestion propagation relationship determination module is configured to:
  • the congestion propagation graph is constructed by using the congestion starting section as the root node, and the first congestion propagation section and the second congestion propagation section as leaf nodes.
  • the congestion propagation state value determination module is configured as:
  • the congestion propagation state value determination module determines the congestion of the congestion propagation section based on the historical congestion correlation of the congestion propagation section and the next-level congestion propagation section in the target congestion propagation path and the congestion situation value of the next-level congestion propagation section When propagating state values, it is configured as:
  • the congestion propagation state value of the congestion propagation section is determined.
  • the congestion propagation state value determination module determines the relationship between the congestion propagation section and the next-level congestion propagation section in the target congestion propagation path based on the historical congestion correlation between the congestion propagation section and the next-level congestion propagation section in the target congestion propagation path.
  • the historical propagation probability is configured as:
  • the historical propagation probability of the congestion propagation section and the next level of congestion propagation section in the target congestion propagation path is determined.
  • the identification device for the above-mentioned road network traffic bottleneck also includes:
  • the congestion situation value determining module is configured to determine the congestion situation value of the congestion propagation section based on the congestion duration, traffic delay, and congestion queue length of the congestion propagation section.
  • the bottleneck identification module is configured as any of the following:
  • the congestion starting road sections are sorted, and the congestion starting road sections with a preset number before the sorting are determined as bottleneck road sections.
  • the identification device for the above-mentioned road network traffic bottleneck also includes:
  • the congestion correlation determination module is configured to determine the congestion correlation between each congested road section based on the number of identical vehicles passing through each congested road section in the current observation period.
  • the identification device for the above-mentioned road network traffic bottleneck also includes:
  • the congested road section determination module is configured to determine the congested road section from the observed road sections based on the average speed of vehicles passing through the observed road section.
  • the above-mentioned modules of the road network traffic bottleneck identification device in the embodiment of the present disclosure have the function of implementing the corresponding steps of the road network traffic bottleneck identification method in the embodiment shown in FIG. 1 .
  • This function may be implemented by hardware, or may be implemented by executing corresponding software on the hardware.
  • the hardware or software includes one or more modules corresponding to the above functions.
  • the above-mentioned modules may be software and/or hardware, and each of the above-mentioned modules may be realized independently, or multiple modules may be integrated and realized.
  • For the functional description of each module of the above road network traffic bottleneck identification device please refer to the corresponding description of the road network traffic bottleneck identification method in the embodiment shown in FIG. 1 , which will not be repeated here.
  • the acquisition, storage and application of the user's personal information involved are in compliance with relevant laws and regulations, and do not violate public order and good customs.
  • the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.
  • the electronic device includes: at least one processor; and a memory connected in communication with the at least one processor; wherein, 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.
  • this electronic device determines the congestion propagation relationship caused by the congestion starting road section based on the congestion correlation between each congestion road section by taking any congested road section as the congestion starting road section, and based on the congestion propagation relationship contained in the congestion propagation relationship.
  • the congestion situation value of each congestion propagation section, and the historical congestion correlation between adjacent congestion transmission sections determine the congestion propagation status value of the congestion starting section, and determine the congestion based on the congestion situation value and the congestion propagation status value of the congestion starting section
  • the congestion comprehensive state value of the starting road section so as to determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value. Based on this scheme, the effect of congestion propagation can be included in the comprehensive consideration of traffic bottleneck identification, which improves the accuracy of traffic bottleneck identification and provides a better basis for improving the efficiency of traffic network operation by sorting out traffic bottlenecks.
  • the readable storage medium is a non-transitory computer-readable storage medium storing computer instructions, wherein when the computer instructions are executed by a computer, the method for identifying road network traffic bottlenecks provided by the embodiments of the present disclosure is implemented.
  • this readable storage medium determines the congestion propagation relationship caused by the congestion starting road section based on the congestion correlation between each congested road section by using any congested road section as the congestion starting road section, and based on the congestion propagation relationship.
  • the congestion situation value of each congestion propagation section included, and the historical congestion correlation between adjacent congestion propagation sections determine the congestion propagation state value of the congestion starting section, based on the congestion situation value and the congestion propagation state value of the congestion starting section, Determining the congestion comprehensive state value of the congestion starting road section, so as to determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value.
  • the effect of congestion propagation can be included in the comprehensive consideration of traffic bottleneck identification, which improves the accuracy of traffic bottleneck identification and provides a better basis for improving the efficiency of traffic network operation by sorting out traffic bottlenecks.
  • the computer program product includes a computer program, and when the computer program is executed by a processor, the method for identifying a road network traffic bottleneck provided by an embodiment of the present disclosure is implemented.
  • this computer program product determines the congestion propagation relationship caused by the congestion starting road section based on the congestion correlation between each congested road section by using any congested road section as the congestion starting road section, based on the congestion propagation relationship containing The congestion situation value of each congestion propagation section, and the historical congestion correlation between adjacent congestion transmission sections, determine the congestion propagation state value of the congestion starting section, based on the congestion situation value and the congestion propagation state value of the congestion starting section, determine The congestion comprehensive state value of the congestion starting road section, so as to determine the bottleneck road section from the congestion starting road section based on the congestion comprehensive state value.
  • the effect of congestion propagation can be included in the comprehensive consideration of traffic bottleneck identification, which improves the accuracy of traffic bottleneck identification and provides a better basis for improving the efficiency of traffic network operation by sorting out traffic bottlenecks.
  • FIG. 11 shows a schematic block diagram of an example electronic device 2000 that may be used to implement embodiments of the present disclosure.
  • Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers.
  • Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices.
  • the components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.
  • the device 2000 includes a computing unit 2010 that can be executed according to a computer program stored in a read-only memory (ROM) 2020 or loaded from a storage unit 2080 into a random access memory (RAM) 2030. Various appropriate actions and treatments. In the RAM 2030, various programs and data necessary for the operation of the device 2000 can also be stored.
  • the calculation unit 2010, the ROM 2020 and the RAM 2030 are connected to each other through the bus 2040.
  • An input/output (I/O) interface 2050 is also connected to bus 2040 .
  • the I/O interface 2050 includes: an input unit 2060, such as a keyboard, a mouse, etc.; an output unit 2070, such as various types of displays, speakers, etc.; a storage unit 2080, such as a magnetic disk, an optical disk, etc. ; and a communication unit 2090, such as a network card, a modem, a wireless communication transceiver, and the like.
  • the communication unit 2090 allows the device 2000 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.
  • Computing unit 2010 may be various general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of computing units 2010 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc.
  • the calculation unit 2010 executes the identification method of road network traffic bottleneck provided in the embodiment of the present disclosure.
  • the method for identifying road network traffic bottlenecks provided in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 2080 .
  • part or all of the computer program may be loaded and/or installed on the device 2000 via the ROM 2020 and/or the communication unit 2090.
  • the computer program When the computer program is loaded into the RAM 2030 and executed by the computing unit 2010, one or more steps of the method for identifying road network traffic bottlenecks provided in the embodiments of the present disclosure can be executed.
  • the computing unit 2010 may be configured in any other appropriate manner (for example, by means of firmware) to execute the method for identifying road network traffic bottlenecks provided in the embodiments of the present disclosure.
  • programmable processor can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.
  • Program codes for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, so that the program code, when executed by the processor or controller, makes the functions/functions specified in the flowchart and/or block diagram Action is implemented.
  • 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.
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • a 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing.
  • machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read only memory
  • EPROM or flash memory erasable programmable read only memory
  • CD-ROM compact disk read only memory
  • magnetic storage or any suitable combination of the foregoing.
  • the systems and techniques described herein 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 the user. ); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • a keyboard and pointing device eg, a mouse or a trackball
  • Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.
  • the systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system.
  • the components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN) and the Internet.
  • a computer system may include clients and servers.
  • Clients and servers are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other.
  • the server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.
  • steps may be reordered, added or deleted using the various forms of flow shown above.
  • each step described in the present disclosure may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in the present disclosure can be achieved, no limitation is imposed herein.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention concerne un procédé et un appareil pour identifier un étranglement de trafic de réseau routier (1000), et un dispositif électronique (200). Le procédé d'identification d'étranglement du trafic de réseau routier consiste à : prendre l'une quelconque de multiples sections de route encombrées en tant que section de route de point de départ d'encombrement et déterminer, sur la base d'une association d'encombrement entre des sections de route encombrées, une relation de propagation d'encombrement provoquée par la section de route de point de départ d'encombrement; déterminer une valeur d'état de propagation d'encombrement de la section de route de point de départ d'encombrement sur la base du niveau d'encombrement de chaque section de route de propagation d'encombrement contenue dans la relation de propagation d'encombrement et d'une association d'encombrement historique entre des sections de route de propagation d'encombrement adjacentes; déterminer une valeur d'état d'encombrement complète de la section de route de point de départ d'encombrement sur la base du niveau d'encombrement de la section de route de point de départ d'encombrement et de la valeur d'état de propagation d'encombrement; et déterminer la section de route d'étranglement dans la section de route de point de départ d'encombrement sur la base de la valeur d'état d'encombrement complète.
PCT/CN2021/128433 2021-07-09 2021-11-03 Procédé et appareil pour identifier un étranglement de trafic de réseau routier, et dispositif électronique WO2023279603A1 (fr)

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