A generalized estimation method and system for saturated headway at urban road signalized intersections

Abstract

The present invention relates to the field of urban road traffic technology, and discloses a generalized estimation method and system for saturated headway at urban road signal intersections, comprising obtaining license plate recognition data and extracting an intersection signal cycle; matching the license plate recognition data with floating vehicle data, extracting saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles; screening out factors influencing the saturated headway; using the saturated headway samples as input, and constructing a probability estimation model of the saturated headway and various saturated headway influencing factors based on the NGBoost model according to the saturated headway influencing factors; using each saturated headway influencing factor as input to the probability estimation model, and outputting a generalized estimation result of the saturated headway. The beneficial effect of the present invention is that it can generalize the saturated headway based on existing and easily obtainable license plate recognition data and floating vehicle data.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban road traffic, and relates to a generalized estimation method and system for saturated headway at a signalized intersection on an urban road. Background Art

[0002] Signalized intersections on urban roads are key nodes where the contradiction between traffic supply and demand is prominent. Accurately characterizing and understanding the supply capacity characteristics of signalized intersections is crucial for achieving refined and intelligent intersection traffic management and control. In traffic engineering, headway is a key indicator of traffic flow conditions. Saturated headway reflects the efficiency of vehicles passing through an intersection under saturated flow conditions and is a key indicator of intersection supply characteristics.

[0003] The existing methods for estimating saturated headway time are as follows: (1) HCM-based methods: by collecting a large amount of data to calculate the relevant correction parameters of the saturated flow rate, the saturated flow rate is estimated, and then the saturated headway time is obtained. The calculation of the correction coefficient involves factors such as the number of lanes, the rate of large vehicles, the slope, the influence of buses, the area type, turning vehicles, pedestrians and non-motor vehicles. (2) Data-driven methods: divided into threshold methods and model methods. The former determines whether the headway time is in a saturated state by setting a threshold. The threshold setting is usually based on the median or average value of the collected samples; the latter classifies the headway time by establishing a relevant model to extract the saturated headway time, such as the hidden Markov chain model method, the Gaussian mixture model method, the K-means clustering method, etc. However, the methods represented by HCM more systematically consider the various factors affecting the saturated headway time. In real applications, a large amount of data needs to be collected to calibrate the model parameters. Many parameters are difficult to fully obtain on real urban roads. However, the data-driven saturated headway estimation model is limited to saturated headway estimation under specific conditions and scenarios, and lacks in-depth analysis of the factors affecting saturated headway, which leads to insufficient generalization of the model.

[0004] Currently, traffic data packages widely available on urban roads in my country fall into two categories: license plate recognition data and floating vehicle data. License plate recognition data refers to the use of traffic surveillance cameras to detect vehicles leaving intersections and obtain vehicle license plates through automatic text recognition technology. This data includes information such as the detector number, detection time, license plate, vehicle type, and lane. Floating vehicle data refers to location data collected from satellite-based vehicles (such as taxis), including time, license plate, passenger status, location, and speed. Using this real-world observable data to analyze the factors affecting saturated headway and develop a generalized estimation model is a challenge that currently requires in-depth research and resolution. Summary of the Invention

[0005] The purpose of the present invention is to provide a generalized estimation method and system for saturated headway time at urban road signalized intersections, which can perform generalized estimation of saturated headway time based on existing and easily accessible license plate recognition data and floating vehicle data, providing a scientific basis for the characterization of supply characteristics of urban signalized intersections.

[0006] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0007] In a first aspect, the present invention provides a generalized method for estimating saturated headway time at a signalized intersection on an urban road, comprising:

[0008] Selecting several potential influencing factors of saturated headway, including road grade, lane type, vehicle type, and weather conditions, and identifying the potential influencing factors to obtain the influencing factors of saturated headway;

[0009] Inputting the screened influencing factors of saturated headway into a pre-built probability estimation model to obtain a generalized estimation result of the saturated headway, wherein the generalized estimation result of the saturated headway is the mean value and probability distribution of the saturated headway;

[0010] The method for constructing the probability estimation model is as follows:

[0011] Based on the NGBoost model, a probability estimation model of the functional relationship between the saturated headway and the influencing factors of the saturated headway in the same period is established.

[0012] In combination with the first aspect, further, a specific method for constructing the probability estimation model is:

[0013] First, initialize the probability estimation model parameters , is the parameter of the target distribution estimated by the probability estimation model. For the normal distribution, .

[0014] Secondly, build a base learner. NGBoost uses base learners (such as decision trees) to gradually approximate the parameters of the target distribution.

[0015] Finally, for each round of iterative updates, perform the following steps:

[0016] Calculate the negative log-likelihood (NLL):

[0017]

[0018] in, is the negative log-likelihood, is the target variable, are the parameters estimated by the probability estimation model in the current iteration;

[0019] Compute the negative log-likelihood with respect to Gradient:

[0020]

[0021] in, is the negative log-likelihood about The gradient, are the parameters estimated by the probability estimation model in the current iteration, is the negative log-likelihood;

[0022] Compute the Fisher information matrix:

[0023]

[0024] in, is the Fisher information matrix, is the expectation operator, is the target variable, are the parameters estimated by the probability estimation model in the current iteration;

[0025] Natural Gradient Update:

[0026]

[0027] in, is the natural gradient, is the Fisher information matrix, is the negative log-likelihood about gradient;

[0028] Fitting Natural Gradients Using Base Learners :

[0029]

[0030] in, is the predicted increment of the base learner in the current iteration, is the current iteration number, are the characteristics of the training data, is the natural gradient, is the base learner;

[0031] Update the parameters of the estimated target distribution :

[0032]

[0033] in, It is The update parameters of the step, is the learning rate, is the predicted increment of the base learner in the current iteration.

[0034] In combination with the first aspect, further, the training method of the probability estimation model is: using the extracted saturated headway samples to train and test the probability estimation model, and determining the optimal hyperparameter combination of the probability estimation model based on grid search.

[0035] The optimal hyperparameter combination of the probability estimation model is as follows:

[0036] (1) Learning rate:

[0037] Control the weight adjustment amplitude of each tree and affect the convergence speed of the model.

[0038] (2) The number of base learners (n estimators):

[0039] Specifies the number of base learners to be built, affecting the complexity of the model.

[0040] (3) Minibatch fraction:

[0041] It is used to specify the proportion of training data used in each iteration step during the training process, which determines the amount of data used in each iterative update.

[0042] (4) Column sampling ratio (col sample):

[0043] Used to specify the subsample ratio of feature columns to use when building each base learner, controlling the number of features that each base learner can see.

[0044] In combination with the first aspect, further, the method for extracting the saturated headway samples is:

[0045] Acquiring historical license plate recognition data, identifying a vehicle queue using the historical license plate recognition data, and extracting an intersection signal cycle based on the vehicle queue;

[0046] Get historical floating vehicle data;

[0047] The historical license plate recognition data is matched with the historical floating vehicle data to extract saturated headway time samples of vehicles queuing at the intersection under all intersection signal cycles.

[0048] In combination with the first aspect, further, a specific method for obtaining the intersection signal cycle is:

[0049] Pre-process the historical license plate recognition data to obtain the headway time;

[0050] Based on the headway, a headway threshold gap is set to identify a vehicle queue;

[0051] Starting from the signal cycle determined by the head vehicle of each vehicle queue, the historical license plate recognition data is divided into cycles to obtain the intersection signal cycle.

[0052] In combination with the first aspect, further, the step of preprocessing the historical license plate recognition data includes dividing the license plate recognition data according to the lanes of the intersection, removing the license plate recognition data of repeated detection, and subtracting the continuous detection time of the vehicle to calculate the headway.

[0053] In combination with the first aspect, further, based on the headway time gap, a specific method for setting a headway time gap threshold value to identify a vehicle queue is as follows:

[0054] For each headway, if it is less than the headway threshold gap, the vehicle is added to the vehicle queue; otherwise, a new vehicle queue is created and each vehicle queue is numbered.

[0055] Filter out the vehicle queues whose number of vehicles is less than the vehicle number threshold num;

[0056] Merge adjacent vehicle queues that do not meet the minimum red light interval, and update the headway and vehicle queue number;

[0057] Starting from the signal cycle determined by the head vehicle of each vehicle queue, the license plate recognition data is divided into cycles to obtain the intersection signal cycle.

[0058] In combination with the first aspect, further, the specific method of matching the historical license plate recognition data with the historical floating vehicle data to extract the saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles is as follows:

[0059] For each intersection signal cycle, the historical floating vehicle data in the parked state is matched with the historical license plate recognition data one by one based on the vehicle license plate and time;

[0060] The headway of the matched vehicle and its preceding vehicle within the same intersection signal cycle is extracted as the saturated headway sample.

[0061] In combination with the first aspect, further, a specific method for obtaining the factors affecting the saturated headway is as follows:

[0062] Several potential influencing factors of saturated headway are selected; the potential influencing factors include road grade, lane type, vehicle type, weather conditions, etc., and the overall factors must be easy to obtain and highly generalizable.

[0063] Perform VIF test on the potential influencing factors to determine whether there is collinearity among the features; the specific method is as follows:

[0064] (1) Take each potential influencing factor as the response variable and perform linear regression on all other potential influencing factors.

[0065] (2) Calculate the regression model The value, also known as the coefficient of determination, indicates the extent to which the remaining potential influencing factors can explain the variability of the potential influencing factor.

[0066] (3) Calculate VIF value:

[0067]

[0068] in, is the coefficient of determination of the above regression model.

[0069] (4) Screen out features with collinearity based on the VIF value. The larger the VIF value, the stronger the linear relationship between the potential influencing factor and other potential influencing factors, and further correlation test is performed.

[0070] Obtain alternative influencing factors for saturated headway; perform correlation tests on potential influencing factors with collinearity based on the Spearman correlation coefficient, screen out features with high correlation with other potential influencing factors, and obtain alternative influencing factors; the calculation formula for the Spearman correlation coefficient is as follows:

[0071]

[0072] in, is the Spearman correlation coefficient, It is The difference between the ranking of potential factors affecting the data in the two saturated headways is is the number of saturated headway samples.

[0073] The KS test determines whether an alternative influencing factor has a significant impact on saturated headway. The KS test primarily compares two empirical cumulative distribution functions (ECDFs) to determine whether two samples with different eigenvalues ​​of a given alternative influencing factor come from the same distribution. The KS test value is the maximum absolute difference between the two ECDFs. A larger value indicates a greater difference between the sample and the hypothesized distribution. The KS test value is calculated as follows:

[0074]

[0075] in, is the KS test quantity, The sample size of saturated headway is The empirical cumulative distribution function of The sample size of saturated headway is The empirical cumulative distribution function of represents the size of the first sample, represents the size of the second sample, Indicates the value of the variable (i.e., saturated headway).

[0076] Determine the influencing factors of the saturated headway, where the influencing factors of the saturated headway are those that satisfy the conditions of no obvious collinearity between the features after the collinearity test and the KS test and have a significant impact on the saturated headway.

[0077] In a second aspect, the present invention provides a generalized estimation system for saturated headway time at a signalized intersection on an urban road, comprising:

[0078] a saturated headway influencing factor module configured to select a number of potential influencing factors of the saturated headway, the potential influencing factors including road grade, lane type, vehicle type, and weather conditions, and identify the potential influencing factors to obtain the saturated headway influencing factors;

[0079] A saturated headway probability estimation module is configured to input the filtered saturated headway influencing factors into a pre-built probability estimation model to obtain a generalized estimation result of the saturated headway;

[0080] The method for constructing the probability estimation model is as follows:

[0081] Based on the NGBoost model, a probability estimation model of the functional relationship between the saturated headway and the influencing factors of the saturated headway in the same period is established.

[0082] In combination with the second aspect, the present invention further provides a generalized saturated headway estimation system including:

[0083] an intersection signal cycle extraction module configured to obtain historical license plate recognition data, identify vehicle queues using the historical license plate recognition data, and extract the intersection signal cycle based on the vehicle queues;

[0084] A floating vehicle data acquisition module is configured to acquire historical floating vehicle data;

[0085] The saturated headway time sample extraction module is configured to match the historical license plate recognition data with the historical floating vehicle data, and extract the saturated headway time samples of vehicles queuing at the intersection under all intersection signal cycles.

[0086] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned generalized saturated headway estimation method are implemented.

[0087] In a fourth aspect, the present invention provides a computer device, comprising:

[0088] memory for storing computer programs;

[0089] A processor is used to execute the computer program to implement the steps of the above-mentioned saturated headway generalization estimation method.

[0090] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned saturated headway generalization estimation method.

[0091] The present invention divides the signal period based on license plate recognition data, and identifies vehicles in the intersection queue by matching historical license plate recognition data with historical floating vehicle data, thereby extracting saturated headway samples. The collinearity between factors is determined based on the VIF test and correlation test, and the degree of influence of each factor on the saturated headway is determined based on the KS test, thereby screening out the factors affecting the saturated headway. A relationship model between the saturated headway and the factors affecting the saturated headway is constructed based on the NGBoost model, achieving a generalized estimation of the mean and probability distribution of the saturated headway under dynamic and static factors such as different road grades (such as main roads, secondary roads, and branch roads), lane types (such as left-turn lanes, straight left lanes, straight lanes, and straight right lanes), vehicle types (such as small cars and large cars), and weather conditions (such as rainfall and snowfall).

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] (1) This paper uses license plate recognition data and floating vehicle data that are widely available on urban roads in my country to extract saturated headway samples at signalized intersections, reveal the key influencing factors of saturated headway, and construct a generalized estimation model with the help of machine learning methods, providing a scientific basis for the characterization of supply characteristics of urban signalized intersections.

[0094] (2) Compared with the HCM-based estimation method, the proposed method takes into account the easily accessible factors affecting saturated headway, which significantly improves the generalization of the estimation model in practical applications.

[0095] (3) Compared with the data-driven method, the probability estimation model based on NGBoost constructed in this paper further analyzes the influencing factors of saturated headway and can output the probability distribution of saturated headway, thereby improving the robustness of the model and providing good interpretability for the estimation results.

[0096] (4) The present invention can provide scientific and reliable intersection traffic supply characteristic parameters for urban road traffic flow modeling, traffic signal timing optimization, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 A flowchart of the generalized estimation method for saturated headway according to the present invention;

[0098] Figure 2 Schematic diagram of the flow of the generalized estimation method for saturated headway of the present invention;

[0099] Figure 3 This is a schematic diagram of the signal period extraction result in the present invention;

[0100] Figure 4 Schematic diagram comparing the probability density distribution of saturated headway times in different lanes in the present invention, where Figure (4a) is the left-turn lane, Figure (4b) is the through lane 1, and Figure (4c) is the through lane 2;

[0101] Figure 5 Schematic diagram of the saturated headway probability distribution estimation result in the present invention;

[0102] Figure 6 Schematic diagram of the comparison of saturated headway estimation intervals at different confidence levels in the present invention, where Figure (6a) is the 80% confidence interval, Figure (6b) is the 85% confidence interval, and Figure (6c) is the 90% confidence interval. DETAILED DESCRIPTION

[0103] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0104] The term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects.

[0105] Example 1

[0106] like Figure 1 As shown, the generalized estimation method for saturated headway time at a signalized intersection on an urban road proposed by the present invention includes the following steps:

[0107] Step S1: Obtain historical license plate recognition data, use the historical license plate recognition data to identify vehicle queues, and extract the intersection signal cycle based on the vehicle queue

[0108] The method for extracting intersection signal cycle is as follows:

[0109] Step S11 , pre-processing the historical license plate recognition data to obtain the headway time, including: dividing the license plate recognition data by lanes at the intersection, removing the license plate recognition data of repeated detections, and subtracting the continuous detection time of the vehicles to calculate the headway time.

[0110] Step S12 , starting with the first vehicle in each vehicle queue determining the signal cycle, divides the historical license plate recognition data into cycles to obtain the intersection signal cycle.

[0111] (1) Based on the headway time obtained from historical license plate recognition data, a headway time threshold gap is set to identify vehicle queues. The specific method is as follows: for each headway time, if it is less than the headway time threshold gap, the vehicle is added to the queue; otherwise, a new vehicle queue is added, and each vehicle queue is numbered.

[0112] (2) Filter out the vehicle queues whose number of vehicles is less than the vehicle number threshold num.

[0113] (3) Merge adjacent vehicle queues that do not meet the minimum red light interval and update the headway and queue number.

[0114] (4) Starting from the signal cycle determined by the head vehicle in each vehicle queue, the license plate recognition data is divided into cycles.

[0115] The pseudo code of the intersection signal cycle extraction algorithm based on the vehicle queue in step S12 is as follows:

[0116] In the above pseudo code, m represents the number of the vehicle queue. represents a vehicle queue numbered m, n represents the total number of vehicles passing a lane in a continuous period, i represents the i-th vehicle, i=1,2,3…,n, represents the headway of the i-th vehicle, and red represents the minimum red light interval.

[0117] Step S2: Match the historical license plate recognition data with the historical floating vehicle data to extract the saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles. The specific steps are as follows:

[0118] Step S21 , for each intersection signal cycle, based on the vehicle license plate and time, the historical floating vehicle data in the parked state is matched with the historical license plate recognition data one by one.

[0119] Step S22 , extracting the headway of the matched vehicle and its preceding vehicle within the same period as a saturated headway sample.

[0120] Step S3: perform collinearity determination based on VIF test and correlation test, perform feature influence determination based on KS test, and select appropriate factors influencing saturation headway. The selection process of factors influencing saturation headway is as follows:

[0121] Step S31 : Select several potential influencing factors of saturated headway, including but not limited to road grade, lane type, vehicle type, weather conditions, etc., which should generally be easy to obtain and highly generalizable.

[0122] Step S32: Perform VIF test on each influencing factor to determine whether there is collinearity among the features:

[0123] (1) Take each potential influencing factor as the response variable and perform linear regression on all other potential influencing factors.

[0124] (2) Calculate the regression model The value, also known as the coefficient of determination, indicates the extent to which the remaining potential influencing factors can explain the variability of the potential influencing factor.

[0125] (3) Calculate VIF value:

[0126]

[0127] in, is the coefficient of determination of the above regression model.

[0128] (4) Screen out features with collinearity based on the VIF value. The larger the VIF value, the stronger the linear relationship between the potential influencing factor and other potential influencing factors, and further correlation test is performed.

[0129] Step S33: Obtain candidate influencing factors for saturated headway. Perform a correlation test on the influencing factors with collinearity based on the Spearman correlation coefficient, and filter out features with high correlation with other factors. The calculation formula for the Spearman correlation coefficient is as follows:

[0130]

[0131] in, is the Spearman correlation coefficient, It is The difference between the ranking of potential factors affecting the data in the two saturated headways is is the number of saturated headway samples.

[0132] In step S34, a KS test is performed to determine whether the candidate feature has a significant impact on saturated headway. The KS test primarily compares two empirical cumulative distribution functions (ECDFs) to determine whether two samples come from the same distribution. The KS test value is the maximum absolute difference between the two ECDFs. A larger value indicates a greater difference between the sample and the hypothesized distribution. The KS test value is calculated as follows:

[0133]

[0134] in, is the KS test quantity, The sample size of saturated headway is The empirical cumulative distribution function of The sample size of saturated headway is The empirical cumulative distribution function of represents the size of the first sample, represents the size of the second sample, Indicates the value of the variable (i.e., saturated headway).

[0135] Step S35, determining the factors affecting the saturated headway. The factors affecting the saturated headway are those that satisfy the conditions of no obvious collinearity between the features and significant influence on the saturated headway after undergoing collinearity test and KS test. Generally speaking, if the KS test quantity , it is generally believed that there is a significant impact; otherwise, there is no significant impact.

[0136] In step S4, the saturated headway extracted in step S2 is used as input. Based on the factors affecting the saturated headway determined in step S3, a probability estimation model for the saturated headway and various influencing factors is established based on the NGBoost model. The model parameters are determined based on grid search. With various influencing factors as input, the model outputs a generalized estimation result of the saturated headway.

[0137] The specific steps are as follows:

[0138] Step S41: Based on the NGBoost model, a probability estimation model is established for the functional relationship between the saturated headway and the factors affecting the saturated headway in the same period. First, the probability estimation model parameters are initialized. , is the parameter of the target distribution estimated by the probability estimation model. For the normal distribution, Next, we build a base learner. NGBoost uses base learners (such as decision trees) to gradually approximate the parameters of the target distribution. Finally, for each round of iterative updates, we perform the following steps:

[0139] (1) Calculate the negative log-likelihood (NLL):

[0140]

[0141] in, is the negative log-likelihood, is the target variable, are the parameters estimated by the probability estimation model in the current iteration.

[0142] (2) Calculate the negative log-likelihood about Gradient:

[0143]

[0144] in, is the negative log-likelihood about The gradient, are the parameters estimated by the probability estimation model in the current iteration, is the negative log-likelihood.

[0145] (3) Calculate the Fisher information matrix:

[0146]

[0147] in, is the Fisher information matrix, is the expectation operator, is the target variable, are the parameters estimated by the probability estimation model in the current iteration.

[0148] (4) Natural gradient update:

[0149]

[0150] in, is the natural gradient, is the Fisher information matrix, is the negative log-likelihood about gradient.

[0151] (5) Fitting natural gradient using base learners :

[0152]

[0153] in, is the predicted increment of the base learner in the current iteration, is the current iteration number, are the characteristics of the training data, is the natural gradient, is the base learner.

[0154] (6) Update the parameters of the estimated target distribution:

[0155]

[0156] in, It is The update parameters of the step, is the learning rate, is the predicted increment of the base learner in the current iteration.

[0157] In step S42, the saturated headway extracted in step S2 is input and used to train and test the model. The optimal NGBoost hyperparameter combination is found based on a grid search. The important hyperparameters of the model are as follows:

[0158] (1) Learning rate:

[0159] Control the weight adjustment amplitude of each tree and affect the convergence speed of the model.

[0160] (2) The number of base learners (n estimators):

[0161] Specifies the number of base learners to be built, affecting the complexity of the model.

[0162] (3) Minibatch fraction:

[0163] It is used to specify the proportion of training data used in each iteration step during the training process, which determines the amount of data used in each iterative update.

[0164] (4) Column sampling ratio (col sample):

[0165] Used to specify the subsample ratio of feature columns to use when building each base learner, controlling the number of features that each base learner can see.

[0166] In step S43, the learned NGBoost model is a generalized estimation model for saturated headway. In practical applications, the model takes various influencing factors as input and outputs the probability distribution of saturated headway.

[0167] Example 2

[0168] This example uses an area near a high-speed rail station in a certain city as an example scenario. This area covers approximately 25 square kilometers and includes approximately 30 urban roads and 50 signalized intersections. This example extracts saturated headway times at 11 intersections in this area from July to August 2018 and further implements generalized estimation.

[0169] Taking the north entrance of a certain intersection as an example, the data of August 8, 2018 was selected. After data preprocessing, the signal cycle of the intersection was extracted based on the vehicle queue. Figure 3 shown.

[0170] Furthermore, the headway time is divided based on the signal cycle, and the license plate recognition data is matched with the floating vehicle data to obtain the saturated headway time of each lane of the entrance road in July 2018. The probability density distribution is as follows: Figure 4 As shown, Figure (4a) is the left turn lane, Figure (4b) is the through lane 1, and Figure (4c) is the through lane 2. The data statistical results are shown in Table 1.

[0171] After collinearity judgment and influence degree judgment, the factors affecting saturated headway are as follows: queue position, rainfall, travel time, road grade, lane type, preceding vehicle model, current vehicle model, and central median.

[0172] Based on the NGBoost model, a probability estimation model for saturated headway is constructed. The extracted saturated headway is used for model training to estimate the normal distribution probability density of saturated headway. The results are as follows: Figure 5 As shown. Construct the estimation intervals under different confidence levels respectively, and the results are as follows Figure 6 As shown, Figure (6a) is the 80% confidence interval, Figure (6b) is the 85% confidence interval, and Figure (6c) is the 90% confidence interval.

[0173] The probability estimation results are evaluated by the coverage of the estimated interval (PICP) and the normalized average width (PINAW). The above indicators evaluate the model performance from the two aspects of interval reliability and interval width. Figure 6 As can be seen from Table 2, the estimated interval of this model covers a large proportion of the actual observation values, the interval reliability is high, and the interval width is small, so the estimation effect is good.

[0174] Example 2

[0175] Based on the same inventive concept as Example 1, this example introduces a generalized estimation system for saturated headway time at signalized intersections on urban roads, including:

[0176] a saturated headway influencing factor module configured to select a number of potential influencing factors of the saturated headway, including road grade, lane type, vehicle type, and weather conditions, and identify the potential influencing factors to obtain the saturated headway influencing factors;

[0177] A saturated headway probability estimation module is configured to input the filtered saturated headway influencing factors into a pre-built probability estimation model to obtain a generalized estimation result of the saturated headway;

[0178] Among them, the construction method of the probability estimation model is:

[0179] Based on the NGBoost model, a probability estimation model of the functional relationship between the saturated headway and the influencing factors of the saturated headway in the same period is established.

[0180] In a specific implementation of this embodiment, the saturated headway generalization estimation system of the present invention further includes:

[0181] an intersection signal cycle extraction module configured to obtain historical license plate recognition data, identify vehicle queues using the license plate recognition data, and extract the intersection signal cycle based on the vehicle queues;

[0182] A floating vehicle data acquisition module is configured to acquire historical floating vehicle data;

[0183] The saturated headway sample extraction module is configured to match historical license plate recognition data with historical floating vehicle data, and extract the saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles.

[0184] Example 3

[0185] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned generalized saturated headway estimation method are implemented.

[0186] Example 4

[0187] Based on the same inventive concept as other embodiments, this embodiment introduces a computer device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-mentioned saturated headway generalization estimation method.

[0188] Example 5

[0189] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned generalized saturated headway estimation method are implemented.

[0190] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0191] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0192] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0193] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0194] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A generalized estimation method for saturated headway time at urban road signalized intersections, characterized by: include: Selecting several potential influencing factors of saturated headway, including road grade, lane type, vehicle type, and weather conditions, and identifying the potential influencing factors to obtain the influencing factors of saturated headway; Input the filtered factors affecting the saturated headway into the pre-built probability estimation model to obtain the generalized estimation result of the saturated headway; The method for constructing the probability estimation model is as follows: Based on the NGBoost model, a probability estimation model of the functional relationship between the saturated headway and the influencing factors of the saturated headway in the same period is established; The training method of the probability estimation model is as follows: using the extracted saturated headway samples to train and test the probability estimation model, and determining the optimal hyperparameter combination of the probability estimation model based on grid search; The method for extracting the saturated headway samples is as follows: Acquiring historical license plate recognition data, identifying a vehicle queue using the historical license plate recognition data, and extracting an intersection signal cycle based on the vehicle queue; Get historical floating vehicle data; Matching the historical license plate recognition data with the historical floating vehicle data, and extracting saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles; The specific method for obtaining the intersection signal cycle is: Pre-process the historical license plate recognition data to obtain the headway time; Based on the headway, a headway threshold gap is set to identify a vehicle queue; Starting from the signal cycle determined by the head vehicle of each vehicle queue, the historical license plate recognition data is divided into cycles to obtain the intersection signal cycle; Based on the headway time gap, the specific method for setting the headway time gap threshold to identify the vehicle queue is as follows: For each headway, if it is less than the headway threshold gap, the vehicle is added to the vehicle queue; otherwise, a new vehicle queue is created and each vehicle queue is numbered. Filter out the vehicle queues whose number of vehicles is less than the vehicle number threshold num; Merge adjacent vehicle queues that do not meet the minimum red light interval, and update the headway and vehicle queue number; Starting from the signal cycle determined by the head vehicle of each vehicle queue, the license plate recognition data is divided into cycles to obtain the intersection signal cycle.

2. The generalized estimation method for saturated headway at urban road signalized intersections according to claim 1 is characterized in that: The specific method for matching the historical license plate recognition data with the historical floating vehicle data and extracting the saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles is as follows: For each intersection signal cycle, the historical floating vehicle data in the parked state is matched with the historical license plate recognition data one by one based on the vehicle license plate and time; The headway of the matched vehicle and its preceding vehicle within the same intersection signal cycle is extracted as the saturated headway sample.

3. The generalized estimation method for saturated headway at urban road signalized intersections according to claim 1 is characterized in that: The method to obtain the factors affecting the saturated headway is: Based on the VIF test and correlation test, several potential influencing factors of saturated headway were selected for collinearity discrimination, and the alternative influencing factors of saturated headway were obtained. Based on the KS test, the characteristic influence degree of the alternative influencing factors of saturated headway was judged, and then the influencing factors of saturated headway were obtained.

4. A generalized estimation system for saturated headway at urban road signalized intersections, characterized by: A generalized estimation method for saturated headway at a signalized intersection on an urban road for implementing any one of claims 1 to 3, comprising: a saturated headway influencing factor module configured to select a number of potential influencing factors of the saturated headway, the potential influencing factors including road grade, lane type, vehicle type, and weather conditions, and identify the potential influencing factors to obtain the saturated headway influencing factors; A saturated headway probability estimation module is configured to input the filtered saturated headway influencing factors into a pre-built probability estimation model to obtain a generalized estimation result of the saturated headway; The method for constructing the probability estimation model is as follows: Based on the NGBoost model, a probability estimation model of the functional relationship between the saturated headway and the influencing factors of the saturated headway in the same period is established.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the generalized saturated headway estimation method according to any one of claims 1 to 3 are implemented.

6. A computer device, characterized in that: include: memory for storing computer programs; A processor is configured to execute the computer program to implement the steps of the generalized saturated headway estimation method according to any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that: When the computer program is executed by a processor, the steps of the generalized saturated headway estimation method according to any one of claims 1 to 3 are implemented.

Images