Road network analysis method and system related to intelligent traffic

Abstract

The invention relates to the technical field of road network analysis, and discloses a road network analysis method and system related to intelligent traffic, and the method comprises the steps: obtaining the scale and regional features of a road network, and dividing the whole road network into three levels: a bottom-layer road network, a middle-layer road network, and a high-layer road network according to the scale and regional features of the road network; deploying an intelligent signal control module in each underlying road network, collecting real-time data of the underlying road network, and carrying out underlying road network local optimization on signal lamp periods and phase distribution according to the real-time data of the underlying road network; deploying a regional control module in the middle-layer road network, receiving the real-time data uploaded by the bottom-layer road network, calculating the overall load of the middle-layer road network through regional analysis, and carrying out coordination control on the middle-layer road network according to the overall load of the middle-layer road network; and deploying a global control module in the high-level road network, and issuing a scheduling instruction to the middle-level road network according to the real-time data and the overall load of the middle-level road network to realize the analysis and optimization of the overall road network. And greener and more intelligent urban traffic management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of road network analysis, and specifically to a road network analysis method and system related to intelligent transportation. Background Art

[0002] With the continuous acceleration of the urbanization process and the rapid growth of the motor vehicle ownership, the urban traffic pressure has become increasingly prominent, and problems such as traffic congestion, exhaust emissions, and travel delays have become more and more prominent. The traditional traffic management mode usually takes intersections as units and alleviates the vehicle congestion on local road sections through preset or simple adaptive signal timing schemes. However, this mode is often unable to cope when faced with large-scale road networks and rapidly changing traffic flows, and it is easy to achieve a localized treatment effect of "treating the head when it hurts and treating the foot when it hurts". In addition, conventional vehicle route planning mostly focuses on the shortest path or the shortest time from the starting point to the end point, and fails to effectively integrate multi-dimensional information such as dynamic signal timing, road network carrying capacity, and real-time traffic status, resulting in vehicles gathering in the same road section or intersection during peak periods, thereby exacerbating the traffic congestion in local areas.

[0003] Therefore, how to achieve the collaborative management of different levels of traffic control units at the road network analysis level and organically combine signal timing with vehicle route planning, so as to balance the traffic flow, improve the traffic efficiency, and reduce the traffic delay in a wider range has become one of the key problems in the current intelligent transportation field. If, on the basis of the mutual cooperation between the global and the local, with the help of the efficient fusion of real-time data, and through adaptive or predictive algorithms to adjust the signal scheme and path recommendation strategy in real time, the urban congestion can be greatly alleviated, the environmental load can be reduced, and a technical basis can be provided for further expanding the multi-objective optimization such as traffic safety, public transportation priority, and travel guarantee for special groups in the future. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing technical means usually lack a global planning mechanism for hierarchical cooperation. The optimization of signal timing at a single level can only cover limited intersections or small areas, while the global planning lacks a close interaction with road surface control and it is difficult to achieve the unified allocation of road network resources.

[0006] To solve the above technical problem, the present invention provides the following technical solution: A road network analysis method related to intelligent transportation, including: obtaining the scale and regional characteristics of the road network, and dividing the overall road network into three levels: the underlying road network, the middle-level road network, and the high-level road network according to the scale and regional characteristics of the road network;

[0007] Deploy intelligent signal control modules in each underlying road network, collect real-time data of the underlying road network, and perform local optimization of the signal light cycle and phase allocation for the underlying road network according to the real-time data of the underlying road network;

[0008] Deploy area control modules in the middle-level road network, receive the real-time data uploaded by the underlying road network, calculate the overall load of the middle-level road network through area analysis, and perform coordinated control on the middle-level road network according to the overall load of the middle-level road network;

[0009] Deploy global control modules in the high-level road network, issue scheduling instructions to the middle-level road network according to the real-time data and the overall load of the middle-level road network, and achieve overall road network analysis and optimization.

[0010] As a preferred solution of a road network analysis method related to intelligent transportation according to the present invention, wherein: the road network scale and regional characteristics include road structure information, historical traffic flow and load data, and geographical and regional elements;

[0011] The real-time data includes traffic flow and vehicle speed data, queue length data, vehicle data, and signal light data.

[0012] As a preferred solution of a road network analysis method related to intelligent transportation according to the present invention, wherein: the underlying road network consists of an intersection and adjacent road sections at the intersection. Set a detection period T in the underlying road network, collect real-time data of the intersection and adjacent road sections within each detection period T, and judge the congestion situation of the detection period T according to the real-time data;

[0013] Suppose there are n inlet lanes at the intersection, and obtain the average flow rate q of the i-th inlet lane during the detection period T according to the traffic flow and vehicle speed data i , and calculate the saturation degree of each inlet lane, expressed as:

[0014]

[0015] Among them, X i represents the saturation degree of the i-th inlet lane; S i represents the saturation flow rate of the i-th inlet lane, which is determined according to the lane width and signal timing standard;

[0016] Calculate the congestion degree of the intersection according to the queue length and saturation degree of the intersection, expressed as:

[0017]

[0018] Among them, C represents the congestion degree of the intersection, α represents the weight coefficient; L i represents the queue length of the i-th inlet lane; L max represents the maximum queue length; β represents the weight coefficient;

[0019] Set the maximum congestion level C through expert evaluation max , when the underlying road network detects that there is an approach lane with X i greater than or equal to 1, or the congestion level C ≥ C max , it is determined that the underlying road network is congested during the detection period T, triggering local optimization of the underlying road network, and at the same time uploading the real-time data during the detection period T to the middle-level road network to obtain the middle-level intervention coefficient.

[0020] As a preferred solution of a road network analysis method related to intelligent transportation according to the present invention, wherein: the local optimization of the underlying road network includes calculating the initial signal cycle according to the saturation of the approach lane through Webster's formula:

[0021]

[0022] wherein, R raw represents the initial signal cycle; M represents the total lost time;

[0023] Adjust the initial signal cycle according to the middle-level intervention coefficient issued by the middle-level road network to obtain the actually executed signal cycle, expressed as:

[0024] R local = clip(R raw ·K region , R old - Δ local , R old + Δ local )

[0025] wherein, R local represents the actually executed signal cycle; clip means restricting R raw ·K region to between R old - Δ local and R old + Δ local ; K region represents the middle-level intervention coefficient; Δ local represents the underlying road network adjustment limit;

[0026] According to the actually executed signal cycle R local , perform the green light duration allocation for each phase, expressed as:

[0027]

[0028] wherein, g j represents the green light duration of the jth phase; X j represents the saturation of the approach lane corresponding to phase j; m represents the total number of phases at the intersection; X kIt represents the saturation of the approach corresponding to phase k; k represents the kth phase.

[0029] As a preferred solution of a road network analysis method related to intelligent transportation according to the present invention, wherein: the coordinated control of the middle-level road network includes that the middle-level road network is composed of multiple adjacent bottom-level road networks. When the bottom-level road networks controlled by the middle-level road network request the middle-level intervention coefficient, the approach saturation of all the bottom-level road networks controlled by the middle-level road network is obtained, and the regional average saturation of the middle-level road network is calculated, which is expressed as:

[0030]

[0031] Wherein, X avg represents the regional average saturation of the middle-level road network; N represents the number of bottom-level road networks controlled by the middle-level road network; r represents the rth bottom-level road network;

[0032] Calculate the middle-level intervention coefficient K according to the regional average saturation of the middle-level road network region , which is expressed as:

[0033] K region = max(0.5, min(1.5, 1 + γ·(X avg - X0)))

[0034] Wherein, max represents taking the maximum value; min represents taking the minimum value; γ represents the coefficient for adjusting the sensitivity; X0 represents the critical saturation.

[0035] As a preferred solution of a road network analysis method related to intelligent transportation according to the present invention, wherein: the coordinated control of the middle-level road network further includes setting a green wave band in the middle-level road network, obtaining the distance d between adjacent intersections controlled by the middle-level road network ij , determining the recommended driving speed v according to the traffic flow and vehicle speed data ref , calculating the driving time for a vehicle to travel from one intersection to the next intersection:

[0036]

[0037] Wherein, T ab represents the driving time from intersection a to intersection b;

[0038] Obtain the actual executed signal light cycle R of each bottom-level intersection local , select the nearest integer to align the signal light cycle, and obtain the unified basic cycle R;

[0039] Taking the starting intersection of the green wave band as the time zero point, setting the starting time of the main phase green light of the starting intersection as 0 seconds, and calculating the phase difference according to the unified basic cycle R, which is expressed as:

[0040] Ob =(O a +T ab ) mod R

[0041] Wherein, O b represents the phase difference of intersection b; O a represents the phase difference of intersection a; mod R represents the modulo operation on the unified basic cycle R;

[0042] According to the unified basic cycle R of the green wave band, restrictions are imposed on the adjustment limit values of the underlying road network on the green wave band, expressed as:

[0043] Δ local = θ·(R - M)

[0044] Wherein, Δ local represents the adjustment limit value of the underlying road network; θ represents the adjustment coefficient; the middle-level road network will send the unified basic cycle R, the phase difference, and the restricted Δ local to the underlying road network to complete the setting of the green wave band.

[0045] As a preferred solution of a road network analysis method related to intelligent transportation according to the present invention, wherein: the high-level road network is composed of all middle-level road networks, covers the overall road network area, summarizes and stores the real-time data of the overall road network, analyzes the periodic trend and issues scheduling instructions to the middle-level road network to achieve the optimization of the overall road network analysis.

[0046] A road network analysis system related to intelligent transportation adopting any of the methods of the present invention, wherein: an intelligent signal control module, which collects the real-time data of the underlying road network and locally optimizes the signal light cycle and phase allocation of the underlying road network according to the real-time data of the underlying road network;

[0047] A regional control module, which receives the real-time data uploaded by the underlying road network, calculates the overall load of the middle-level road network through regional analysis, and coordinates and controls the middle-level road network according to the overall load of the middle-level road network;

[0048] A global control module, which issues scheduling instructions to the middle-level road network according to the real-time data and the overall load of the middle-level road network to achieve the optimization of the overall road network analysis.

[0049] A computer device, comprising: a memory and a processor; the memory stores a computer program, including: when the processor executes the computer program, the steps of any of the methods in the present invention are implemented.

[0050] A computer-readable storage medium, on which a computer program is stored, including: when the computer program is executed by a processor, the steps of any of the methods in the present invention are implemented.

[0051] Advantages of the present invention: The method of the present invention effectively avoids congestion and conflicts caused by each traffic node acting independently through hierarchical collaborative control of the bottom layer, middle layer, and high layer, combined with real-time multi-source data and adaptive algorithms. The bottom layer can slightly adjust the cycle to quickly respond to local traffic flow fluctuations. The middle layer coordinates the green wave bands of road sections and regional coordination. The high layer conducts global situation analysis and policy issuance. The three complement each other, significantly improving the overall efficiency and emergency response ability of the urban road network. Compared with traditional fixed or single-point signal control, the present invention has higher flexibility and self-learning ability in a rapidly changing traffic environment, can accurately relieve vehicle congestion during peak hours, and takes into account multiple objectives such as public transportation priority and pedestrian safety, realizing a greener and smarter urban traffic management, thus providing an efficient, safe, and sustainable solution for urban traffic modernization. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0053] Figure 1 It is a general flowchart of a road network analysis method related to intelligent transportation provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0055] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a road network analysis method related to intelligent transportation, including:

[0056] S1: Obtain the road network scale and regional characteristics, and divide the overall road network into three levels: the bottom-layer road network, the middle-layer road network, and the high-layer road network according to the road network scale and regional characteristics.

[0057] Furthermore, obtain the road network scale and regional characteristics through multiple sources such as roadside sensors and the database of the traffic management center, mainly including road structure information, historical traffic flow, and geographical and regional elements. Specifically, the road structure information includes road grades (expressways, arterial roads, secondary arterial roads, feeder roads), the number of lanes, road speed limits, section lengths, and key nodes (ramps, tunnels, bridges), etc.

[0058] The historical traffic flow covers the historical traffic volume, vehicle speed, queue length, road capacity, congestion duration, section saturation degree, etc. of the road, and is used to evaluate the immediate bearing capacity and long-term operating status of the road.

[0059] Geographical and regional elements such as regional area, population and vehicle distribution density, commercial or residential area distribution, special locations (schools, hospitals, business districts, industrial parks, etc.), and major traffic generation or attraction sources within the region.

[0060] Divide the overall road network into three levels: the underlying road network, the middle-level road network, and the high-level road network according to the road network scale and regional characteristics.

[0061] The underlying road network consists of an intersection and the adjacent sections of the intersection, and an intelligent signal control module is deployed to be responsible for collecting and processing traffic flow, queue length, vehicle waiting time, etc. of this intersection or section, and locally optimizing the signal cycle and phase allocation to strive to reduce the congestion situation at this intersection / section.

[0062] Define each intersection and its adjacent sections as an independent underlying road network unit, allowing adjacent sections to overlap between multiple underlying road network units, that is, the section data between two intersections is simultaneously adopted by the intelligent signal control modules of both intersections.

[0063] The same section of the road belongs to two underlying road network units, each having an independent "perspective" on the section data. When different intersections analyze this section of the road, they often focus on different incoming and outgoing vehicle flows. Number each section, and when uploading data, only upload the section data with the same number once.

[0064] The middle-level road network consists of one or more adjacent underlying road networks, and is responsible for comprehensively considering the traffic flow conditions, weaving section conditions, historical data, etc. of the main intersections in the region to form coordinated control of local vehicle flows. According to the overall regional traffic load situation, dynamically fine-tune the signal timing of key intersections in the region, or interact with the navigation platform to give recommended driving routes for vehicles within the road network.

[0065] It should be noted that when dividing the middle - layer road network, clustering can be carried out according to historical traffic flow, congestion period rules, vehicle type distribution, etc.; a group of intersections with relatively similar traffic conditions and high mutual influence degrees are merged together to facilitate unified scheduling at the middle - layer. Secondly, administrative divisions (streets, districts and counties) or functional areas (development zones, business districts) are often natural bases for dividing the middle - layer road network.

[0066] The high - layer consists of all middle - layer road networks, covering the overall road network area, summarizing and storing real - time data of the overall road network, and issuing corresponding scheduling instructions to the control centers in each region, such as global strategies like ensuring the smoothness of main roads during peak hours, restricting traffic flow in specific areas, or traffic dispatching around large - scale event venues.

[0067] By dividing the road network into three levels: the bottom - layer, the middle - layer, and the high - layer, it is possible to better achieve the refinement and stratification of traffic management. The bottom - layer road network focuses on the optimization of local intersections, the middle - layer road network is responsible for regional coordination, and the high - layer road network conducts global scheduling. This hierarchical structure helps to improve the efficiency and response speed of traffic management.

[0068] S2: Deploy intelligent signal control modules in each bottom - layer road network, collect real - time data of the bottom - layer road network, and perform local optimization of the signal light cycle and phase allocation for the bottom - layer road network according to the real - time data of the bottom - layer road network.

[0069] Furthermore, the real - time data includes traffic flow and vehicle speed data, queue length data, vehicle data, and signal light data.

[0070] Specifically, through detectors at the bottom - layer (such as induction loops, microwave radars, camera recognition, etc.), obtain the number of vehicles passing through a section or intersection per unit time, as well as the average vehicle speed or real - time speed distribution. Continuously monitor the position of the end of the vehicle queue, calculate the number and length of waiting vehicles to obtain queue length data. Use cameras or intelligent detection algorithms to distinguish different types of vehicles such as cars, buses, trucks, etc., and determine their driving directions (left - turn, straight - ahead, right - turn). Record the signal timing plan (green - light, yellow - light, red - light durations) and execution time periods for the current intersection, and report its actual execution status (such as whether there are faults or deviations) to the middle - layer.

[0071] Even further, based on the real - time data collected from the bottom - layer road network, evaluate the congestion situation of the corresponding intersections. It should be noted that the method of the present invention is aimed at the analysis and multi - layer collaborative optimization of road networks with conventional traffic congestion, and temporarily does not consider special events such as traffic accidents and bad weather. Special event information can be obtained by connecting the high - layer road network with external platforms (such as traffic police accident information, meteorological station data, etc.), and overall scheduling instructions are issued based on the special event information.

[0072] Specifically, a detection period T is set for the underlying road network. Real-time data of intersections and adjacent road sections are collected within each detection period T, and the congestion situation of the detection period T is judged based on the real-time data.

[0073] Suppose there are n inlet lanes at an intersection (including different turning directions, such as left turn, straight, and right turn), and each phase contains the permitted turning lane groups. The average flow rate q of the i-th inlet lane within the detection period T is obtained based on the traffic flow and vehicle speed data. i , and the saturation degree of each inlet lane is calculated, expressed as:

[0074]

[0075] Among them, X i represents the saturation degree of the i-th inlet lane; S i represents the saturation flow rate of the i-th inlet lane. The saturation flow rate can be queried through the traffic engineering manual or design specifications during road design, or traffic flow observations can be carried out on existing roads or under similar conditions:

[0076] Select a high-flow and unobstructed time period or road section ("near saturation" state) for vehicle flow statistics;

[0077] Record the maximum number of vehicles passing through per unit time, as well as the actual width of the lane and signal timing at the same time. After accumulating sufficient observation data, a more locally realistic saturation flow rate can be obtained.

[0078] The congestion degree of the intersection is calculated based on the queue length and saturation degree of the intersection, expressed as:

[0079]

[0080] Among them, C represents the congestion degree of the intersection, and α represents the weight coefficient; L i represents the queue length of the i-th inlet lane; L max represents the maximum queue length; β represents the weight coefficient.

[0081] Set the maximum value C of the congestion degree through expert evaluation max , when it is detected in the underlying road network that the X i of the inlet lane is greater than or equal to 1, or the congestion degree C ≥ C max , it is judged that the underlying road network is congested within the detection period T, triggering local optimization of the underlying road network, and at the same time uploading the real-time data within the detection period T to the middle-level road network to obtain the middle-level intervention coefficient.

[0082] Furthermore, a tunable micro interval (such as "±3 seconds" or "±5 seconds") is set for each underlying intersection to limit the adjustment range of the underlying road network and prevent a greater impact on adjacent road sections. According to the saturation degree of the inlet lane, the initial signal light cycle is calculated through Webster's formula:

[0083]

[0084] Among them, R raw represents the initial signal light cycle; M represents the total lost time. The signal light cycle refers to the total time required for all phases (green light, yellow light, red light, etc.) of a set of traffic lights to run once. The total lost time includes the sum of the yellow light duration, the all-red light duration, and the safety interval time during phase switching.

[0085] The initial signal light cycle R raw is an "optimal" cycle without other intervention constraints. This value can, under a given traffic flow, try to balance the traffic demands of each lane (or phase) as much as possible and minimize the overall vehicle delay. In reality, the intersection must cooperate with the surrounding road network or make a smooth connection with the previous cycle. Therefore, the initial signal light cycle often needs to be adjusted (such as amplitude limiting, phase timing reallocation, etc.) before it can be finally implemented.

[0086] According to the middle-level intervention coefficient issued by the middle-level road network, the initial signal light cycle is adjusted to obtain the actually implemented signal light cycle, expressed as:

[0087] R local = clip(R raw ·K region , R old -Δ local , R old +Δ local )

[0088] Among them, R local represents the actually implemented signal light cycle; clip means restricting R raw ·K region to between R old -Δ local and R old +Δ local ; K region represents the middle-level intervention coefficient; Δ local represents the bottom-level road network adjustment limit.

[0089] According to the actually implemented signal light cycle R local , the green light duration of each phase is allocated, expressed as:

[0090]

[0091] Among them, g j represents the green light duration of the jth phase; X j represents the saturation of the approach corresponding to phase j; m represents the total number of phases at the intersection; X kdenotes the saturation of the approach corresponding to phase k; k represents the k-th phase.

[0092] It should be noted that the adjustment limit value Δ of the underlying road network local adopts a hierarchical fixed limit strategy. According to the importance of the intersection, the road grade where it is located, and the traffic fluctuation range, different Δ values are assigned to different intersections local , for example, the Δ value of the core intersection of the arterial road local is set to 3 seconds, and the Δ value of the secondary arterial road or ordinary intersection local is set to 7 seconds.

[0093] By calculating the saturation of the approach and the congestion degree of the intersection, the congestion situation can be identified in a timely manner and local optimization can be triggered. At the same time, by setting the adjustment range of the underlying road network in the microcell interval, the impact on adjacent sections can be avoided, ensuring the smoothness and continuity of the optimization. According to the middle-layer intervention coefficient issued by the middle-layer road network, the signal cycle is dynamically adjusted, which can optimize the signal timing from a global perspective, reduce vehicle delay and queue length, and improve the traffic capacity of the overall road network.

[0094] S3: Deploy a regional control module in the middle-layer road network, receive the real-time data uploaded by the underlying road network, calculate the overall load of the middle-layer road network through regional analysis, and coordinate and control the middle-layer road network according to the overall load of the middle-layer road network.

[0095] Furthermore, when the underlying road network controlled by the middle-layer road network requests the middle-layer intervention coefficient, obtain the approach saturation of all underlying road networks controlled by the middle-layer road network, and calculate the regional average saturation of the middle-layer road network, which is expressed as:

[0096]

[0097] Among them, X avg represents the regional average saturation of the middle-layer road network; N represents the number of underlying road networks controlled by the middle-layer road network; r represents the r-th underlying road network. X avg is used to comprehensively evaluate the traffic pressure situation of a certain middle-layer road network range. Since the phases and lanes among the underlying road networks controlled by the middle-layer road network are not necessarily exactly the same, if a certain area contains multiple sections (or lanes), the X i value of each lane indicates its relative load. Adding these loads directly can be regarded as the total saturated occupancy of all key lanes in the area, and at the same time can reflect the overall traffic flow in the middle-layer area.

[0098] Calculate the middle-layer intervention coefficient K according to the regional average saturation of the middle-layer road network region , which is expressed as:

[0099] K region = max(0.5, min(1.5, 1 + γ·(X avg - X0)))

[0100] Among them, max represents taking the maximum value; min represents taking the minimum value; γ represents the coefficient for adjusting the sensitivity; X0 represents the critical saturation. If X avg ≥X0 indicates that the regional pressure is high and the cycle needs to be increased. K region will be > 1; if X avg <X0, then compress the cycle.

[0101] When the overall average congestion degree of a region is low, it means that most intersections are not very congested. The middle layer tends to shorten the cycle, allowing the signal lights to switch in a faster cycle, reducing the idle time of vehicles at red lights, and also improving the flexibility of pedestrian passage or other traffic flows. Therefore, the K issued by the middle layer region usually will be less than or close to 1 (such as 0.9 or 1.0), and will not arbitrarily increase the cycle.

[0102] If the regional congestion degree is generally high, then the K issued by the middle layer region is relatively large. At this time, even if there is only moderate congestion at the bottom-level intersections, multiplying by a coefficient > 1 will result in a relatively larger signal light cycle. For all intersections, the overall signal light cycle becomes longer, which also means that each key phase can be allocated more green light time to disperse the traffic flow, and at the same time the red light will also become longer. However, from a global perspective, increasing the signal light cycle is beneficial to reducing large-scale backlogs and secondary queuing. If a single intersection is still very congested, it can more easily obtain the allocated green light bonus in the large signal light cycle.

[0103] Furthermore, the coordinated control of the middle-layer road network also includes setting a green wave belt in the middle-layer road network. The green wave belt refers to a traffic control method in which, between multiple continuous roads or adjacent intersections, by uniformly or coordinately adjusting the signal light cycles and phases of these intersections, vehicles can continuously encounter green lights and reduce stop-and-wait under a certain vehicle speed or time window.

[0104] When the middle-layer road network monitors that the traffic flow at multiple adjacent intersections on a main road section is large, or it is necessary to ensure the traffic efficiency in a certain direction, it will synchronize or schedule the signal lights of these continuous intersections in a time-sharing manner.

[0105] Specifically, obtain the distance d between adjacent intersections controlled by the middle-layer road network ij , determine the recommended driving speed v according to the traffic flow and vehicle speed data ref , and calculate the driving time for a vehicle to travel from one intersection to the next intersection:

[0106]

[0107] Among them, T ab represents the driving time from intersection a to intersection b. The recommended driving speed v refCalculate based on road speed limits, historical average driving speeds, and real-time traffic flow. For the intermediate road network, calculate the speed v of the green wave belt through a weighted fusion method ref .

[0108] Obtain the actual signal light cycle R executed at each underlying intersection local , select the nearest integer to align the signal light cycle, and obtain a unified basic cycle R. For example, if the original cycle of intersection 1 is 108 seconds and the original cycle of intersection 2 is 112 seconds, both can be fine-tuned to 110 seconds, or one of them can be trimmed to 110 and the other can be adjusted to 110±2, and finally unified to 110 seconds.

[0109] Take the starting intersection of the green wave belt as the time zero point, set the starting moment of the main phase green light at the starting intersection to 0 seconds, and calculate the phase difference according to the unified basic cycle R, expressed as:

[0110] O b =(O a +T ab )modR

[0111] Among them, O b represents the phase difference of intersection b; O a represents the phase difference of intersection a; modR represents the modulo operation on the unified basic cycle R, that is, the theoretical delay value is converted into the interval [0,R). Because the signal light has a cycle of R seconds, if the phase offset exceeds R, it will cycle to the next round and finally fall within [0,R).

[0112] When setting a green wave at multiple intersections in the middle layer, a reference starting intersection a will be selected, and its O a =0 or set as a reference value, and then calculate O b downstream or upstream and downstream intersections in turn. Through the interlocking, the starting moment of the green light at each intersection can be calculated. As long as the vehicle generally maintains a driving time of T ab (that is, driving at the recommended speed), it can encounter the green light in sequence.

[0113] In practice, it is impossible to align perfectly, and the phase combination of intersection b (left turn, pedestrian red light, etc.) needs to be considered. Therefore, it is often necessary to make fine-tuning up and down or tolerate a certain error (±2 to 5 seconds) on the theoretical O b to ensure the key phases of intersection j.

[0114] Since each intersection needs to be connected to adjacent intersections, the phase offsets of a series of intersections often require algorithm iteration (such as based on space-time diagrams or computer simulations) to find the solution with the smallest global error. The iteration process can be solved using existing methods such as TRANSYT, Synchro, and SCOOT.

[0115] Based on the unified basic cycle R of the green wave band, restrictions are imposed on the adjustment limit values of the underlying road network on the green wave band, expressed as:

[0116] Δ local = θ·(R - M)

[0117] Among them, Δ local represents the adjustment limit value of the underlying road network; θ represents the adjustment coefficient; when the green wave cycle R is very long, indicating high traffic flow or strong demand on the main road, Δ local can also be increased accordingly, but it is still limited by α; when R is shortened, Δ local will become smaller accordingly to protect the green wave. Δ is dynamically calculated according to the unified basic cycle R of the green wave band local so that the underlying layer will not continuously accumulate offsets due to a certain fixed large amplitude, resulting in vehicles running a red light at the next intersection.

[0118] The middle-level road network will send the unified basic cycle R, phase difference, and the restricted Δ local to the underlying road network to complete the setting of the green wave band.

[0119] Traditional green wave band design usually bases on a fixed or small-range adaptive signal cycle, manually or with offline tools configures the cycle and phase difference of adjacent intersections, and then executes after completion; if dynamic adjustment is required, it often only makes limited responses or requires manual intervention.

[0120] The method of the present invention can quickly adapt the cycle and phase at each intersection of the underlying layer within a Δ local (the range of fine-tuning amplitude) to solve small-scale congestion; through the middle-level intervention coefficient K region , and dynamically analyzing the regional average congestion degree or saturation degree, provides directive or corrective parameters for the cycle and offset of the green wave band. During the operation of the green wave band, the underlying layer can still perform small-range adaptive fine-tuning without destroying the overall green wave structure of the main road; if there is serious local congestion, it will report to the middle-level road network, allowing it to make larger-scale or cross-intersection strategy updates within the overall planning scope of the green wave band.

[0121] S4: Deploy a global control module in the high-level road network, and issue scheduling instructions to the middle-level road network according to real-time data and the overall load of the middle-level road network to achieve overall road network analysis and optimization.

[0122] Furthermore, the high-level road network consists of all middle-level road networks, covers the overall road network area, aggregates and stores the real-time data of the overall road network, analyzes the periodic trend and issues scheduling instructions to the middle-level road network to achieve overall road network analysis and optimization.

[0123] The high-level road network aggregates regional load indicators (such as average congestion degree, traffic flow saturation, and segmented queue length) from multiple middle levels, combines with external databases (weather, holidays, major events, public transportation operations, etc.), and comprehensively perceives the overall traffic situation.

[0124] Through time series analysis models, such as LMST, etc., analyze the peak time periods of arterial roads, and record the green wave band settings in historical peak time periods. Turn on the green wave band before the peak time period arrives to prevent congestion. Secondly, connect with external platforms (such as traffic police accident information, meteorological station data, etc.) to obtain special event information, and issue dispatching instructions to the corresponding middle-level road network based on the special event information, including but not limited to:

[0125] Traffic restriction or peak shifting: During high-pollution weather, serious accidents or large-scale summits, restrict some vehicles from entering a certain area;

[0126] Public transportation priority: Issue instructions to the signal machines along the designated bus lines, requiring the middle level to allocate more green light time (achieved by increasing the upper limit of K region or Δ local );

[0127] Parking and guidance: Combine the remaining space data of parking lots, and real-time induce vehicles to go to parking lots with available spaces to relieve the traffic pressure on the road surface;

[0128] Special vehicle channels: Combine intelligent light control and V2X to guide emergency rescue vehicles or VIP channels.

[0129] The method of the present invention effectively avoids congestion and conflicts caused by each traffic node acting independently through hierarchical collaborative control of the bottom layer, middle layer, and high layer, combined with real-time multi-source data and adaptive algorithms. The bottom layer can slightly adjust the cycle to quickly respond to local traffic flow fluctuations, the middle layer coordinates the green wave band of the road section and regional coordination, and the high layer conducts global situation analysis and strategy issuance. The three complement each other, significantly improving the overall efficiency and emergency response ability of the urban road network. Compared with traditional fixed or single-point signal control, the present invention has higher flexibility and self-learning ability in a rapidly changing traffic environment, can accurately relieve vehicle congestion during peak periods, and takes into account multiple goals such as public transportation priority and pedestrian safety, realizing a more green and intelligent urban traffic management, thereby providing an efficient, safe and sustainable solution for urban traffic modernization.

[0130] Embodiment 2, in the exemplary embodiment, a road network analysis system related to intelligent transportation is further provided, including an intelligent signal control module, a regional control module, and a global control module.

[0131] The deployment location of the intelligent signal control module is at the intersections of the underlying road network, where it collects real-time data (such as vehicle flow, vehicle speed, queue length) of the current intersection or adjacent road sections, and performs adaptive signal timing and phase adjustment.

[0132] The intelligent signal control module sends real-time data packets of the underlying road network to the area control module, including traffic statistics, cycle and phase adjustment amounts, queue length, and abnormal events; when congestion cannot be handled locally or an emergency occurs (such as an accident), it triggers congestion alerts or emergency reports. It receives fine-tuning limits, priority timing parameters, or special instructions (such as the green wave period of the main road) from the area control module, and after safely parsing the instructions, it executes them locally or performs secondary fusion.

[0133] The area control module collects and fuses real-time data and congestion alerts uploaded from multiple underlying intersections to form the overall load and traffic status of the area. According to the area-level analysis results, it performs coordinated scheduling on several intersections or key road sections, such as setting green wave belts and issuing diversion instructions.

[0134] The area control module periodically reports the overall load of the middle-level road network and area-level traffic indicators (such as saturation, congestion degree, total queue length, etc.) to the global control module, and transmits important alert or emergency event information; it issues signal timing correction instructions or fine-tuning limit parameters to the underlying intersections to ensure that local optimization is consistent with the overall area. It obtains various traffic flows and event reports from the underlying road network; it receives the macro-scheduling strategies issued by the global control module, such as main road priority, temporary traffic restrictions, or bus priority plans.

[0135] The global control module integrates city-wide or cross-regional data, executes global situation prediction and optimization algorithms, identifies major congestion points, vehicle flow corridors, and the impacts of key activities; formulates and issues macro-scheduling instructions (such as area priorities, unified cycle upper limits, cross-regional diversion plans).

[0136] The global control module issues to each area control module in the form of overall scheduling instructions, such as green waves on the main road during peak hours, traffic restrictions and diversions around activity areas, and priority for emergency rescue channels. It feeds back the urban traffic operation situation to the municipal management department or other public platforms, and at the same time receives area load indicators and summary of alert events from the middle level; external data (such as bus operations, weather, major event arrangements, etc.) for comprehensive evaluation and long-term scheduling planning.

[0137] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes of various kinds.

[0138] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0139] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways when necessary, and then storing it in a computer memory.

[0140] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A road network analysis method related to intelligent transportation, characterized in that, Including: Obtain the road network scale and regional characteristics, and divide the overall road network into three levels: the underlying road network, the middle-level road network, and the high-level road network according to the road network scale and regional characteristics; Deploy an intelligent signal control module in each underlying road network, collect real-time data of the underlying road network, and perform local optimization of the underlying road network on the signal light cycle and phase allocation according to the real-time data of the underlying road network; Deploy a regional control module in the middle-level road network, receive the real-time data uploaded by the underlying road network, calculate the overall load of the middle-level road network through regional analysis, and perform coordinated control on the middle-level road network according to the overall load of the middle-level road network; Deploy a global control module in the high-level road network, issue a scheduling instruction to the middle-level road network according to the real-time data and the overall load of the middle-level road network, and realize the analysis and optimization of the overall road network.

2. The road network analysis method related to intelligent transportation according to claim 1, characterized in that: The road network scale and regional characteristics include road structure information, historical traffic flow and load data, and geographical and regional elements; The real-time data includes traffic flow and vehicle speed data, queue length data, vehicle data, and signal light data.

3. The road network analysis method related to intelligent transportation according to claim 2, wherein: The underlying road network consists of an intersection and adjacent road sections. Set a detection period T in the underlying road network, collect real-time data of the intersection and adjacent road sections within each detection period T, and judge the congestion situation of the detection period T according to the real-time data; Suppose there are n approach lanes at an intersection, and the average traffic flow q of the i-th approach lane in the detection period T is obtained according to the traffic flow and vehicle speed data i , and the saturation of each approach lane is calculated and expressed as: Among them, X i represents the saturation degree of the i-th approach; S i represents the saturation flow rate of the i-th approach, which is determined according to the lane width and signal timing standard; Calculate the congestion degree of the intersection according to the queue length and saturation degree of the intersection, expressed as: Among them, C represents the congestion level of the intersection, α represents the weight coefficient; L i represents the queue length of the i-th approach; L max represents the maximum queue length; β represents the weight coefficient; Set the maximum congestion level C through expert evaluation max When the underlying road network detects that there is an approach X i greater than or equal to 1, or the congestion level C ≥ C max At this time, it is determined that the underlying road network is congested during the detection period T, triggering local optimization of the underlying road network, and at the same time uploading the real-time data during the detection period T to the middle-level road network to obtain the middle-level intervention coefficient.

4. A road network analysis method related to intelligent transportation according to claim 3, characterized in that: The local optimization of the underlying road network includes calculating the initial signal light cycle according to the saturation degree of the approach lane through the Webster formula: Among them, R raw represents the initial signal light cycle; M represents the total lost time; Adjust the initial signal light cycle according to the middle-level intervention coefficient issued by the middle-level road network to obtain the actually executed signal light cycle, expressed as: R local = clip(R raw ·K region , R old -Δ local , R old +Δ local ) Among them, R local represents the actual executed signal light cycle; clip represents restricting R raw ·K region to between R old -Δ local and R old +Δ local ; K region represents the middle layer intervention coefficient; Δ local represents the bottom layer road network adjustment limit value; According to the actual signal light cycle R local , perform the allocation of the green light duration for each phase, expressed as: Among them, g j represents the green light duration of the j-th phase; X j represents the saturation degree of the approach corresponding to phase j; m represents the total number of phases at the intersection; X k represents the saturation degree of the approach corresponding to phase k; k represents the k-th phase.

5. The road network analysis method related to intelligent transportation according to claim 4, wherein: The coordinated control of the middle-level road network includes that the middle-level road network consists of multiple adjacent underlying road networks. When the underlying road network controlled by the middle-level road network requests the middle-level intervention coefficient, obtain the saturation degree of the approach lanes of all underlying road networks controlled by the middle-level road network, and calculate the regional average saturation degree of the middle-level road network, expressed as: Among them, X avg represents the regional average saturation of the middle-level road network; N represents the number of underlying road networks controlled by the middle-level road network; r represents the r-th underlying road network; Calculate the middle-level intervention coefficient K based on the regional average saturation of the middle-level road network region , expressed as: K region = max(0.5, min(1.5, 1 + γ·(X avg - X0))) Where, max represents taking the maximum value; min represents taking the minimum value; γ represents the coefficient of adjustment sensitivity; X0 represents the critical saturation degree.

6. The road network analysis method related to intelligent transportation according to claim 5, characterized in that: The coordinated control of the middle-level road network further includes setting a green wave band in the middle-level road network and obtaining the distance d between adjacent intersections controlled by the middle-level road network ij , determining the recommended driving speed v according to traffic flow and vehicle speed data ref , calculating the driving time for a vehicle to travel from one intersection to the next intersection: Among them, T ab represents the travel time from intersection a to intersection b; Obtain the actual executed signal light cycle R of each underlying intersection local , select the nearest integer to align the signal light cycle, and obtain the unified basic cycle R; Take the starting intersection of the green wave belt as the time zero point, set the starting time of the main phase green light of the starting intersection as 0 seconds, and calculate the phase difference according to the unified basic cycle R, expressed as: O b =(O a +T ab ) mod R Among them, O b represents the phase difference of intersection b; O a represents the phase difference of intersection a; modR represents the modulo operation on the unified basic cycle R; Make a limit on the adjustment limit value of the underlying road network on the green wave belt according to the unified basic cycle R of the green wave belt, expressed as: Δ local = θ·(R - M) Among them, Δ local represents the adjustment limit value of the underlying road network; θ represents the adjustment coefficient; the middle-layer road network will unify the basic cycle R, phase difference, and the restricted Δ local and send them to the underlying road network to complete the setting of the green wave band.

7. The road network analysis method related to intelligent transportation according to claim 6, characterized in that: The high-level road network consists of all middle-level road networks, covers the overall road network area, aggregates and stores the real-time data of the overall road network, analyzes the periodic trend and issues a scheduling instruction to the middle-level road network to realize the analysis and optimization of the overall road network.

8. A road network analysis system related to intelligent transportation, which is applied to a road network analysis method related to intelligent transportation according to any one of claims 1 to 7, characterized in that, Including, An intelligent signal control module that collects real-time data of the underlying road network and performs local optimization of the underlying road network on the signal light cycle and phase allocation according to the real-time data of the underlying road network; A regional control module that receives the real-time data uploaded by the underlying road network, calculates the overall load of the middle-level road network through regional analysis, and performs coordinated control on the middle-level road network according to the overall load of the middle-level road network; A global control module that issues a scheduling instruction to the middle-level road network according to the real-time data and the overall load of the middle-level road network to realize the analysis and optimization of the overall road network.

9. A computer device, comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of a road network analysis method related to intelligent transportation as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of a road network analysis method related to intelligent transportation as described in any one of claims 1-7 are implemented.

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