A method and system for real-time monitoring of road traffic conditions

Abstract

This invention belongs to the field of Internet of Things (IoT) monitoring technology and provides a method and system for real-time monitoring of road traffic conditions. The method includes the following steps: S1: measuring the vehicle flow velocity V and traffic flow C on the outermost lane of the road to be monitored; S2: calculating the total traffic flow CO of the entire one-way lane using the traffic flow C obtained in step S1; S3: calculating the moving average of M consecutive vehicle flow velocity measurements and the moving average of N consecutive vehicle flow flow measurements based on the vehicle flow velocity V and the total traffic flow CO; and S4: calculating and determining the road traffic condition. According to this invention, a low-power data acquisition design is adopted, combining vehicle flow velocity and traffic flow to determine the road traffic condition. This significantly reduces the accuracy requirements for measuring vehicle flow velocity and traffic flow, not only meeting the requirements of low-power operation but also improving the fault tolerance of monitoring, thus satisfying the needs of intelligent traffic management for secure, low-cost, refined, and intelligent traffic condition data.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) monitoring technology, specifically, it relates to a method and system for real-time monitoring of road traffic conditions. Background Technology

[0002] With the acceleration of urbanization, urban traffic congestion has become an increasingly prominent problem. Traffic jams not only reduce travel efficiency but also affect urban economic activities and social order. Therefore, traffic management departments urgently need to introduce advanced traffic management methods to alleviate traffic congestion, improve transportation efficiency, optimize citizens' travel experience, and thus promote the comprehensive and harmonious development of the city.

[0003] Existing methods for monitoring road traffic conditions mainly include:

[0004] (1) Crowdsourcing model monitoring

[0005] Map service providers employ a crowdsourcing model (To C), relying on data feedback from a large number of users to update traffic information. When using these services, users' mobile devices may upload personal information such as vehicle speed and location, forming a dynamic, real-time traffic information network. The advantage of this approach is rapid data updates and wide coverage, but it also carries the risk of user privacy data leaks. Furthermore, crowdsourcing monitoring primarily serves individual users, making it difficult for traffic management departments to control the initiative in data collection and management.

[0006] (2) Manual monitoring

[0007] Taxi drivers, as road users, can experience traffic conditions in real time and report them through specific channels. This method provides relatively direct and real-time information, but it is limited by the number of taxi drivers and the frequency of reporting. Furthermore, due to limited coverage, the accuracy and objectivity of the data are difficult to guarantee.

[0008] (3) Camera monitoring

[0009] Traffic cameras are a traditional method of traffic monitoring, used to capture traffic conditions in real time by installing cameras at key road sections. However, due to the high installation and maintenance costs of cameras, the relatively limited number of monitoring points, and the inability to achieve full coverage, blind spots and dead zones exist. Furthermore, camera monitoring is greatly affected by factors such as weather and lighting, and requires significant manpower for monitoring and analysis, resulting in high maintenance costs.

[0010] (4) Unmanned aerial vehicle (UAV) monitoring

[0011] Drone monitoring is an emerging traffic monitoring method in recent years, offering high flexibility and real-time capabilities. Drones can quickly reach specific areas to conduct aerial monitoring, capturing road traffic conditions and providing real-time data support for traffic management departments. However, drone monitoring is also limited by factors such as battery life, airspace restrictions, and data processing capabilities. Furthermore, drone monitoring requires professional personnel for operation, resulting in high procurement and maintenance costs.

[0012] In summary, existing technologies such as crowdsourcing and manual reporting provide diverse and dispersed sources of road traffic status data, making it difficult for governments to fully grasp and manage this data. While monitoring methods such as cameras and drones are more direct and controllable, they are limited by high equipment costs, high maintenance costs, and high costs of hiring technical personnel, making it difficult to achieve comprehensive coverage and efficient management. As a result, existing traffic status monitoring methods cannot meet the needs of intelligent traffic management that requires secure, low-cost, refined, and intelligent traffic status data. Summary of the Invention

[0013] The purpose of this invention is to provide a method for real-time monitoring of road traffic conditions to solve the technical problems existing in the prior art.

[0014] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0015] A method for real-time monitoring of road traffic conditions includes the following steps:

[0016] Step S1: Using low-power technology, measure the vehicle flow velocity V and flow rate C on the outermost lane of the road to be monitored;

[0017] Step S2: Calculate the total traffic flow C of the entire one-way lane based on the traffic flow C obtained in step S1. O ;

[0018] Step S3: Based on the vehicle flow velocity V and total traffic volume C O The sliding average value of M consecutive vehicle flow velocity measurements is calculated as V. cur_mean The moving average of N consecutive vehicle flow measurement data is C. cur_mean ;

[0019] Step S4: According to the V cur_mean The C cur_mean Speed ​​limit V of the road to be monitored max The historical maximum traffic volume of the road to be monitored is C. max The road traffic conditions are calculated and determined as follows:

[0020] (1) When V cur_mean ≥K1·V max , or, (V cur_mean>K4·V max )&(C cur_mean <P1·C max When the traffic conditions are clear, the current road traffic status is determined to be smooth.

[0021] (2) When K2·V max ≤V cur_mean <K1·V max Or, P1·C max ≤C cur_mean <P2·C max At that time, the current road traffic condition is determined to be relatively congested;

[0022] (3) When K3·V max ≤V cur_mean <K2·V max Or, P2·C max ≤C cur_mean <P3·C max At that time, the current road traffic condition is determined to be congested;

[0023] (4) When V cur_mean <K3·V max Or, P3·C max ≤C cur_mean At that time, the current road traffic condition was determined to be severe congestion.

[0024] In one implementation, step S1 is specifically implemented as follows:

[0025] Step S1.1: Along the lane direction, two sets of sensors are arranged at intervals on the outermost lane side: transmitting sensor T1 and receiving sensor R1 form one set, and transmitting sensor T2 and receiving sensor R2 form the other set;

[0026] Step S1.2: The transmitting sensor T1 periodically transmits signals. When a vehicle passes by the location corresponding to the transmitting sensor T1, the receiving sensor R1 receives the signal, records the time when it receives the signal as t1, and proceeds to the next step.

[0027] Step S1.3: Turn off transmitting sensor T1, turn on transmitting sensor T2 and send a signal. When a vehicle passes by the location corresponding to transmitting sensor T2, receiving sensor R2 receives the signal, records the time when it receives the signal as t2, and proceeds to the next step.

[0028] Step S1.4: Calculate the vehicle flow velocity V: V = L / (t2-t1), where L is the distance between the receiving sensor R1 and the receiving sensor R2; Calculate the flow rate C: C = the number of times the receiving sensor R1 receives the signal, and remove duplicate data.

[0029] In one embodiment, the method for eliminating duplicate data in step S1.4 is as follows:

[0030] Step S1.4.1: Obtain the time series data of the vehicle passing signal collected by the receiving sensor R1, denoted as {t ˋ 1,t ˋ 1,…,t ˋ i ,t ˋ n}, where t ˋ i represents the i-th signal reception timestamp;

[0031] Step S1.4.2: Calculate the adjacent signal time interval sequence {Δt ˋ 1,Δt ˋ 1,…,Δt ˋ i ,Δt<s000043> n}, where Δt ˋ i =t ˋ i+1 - t ˋ ;

[0032] Step S1.4.3: Calculate the statistical feature quantities based on the time interval sequence:

[0033] Average time interval: ;

[0034] Standard deviation of time interval: ;

[0035] [[ID=?3]]Step S1.4.4: Set the dynamic decision threshold T = μ - β • σ, where β is a preset coefficient;

[0036] [[ID=?6]]Step S1.4.5: Traverse the time interval sequence. If there exists Δt ˋ i < T, then determine that t ˋ i+1 is a duplicate signal and eliminate it.

[0037] In one embodiment, it further includes step S1.4.6: Update μ and σ with the most recent Y time intervals as samples.

[0038] In one embodiment, the calculation method of the total traffic flow C O in step S2 is as follows:

[0039] Step S2.1: Dynamically allocate the weight coefficients of each lane according to the number of lanes A of the road to be monitored: α i(i=1,2,…,A) where the initial weight coefficient of the outermost lane is 1, and the initial weight coefficient of the other lanes is 1.2;

[0040] Step S2.2: Combine the time period correction factor γ and calculate the total traffic flow C according to the following formula. O :

[0041] ;

[0042] In the formula, γ=1.2 during rush hour, γ=0.7 at night, and γ=1 at other times.

[0043] In one implementation, the weighting coefficient α in step S2.1 i The dynamic allocation method is as follows:

[0044] Step S2.1.1: Discretize and number the lanes in the same direction according to their spatial location. The outermost lane has a level L1=1, the adjacent lane of the outermost lane has a level L2=2, and the middle lane has a level L... c Determined based on the total number of lanes, A;

[0045] Step S2.1.2: Construct the hierarchical attenuation factor:

[0046] δ i =K Li-1

[0047] In the formula, K is the hierarchical attenuation coefficient, and L i This is the level number for lane i;

[0048] Step S2.1.3: Obtain the weight coefficient of the corresponding lane based on the hierarchical attenuation factor:

[0049] α i =α0•δ i

[0050] In the formula, α0 is the initial weight coefficient of the lane.

[0051] In one implementation, the intermediate lane level is calculated as follows:

[0052] (1) When A is odd, the middle lane level L C =(A+1) / 2;

[0053] (2) When A is an even number, the middle lane level L C =A / 2.

[0054] To achieve the above objectives, the present invention also provides a real-time road traffic condition monitoring system, comprising:

[0055] A low-power vehicle flow velocity acquisition terminal is deployed on one side of the outermost lane of the road to be monitored, and uses low-power technology to measure the vehicle flow velocity V and flow rate C on the outermost lane of the road to be monitored.

[0056] The road traffic condition monitoring platform communicates with the low-power vehicle flow rate acquisition terminal and is configured with:

[0057] Total traffic flow calculation module: Calculates the total traffic flow C of the entire one-way lane based on the traffic flow C. O ;

[0058] Intermediate calculation module: based on the vehicle flow velocity V and total traffic volume C O The sliding average value of M consecutive vehicle flow velocity measurements is calculated as V. cur_mean The moving average of N consecutive vehicle flow measurement data is C. cur_mean ;

[0059] Judgment module: Based on the V cur_mean The C cur_mean Speed ​​limit V of the road to be monitored max The historical maximum traffic volume of the road to be monitored is C. max The road traffic conditions are calculated and determined as follows:

[0060] (1) When V cur_mean ≥K1·V max , or, (V cur_mean >K4·V max )&(C cur_mean <P1·C max When the traffic conditions are clear, the current road traffic status is determined to be smooth.

[0061] (2) When K2·V max ≤V cur_mean <K1·V max Or, P1·C max ≤C cur_mean <P2·C max At that time, the current road traffic condition is determined to be relatively congested;

[0062] (3) When K3·V max ≤V cur_mean <K2·V max Or, P2·C max ≤C cur_mean <P3·C max At that time, the current road traffic condition is determined to be congested;

[0063] (4) When V cur_mean <K3·V max Or, P3·Cmax ≤C cur_mean At that time, the current road traffic condition is determined to be severe congestion;

[0064] In the above formula, K1, K2, K3, and K4 are the vehicle velocity boundary state coefficients, and P1, P2, and P3 are the vehicle flow rate boundary state coefficients.

[0065] In one embodiment, the low-power vehicle flow rate acquisition terminal includes a microprocessor, two sets of sensors communicating with the microprocessor: a transmitting sensor T1 and a receiving sensor R1 as one set, and a transmitting sensor T2 and a receiving sensor R2 as another set, a wireless communication module communicating with the microprocessor, and a power supply module for powering the device.

[0066] In one embodiment, both sets of sensors are ultrasonic sensors, infrared light sensors, laser sensors, thermal sensors, or microwave radar sensors.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] (1) According to the present invention, a low-power data acquisition design scheme is adopted, based on V cur_mean C cur_mean Speed ​​limit V max And the historical maximum traffic volume is C max By combining vehicle speed and traffic volume to determine road traffic conditions, the accuracy requirements for measuring vehicle speed and traffic volume can be significantly reduced. This not only meets the requirements for low-power operation but also improves the fault tolerance of monitoring, satisfying the needs of intelligent traffic management for secure, low-cost, refined, and intelligent traffic status data.

[0069] (2) According to the present invention, only low-power vehicle flow velocity acquisition terminals need to be deployed at the edge of the lane. The flow velocity and traffic flow are calculated by time difference. Basic vehicle flow and speed monitoring can be achieved without additional equipment. Compared with the prior art, it not only has lower requirements for installation location accuracy, but also does not require damage to the roadbed or laying of cables. Moreover, the installation cost is low and it is suitable for rapid deployment. On the other hand, the hardware cost and operating power consumption are significantly reduced, making it suitable for large-scale promotion and application.

[0070] (3) According to the present invention, the weight coefficient of each lane is dynamically allocated based on the single monitoring data of the outermost lane. At the same time, a time period correction factor is introduced. No other monitoring data is required, which is in line with the low power consumption operation design of the present technical solution. Without increasing power consumption, the calculation accuracy of total traffic flow is higher. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating Embodiment 1 of the present invention.

[0072] Figure 2 This is a schematic diagram illustrating the front of the vehicle reaching directly in front of sensor T1-R1, as exemplified in Embodiment 1 of the present invention.

[0073] Figure 3 This is a schematic diagram illustrating the front of the vehicle reaching directly in front of sensor T2-R2, as shown in Embodiment 1 of the present invention.

[0074] Figure 4 This is a schematic diagram of the sensor's working process in Embodiment 1 of the present invention.

[0075] Figure 5 This is a schematic diagram of the principle of Embodiment 2 of the present invention. Detailed Implementation

[0076] To enable those skilled in the art to have a clearer understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described below are merely for illustrative purposes and to facilitate understanding. The technical solutions provided by the present invention are not limited to those provided in the following embodiments, nor should they limit the scope of protection of the present invention.

[0077] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0078] Example 1

[0079] like Figure 1 As shown in Figure 4, this embodiment provides a method for real-time monitoring of road traffic conditions. The design principle of this method is as follows: a large number of low-power vehicle flow rate acquisition terminals are used to cover as many roads as possible. The low-power vehicle flow rate acquisition terminals perform real-time low-precision measurements of vehicle speed and traffic flow on each road. The traffic data (real-time vehicle speed and traffic flow) of each road are aggregated to the cloud platform through the Internet of Things. The cloud platform processes this massive amount of traffic data to obtain real-time monitoring and prediction information of road traffic conditions, thereby realizing a low-power, low-cost and wide-area coverage road traffic monitoring solution.

[0080] In this embodiment, the specific steps of the real-time road traffic condition monitoring method are as follows:

[0081] 1. Step S1: Using low-power technology, measure the vehicle flow velocity V and flow rate C on the outermost lane of the road to be monitored.

[0082] The specific implementation of this step is as follows:

[0083] Step S1.1: Along the lane direction, two sets of sensors are arranged at intervals on the outermost lane side: transmitting sensor T1 and receiving sensor R1 form one set, and transmitting sensor T2 and receiving sensor R2 form the other set; preferably, conventional sensors such as ultrasonic sensors, infrared light sensors, laser sensors, thermal sensors, or microwave radar sensors can be used; the sensor data is transmitted to the cloud platform (i.e., road traffic condition monitoring platform) via a wireless network, and the wireless network adopts existing mature mobile communication networks such as 4G, NB-IoT, 5G, and 6G; based on the above design, basic traffic flow and speed monitoring is realized, and the hardware deployment is fast, the cost is low, and the operating power consumption is low.

[0084] Step S1.2: The transmitting sensor T1 periodically sends a signal. When a vehicle passes by the location corresponding to the transmitting sensor T1, the receiving sensor R1 receives the signal, records the time of receiving the signal as t1, and proceeds to the next step; if no vehicle passes by, the receiving sensor R1 does not receive the signal.

[0085] Step S1.3: Turn off transmitting sensor T1, turn on transmitting sensor T2 and send a signal. When a vehicle passes by the location corresponding to transmitting sensor T2, receiving sensor R2 receives the signal, records the time when it receives the signal as t2, and proceeds to the next step.

[0086] Data collection example: Figure 2 and 3 As shown, assuming there are a total of 4 lanes in one direction, only the vehicle flow speed V and flow rate C in the outermost lane are measured; when the front of the vehicle reaches the sensor T1-R1, the time t1 is obtained, and when the front of the vehicle reaches the sensor T2-R2, the time t2 is obtained.

[0087] Step S1.4: Calculate the vehicle flow rate V: V = L / (t2-t1), where L is the distance between receiving sensor R1 and receiving sensor R2; Calculate the flow rate C: C = the number of times the receiving sensor R1 receives a signal. Each time the receiving sensor R1 receives a signal, it is considered that a vehicle has passed, and the vehicle flow rate is increased by 1. In order to avoid the same vehicle being counted repeatedly, an algorithm is needed to remove duplicate data.

[0088] The duplicate data removal algorithm used in this embodiment is as follows:

[0089] Step S1.4.1: Acquire the time series data of vehicle passage signals collected by the receiving sensor R1, denoted as {t} ˋ 1,t ˋ 1,…,t ˋ i ,t ˋ n}, where t ˋ i represents the i-th signal reception timestamp;

[0090] Step S1.4.2: Calculate the adjacent signal time interval sequence {Δt ˋ 1, Δt ˋ 1, …, Δt ˋ i , Δt ˋ n} where Δt ˋ i = t ˋ i+1 - t ˋ ;

[0091] Step S1.4.3: Calculate the statistical feature quantity based on the time interval sequence:

[0092] Average time interval: ;

[0093] Standard deviation of time interval: ;

[0094]

[0095] Step S1.4.4: Set the dynamic decision threshold T = μ - β • σ, where β is a preset coefficient, and the reference value range is 1.5 - 3. The preferred value method: Obtain the time interval distribution under normal traffic flow conditions, and determine the value of β based on the lower limit of the 99% confidence interval;

[0095] Step S1.4.5: Traverse the time interval sequence. If there exists Δt ˋ i < T, then determine t ˋ i+1 as a repeated signal and eliminate it;

[0096] Step S1.4.6: Update μ and σ with the recent Y time intervals as samples. Preferably, Y ≥ 30. Through this design, dynamic adaptation to changes in traffic flow density can be achieved.

[0097] Through the above settings, by dynamically setting the decision threshold using the mean and standard deviation of the time interval, the sensitivity can be automatically adjusted according to the traffic flow density. When congested, the vehicle distance is small, the threshold is reduced to avoid mis-elimination; when unobstructed, the vehicle distance is large, the threshold is increased to avoid missed detection.

[0098] 2. Step S2: Calculate the total traffic volume C of the entire one-way lane based on the traffic flow C in Step S1 O .

[0099] The specific implementation of this step is as follows:

[0100] Step S2.1: Dynamically allocate the weight coefficient of each lane according to the number of lanes A of the road to be monitored: αi (i=1,2,…,A) where the initial weight coefficient of the outermost lane is 1, and the initial weight coefficient of the other lanes is 1.2;

[0101] Step S2.2: Combine the time period correction factor γ and calculate the total traffic flow C according to the following formula. O :

[0102] ;

[0103] In the formula, γ=1.2 during peak commuting hours (e.g., 8:00 AM to 10:00 AM), γ=0.7 at night (e.g., 10:00 PM to 6:00 AM the next day), and γ=1 at other times.

[0104] With the above settings, the weight coefficients of each lane are dynamically allocated based on the single monitoring data of the outermost lane. At the same time, a time period correction factor is introduced, which eliminates the need for other monitoring data and conforms to the low-power operation design of this technical solution. This results in higher accuracy in the calculation of total traffic flow without increasing power consumption.

[0105] Preferably, the weighting coefficient α i The dynamic allocation method is as follows:

[0106] Step S2.1.1: Discretize and number the lanes in the same direction according to their spatial location. The outermost lane has a level L1=1, the adjacent lane of the outermost lane has a level L2=2, and the middle lane has a level L... c The middle lane level is determined based on the total number of lanes A; specifically, the calculation method for the middle lane level is as follows: (1) When A is odd, the middle lane level L C =(A+1) / 2; (2) When A is even, the middle lane level L C =A / 2;

[0107] Step S2.1.2: Construct the hierarchical attenuation factor:

[0108] δ i =K Li-1

[0109] In the formula, K is the hierarchical attenuation coefficient (a set value, ranging from 0.5 ≤ K ≤ 0.7), and L... i This is the level number for lane i; the significance of this attenuation factor is: based on the center-of-traffic concentration effect, the attenuation of the central lane is slower, which is consistent with the actual traffic flow distribution pattern;

[0110] Step S2.1.3: Obtain the weight coefficient of the corresponding lane based on the hierarchical attenuation factor:

[0111] α i =α0•δ i

[0112] In the formula, α0 is the initial weight coefficient of the lane.

[0113] By setting up the above system, the actual weight coefficient of each lane is dynamically allocated based on the initial weight coefficient of the lane and the hierarchical attenuation factor, which conforms to the actual traffic flow distribution pattern and improves the calculation accuracy.

[0114] 3. Step S3: Based on vehicle flow velocity V and total traffic volume C O The sliding average value of M consecutive vehicle flow velocity measurements is calculated as V. cur_mean The moving average of N consecutive vehicle flow measurement data is C. cur_mean .

[0115] In this step, the moving average is a commonly used data processing technique that smooths short-term fluctuations and reveals long-term trends by calculating the average value of data within a continuous window in a sequence. It is an existing and mature technique, so it will not be elaborated on here.

[0116] 4. Step S4: According to V cur_mean C cur_mean Speed ​​limit V of the road to be monitored max (Unit: km / h) and the historical maximum traffic volume of the road to be monitored is C. max (Unit: vehicles / minute) Calculate and determine road traffic conditions.

[0117] There are four ways to determine the road traffic status based on the above data, as follows:

[0118] (1) Smooth

[0119] When V cur_mean ≥K1·V max , or, (V cur_mean >K4·V max )&(C cur_mean <P1·C max When the traffic conditions are clear, the current road traffic status is determined to be smooth.

[0120] (2) Relatively congested

[0121] When K2·V max ≤V cur_mean <K1·V max Or, P1·C max ≤C cur_mean <P2·C max At that time, the current road traffic condition is determined to be relatively congested;

[0122] (3) Congestion

[0123] When K3·V max ≤V cur_mean <K2·Vmax Or, P2·C max ≤C cur_mean <P3·C max At that time, the current road traffic condition is determined to be congested;

[0124] (4) Severe congestion

[0125] When V cur_mean <K3·V max Or, P3·C max ≤C cur_mean At that time, the current road traffic condition is determined to be severe congestion;

[0126] Wherein, K1, K2, K3, and K4 are the vehicle flow velocity boundary state coefficients, and P1, P2, and P3 are the vehicle traffic volume boundary state coefficients.

[0127] The above coefficients are derived from the basic traffic flow map and are existing known coefficients. The methods used to obtain them are existing technologies, as illustrated below: Data Acquisition: Deploy detectors (such as loop detectors, microwave radar, video cameras, and floating car GPS data) on the target road. Data Preprocessing: Collect raw data over a period of time (e.g., one month), including the passage time and speed of each vehicle, and then aggregate and calculate the time-averaged flow rate (Q, vehicles / hour) and time-averaged speed (V, kilometers / hour), and derive the average density (K, vehicles / km). Scatter Plotting: Plot the data as a scatter plot, typically resulting in two graphs: Flow-Density Plot (QK Plot): The horizontal axis represents density K, and the vertical axis represents flow rate Q. Speed-Density Plot (VK Plot): The horizontal axis represents density K, and the vertical axis represents speed V. Model Fitting and Key Point Identification: Observing the QK plot, a distinct inverted U-shaped curve will be seen. The horizontal axis (density) of the curve's peak is the critical density K2, and the vertical axis is the maximum capacity P2. On the VK plot, it can be seen that speed decreases as density increases. The density corresponding to the inflection point where the curve begins to deviate significantly from the initial stationary value can be considered as the boundary point K1 between free flow and critical flow. After the curve drops sharply, it enters a state of complete congestion and stagnation, with the corresponding density being approximately the blocking density K4. K3 is usually located between the critical density and the blocking density, used to distinguish between mild and severe congestion. P1 and P3 are flow rates, usually determined on the QK diagram. P1 is the relatively high flow rate in the free flow state, but not reaching its maximum value, while P3 is the flow rate level after decreasing in the congested state.

[0128] The values ​​of the coefficients are not uniform, fixed constants, but rather derived from long-term, extensive observation and statistical analysis of traffic data (flow rate, speed, density) on specific roads (such as a section of highway or urban expressway). The meanings of each coefficient are as follows:

[0129] K1 (Critical density of free flow): Function: To determine whether the traffic flow starts to change from the "completely free" state to the "unstable" state. When the measured density < K1, vehicles are hardly disturbed and can drive freely. Once the density > K1, drivers start to influence each other and need to be vigilant.

[0130] K2 (Critical density / Optimal density): Function: This is one of the most important coefficients. It corresponds to the maximum traffic capacity (P2) of the road. One of the goals of traffic management is to keep the density near K2 to maximize the road use efficiency. When the density exceeds K2, it means the traffic flow starts to become unstable and congestion is likely to occur.

[0131] K3 (Critical density of congestion): Function: Used to distinguish between "synchronized flow / mild congestion" and "severe congestion". When the density > K3, the traffic flow has entered the forced congestion state, with a very low speed and a significant decrease in traffic volume. It is the threshold for triggering the highest-level congestion warning.

[0132] K4 (Jam density): Function: The theoretically maximum density, representing the state where vehicles are almost completely stalled (such as queuing for a red light). Used to calculate the maximum queue length and the theoretical congestion range.

[0133] P1 (High traffic volume of free flow): Function: Represents the relatively high traffic volume level that can be achieved in the free flow state. Used to evaluate the service level of the road under unobstructed conditions.

[0134] P2 (Maximum traffic capacity): Function: The "theoretical limit" of the road. It is a core parameter in road design and traffic planning. One of the ultimate goals of all traffic control strategies (such as signal timing, ramp control) is to make the actual traffic volume on the road as close as possible to P2.

[0135] P3 (Congested traffic volume): Function: Indicates that in the congested state, the actual traffic capacity of the road will decrease. P3 < P2. This value helps managers understand how much the traffic capacity is lost due to congestion.

[0136] Through the above solution, by combining the vehicle flow velocity and the vehicle traffic volume to determine the traffic state of the road, the measurement accuracy requirements for the vehicle flow velocity and the vehicle traffic volume can be significantly reduced, and the fault tolerance rate of the system can be improved.

[0137] Embodiment 2

[0138] As Figure 5 shown, this embodiment provides a real-time traffic state monitoring system for roads, which is used to implement the real-time traffic state monitoring method provided in Embodiment 1, and specifically includes:

[0139] A low-power vehicle flow velocity acquisition terminal is deployed on the outermost lane of the road to be monitored and uses low-power technology to measure the vehicle flow velocity V and flow rate C in the outermost lane. Typical characteristics of a low-power vehicle flow velocity acquisition terminal include:

[0140] (1) An embedded architecture of MCU (microprocessor) + flow rate measurement sensor + wireless communication module is adopted;

[0141] (2) Typical flow rate and velocity measurement sensors include: ultrasonic, infrared, laser, thermal, microwave radar, and other sensors.

[0142] (3) Permissible measurement error for flow rate and velocity: 20% to 50%.

[0143] (4) The vehicle flow rate acquisition terminal has extremely low power consumption, with an average operating current in the mA level or even as low as µA.

[0144] The hardware of the low-power vehicle flow rate acquisition terminal can all adopt existing mature hardware structures. For example, in one implementation, the low-power vehicle flow rate acquisition terminal includes a microprocessor, two sets of sensors communicating with the microprocessor: transmitting sensor T1 and receiving sensor R1 as one set, transmitting sensor T2 and receiving sensor R2 as another set, a wireless communication module communicating with the microprocessor, and a power supply module for power supply. The wireless communication module can adopt mature wireless communication modules such as 4G communication modules and 5G communication modules. The power supply module can be powered by a disposable lithium battery or a rechargeable lithium battery + solar cell to ensure a longer continuous working time for the low-power vehicle flow rate acquisition terminal.

[0145] The real-time road traffic condition monitoring system also includes a road traffic condition monitoring platform, which communicates with a low-power vehicle flow rate acquisition terminal. The data collected by the low-power vehicle flow rate acquisition terminal is transmitted to the road traffic condition monitoring platform via a wireless network. The road traffic condition monitoring platform is deployed on a mobile communication network data server.

[0146] In one implementation scheme, the road traffic condition monitoring platform is configured with:

[0147] Total traffic flow calculation module: Calculates the total traffic flow C of the entire one-way lane based on the traffic flow C. O ;

[0148] Intermediate calculation module: based on vehicle flow velocity V and total traffic volume C O The sliding average value of M consecutive vehicle flow velocity measurements is calculated as V. cur_mean The moving average of N consecutive vehicle flow measurement data is C. cur_mean ;

[0149] Judgment module: Based on V cur_mean C cur_mean Speed ​​limit V of the road to be monitored max The historical maximum traffic volume of the road to be monitored is C. max The road traffic conditions are calculated and determined as follows:

[0150] (1) When V cur_mean ≥K1·V max , or, (V cur_mean >K4·V max )&(C cur_mean <P1·C max When the traffic conditions are clear, the current road traffic status is determined to be smooth.

[0151] (2) When K2·V max ≤V cur_mean <K1·V max Or, P1·C max ≤C cur_mean <P2·C max At that time, the current road traffic condition is determined to be relatively congested;

[0152] (3) When K3·V max ≤V cur_mean <K2·V max Or, P2·C max ≤C cur_mean <P3·C max At that time, the current road traffic condition is determined to be congested;

[0153] (4) When V cur_mean <K3·V max Or, P3·C max ≤C cur_mean At that time, the current road traffic condition is determined to be severe congestion;

[0154] In the above formula, K1, K2, K3, and K4 are the vehicle velocity boundary state coefficients, and P1, P2, and P3 are the vehicle flow rate boundary state coefficients.

[0155] It should be noted that the functional modules of the above-mentioned road traffic condition monitoring platform correspond one-to-one with the steps of the real-time road traffic condition monitoring method provided in Embodiment 1. The specific functions implemented are the same as those of the real-time road traffic condition monitoring method provided in Embodiment 1, and the beneficial effects achieved are also the same as those achieved by the real-time road traffic condition monitoring method provided in Embodiment 1.

[0156] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0157] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for real-time monitoring of road traffic conditions, characterized in that, Includes the following steps: Step S1: Using low-power technology, measure the vehicle flow velocity V and flow rate C on the outermost lane of the road to be monitored; Step S2: Calculate the total traffic flow C of the entire one-way lane based on the traffic flow C obtained in step S1. O In step S2, the total traffic flow C O The calculation method is as follows: Step S2.1: Based on the number of lanes A of the road to be monitored, dynamically allocate the weight coefficient α for each lane. i (i=1,2,…,A), where the initial weight coefficient of the outermost lane is 1, and the initial weight coefficient of other lanes is 1.2; Step S2.2: Combine the time period correction factor γ, and calculate the total traffic flow C according to the following formula. O : In the formula, γ=1.2 during rush hour, γ=0.7 at night, and γ=1 at other times; In step S2.1, the weighting coefficient α i The dynamic allocation method is as follows: Step S2.1.1: Discretize and number the lanes in the same direction according to their spatial location. The outermost lane has a level L1=1, the adjacent lane of the outermost lane has a level L2=2, and the middle lane has a level L... c Determined based on the total number of lanes, A; Step S2.1.2: Construct the hierarchical attenuation factor: d i =K Li-1 In the formula, K is the hierarchical attenuation coefficient, and L i This is the level number for lane i; Step S2.1.3: Obtain the weight coefficient of the corresponding lane based on the hierarchical attenuation factor: a i =α0•δ i In the formula, α0 is the initial weight coefficient of the lane; Step S3: Based on the vehicle flow velocity V and total traffic volume C O The sliding average value of M consecutive vehicle flow velocity measurements is calculated as V. cur_mean The moving average of N consecutive vehicle flow measurement data is C. cur_mean ; Step S4: According to the V cur_mean The C cur_mean Speed ​​limit V of the road to be monitored max The historical maximum traffic volume of the road to be monitored is C. max The road traffic conditions are calculated and determined as follows: (1) When V cur_mean ≥K1·V max , or, (V cur_mean >K4·V max )&(C cur_mean <P1·C max When the traffic conditions are clear, the current road traffic status is determined to be smooth. (2) When K2·V max ≤V cur_mean < K1·V max Or, P1·C max ≤C cur_mean <P2·C max At that time, the current road traffic condition is determined to be relatively congested; (3) When K3·V max ≤V cur_mean < K2·V max Or, P2·C max ≤C cur_mean <P3·C max At that time, the current road traffic condition is determined to be congested; (4) When V cur_mean < K3·V max Or, P3·C max ≤C cur_mean At that time, the current road traffic condition is determined to be severe congestion; In the above formula, K1, K2, K3, and K4 are the vehicle velocity boundary state coefficients, and P1, P2, and P3 are the vehicle flow rate boundary state coefficients.

2. The method for real-time monitoring of road traffic conditions according to claim 1, characterized in that, The specific implementation of step S1 is as follows: Step S1.1: Along the lane direction, two sets of sensors are arranged at intervals on the outermost lane side: transmitting sensor T1 and receiving sensor R1 form one set, and transmitting sensor T2 and receiving sensor R2 form the other set; Step S1.2: The transmitting sensor T1 periodically transmits signals. When a vehicle passes by the location corresponding to the transmitting sensor T1, the receiving sensor R1 receives the signal, records the time when it receives the signal as t1, and proceeds to the next step. Step S1.3: Turn off transmitting sensor T1, turn on transmitting sensor T2 and send a signal. When a vehicle passes by the location corresponding to transmitting sensor T2, receiving sensor R2 receives the signal, records the time when it receives the signal as t2, and proceeds to the next step. Step S1.4: Calculate the vehicle flow velocity V: V = L / (t2-t1), where L is the distance between the receiving sensor R1 and the receiving sensor R2; Calculate the flow rate C: C = the number of times the receiving sensor R1 receives the signal, and remove duplicate data.

3. The method for real-time monitoring of road traffic conditions according to claim 2, characterized in that, The method for removing duplicate data in step S1.4 is as follows: Step S1.4.1: Acquire the time series data of vehicle passage signals collected by the receiving sensor R1, denoted as {t} ˋ 1,t ˋ 1,…,t ˋ i ,t ˋ n }, where t ˋ i This represents the timestamp of the i-th signal reception. Step S1.4.2: Calculate the time interval sequence between adjacent signals {Δt} ˋ 1,Δt ˋ 1,…,Δt ˋ i ,Δt ˋ n }where Δt ˋ i =t ˋ i+1 - t ˋ ; Step S1.4.3: Calculate statistical features based on the time interval sequence: Average time interval: ; Standard deviation of time interval: ; Step S1.4.4: Set the dynamic judgment threshold T=μ-β•σ, where β is a preset coefficient; Step S1.4.5: Traverse the time interval sequence. If there exists Δt ˋ i < T, then determine that t ˋ i+1 is a repeated signal and eliminate it.

4. The method for real-time monitoring of road traffic conditions according to claim 3, characterized in that, It also includes step S1.4.6: updating μ and σ for the sample based on the most recent Y time intervals.

5. The method for real-time monitoring of road traffic conditions according to claim 4, characterized in that, The calculation method for the intermediate lane level is as follows: (1) When A is odd, the middle lane level L C =(A+1) / 2; (2) When A is even, the middle lane level L C =A / 2.

6. A real-time road traffic condition monitoring system, characterized in that, include: A low-power vehicle flow velocity acquisition terminal is deployed on one side of the outermost lane of the road to be monitored, and uses low-power technology to measure the vehicle flow velocity V and flow rate C on the outermost lane of the road to be monitored. The road traffic condition monitoring platform communicates with the low-power vehicle flow rate acquisition terminal and is configured with: Total traffic flow calculation module: Calculates the total traffic flow C of the entire one-way lane based on the traffic flow C. O Total traffic volume C O The calculation method is as follows: First, based on the number of lanes A of the road to be monitored, dynamically allocate the weight coefficient α for each lane. i (i=1,2,…,A), where the initial weight coefficient of the outermost lane is 1, and the initial weight coefficient of other lanes is 1.2; then, combined with the time period correction factor γ, the total traffic flow C is calculated according to the following formula. O : In the formula, γ=1.2 during rush hour, γ=0.7 at night, and γ=1 at other times; Intermediate calculation module: based on the vehicle flow velocity V and total traffic volume C O The sliding average value of M consecutive vehicle flow velocity measurements is calculated as V. cur_mean The moving average of N consecutive vehicle flow measurement data is C. cur_mean ; Judgment module: Based on the V cur_mean The C cur_mean Speed ​​limit V of the road to be monitored max The historical maximum traffic volume of the road to be monitored is C. max The road traffic conditions are calculated and determined as follows: (1) When V cur_mean ≥K1·V max , or, (V cur_mean >K4·V max )&(C cur_mean <P1·C max When the traffic conditions are clear, the current road traffic status is determined to be smooth. (2) When K2·V max ≤V cur_mean < K1·V max Or, P1·C max ≤C cur_mean <P2·C max At that time, the current road traffic condition is determined to be relatively congested; (3) When K3·V max ≤V cur_mean < K2·V max Or, P2·C max ≤C cur_mean <P3·C max At that time, the current road traffic condition is determined to be congested; (4) When V cur_mean < K3·V max Or, P3·C max ≤C cur_mean At that time, the current road traffic condition is determined to be severe congestion; In the above formula, K1, K2, K3, and K4 are the vehicle velocity boundary state coefficients, and P1, P2, and P3 are the vehicle flow rate boundary state coefficients.

7. The real-time road traffic condition monitoring system according to claim 6, characterized in that, The low-power vehicle flow rate acquisition terminal includes a microprocessor, two sets of sensors communicating with the microprocessor: a transmitting sensor T1 and a receiving sensor R1 as one set, and a transmitting sensor T2 and a receiving sensor R2 as another set, a wireless communication module communicating with the microprocessor, and a power supply module for power supply.

8. The real-time road traffic condition monitoring system according to claim 7, characterized in that, Both sets of sensors employ ultrasonic sensors, infrared light sensors, laser sensors, thermal sensors, or microwave radar sensors.

Images