Urban road congestion evaluation method considering macro-micro data coupling

Abstract

The invention provides an urban road congestion evaluation method considering macro-micro data coupling, and the method comprises the following steps: S1, obtaining GIS geographic information data, and carrying out the modeling of a traffic network through a complex network theory; s2, preprocessing the vehicle GPS track data, and extracting speed information of each road section to obtain a traffic state index; s3, obtaining a seepage threshold value according to the speed information of each road section; s4, according to the percolation threshold value, obtaining an operation state threshold value of each road section; and S5, obtaining an operation state index according to the traffic state index, and obtaining a system congestion index SCI in combination with an operation state threshold. The invention provides a macroscopic and microscopic coupled road congestion evaluation method, aiming at the problem of urban road congestion condition evaluation, the type and quality difference of detectors, the uncertainty of cost effectiveness and data real-time performance caused by introduction of multi-source data are avoided, and the accuracy of urban road congestion condition evaluation is improved. Meanwhile, the problem that detailed information of specific road sections is lost due to the fact that traffic conditions are described only from the macroscopic perspective is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic network evaluation, and in particular to an urban road congestion evaluation method considering macro-micro data coupling. Background Art

[0002] With the development of social economy and the acceleration of urbanization, the contradictions brought about by the limited urban traffic resources have become increasingly prominent, specifically manifested in the imbalance between demand and supply of urban road networks, environmental pollution and traffic safety issues. In order to cope with complex traffic problems, a series of traffic control and management solutions are needed. Urban traffic status reflects the time-varying, multi-scale, multi-variable and random characteristics of traffic congestion in urban road networks. Quantifying the degree of traffic congestion can enable transportation management departments to grasp the spatiotemporal information of congested sections, which is the basis for implementing traffic control and management. Traffic flow in urban road networks is complex and dynamic, and it is a challenging task to comprehensively evaluate the congestion level of each section. Therefore, it is urgent to establish a set of scientific urban road section traffic status evaluation methods to provide a reference for efficient decision-making in traffic management.

[0003] Chinese invention patent: The announcement number is "CN106781491B", and the name is "A system for evaluating urban road traffic status", which discloses an urban road traffic status evaluation system, including a data acquisition unit, a data processing unit, a data transmission unit and a traffic status evaluation unit; the data acquisition unit is used to collect traffic parameters at key locations, the data processing unit is used to process the collected data and transmit it to the traffic status evaluation unit through the data transmission unit; the traffic status evaluation unit is used to evaluate the road traffic status according to the processed traffic parameter values. This technical solution comprehensively considers vehicle status information and road traffic status information, establishes a comprehensive and efficient road traffic status evaluation system, and realizes effective evaluation of road traffic status. However, this technical solution has strict requirements on the setting of road sensors, and cannot be evaluated on sections without sensor data support.

[0004] Chinese invention patent: Announcement number is "CN108777064A", and the name is "A traffic state evaluation system based on information fusion". It discloses a traffic state evaluation system based on information fusion, including a traffic information detection module, a data transmission module, a data preprocessing module, a feature extraction module, a state evaluation module and a result evaluation module. The traffic information detection module is used to collect traffic information, the data transmission module is used to transmit the traffic information to the data preprocessing module, the data preprocessing module is used to filter and de-duplicate the traffic information, the feature extraction module is used to extract features and normalize the processed traffic information, the state evaluation module is used to evaluate the traffic state, and the result evaluation module is used to count the accuracy of the traffic state evaluation results, and issue an early warning when the accuracy is low. This technical solution adopts evidence theory to fuse traffic monitoring information, and overcomes the defects of evidence theory in processing conflicting evidence by improving the evidence theory. However, the information fusion process in this technical solution is rough and single-dimensional, which will cause inaccurate traffic state evaluation results.

[0005] Chinese invention patent: Announcement number is "CN116030631A", and the name is "A real-time traffic congestion status assessment method based on drone aerial video". It discloses a real-time traffic congestion status assessment method based on drone aerial video, which is divided into a traffic flow statistics module and a road time occupancy calculation module. The traffic flow statistics module records the tracked vehicle ID and detection frame coordinate value. When the change of the detection frame coordinate value of the same ID vehicle is greater than the threshold, the traffic flow is increased by 1; the road time occupancy calculation module sets N virtual coils with dynamically changing positions, records the number of times each virtual coil is occupied by a vehicle in each frame image, and calculates the road time occupancy. The traffic congestion status is assessed by the traffic flow and road time occupancy within a specified time period. This technical solution solves the problems of existing methods requiring a lot of manpower and material resources, equipment being prone to aging and damage, and relatively complex implementation. It takes into account accuracy, real-time and economy, and is of great significance for realizing traffic congestion status judgment and traffic scheduling management. However, the detection range of this technical solution is relatively limited, and the congestion status assessment index is single, which easily causes the assessment result to be inconsistent with the actual situation. Summary of the invention

[0006] In order to solve the problem in the above-mentioned prior art that the evaluation method has a strict road sensor setting scheme and a single congestion status evaluation indicator, which leads to the inability to evaluate or inaccurate evaluation status. The present invention proposes an urban road congestion evaluation method that considers macro-micro data coupling, and proposes a road congestion evaluation method that couples macro and micro data. Aiming at the evaluation of urban road congestion status, it avoids the type and quality differences of detectors, cost-effectiveness and data real-time uncertainty caused by the introduction of multi-source data, and solves the problem of missing detailed information on specific road sections caused by describing traffic conditions only from a macro perspective.

[0007] The present invention is achieved through the following technical solution: comprising the following steps:

[0008] S1: Obtain GIS geographic information data within the evaluation road network area, and use complex network theory to model the traffic network within the evaluation road network area;

[0009] S2: Preprocess the vehicle GPS trajectory data in the evaluation road network area to extract the speed information of each road section, where the speed information of each road section includes the current speed v of each road section within the time interval t. ij (t) and limit the maximum speed Obtain the traffic status index U(T) of the road network area within the time interval T;

[0010] S3: According to the current speed v of each road section within the time interval t ij (t) and limit the maximum speed Get the seepage threshold q in the evaluation road network area c (T);

[0011] S4: According to the percolation threshold q c (T), and obtain the operating status threshold θ(T) of each road section;

[0012] S5: According to the traffic status index U(t) of the road network area in the time interval T, the operation status index P(t) of the road network area is obtained, and combined with the operation status threshold θ(T), the system congestion index SCI is obtained.

[0013] As a further preference, the specific steps of step S1 are as follows:

[0014] S1-1. Model the traffic network as a graph G(R, E, W), where R = {r 1 , ..., r n} is a set of nodes, n is the number of nodes, E={e ij , i, j = 1, ..., k} is a set of road segments, e ij For connecting node r i and node r jroad sections, k is the number of road sections, W = {w ij (t), i, j = 1, ..., k} is the time weight matrix, where w ij (t) represents the road segment e ij The operating status within the time interval t.

[0015] As a further preference, the specific steps of step S2 are as follows:

[0016] S2-1. Obtain the vehicle GPS trajectory data in the evaluation road network area, perform preprocessing, and obtain the speed information of each road section, that is, the current speed v of each road section ij (t) and the maximum speed limit within the time interval t The maximum speed limit within the time interval t is the 95th percentile of the current speed in time interval t;

[0017] S2-2. Based on the speed information of each road section, the traffic status index U(T) in the evaluation road network area within the time interval T is obtained by formula (1).

[0018] U(T)=g(w ij (t)), t = t m , T = t M (1)

[0019] In the formula, Time interval t M Greater than time interval t m .

[0020] As a further preferred embodiment, the time interval t M is 60 minutes, the time interval t m for 5 minutes.

[0021] As a further preference, the specific steps of step S3 are as follows:

[0022] S3-1. According to the current speed v of the road section within the time interval t ij (t), time interval t M and the time interval t m , we can get the e of each road section in time period T by formula (2): ij The average speed v ij (T):

[0023]

[0024] S3-2. According to each section e in time period T ij The average speed v ij (T) and limit the maximum speed The functional status f of each road section is obtained by formula (3): ij (T):

[0025]

[0026] Where q(T) is the threshold of the road segment during the time interval T;

[0027] S3-3. Select f in step S3-2 ij The second largest connected cluster in the connected cluster formed by the road segment with (T) = 1 reaches the maximum value q(T), and the permeation threshold q of the evaluation road network area is obtained. c (T).

[0028] As a further preference, the value range of q(T) is 0-1, wherein 0 indicates that the road section is completely blocked and all vehicles are stationary, and 1 indicates that the road section is freely flowing and all vehicles are traveling at the maximum speed.

[0029] As a further preference, the specific steps of step S4 are as follows:

[0030] S4-1. According to the percolation threshold q c (T), the penetration threshold q of the evaluation road network area is obtained by formula (4) c (T) change value

[0031]

[0032] in, To pass the closed section e ij and measure the updated infiltration threshold of the modified evaluation road network area;

[0033] S4-2. According to the percolation threshold q c (T) and the percolation threshold q c (T) change value Obtain the operating status threshold θ(T) of the evaluated road network area;

[0034] θ(T) is calculated by evaluating the road segments e in the road network area. ij The dynamic threshold value of the operating state θ ij (T) composition, θ ij (T) is obtained from formula (5),

[0035]

[0036] Where λ is the correction factor.

[0037] As a further preferred embodiment, the specific steps of step S5 are as follows:

[0038] S5-1. According to the traffic status index U(T) of the road network within the time interval T obtained in step S2-2, the operating status index P(t) for evaluating the road network area is obtained, where P(t) = U(T);

[0039] S5-2. The free flow range of the evaluation road network area is obtained according to the operating status index P(t) of each section in the evaluation road network area from 0:00 to 6:00 in the morning. The minimum value of the free flow range is the lower limit index P(t pre );

[0040] S5-3. According to the operating state index P(t) and the operating state threshold θ(T), the resistance coefficient R is obtained by formula (6): e (t),

[0041]

[0042] S5-4. According to the operating status index P(t) and the lower limit index P(t pre ), the congestion level C is obtained from formula (7) m (t),

[0043]

[0044] S5-5. According to the resistance coefficient R e (t) and the congestion level C m (t), according to the formula SCI(t) = C m (t)+R e (t) Obtain the system congestion index SCI.

[0045] As a further preference, the preprocessing method for the vehicle GPS trajectory data in step S2-1 is: matching the vehicle GPS trajectory data with the road network of the evaluation road network area through a map matching algorithm, and using the DBSCAN clustering method to remove the densely accumulated GPS data at the locations where stationary vehicles or other vehicles have been parked for a long time.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The method of the present invention evaluates the road congestion condition by generating a systematic congestion index from the macro-micro coupling, which fills the gap between the evaluation of a single road section and the macro evaluation of the road network, can provide a more accurate evaluation of the road network traffic status, and is helpful to implement effective traffic management and control measures on specific road sections.

[0048] 2. The method of the present invention uses the percolation theory to describe the macroscopic traffic status of the road network, and uses this as the evaluation baseline for correction, so as to obtain a reliable congestion assessment without introducing additional data sources.

[0049] 3. The present invention avoids the problems of detector type and quality differences, cost-effectiveness and inaccurate real-time data brought about by introducing multiple data sources (such as inductive loops, traffic monitoring videos, and radio frequency identification), and the evaluation data source acquisition method is simple and efficient.

[0050] 4. The present invention uses the DBSCAN clustering method for data preprocessing, which can effectively remove noise data and improve the reliability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The figure is a flow chart of the evaluation method in the present invention.

[0052] Figure 2 It is the road congestion status assessment framework of the present invention.

[0053] Figure 3(a) is a GPS data distribution diagram.

[0054] Figure 3(b) shows the speed distribution of the road section after GIS preprocessing.

[0055] Figure 4(a) is a schematic diagram of the microwave detector layout.

[0056] Figure 4(b) is a schematic diagram of the congestion assessment results using SCI as an indicator.

[0057] Figure 4(c) is a schematic diagram of the congestion assessment results using occupancy rate as an indicator.

[0058] Figure 4(d) is a schematic diagram of the congestion assessment results using relative speed as an indicator.

[0059] Figure 5 This is a schematic diagram of visualizing the road network congestion assessment using SCI as an indicator. DETAILED DESCRIPTION

[0060] Advantages and features of the present invention will be illustrated and explained by the following non-limiting description of preferred embodiments thereof, which are given by way of example only with reference to the accompanying drawings.

[0061] like Figure 1 As shown, the present invention proposes an urban road congestion evaluation method considering macro-micro data coupling, and the method comprises the following steps:

[0062] S1: Obtain GIS geographic information data within the evaluation road network area, and use complex network theory to model the transportation network within the evaluation road network area.

[0063] S1-1. Obtain GIS geographic information data in the evaluation road network area and model the traffic network in the evaluation road network area. The traffic network is modeled as a graph G(R, E, W), where R = {r 1, ..., r n} is a set of nodes (i.e., road intersections), n is the number of nodes, E = {e ij , i, j = 1, ..., k} is a set of road segments, e ij For connecting node r i and node r j road sections, k is the number of road sections, W = {w ij (t), i, j = 1, ..., k} is a series of time weight matrices, where w ij (t) represents the road segment e ij The operating status within the time interval t.

[0064] S2: Preprocess the vehicle GPS trajectory data in the evaluation road network area, extract the speed information of each road section, and obtain the traffic status index (traffic congestion degree) U(T) of the evaluation road network area within the time interval T.

[0065] S2-1. Obtain the vehicle GPS trajectory data within the evaluation road network area, perform preprocessing, and obtain the speed information of each road section.

[0066] Obtain vehicle GPS trajectory data in the evaluation road network area and extract speed information for each road section. Use a map matching algorithm to match the obtained GPS data with the road network in the evaluation road network area. Use the DBSCAN clustering method to remove densely accumulated GPS data at locations where stationary vehicles or other vehicles have been parked for a long time; for sections where speed data cannot be collected during certain periods of time, use an interpolation method to fill in the missing data; for roads without any speed data, use the average speed of the connecting roads as a substitute. Repeat this process at a time interval t until the time interval reaches T, and obtain the GPS data of all sections within the time interval T, and plot it into a graph, as shown in Figure 3(a). Use a time interval t of 5 minutes to use GPS data to obtain the current speed v of each section. ij (t), plotted as a graph, as shown in Figure 3(b), and the maximum speed limit within the time interval t The maximum speed limit within the time interval t is the 95th percentile of the current speed in the time interval t. The current speed v of each road section in the time interval t ij (t) and limit the maximum speed It is the speed information of each road section.

[0067] S2-2. Based on the speed information of each road section, the traffic status index (traffic congestion level) U(T) in the road network area within the time interval T is obtained and determined by formula (1):

[0068] U(T)=g(w ij (t)),t=tm ,T=t M (1)

[0069]

[0070] In formula (1), according to the spatiotemporal mapping function g(·) and the road segment e ij The operating state w in the time interval t ij (t) to obtain the traffic status index U(T) in the evaluation road network area within the time interval T. Among them, the time interval t M Greater than time interval t m , the specific time interval t M is 60 minutes, the time interval t m for 5 minutes.

[0071] In formula (2), w ij (t) is the current speed v ij (t) and its maximum speed limit within the time interval t The ratio of v ij (t) is the road segment e ij The current speed in time interval t, The 95th percentile of its historical speed.

[0072] S3: Analyze the congestion diffusion effect of the road network based on the percolation theory and obtain the percolation threshold q in the evaluation road network area c (T), as the traffic status information at the macro road network level.

[0073] S3-1. According to the current speed v of the road section within the time interval t ij (t), time interval t M and the time interval t m Get each road section e in time period T ij The average speed v ij (T), determined by formula (3):

[0074]

[0075] S3-2. According to each section e in time period T ij The average speed v ij (T) and limit the maximum speed Get the functional status f of each road segment ij (T), determined by formula (4):

[0076]

[0077] Where q(T) is the threshold of the road segment during the time interval T. Based on the speed level considered acceptable, q(T) is used to determine whether the road segment has reached maximum functionality during the time interval T. The value range of q(T) is 0 to 1, where 0 means that the road segment is completely blocked and all vehicles are stationary, and 1 means that the road segment is free flowing and all vehicles are traveling at maximum speed. If the average operating state recorded during the time interval T is Greater than section e ij If the running status recorded by q(T) in the figure is not correct, the section is considered to be a normal section (i.e., ij (T) = 1), otherwise, it is a dysfunctional section (i.e., f ij (T) = 0).

[0078] S3-3. Select f in step S3-2 ij The second largest connected cluster in the connected cluster formed by the road segment with (T) = 1 reaches the maximum value q(T), and the permeation threshold q of the evaluation road network area is obtained. c (T).

[0079] Using the percolation theory, record f in step S3-2 ij After removing the dysfunctional road segments in the network, each road segment will form different dual-connected road segments within a given time interval T. The q(T) value when the second largest connected cluster reaches the maximum value is defined as the permeation threshold q of the road network. c (T). This reflects the optimal state that the evaluated road network area can achieve within the time period T, and the vehicle can travel smoothly at a relative speed on most sections of the road. c (T) represents the phase transition point of the connectivity of the functional transportation network, which can effectively measure the maximum operating state and reflect its global efficiency.

[0080] S4: According to the percolation threshold q c (T), and obtain the operation state threshold (Operation State Threshold, OST)θ(T) of each road section.

[0081] S4-1. According to the percolation threshold q c (T) Get the infiltration threshold q of the evaluation road network area c (T) change value Determined by formula (5):

[0082]

[0083] in, To pass the closed section (ie section e ij The operation state is converted to 0) and the updated penetration threshold of the modified evaluation road network area is measured.

[0084] S4-2. According to the percolation threshold q c (T) and the percolation threshold q c (T) change value The operation state threshold (Operation State Threshold, OST)θ(T) of the evaluation road network area is obtained.

[0085] θ(t) is calculated by evaluating the road segments e in the road network area. ij The dynamic threshold value of the operating state θ ij (T) composition. Structure of θ ij (T) can be expressed by formula (6):

[0086]

[0087] where λ is the correction factor. Note that if the road segment e ij The running status increases from 0 to 1 and exceeds If the value of is large, the road section tends to improve the efficiency of the road network and obtain a better macro traffic state (i.e., alleviate traffic congestion).

[0088] S5: According to the operating status threshold θ(T), the system congestion index SCI (Systemic Congestion Index) is obtained to evaluate the congestion status of each road section in the road network area.

[0089] S5-1. Based on the traffic status index U(T) of the road network within the time interval T obtained in step S2-2, obtain the operating status index P(t) of the evaluation road network area, where P(t) = U(T). Figure 2 shown.

[0090] S5-2. The free flow range of the evaluation road network area is obtained according to the operating status index P(t) of each section in the evaluation road network area from 0:00 to 6:00 in the morning. The minimum value of the free flow range is the lower limit index P(t pre ).

[0091] The traffic operation state within the free flow range is a non-congested state. When the operation state index P(t) is lower than the lower limit index P(t) of the free flow range, pre ), congestion will occur, such as Figure 2 shown.

[0092] S5-3. Obtain the resistance coefficient R according to the operating state index P(t) and the operating state threshold θ(T) e (t). It indicates the severity of the congestion caused by the phase change of traffic state on a certain road section spreading to the adjacent road section, which is closely related to the operating state threshold θ(T). eThe expression of (t) is shown in formula (7):

[0093]

[0094] If P(t)<θ(T), the part beyond the lower bound is represented by R e (t) = θ(T) - P(t). The critical threshold of congestion diffusion is (C t ): When P(t)<θ(T), the road section will cause congestion diffusion between adjacent road sections, thereby accelerating the diffusion of congestion, such as Figure 2 As shown. t (t) can be expressed by formula (8):

[0095] C t (t) = P(t pre )-θ(T) (8)

[0096] S5-4. According to the operating status index P(t) and the lower limit index P(t pre ), and get the congestion level C m (t) is used to measure the degree of traffic congestion. It is determined by formula (9):

[0097]

[0098] When the road section operation status index P(t) is at the lower limit of the free flow range, that is, P(t pre )>P(t). The degradation of the running state can be regarded as the formation process of congestion, where C m (t) = P(t pre )-P(t). If P(t pre )≤P(t), P(t) is still within the free flow range, where C m (t) = 0, such as Figure 2 shown.

[0099] S5-5. According to the resistance coefficient R e (t) and the congestion level C m (t), the system congestion index SCI (Systemic Congestion Index) is obtained, which is used to evaluate the traffic status of urban roads. It is expressed by formula (10):

[0100] SCI(t)=C m (t)+R e (t) (10)

[0101] In the process of changing the operation status of the road section, the formation and dissipation of congestion are corresponding at the macro scale. Then, θ(T) is also affected by the macro traffic status.

[0102] To further verify the feasibility and superiority of the urban road congestion evaluation method considering macro-micro data coupling described in the present invention. A local road network in Kunshan, China was selected. There are 209 road sections and 144 intersections in the area. The taxi GPS data set was recorded every 5 seconds from January 4 to January 6, 2018. It was preprocessed according to steps S1 to S2 to obtain Figures 3(a) and 3(b). Figure 3(a) is a GPS data distribution diagram of a local road network in Kunshan, China from January 4 to January 6, 2018. Figure 3(b) is the spatial distribution of speed at 12:00 on January 4, 2018. The local road network data is substituted into steps S3 to S5 in turn to obtain the percolation threshold q of the local road network. c (T), the operating state threshold value of each road section θ(T), the resistance coefficient R e (t) and the congestion level C m (t), and finally obtain the system congestion index SCI. After matching the system congestion index SCI with the map of the local road network, we can get Figure 5 The road network congestion status assessment diagram is shown. Figure 5 This is a road network congestion status assessment diagram for the three time periods of 6:30, 7:30 and 8:30 in the morning. It can assess the road congestion status of this local area in Kunshan.

[0103] The road network congestion status assessment visualization using the system congestion index SCI proposed by the present invention is as follows Figure 5 As shown in the figure. At around 6:30 in the morning, SCI identified that most sections were relatively stable. However, during the morning peak period (i.e. 7:30), the congestion level escalated significantly, with a significant increase in the number of sections within the SCI range of 0.4-0.6, and a further surge in the number of sections marked in red, indicating severe congestion. After 1 hour (i.e. 8:30), the congestion gradually eased and most sections reached a relatively stable state.

[0104] There are many indicators used to measure the traffic conditions of urban roads. In order to verify the effectiveness of the system congestion index (SCI) proposed in the present invention, relative speed (RV) and occupancy rate are introduced to evaluate the degree of congestion and compare them with the trend of the system congestion index (SCI). The occupancy rate can be constructed based on the density and average speed of each road section. Considering the high detection cost, only a few road sections are currently equipped with microwave detectors that can provide occupancy information.

[0105] As shown in Figure 4(a), the main roads selected for analysis include sections 2, 12, 44, 62, and 76. Among them, sections 12 and 76 are equipped with microwave detectors.

[0106] Figures 4(b), 4(c) and 4(d) show the congestion levels estimated by the system congestion index SCI, occupancy rate and relative speed RV, respectively. The congestion level varies with space and time. As shown in Figure 4(d), there is a relatively high level of congestion in the early morning hours. Since the traffic demand in the early morning hours is usually lower than that in the daytime, the occupancy rate observed in Figure 4(c) also proves this. The situation shown in Figure 4(d) can be explained by the fact that the taxi trajectories at the low GPS sampling rate in the early morning lead to unreliable and counterintuitive assessments, while the system congestion index SCI and the original occupancy rate assessment provided by the present invention will not have such errors, so the system congestion index SCI provided by the present invention is more accurate than the assessment of the relative speed RV.

[0107] As can be seen from Figure 4(b) and Figure 4(c), higher congestion levels can be observed during the morning peak (6:00 to 8:00 a.m.) and evening peak (7:00 to 9:00 p.m.) compared to other time periods. In addition, the congestion changes in different sections of this trunk road are significant during the same period of the day, while the changes in Figure 4(d) are relatively low. It can be seen that the system congestion index SCI provided by the present invention is more accurate than the evaluation of relative speed RV; compared with the need to set up microwave detectors for occupancy evaluation, the system congestion index SCI provided by the present invention saves more resources and costs.

[0108] In addition to the above embodiments, the present invention may also have other implementation modes. Any technical solutions formed by equivalent replacement or equivalent transformation shall fall within the protection scope required by the present invention.

Claims

1. An urban road congestion evaluation method considering macro-micro data coupling, characterized by: The steps include: S1: Obtain GIS geographic information data in the evaluation road network area, and use complex network theory to model the traffic network in the evaluation road network area; S2: Preprocess the vehicle GPS trajectory data in the evaluation road network area to extract the speed information of each road section, where the speed information of each road section includes the current speed v of each road section within the time interval t. ij (t) and limit the maximum speed Obtain the traffic status index U(T) of the road network area within the time interval T; S3: According to the current speed v of each road section within the time interval t ij (t) and limit the maximum speed Get the seepage threshold q in the evaluation road network area c (T); S4: According to the percolation threshold q c (T), and obtain the operating status threshold θ(T) of each road section; S5: According to the traffic status index U(T) of the road network area in the time interval T, the operation status index P(T) of the road network area is obtained, and combined with the operation status threshold θ(T), the system congestion index SCI is obtained.

2. The urban road congestion evaluation method considering macro-micro data coupling according to claim 1 is characterized by: The specific steps of step S1 are as follows: S1-1. Model the traffic network as a graph G(R, E, W), where R = {r1, ..., r n } is a set of nodes, n is the number of nodes, E={e ij , i, j = 1, ..., k} is a set of road segments, e ij For connecting node r i and node r j road sections, k is the number of road sections, W = {w ij (t), i, j = 1, ..., k} is the time weight matrix, where w ij (t) represents the road segment e ij The operating status within the time interval t.

3. The urban road congestion evaluation method considering macro-micro data coupling according to claim 2 is characterized by: The specific steps of step S2 are as follows: S2-1. Obtain the vehicle GPS trajectory data in the evaluation road network area, perform preprocessing, and obtain the speed information of each road section, that is, the current speed v of each road section ij (t) and the maximum speed limit within the time interval t The maximum speed limit within the time interval t is the 95th percentile of the current speed in time interval t; S2-2. Based on the speed information of each road section, the traffic status index U(T) in the evaluation road network area within the time interval T is obtained by formula (1). U(T)=g(w ij (t)),t=t m ,T=t M (1) In the formula, Time interval t M Greater than time interval t m .

4. The urban road congestion evaluation method considering macro-micro data coupling according to claim 3 is characterized by: Time interval t M is 60 minutes, the time interval t m for 5 minutes.

5. The urban road congestion evaluation method considering macro-micro data coupling according to claim 4 is characterized by: The specific steps of step S3 are as follows: S3-1. According to the current speed v of the road section within the time interval t ij (t), time interval t M and the time interval t m , we can get the e of each road section in time period T by formula (2): ij The average speed v ij (T): S3-2. According to each section e in time period T ij The average speed v ij (T) and limit the maximum speed The functional status f of each road section is obtained by formula (3): ij (T): Where q(T) is the threshold of the road segment during the time interval T; S3-3. Select f in step S3-2 ij The second largest connected cluster in the connected cluster formed by the road segment with (T) = 1 reaches the maximum value q(T), and the permeation threshold q of the evaluation road network area is obtained. c (T).

6. The urban road congestion evaluation method considering macro-micro data coupling according to claim 5 is characterized by: The value range of q(T) is 0-1, where 0 indicates that the road section is completely blocked and all vehicles are stationary, and 1 indicates that the road section is free flowing and all vehicles are traveling at the maximum speed.

7. The urban road congestion evaluation method considering macro-micro data coupling according to claim 6 is characterized by: The specific steps of step S4 are as follows: S4-1. According to the percolation threshold q c (T), the penetration threshold q of the evaluation road network area is obtained by formula (4) c (T) change value in, To pass the closed section e ij and measure the updated infiltration threshold of the modified evaluation road network area; S4-2. According to the percolation threshold q c (T) and the percolation threshold q c (T) change value Obtain the operating status threshold θ(T) of the evaluated road network area; θ(T) is calculated by evaluating the road segments e in the road network area. ij The dynamic threshold value of the operating state θ ij (T) composition, θ ij (T) is obtained from formula (5), Where λ is the correction factor.

8. The urban road congestion evaluation method considering macro-micro data coupling according to claim 7 is characterized by: The specific steps of step S5 are as follows: S5-1. According to the traffic status index U(T) of the road network within the time interval T obtained in step S2-2, the operating status index P(t) for evaluating the road network area is obtained, where P(t) = U(T); S5-2. The free flow range of the evaluation road network area is obtained according to the operating status index P(t) of each section in the evaluation road network area from 0:00 to 6:00 in the morning. The minimum value of the free flow range is the lower limit index P(t pre ); S5-3. According to the operating state index P(t) and the operating state threshold θ(T), the resistance coefficient R is obtained by formula (6): e (t), S5-4. According to the operating status index P(t) and the lower limit index P(t pre ), the congestion level C is obtained by formula (7) m (t), S5-5. According to the resistance coefficient R e (t) and the congestion level C m (t), according to the formula SCI(t) = C m (t)+R e (t) Obtain the system congestion index SCI.

9. The urban road congestion evaluation method considering macro-micro data coupling according to any one of claims 3 to 8, characterized in that: The preprocessing method for vehicle GPS trajectory data in step S2-1 is: The vehicle GPS trajectory data is matched with the road network in the evaluation area through a map matching algorithm, and the densely accumulated GPS data at locations where stationary vehicles or other vehicles have been parked for a long time are removed using the DBSCAN clustering method.

Images