Expressway multi-tunnel same-direction separation induction method and system

Abstract

The invention relates to the technical field of traffic data processing, and particularly discloses an expressway multi-tunnel same-direction separation induction method and system, and the method comprises the steps: S01, obtaining the vehicle flow and vehicle speed collected in real time at a distance from a fork in front of a tunnel on an expressway, so as to obtain first data; s02, acquiring the current vehicle flow and vehicle speed in the tunnel to obtain second data; s03, obtaining a first longest unit time vehicle entering the point a and a second longest unit time vehicle passing through the current point a according to the first data and the second data; and S04, based on the obtained longest vehicle in the first unit time and the longest vehicle in the second unit time, whether the maximum vehicle at the point a at the current time exceeds the maximum vehicle load in the tunnel unit time or not is judged. According to the invention, traffic information is collected and analyzed in real time, and induction information is generated and transmitted, so that the vehicle can safely and orderly run when approaching the fork in front of the tunnel.

Description

Technical Field

[0001] The invention relates to the technical field of traffic data processing, and in particular to a method and system for same-direction separation induction of multiple tunnels on a highway. Background Art

[0002] At present, the infrastructure of expressways is mainly based on the reconstruction and expansion of existing roads. The multi-tunnel mode of expressways can effectively increase the traffic capacity and is a common mode of reconstruction and expansion at this stage. However, the multi-tunnel mode will also cause a complex traffic environment. Considering the inertial thinking of drivers and passengers, if they are not effectively guided, it is easier for incidents to occur at the tunnel entrance or inside the tunnel.

[0003] The current processing methods include the coexistence of road sign prompts and navigation software prompts or one of them, so as to provide clear guidance at the front end of the fork in front of the tunnel, which can effectively reduce the time for vehicles to choose a path and reduce the number of sudden braking of vehicles, thereby avoiding sudden braking that causes vehicles behind to be unable to avoid in time, thereby causing traffic accidents.

[0004] However, this type of prompt is a passive prompt and cannot achieve good foresight. In short, the purpose of the above methods is to remind the driver to drive carefully and slow down when reaching the fork in the road, so that he can make a timely response when encountering an emergency. It cannot foresee in advance and prompt the vehicle that is about to reach the front end of the fork in front of the tunnel to slow down to ensure that the passing vehicles avoid or stagger according to the prompt, thereby preventing the occurrence of danger. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for same-direction separation induction of multiple tunnels on a highway to solve the above-mentioned problems.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for inducing same-direction separation of multiple tunnels on a highway, comprising:

[0008] S01, obtaining the vehicle flow and vehicle speed collected in real time from the fork before the tunnel on the expressway to obtain first data;

[0009] S02, obtaining the vehicle flow and vehicle speed in the tunnel to obtain second data;

[0010] S03, according to the first data and the second data, obtaining the vehicle with the longest first unit time entering point a and the vehicle with the longest second unit time passing through the current point a;

[0011] S04. Based on the obtained longest vehicle per unit time in the first unit time and the longest vehicle per unit time in the second unit time, determine whether the maximum vehicle at point a at the current time exceeds the maximum vehicle load per unit time in the tunnel. If so, determine it as a risk point.

[0012] S05. Based on the acquired risk points and according to the number of tunnel lanes, a separation strategy is executed to prompt vehicles entering point a;

[0013] The point a is the entrance of the current expressway into the tunnel.

[0014] Preferably, the vehicle flow and vehicle speed collected in real time in step S01 include:

[0015] S11, dividing the highway into several sections L1, L2, Ln, before the fork in the tunnel;

[0016] S12, based on the preset collection window period, the vehicle flow of L1, L2, and Ln is collected simultaneously, and the first vehicle of the L section is taken as the reference, and the vehicles within the unit length of 20m are regarded as a group of vehicles with the longest unit time, so as to obtain n groups of vehicles with the longest unit time;

[0017] S13, obtaining the maximum vehicle speed f of each group of vehicles with the longest unit time max and minimum vehicle speed f min ;

[0018] The total length of L1, L2, and Ln in step S11 is not less than 300 m, and the length of each L is not less than 50 m.

[0019] Preferably, the longest vehicle per unit time obtained in step S12 includes a prediction of the longest vehicle per unit time in the future, and the steps include:

[0020] S121, determining each vehicle in the n groups of vehicles with the longest unit time, and marking them as C1, C2, Cn;

[0021] S122, continuously collect n groups of vehicles with the longest unit time in four window periods, and extract the vehicle speeds for the same Cn in the four window periods to obtain vehicle speed data sets v1, v2, v3 and v4;

[0022] S123, the acceleration between the i-th window and the i+1-th window is (VI+1-vi) / Δt, and all adjacent windows are traversed to find the average value to obtain the average acceleration, where Δt is the time interval between adjacent windows;

[0023] S124. Based on the determined average acceleration of Cn, determine the maximum weighing per unit time of Ln of the adjacent point a.

[0024] Preferably, the second data in step S02 includes vehicle flow and vehicle speed within 10 m in the opposite driving direction of the tunnel with point a as the origin.

[0025] Preferably, the step S03 of determining the vehicle with the longest first unit time entering point a includes determining the entry time of the first vehicle of the adjacent point a in Ln adjacent to point a as the first entry time, and the vehicle flow on Ln within an interval of 1 minute is the maximum vehicle of the first entry time, thereby obtaining multiple maximum vehicles of entry time, namely S1, S2, Sn;

[0026] The vehicle with the longest second unit time passing through the current point a in step S03 is the largest vehicle in the sum of all lanes within 30 seconds at the same time as Sn.

[0027] Preferably, the step S04 determines whether the maximum vehicle at point a at the current time exceeds the maximum vehicle load per unit time of the tunnel, including:

[0028] S41, obtaining the largest vehicle at point a based on the first longest vehicle per unit time and the second longest vehicle per unit time;

[0029] S42, based on the preset maximum number of vehicles that can be allowed to pass through all lanes of the tunnel passing through the current point a per unit time and the maximum number of vehicles at point a, if it is a negative value, it is a risk point, if not negative, it is normal.

[0030] Preferably, the step S05 executes a separation strategy according to the number of tunnel lanes to prompt vehicles entering point a and vehicles passing through point a, including:

[0031] S51, assuming that the distance from point a is D meters, the vehicle accelerates from the fork to the speed limit V limit The acceleration required to decelerate to a stop is -b (m / s2).

[0032] S52, the time of the acceleration phase is The distance during the acceleration phase is

[0033] S53. The vehicle needs to decelerate to the speed limit before entering the tunnel. The deceleration phase time is Where V entry is the speed of the vehicle before entering the tunnel;

[0034] S54: The time required for vehicles entering point a to travel at the speed limit shall prevail.

[0035] A highway multi-tunnel same-direction separation induction system, used to run the highway multi-tunnel same-direction separation induction method described in the above technical solution, comprising:

[0036] A data collection module is provided on the highway at a predetermined distance from the fork before the tunnel, wherein the information collection device includes a single vehicle monitor, a speed sensor and a road surface condition monitor, and is used to collect the current road traffic flow, vehicle speed and road surface condition information in real time;

[0037] The data analysis module analyzes the real-time collected data, organizes the analysis data and uploads it;

[0038] The strategy decision module dynamically adjusts the induction strategy according to the evaluation results of the analysis data to prompt the vehicles entering the fork before the tunnel to slow down and the entry time.

[0039] In the above technical solution, the present invention provides a method and system for inducing same-direction separation of multiple tunnels on a highway, which has the following beneficial effects:

[0040] 1. It realizes the intelligent management and optimization of traffic conditions at highways and tunnel entrances, which helps to promote the intelligent development of the transportation industry and improve the scientific and technological level and modernization of traffic management.

[0041] 2. By breaking down the road sections into smaller pieces and setting the vehicles within a unit length as a group of vehicles with the longest unit time, the changes in vehicle flow can be more accurately understood.

[0042] 3. Obtaining the maximum and minimum speeds of each group of vehicles with the longest unit time helps to understand the driving status of the vehicles, provides reliable data support for subsequent risk assessment and separation strategies, and improves the accuracy and effectiveness of the entire induction method.

[0043] 4. By determining the speed data of each vehicle and continuously collecting multiple window cycles and calculating the average acceleration, the maximum number of vehicles in adjacent sections in the future unit time can be predicted, so that potential traffic congestion risks can be perceived in advance, providing a time advantage for timely adoption of separation strategies.

[0044] 5. The prediction method based on average acceleration improves the accuracy and reliability of the prediction, and helps to achieve more accurate traffic induction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 A flowchart provided for an embodiment of the present invention;

[0047] Figure 2 The embodiment of the present invention provides Figure 1 Flowchart of step S01;

[0048] Figure 3 The embodiment of the present invention provides Figure 2 Flowchart of step S12;

[0049] Figure 4 The embodiment of the present invention provides Figure 1 Flowchart of step S04;

[0050] Figure 5 A schematic diagram of a module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] like Figure 1-3 As shown, a method for inducing same-direction separation of multiple tunnels on a highway comprises:

[0053] S01, obtaining the vehicle flow and vehicle speed collected in real time from the fork before the tunnel on the expressway to obtain first data;

[0054] S02, obtaining the vehicle flow and vehicle speed in the current tunnel to obtain second data;

[0055] S03, according to the first data and the second data, obtaining the vehicle with the longest first unit time entering point a and the vehicle with the longest second unit time passing through the current point a;

[0056] S04. Based on the obtained longest vehicle per unit time in the first unit time and the longest vehicle per unit time in the second unit time, determine whether the maximum vehicle at point a at the current time exceeds the maximum vehicle load per unit time in the tunnel. If so, determine it as a risk point.

[0057] S05. Based on the acquired risk points and the number of tunnel lanes, a separation strategy is executed to prompt vehicles entering point a;

[0058] Among them, point a is the entrance of the current highway into the tunnel (the present application method is applicable to vehicle control at multiple points a in the tunnel).

[0059] Furthermore, the second data in step S02 includes the vehicle flow and vehicle speed within 10m in the opposite direction of the tunnel with point a as the origin. The selection of the collection range helps to more comprehensively understand the traffic conditions near the tunnel entrance, including vehicles about to enter the tunnel and vehicles just exiting the tunnel. This comprehensive data collection provides richer information for risk assessment and helps to more accurately determine whether there is a risk of traffic congestion at the tunnel entrance.

[0060] Furthermore, in step S03, the vehicle with the longest first unit time entering point a includes determining the entry time of the first vehicle of the adjacent point a in the Ln of the adjacent point a as the first entry time, and the vehicle flow on Ln within the interval of 1 minute is the maximum vehicle of the first entry time, thereby obtaining multiple maximum vehicles of the entry time, namely S1, S2, Sn; and the vehicle with the longest second unit time passing through the current point a in step S03 of the embodiment is the maximum vehicle of the sum of all lanes within 30 seconds at the same time as Sn. By determining the entry time of the first vehicle of the adjacent point a in the Ln of the adjacent point a and the vehicle flow within the interval of 1 minute, as well as the maximum vehicle of the sum of all lanes within 30 seconds at the same time, the vehicle flow information of the two key time points can be accurately obtained. It is helpful to more accurately judge whether there is a risk of traffic congestion at the tunnel entrance, and formulate a suitable separation strategy accordingly.

[0061] In the above technology, by collecting the vehicle flow and vehicle speed (first data) on the highway before the fork in the tunnel, as well as the vehicle flow and vehicle speed (second data) in the current tunnel in real time, it is possible to realize dynamic monitoring of the traffic conditions of the highway and the tunnel entrance. Thus, real-time and accurate data support is provided for traffic dispatching and management. And by calculating the longest vehicle in the first unit time entering the tunnel entrance (point a) and the longest vehicle in the second unit time passing the current point a, and comparing them with the maximum vehicle load in the tunnel unit time. Once the load is exceeded, it can be automatically determined as a risk point and an early warning is issued in time. Thus, the comprehensive application of real-time monitoring, risk assessment, early warning and intelligent separation strategies can be achieved, which helps to reduce the occurrence of traffic accidents, reduce the economic losses and social impacts caused by traffic congestion, and at the same time improve the public's satisfaction and trust in traffic management.

[0062] As an embodiment further provided by the present invention, Figure 2 As shown, the vehicle flow and vehicle speed collected in real time in step S01 include:

[0063] S11, divide the highway into several sections L1, L2, Ln, before the fork in the tunnel;

[0064] S12, based on the preset collection window period, the vehicle flow of L1, L2, and Ln is collected simultaneously, and the first vehicle of the L section is taken as the reference, and the vehicles within the unit length of 20m are regarded as a group of vehicles with the longest unit time, so as to obtain n groups of vehicles with the longest unit time;

[0065] S13, finding the maximum speed f of each group of vehicles with the longest unit time max and minimum vehicle speed f min ;

[0066] The total length of L1, L2, and Ln in step S11 is not less than 300 m, and the length of each L is not less than 50 m.

[0067] By dividing the highway before the fork in the tunnel into several sections L1, L2, and Ln, refined monitoring of traffic flow is achieved. The segmented monitoring method adopted can more accurately capture changes in traffic conditions in different sections, providing more detailed information for subsequent data analysis and traffic management.

[0068] The preset collection window period ensures the real-time and continuity of data, which helps to obtain accurate vehicle flow and speed information. At the same time, taking the first vehicle in the L section as the benchmark, vehicles within a unit length of 20m are regarded as a group of vehicles with the longest unit time. This data sampling method can reduce data fluctuations and improve data stability and accuracy.

[0069] Secondly, by obtaining the maximum speed fmax and minimum speed fmin of each group of vehicles with the longest unit time, the traffic conditions of the road section can be comprehensively evaluated. The maximum speed and minimum speed reflect the smoothness of traffic flow and the speed difference between vehicles, and can identify potential traffic bottlenecks or risk points.

[0070] As another embodiment further provided by the present invention, Figure 3 As shown, the vehicle flow and vehicle speed collected in real time in step S01, and the longest vehicle per unit time obtained in step S12 include the prediction of the longest vehicle per unit time in the future, and the steps include:

[0071] S121, determining each vehicle in the n groups of vehicles with the longest unit time, and marking them as C1, C2, Cn;

[0072] S122, continuously collect n groups of vehicles with the longest unit time in four window periods, and extract the vehicle speeds for the same Cn in the four window periods to obtain vehicle speed data sets v1, v2, v3 and v4;

[0073] S123, the acceleration between the i-th window and the i+1-th window is (VI+1-vi) / Δt, and all adjacent windows are traversed to find the average value to obtain the average acceleration, where Δt is the time interval between adjacent windows;

[0074] S124. Based on the determined average acceleration of Cn, determine the maximum weighing per unit time of Ln of the adjacent point a.

[0075] Specifically, each vehicle in the n groups of maximum vehicle volumes per unit time is uniquely identified (such as C1, C2, ..., Cn) to facilitate the subsequent tracking and analysis of the vehicle's driving trajectory. In order to capture the changing trend of vehicle speed, the n groups of maximum vehicle volumes per unit time of four window periods (each window period represents a period of time, such as 1 minute) are continuously collected. For the same vehicle (such as C1), its vehicle speed data is extracted from four different window periods to form vehicle speed data sets v1, v2, v3 and v4. The acceleration of each vehicle between adjacent window periods is calculated. The acceleration calculation formula is (vi+1-vi) / Δt, where vi and vi+1 are the vehicle speeds of the i-th and i+1-th window periods, respectively, and Δt is the time interval between adjacent window periods. Then, all adjacent window periods are traversed, the acceleration of each vehicle is calculated, and the average value is obtained to obtain the average acceleration of the vehicle. The average acceleration reflects the stability of the vehicle speed change and is a key parameter for predicting future vehicle speed. The maximum vehicle volume per unit time in the future is predicted using the obtained average acceleration.

[0076] It should be noted that the prediction provided in the above embodiment not only takes into account the speed and acceleration of the current vehicle, but also the overall dynamic characteristics of the traffic system. It predicts whether the current vehicle will become part of the first unit time maximum vehicle volume entering point a in the future through acceleration.

[0077] As another embodiment further provided by the present invention, Figure 4 As shown, step S04 determines whether the maximum vehicle at point a at the current time exceeds the maximum vehicle load per unit time in the tunnel, including:

[0078] S41, obtaining the largest vehicle at point a based on the first longest vehicle per unit time and the second longest vehicle per unit time;

[0079] S42. Subtract the maximum number of vehicles that can pass through all lanes of the tunnel at the current point a per unit time from the maximum number of vehicles at point a. If it is a negative value, it is a risk point. If it is not a negative value, it is normal.

[0080] Specifically, the maximum number of vehicles per unit time in the first unit and the maximum number of vehicles per unit time in the second unit: these two data represent the vehicle flow before entering the tunnel entrance and the vehicle flow when passing the tunnel entrance, respectively. By comparing these two data, we can understand the changes in traffic flow near the tunnel entrance. The maximum number of vehicles that can be allowed to pass through all lanes of the tunnel per unit time: this is a preset threshold, which represents the traffic capacity of the tunnel under normal circumstances. In the specific implementation process, by obtaining the maximum number of vehicles at point a based on the longest vehicle per unit time in the first unit and the longest vehicle per unit time in the second unit, and comparing it with the preset maximum number of vehicles that can be allowed to pass through all lanes of the tunnel per unit time, it is possible to accurately determine whether there is a risk of traffic congestion at the tunnel entrance. This judgment method combines the real-time collected vehicle flow data with the preset tunnel traffic capacity, which improves the accuracy and reliability of risk assessment. At the same time, timely adoption of separation strategies based on the judgment results helps to alleviate traffic congestion and improve road traffic efficiency.

[0081] As another embodiment further provided by the present invention, the separation strategy is executed according to the number of tunnel lanes in step S05 to prompt vehicles entering point a, including:

[0082] S51, assuming that the distance from point a is D meters, the vehicle accelerates from the fork to the speed limit V limit The acceleration required to decelerate to a stop is -b (m / s2).

[0083] S52, the time of the acceleration phase is The distance during the acceleration phase is

[0084] S53. The vehicle needs to decelerate to the speed limit before entering the tunnel. The deceleration phase time is Where V entry (m / s2) is the speed of the vehicle before entering the tunnel;

[0085] S54: The time required for vehicles entering point a to travel at the speed limit shall prevail.

[0086] Specifically, by measuring the number of vehicles entering point A and the number of vehicles passing through point A, the maximum number of vehicles supported by all lanes of the tunnel when passing through point A is obtained, and the delay time of vehicles entering point A is calculated, thereby generating a prompt to remind each vehicle entering point A of the entry time, thereby avoiding the peak number of vehicles passing through point A to ensure entry safety.

[0087] Embodiment 2

[0088] like Figure 4 As shown, a highway multi-tunnel same-direction separation induction system is used to run the method provided in Example 1, including.

[0089] A data collection module, wherein an information collection device is arranged at a predetermined distance from the fork before the tunnel on the expressway, the information collection device comprising a single vehicle vehicle detector, a speed sensor and a road surface condition monitor, for collecting information on current road traffic flow, vehicle speed and road surface condition in real time;

[0090] The data analysis module analyzes the real-time collected data, organizes the analysis data and uploads it;

[0091] The strategy decision module dynamically adjusts the induction strategy based on the analysis data evaluation results to prompt vehicles entering the fork before the tunnel to slow down and set the entry time.

[0092] Specifically, by refining the road section and setting the vehicles within a unit length as a group of the longest vehicles per unit time, the changes in vehicle flow can be more accurately grasped. In addition, obtaining the maximum speed and minimum speed of each group of vehicles with the longest unit time helps to understand the driving status of the vehicle. It provides reliable data support for subsequent risk assessment and separation strategies, and improves the accuracy and effectiveness of the entire induction method. And by determining each vehicle and continuously collecting speed data for multiple window cycles, calculating the average acceleration, the maximum traffic flow of adjacent sections in the future unit time can be predicted. This prediction capability enables the induction method to perceive potential traffic congestion risks in advance, providing a time advantage for timely adoption of separation strategies. At the same time, the prediction method based on average acceleration improves the accuracy and reliability of the prediction, which helps to achieve more accurate traffic induction.

[0093] It will be appreciated by those skilled in the art that embodiments of the present invention may provide methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0098] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method in the above embodiments, and the electronic device specifically includes the following contents:

[0099] Processor, memory, communications interface and bus;

[0100] Wherein, the processor, memory, and communication interface communicate with each other via the bus;

[0101] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the method in the above embodiment are implemented.

[0102] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented.

[0103] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Although the embodiment of this specification provides the method operation steps as described in the embodiment or flow chart, more or less operation steps can be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the actual pilot device or terminal product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "includes", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, product or device. Without further restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For the convenience of description, the above device is described by dividing it into various modules according to its function. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] Those skilled in the art will appreciate that the embodiments of this specification may provide methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification.

[0105] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they contradict each other. The above is only an embodiment of the embodiment of this specification and is not intended to limit the embodiment of this specification. For those skilled in the art, the embodiment of this specification may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiment of this specification shall be included within the scope of the claims of the embodiment of this specification.

Claims

1. A method for inducing same-direction separation of multiple tunnels on a highway, characterized in that: include: S01, obtaining the vehicle flow and vehicle speed collected in real time from the fork before the tunnel on the expressway to obtain first data; S02, obtaining the vehicle flow and vehicle speed in the tunnel to obtain second data; S03, according to the first data and the second data, obtaining the vehicle with the longest first unit time entering point a and the vehicle with the longest second unit time passing through the current point a; S04, based on the obtained longest vehicle per unit time in the first unit time and the longest vehicle per unit time in the second unit time, determine whether the maximum vehicle at point a at the current time exceeds the maximum vehicle load per unit time in the tunnel, and if so, determine it as a risk point; S05. Based on the acquired risk points and according to the number of tunnel lanes, a separation strategy is executed to prompt vehicles entering point a; The point a is the entrance of the current expressway into the tunnel.

2. The same-direction separation induction method for multiple tunnels on a highway according to claim 1 is characterized in that: The vehicle flow and vehicle speed collected in real time in step S01 include: S11, dividing the highway into several sections L1, L2, Ln, before the fork in the tunnel; S12, based on the preset collection window period, the vehicle flow of L1, L2, and Ln is collected simultaneously, and the first vehicle of the L section is taken as the reference, and the vehicles within the unit length of 20m are regarded as a group of vehicles with the longest unit time, so as to obtain n groups of vehicles with the longest unit time; S13, obtaining the maximum vehicle speed f of each group of vehicles with the longest unit time max and minimum vehicle speed f min ; The total length of L1, L2, and Ln in step S11 is not less than 300 m, and the length of each L is not less than 50 m.

3. The same-direction separation induction method for multiple tunnels on a highway according to claim 2 is characterized in that: The longest vehicle per unit time obtained in step S12 includes a prediction of the longest vehicle per unit time in the future, and the steps include: S121, determining each vehicle in the n groups of vehicles with the longest unit time, and marking them as C1, C2, Cn; S122, continuously collect n groups of vehicles with the longest unit time in four window periods, and extract the vehicle speeds for the same Cn in the four window periods to obtain vehicle speed data sets v1, v2, v3 and v4; S123, the acceleration between the i-th window and the i+1-th window is (VI+1 - vi) / Δt, and all adjacent windows are traversed to find the average value to obtain the average acceleration, where Δt is the time interval between adjacent windows; S124. Based on the determined average acceleration of Cn, determine the maximum weighing per unit time of Ln of the adjacent point a.

4. The same-direction separation induction method for multiple tunnels on a highway according to claim 1, characterized in that: The second data in step S02 includes the vehicle flow and vehicle speed within 10 m along the opposite driving direction of the tunnel with point a as the origin.

5. The same-direction separation induction method for multiple tunnels on a highway according to claim 1, characterized in that: The step S03 of finding the vehicle with the longest first unit time entering point a includes determining the entry time of the first vehicle of the adjacent point a in Ln adjacent to point a as the first entry time, and the vehicle flow on Ln within an interval of 1 minute as the maximum vehicle of the first entry time, thereby obtaining multiple maximum vehicles of the entry time, namely S1, S2, Sn; The vehicle with the longest second unit time passing through the current point a in step S03 is the largest vehicle in the sum of all lanes within 30 seconds at the same time as Sn.

6. The same-direction separation induction method for multiple tunnels on a highway according to claim 1, characterized in that: The step S04 determines whether the maximum number of vehicles at point a at the current time exceeds the maximum vehicle load per unit time of the tunnel, including: S41, obtaining the largest vehicle at point a based on the first longest vehicle per unit time and the second longest vehicle per unit time; S42, based on the preset maximum number of vehicles that can be allowed to pass through all lanes of the tunnel passing through the current point a per unit time and the maximum number of vehicles at point a, if it is a negative value, it is a risk point, if not negative, it is normal.

7. The same-direction separation induction method for multiple tunnels on a highway according to claim 1, characterized in that: In step S05, the separation strategy is executed according to the number of tunnel lanes to prompt vehicles entering point a and vehicles passing point a, including: S51, assuming that the distance from point a is D meters, the vehicle accelerates from the fork to the speed limit V limit The required acceleration is d, and the deceleration required to slow down to a stop is −b; S52, the time of the acceleration phase is , the distance in the acceleration phase is ; S53. The vehicle needs to decelerate to the speed limit before entering the tunnel. The deceleration phase time is , where V entry is the speed of the vehicle before entering the tunnel; S54: The time required for vehicles entering point a to travel at the speed limit shall prevail. .

8. A highway multi-tunnel same-direction separation induction system, used for running the highway multi-tunnel same-direction separation induction method according to any one of claims 1 to 7, characterized in that: include: A data collection module is provided on the highway at a predetermined distance from the fork before the tunnel, wherein the information collection device includes a vehicle detector, a speed sensor and a road surface condition monitor, and is used to collect the current road traffic flow, vehicle speed and road surface condition information in real time; The data analysis module analyzes the real-time collected data, organizes the analysis data and uploads it; The strategy decision module dynamically adjusts the induction strategy according to the evaluation results of the analysis data to prompt the vehicles entering the fork before the tunnel to slow down and the entry time.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the highway multi-tunnel same-direction separation induction method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the highway multi-tunnel same-direction separation induction method as described in any one of claims 1 to 7 are implemented.

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