Expressway service level evaluation method and device based on charging data

Abstract

The invention belongs to the field of computers, and particularly relates to a highway service level evaluation method and device based on charging data, and the method comprises the steps: obtaining the charging data of a to-be-evaluated highway target section; cleaning the charging data to extract valid data; acquiring traffic parameters of the effective data; respectively calculating a travel time reliability index and a driving comfort index of each set time period in the uplink direction and the downlink direction according to the traffic parameters by taking the set time period as the time granularity, and drawing a scatter diagram according to the results; and performing clustering analysis on the travel time reliability index and the driving comfort index of each set uplink and downlink time period by applying fuzzy C-means clustering, and taking a clustering result as a basis for dividing different types of service levels. According to the method, the expressway service level can be accurately evaluated based on the charging data.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and device for evaluating the service level of a highway based on toll data. Background Art

[0002] With the increase in highway mileage and the growth in the number of cars, the complexity of highway vehicle operation has increased, and some highways have gradually begun to experience traffic congestion. Alleviating traffic congestion and improving highway operation efficiency can be achieved by regulating traffic demand and optimizing traffic supply. The premise of traffic demand management is to master the utilization of traffic resources, that is, to master the travel characteristics of highway travelers. In addition, the service level reflects the relationship between traffic demand and traffic supply, and is a qualitative assessment of service quality. Service level evaluation is crucial in operation management. Summary of the invention

[0003] The embodiments of the present application provide a method and device for evaluating the service level of a highway based on toll data, which can accurately evaluate the service level of a highway based on toll data.

[0004] In a first aspect, an embodiment of the present application provides a method for evaluating a highway service level based on toll data, comprising:

[0005] Obtain toll data for the target section of the highway to be evaluated;

[0006] Cleaning the charging data to extract valid data;

[0007] Acquire traffic parameters of each of the valid data;

[0008] Taking a set time period as the time granularity, respectively calculating the travel time reliability index and the driving comfort index of each set time period in the upward direction and the downward direction according to the traffic parameters, and drawing the results into a scatter plot;

[0009] The fuzzy C-means clustering method is used to perform cluster analysis on the travel time reliability index and driving comfort index of each set time period in both the uplink and downlink directions, and the clustering results are used as the basis for dividing different types of service levels.

[0010] In a second aspect, an embodiment of the present application provides a highway service level evaluation device based on toll data, comprising:

[0011] An acquisition module is used to acquire the toll data of the target section of the expressway to be evaluated;

[0012] A cleaning module, used for cleaning the charging data to extract valid data;

[0013] The analysis module is used to obtain the traffic parameters of each of the valid data; taking the set time period as the time granularity, respectively calculating the travel time reliability index and the driving comfort index of each set time period in the uplink direction and the downlink direction according to the traffic parameters, and drawing the results into a scatter plot; applying fuzzy C-means clustering to perform cluster analysis on the travel time reliability index and the driving comfort index of each set time period in the uplink and downlink directions, and the clustering results are used as the basis for dividing different types of service levels.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements any of the methods described above when the program is executed by a processor.

[0015] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described methods when executing the computer program. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram showing a flow chart of a method for evaluating a highway service level based on toll data according to an embodiment of the present application;

[0018] Figure 2 A schematic diagram showing the reliability of hourly travel time in two directions obtained by the highway service level evaluation method based on toll data in an embodiment of the present application;

[0019] Figure 3 A schematic diagram showing the driving comfort per hour in two directions obtained by the highway service level evaluation method based on toll data in an embodiment of the present application;

[0020] Figure 4 A scatter plot showing the travel time reliability index and the driving comfort index obtained by the highway service level evaluation method based on toll data in an embodiment of the present application;

[0021] Figure 5 A graph showing the relationship between the sum of squared errors and the number of clusters in the highway service level evaluation method based on toll data in an embodiment of the present application is shown;

[0022] Figure 6A graph showing the relationship between the silhouette coefficient and the number of clusters in the highway service level evaluation method based on toll data in an embodiment of the present application is shown;

[0023] Figure 7 A schematic diagram showing a clustering structure of a highway service level evaluation method based on toll data according to an embodiment of the present application is shown;

[0024] Figure 8 A box plot showing the travel time reliability index of various service levels obtained by the highway service level evaluation method based on toll data in an embodiment of the present application;

[0025] Fig. 9 A box plot showing the driving comfort index of various service levels obtained by the highway service level evaluation method based on toll data in an embodiment of the present application;

[0026] Fig.10 A schematic diagram showing the classification accuracy of a classification regression tree using a confusion matrix in a highway service level evaluation method based on toll data according to an embodiment of the present application;

[0027] Fig.11 A schematic diagram showing a decision tree for dividing five service levels using a classification regression tree to generate a method for evaluating the service level of a highway based on toll data according to an embodiment of the present application;

[0028] Fig.12 A schematic diagram showing the structure of a highway service level evaluation device based on toll data according to an embodiment of the present application;

[0029] Fig.13 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0031] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] See also Figure 1 The embodiment of the present application provides a method for evaluating the service level of a highway based on toll data, comprising:

[0034] Obtain toll data for the target section of the highway to be evaluated;

[0035] Clean the charging data to extract valid data;

[0036] Obtain traffic parameters of each valid data;

[0037] Taking the set time period as the time granularity, the travel time reliability index and driving comfort index of each set time period in the upward direction and the downward direction are calculated according to the traffic parameters, and the results are plotted into a scatter plot;

[0038] The fuzzy C-means clustering method is used to perform cluster analysis on the travel time reliability index and driving comfort index of each set time period in both the uplink and downlink directions, and the clustering results are used as the basis for dividing different types of service levels.

[0039] The method of the embodiment of the present application is based on traffic parameters such as vehicle travel time and interval speed. It designs two evaluation indicators, travel time reliability and driving comfort, to evaluate the service level of the highway from the perspective of user needs, and uses cluster analysis to classify the service level, so as to accurately evaluate the service level of the highway.

[0040] The toll data of the target section of the expressway in this application can be obtained from the toll collection system of the expressway operation management department. Valid toll collection data generally includes relevant information such as the time when the vehicle enters and exits the expressway, the name of the toll station, the vehicle model, and traffic parameters such as the vehicle's travel origin and destination (OD).

[0041] In order to eliminate the influence of noise data, the data needs to be cleaned to delete invalid data and retain only valid data. General data cleaning is to retain valid data by deleting incomplete data at the start and end points. However, for the toll data of the target section of the highway, there are many factors that lead to the lack of information on entering and exiting the toll station in the toll data. Using the lack of information on entering and exiting the toll station as the criterion for invalid data may mistakenly delete valid data, resulting in deviations in subsequent clustering. For example, a highway runs through multiple provinces and is interconnected with other highways. Therefore, when studying the data of a highway in a certain province, vehicles may leave or enter the target highway section through the highway interchange, resulting in missing entry and / or exit information. If such toll data is deleted, data on such trips will be missing.

[0042] In the embodiment of the present application, data cleaning is performed by extracting valid data to avoid mistakenly deleting valid data by judging the missing starting and ending toll station information as erroneous data.

[0043] In some embodiments, cleaning the charging data to extract valid data includes:

[0044] (1) Record the toll collection data with complete entry and exit time as valid data. The entry and exit time refers to the time of entering and exiting the toll station;

[0045] (2) If at least one of the entry and exit times of the toll data is missing, if the exit node and / or entry node can be determined based on the toll data from the toll gantry, the toll data shall be recorded as valid data. The exit node and entry node are the nodes for entering or exiting the target section outside the toll station in the target section.

[0046] According to the above two situations, the valid data is determined respectively, and the valid data is retained. Other charging data other than the valid data is deleted as abnormal data to achieve the purpose of accurate data cleaning.

[0047] In the embodiment of the present application, the exit node and the entry node are nodes in the target section that enter or exit the target section outside the toll station. The nodes that enter or exit the target section outside the toll station include the high-speed interconnection node and the provincial boundary node in the target section. For example, a vehicle can enter or exit the target section from a high-speed interconnection node, so that the toll station of the target section fails to record the corresponding information. Alternatively, a vehicle can enter or exit the target section from a provincial boundary node, so that the toll station of the target section fails to record the corresponding information.

[0048] Take the highway connecting Beijing and Tianjin as an example. If a vehicle enters the Beijing section of the highway from Tianjin, the toll data of the Beijing section of the highway will lack the information of the vehicle entering the toll station. If the vehicle enters the Beijing section and then leaves the highway from the highway interchange node, the toll data of the Beijing section of the highway will also lack the information of the vehicle leaving the toll station.

[0049] Alternatively, if a vehicle leaves the Beijing section of the expressway and enters Tianjin, the toll data for the Beijing section of the expressway will lack the information about the vehicle exiting the toll station. If the vehicle enters the expressway from an expressway interchange node, the toll data for the Beijing section of the expressway will also lack the information about the vehicle entering the toll station.

[0050] There are also toll gantries on the highway. In order to facilitate non-stop billing, toll gantries are set up near nodes such as highway interchanges and provincial boundaries. For valid data, the vehicle's entry node or exit node can be determined based on the toll information of the toll gantries. Therefore, if the exit node and / or entry node can be determined based on the toll data of the toll gantries, then the toll data is valid data.

[0051] In some embodiments, at least one of the entrance and exit times of the toll data is missing. If the exit node and / or the entrance node can be determined based on the toll data of the toll gantry, the toll data is recorded as valid data, including: the toll data has the time of entering the toll station. When the time of exiting the toll station is missing, if the last gantry in the toll data is the gantry closest to the highway interchange node or provincial boundary node in front, then the highway interchange node or provincial boundary node closest to the last gantry is the exit node of the toll data, and the toll data is valid data. The toll data with missing entrance and exit times includes at least one gantry information, that is, the vehicle passes through at least one gantry in the target section and is recorded by the gantry. After the vehicle enters the target section from the toll station, if it exits the target section from the highway interchange node or provincial boundary node, the highway interchange node or provincial boundary node that the vehicle exits will be recorded by the gantry, and the last gantry in the toll data of the vehicle is the gantry closest to the highway interchange node or provincial boundary node that it exits.

[0052] In some embodiments, when the toll data has the time of entering the toll station but lacks the time of exiting the toll station, if there are other gantries between the last gantry in the toll data and the nearest highway interchange node or provincial boundary node in front, the toll data is abnormal data.

[0053] In this application, the front refers to the direction of the front of the vehicle, and the rear refers to the direction of the rear of the vehicle.

[0054] In some embodiments, at least one of the entrance and exit times of the toll data is missing. If the exit node and / or the entry node can be determined based on the toll data of the toll gantry, the toll data is recorded as valid data, including: when the toll data has the time of exiting the toll station and the time of entering the toll station is missing, if the first gantry in the toll data is the gantry closest to the highway interchange node or provincial boundary node behind, then the highway interchange node or provincial boundary node closest to the first gantry is the entry node of the toll data, and the toll data is valid data. The toll data with missing entrance and exit times includes at least one gantry information, that is, the vehicle passes through at least one gantry in the target section and is recorded by the gantry. After the vehicle enters the target section from the highway interchange node or provincial boundary node, the vehicle will be recorded by the gantry, and the first gantry in the toll data of the vehicle is the gantry closest to the highway interchange node or provincial boundary node after entering the target section.

[0055] In some embodiments, when the toll data has the time of exiting the toll station but lacks the time of entering the toll station, if there are other gantries between the first gantry in the toll data and the nearest high-speed interchange node or provincial boundary node behind, the toll data is abnormal data. Alternatively, if there are other gantries between the last gantry in the toll data and the nearest high-speed interchange node or provincial boundary node in front, the toll data is abnormal data.

[0056] In some embodiments, if at least one of the entrance and exit times of the toll data is missing, if the exit node and / or the entry node can be determined based on the toll data of the toll gantry, the toll data is recorded as valid data, including: when the toll data is missing the time of exiting the toll station and the time of entering the toll station, if the toll data only includes one gantry, and the gantry is the only gantry between two adjacent nodes, and the nodes include high-speed interchange nodes and provincial boundary nodes, then the nodes adjacent to both sides of the gantry are the entry node and exit node of the toll data, and the toll data is valid data; otherwise, the toll data is abnormal data. The target road section is generally equipped with multiple gantries. When the toll data of the target road section only includes one gantry for the vehicle, and the gantry is the only gantry between two adjacent nodes, it can be considered that the vehicle enters from one of the two adjacent nodes and exits from the other. For example, there is only a first gantry between the provincial boundary node and the first expressway interchange node. If the charging data of the vehicle only includes the first gantry, that is, it is only recorded by the first gantry, and not recorded by other gantries on the target section, such as the second gantry, the third gantry, etc., then it can be considered that the vehicle enters the target section from one of the provincial boundary node and the first expressway interchange node, and exits the target section from the other. The vehicle's driving direction can be determined based on the charging data of the gantry, so the entry node and the exit node can be determined. For example, if the charging data of the first gantry determines that the vehicle enters the target section from the provincial boundary, then the vehicle enters from the provincial boundary node and exits from the first expressway interchange node. Conversely, the vehicle enters the target section from the provincial boundary node and exits the target section from the first expressway node.

[0057] In some embodiments, when the toll data lacks the time of exiting the toll station and the time of entering the toll station, if the toll data includes at least two gantries, and the first gantry in the toll data is the gantry closest to the high-speed interchange node or provincial boundary node behind, and the last gantry in the toll data is the gantry closest to the high-speed interchange node or provincial boundary node in front, then the high-speed interchange node or provincial boundary node closest to the first gantry is the entry node of the toll data, and the high-speed interchange node or provincial boundary node closest to the last gantry is the exit node of the toll data, and the toll data is valid data; otherwise, the toll data is abnormal data. The target road section is generally provided with multiple gantries. When the toll data of the target road section includes at least two gantries, if the first gantry in the toll data is the gantry closest to the high-speed interchange node or provincial boundary node behind, it can be considered that the vehicle enters from the high-speed interchange node or provincial boundary node behind the first gantry, and the node can be used as the starting point of the vehicle in the target road section. After the vehicle enters from the node, it is recorded by the first gantry. If the last gantry in the toll data is the gantry closest to the highway interchange node or provincial boundary node ahead, it can be considered that the vehicle exited from the highway interchange node or provincial boundary node in front of the last gantry, and this node can be used as the end point of the vehicle on the target section.

[0058] For example, there are a first gantry and a second gantry between the provincial boundary node and the first expressway interchange node, the first gantry is close to the provincial boundary node, and the second gantry is close to the first expressway interchange node. If the first gantry in the vehicle's charging data is the first gantry and the last gantry is the second gantry, it can be considered that the vehicle enters the target section from the provincial boundary node and exits the target section from the first expressway interchange node. If the first gantry in the vehicle's charging data is the second gantry and the last gantry is the first gantry, it can be considered that the vehicle enters the target section from the first expressway interchange node and exits the target section from the provincial boundary node.

[0059] In some embodiments, the method of the embodiment of the present application further includes: determining the starting and ending points of each valid data. The valid data can determine the nodes where the vehicle enters or exits the target section, thereby determining the starting and ending points of the vehicle on the target section.

[0060] The starting and ending points of the toll data with complete entry and exit times are the toll station for entering the target road section and the toll station for exiting the target road section. If the toll data includes the toll stations for entering and exiting the target road section, the starting point is the toll station for entering the target road section, and the end point is the toll station for exiting the target road section.

[0061] The starting point of valid data with the time of entering the toll station but missing the time of exiting the toll station is the toll station entering the target section, and the end point is the nearest exit node in front of the last gantry in the valid data. For valid data missing from the toll station, the node where the vehicle exits the target section can be determined based on the gantry, and this node is the end point of the vehicle's journey on the target section. The node where the vehicle exits can be a highway interchange node or a provincial boundary node.

[0062] The starting point of valid data with the time of exiting the toll station but missing the time of entering the toll station is the nearest entry node behind the first gantry in the valid data, and the end point is the toll station exiting the target section. For valid data missing the entry toll station, the node where the vehicle enters the target section can be determined based on the gantry, and this node is the starting point of the vehicle's journey on the target section. The node entered by the vehicle can be a highway interchange node or a provincial boundary node.

[0063] In valid data with missing entrance and exit times, if the valid data includes at least two gantries, the starting point is the nearest entry node behind the first gantry, and the end point is the nearest exit node in front of the last gantry. For valid data missing for both entry and exit toll stations, the node where the vehicle enters the target section can be determined based on the first gantry in the valid data, and this node is the starting point of the vehicle's journey on the target section. The node where the vehicle exits the target section can be determined based on the last gantry in the valid data, and this node is the end point of the vehicle's journey on the target section. The nodes where the vehicle enters and exits can be high-speed interchange nodes or provincial boundary nodes.

[0064] For valid data with missing entry and exit times, if the valid data only includes one gantry, the starting point is the nearest entry node behind the gantry, and the end point is the nearest exit node in front of the gantry. For valid data with missing entry and exit toll stations, the nodes where the vehicle enters and exits the target section can be determined based on the unique gantry, which are used as the starting point and end point of the vehicle's journey on the target section. The node entered by the vehicle can be a high-speed interchange node or a provincial boundary node.

[0065] For valid data missing from the entrance and exit time, the time when the vehicle enters and / or leaves the target section can be determined based on the time information in the toll data recorded by the corresponding gantry. For example, the time recorded by the corresponding gantry can be directly used as the time when the vehicle enters and / or leaves the target section. Alternatively, the time from the gantry to the entry node or exit node can be calculated based on the distance from the gantry to the entry node or exit node and the speed of the vehicle, and the calculated time can be subtracted or added to the time recorded by the corresponding gantry as the entry time or exit time.

[0066] In some embodiments, the travel time reliability index is calculated by the following formula (1):

[0067] R(t)=P{t<(t median +θ*t median )} (1)

[0068] Among them, R(t) is the travel time reliability index, t median is the median of the vehicle travel time on the road section within a specific time period, and θ is a reliability threshold parameter; θ is generally 5%, 10%, 15% or 20%, etc. In the embodiment of the present application, θ=20% is taken as an example.

[0069] The travel time reliability index ranges from 0 to 1. The closer it is to 1, the higher the travel time reliability. When the value is 1, it means that all vehicles can complete the trip within the acceptable travel time threshold.

[0070] Travel time reliability is one of the important indicators for evaluating the operating efficiency and travel quality of the transportation system, reflecting the probability that travelers can complete the trip from the starting point to the destination within an acceptable travel time threshold.

[0071] In some embodiments, the travel time reliability index of each set time period in the up direction and the down direction is calculated according to the traffic parameters, including: dividing the target road section into a plurality of sections according to the toll gate, and calculating the travel time reliability index of each section by formula (1); calculating the travel time reliability index of the target road section according to the following formula (2):

[0072]

[0073] Among them, R represents the travel time reliability index of the target road section, R i It represents the travel time reliability index of the ith interval, and n is the number of intervals on the target road section.

[0074] The toll gate on the target road section divides the target road section into several sections. The travel time reliability of each section can be calculated using formula (1), and the travel time reliability of the entire target road section can be calculated using formula (2). The schematic diagram of the travel time reliability of the entire line in each period is obtained.

[0075] In the embodiment of the present application, the method of the present application is explained by taking a Beijing section of a certain highway as the target section as an example.

[0076] The reliability of the travel time in both the up and down directions of the target road section throughout the day is analyzed at the hourly time granularity. The reliability of the travel time in both directions per hour is as follows: Figure 2 As shown in the figure, the reliability of travel time in both directions at night (0:00-6:00) is close to 1, that is, all travelers can complete their trips within the acceptable travel time threshold. The reliability of travel time throughout the day is also maintained at a high level. In general, the reliability of travel time in the upward direction is slightly lower than that in the downward direction, and the travel quality assurance is not as good as that in the downward direction.

[0077] In some embodiments, the driving comfort index of each set time period in the upward direction and the downward direction is calculated according to the traffic parameters, including: the driving comfort index is used to describe the passenger's feeling of the vehicle speed change when the vehicle passes through each section of the target road section. The larger the driving comfort index, the greater the vehicle speed fluctuation and the worse the driving comfort; the smaller the driving comfort index, the smaller the vehicle speed fluctuation and the better the driving comfort; the driving comfort index is calculated by the following formula (3):

[0078]

[0079] Among them, C v is the driving comfort index of the target road section, vi represents the spatial average speed of the ith interval in a specific time period, It represents the mean of the spatial average vehicle speed in each section of the target road section, and N represents the number of sections in the target road section.

[0080] When people are traveling, in addition to being concerned about whether they can reach their destination on time, they are also concerned about the experience during the trip. For example, under the premise of the same travel time, if the speed fluctuates greatly, the vehicle will be bumpy, the riding experience will be poor, and it may even be easy to get carsick; if the speed changes slightly, the vehicle will drive smoothly and the passengers will have a better experience. The driving comfort index is used to describe the passengers' feelings about the speed changes when the vehicle passes through these intervals. The coefficient of variation can be used to characterize this index. The larger the coefficient of variation, the greater the fluctuation of the vehicle speed and the worse the driving comfort; the smaller the coefficient of variation, the smaller the fluctuation of the vehicle speed and the better the driving comfort.

[0081] The driving comfort of the target road section in both the up and down directions is analyzed at the hourly granularity. The driving comfort of each direction per hour is as follows: Figure 3 As shown. The driving comfort in the downward direction does not fluctuate much throughout the day, and the driving comfort at night is higher than that in the daytime. The driving comfort in the upward direction is worse at night than during the day, which is related to the large number of large trucks entering Beijing at night in the upward direction. In general, the driving comfort in the upward direction changes more than that in the downward direction. 5 to 6 in the morning is an important turning point. Before this, the driving comfort in the downward direction has always been better than that in the upward direction, but after this, the driving comfort in the downward direction is almost not as good as that in the upward direction.

[0082] In the embodiment of the present application, the travel time reliability index and the driving comfort index of the up and down directions every 15 minutes can be calculated with a time granularity of 15 minutes, and the results can be plotted into a scatter plot, such as Figure 4 As shown. Throughout the day, the travel time reliability and driving comfort fluctuations in the upward direction are greater than those in the downward direction. Then, fuzzy C-means clustering is applied to the travel time reliability index and driving comfort index every 15 minutes in the upward and downward directions for cluster analysis. The clustering results will be used as the basis for the classification of different types of service levels. Before clustering begins, the number of clusters needs to be determined first.

[0083] In some embodiments, cluster analysis is performed using fuzzy C-means clustering, including determining the number of clusters. In a specific implementation, determining the number of clusters includes: using the elbow rule and / or the silhouette coefficient to determine the number of clusters for clustering the travel time reliability index and the driving comfort index.

[0084] In some embodiments, the expression of the sum of squared errors and the number of clusters is as shown in formula (4),

[0085]

[0086] Among them, C i represents the i-th cluster, p is C i The sample points in, k is the number of clusters, m is i It is C i The mean of all samples in ;

[0087] The relationship graph between the sum of squared errors and the number of clusters is plotted according to the expression. The relationship graph between the sum of squared errors and the number of clusters presents an elbow shape. The optimal number of clusters corresponding to the elbow is the number of clusters for clustering the travel time reliability index and the driving comfort index.

[0088] As the number of clusters gradually increases, the sample division becomes more refined, the degree of aggregation of each cluster will gradually increase, and the error sum of squares will also decrease. When the number of clusters is less than the optimal number of clusters, the degree of aggregation of each cluster will increase significantly as the number of clusters increases, so the error sum of squares will decrease significantly; and when the number of clusters is greater than the actual number of clusters, the decrease in the degree of aggregation brought about by each increase in the number of clusters will rapidly decrease, so the error sum of squares will decrease sharply, and finally tend to be flat as the number of clusters increases, that is, the relationship between the error sum of squares and the number of clusters presents an elbow shape, and the optimal number of clusters is the number of clusters corresponding to the elbow, and the optimal number of clusters is used as the number of clusters in the embodiment of the present application.

[0089] In the specific implementation, the relationship between the sum of squared errors and the number of clusters is shown in the figure below: Figure 5 As shown in the figure, the best number of clusters can be determined to be 5 by the elbow method.

[0090] In some embodiments, the number of clusters is determined by using the silhouette coefficient, including: the expression of the silhouette coefficient of the sample point is as follows (5):

[0091]

[0092] Among them, S represents the silhouette coefficient, a represents the average distance between the sample point and other sample points in the same cluster, and b represents the average distance between the sample point and all sample points in the nearest cluster;

[0093] The silhouette coefficient of clustering is the average of the silhouette coefficients of all sample points, and its value range is [-1,1]. The closer it is to 1, the better the clustering effect. The cluster with the largest silhouette coefficient is the number of clusters for clustering the travel time reliability index and the driving comfort index.

[0094] In the specific implementation, the relationship between the silhouette coefficient and the number of clusters is shown in the figure Figure 6 As shown, the number of clusters corresponding to the maximum silhouette coefficient is 5. In summary, in the embodiment of the present application, the number of clusters of the fuzzy C-means clustering is set to 5.

[0095] In some embodiments, the method further includes: normalizing the travel time reliability index and the driving comfort index respectively before clustering; the normalization formula is as follows (6):

[0096]

[0097] By normalizing, the value ranges of the travel time reliability index and the driving comfort index used for clustering can be made the same.

[0098] In some embodiments, the clustering results are used as the basis for dividing different types of service levels, including: classifying and defining the travel time reliability and driving comfort of each service level according to the clustering results; and defining different types of service levels according to the travel time reliability definition and driving comfort definition of the same type of service level.

[0099] In the specific implementation, the number of clusters of fuzzy C-means clustering is set to 5, the fuzzy weighted index is set to 2, and the samples are clustered. The membership of some samples is shown in Table 1, and the clustering results are shown in Figure 7 As shown in Table 2, the clustering results are good. The cluster centers are shown in Table 2. The number of samples classified into Class 2 is the largest, and the number of samples classified into Class 4 is the smallest. Among them, Class 2 accounts for 36.98%, Class 4 accounts for 5.73%, Class 1 accounts for 21.88%, and Class 3 and Class 5 account for 17.71% each.

[0100] Table 1 Membership of some samples

[0101]

[0102]

[0103] Table 2 Cluster centers and results

[0104] category Travel time reliability Driving comfort quantity Proportion Category 1 0.96 0.19 42 21.88％ Category 2 0.84 0.07 71 36.98％ Category 3 0.91 0.12 34 17.71％ Category 4 0.99 0.11 11 5.73％ Category 5 0.99 0.34 34 17.71％

[0105] In the specific implementation, the box plot of the travel time reliability index of each service level is as follows Figure 8As shown. The median travel time reliability of the 1st, 4th and 5th service levels is relatively high, greater than 0.95, which means that when traveling under the traffic conditions corresponding to these three service levels, less than 5% of vehicles cannot reach the destination within the acceptable time threshold. The median travel time of the 3rd service level is also above 0.9, but it is slightly worse than the above 3 reliability levels. The median travel time of the 2nd service level is less than 0.85, and the minimum value is less than 0.8, which has the worst reliability among the 5 service levels. Therefore, the travel time reliability corresponding to the 1st, 4th and 5th service levels can be defined as high reliability; the travel time reliability corresponding to the 3rd service level is defined as medium reliability; and the travel time reliability corresponding to the 2nd service level is defined as medium reliability.

[0106] The box diagram of driving comfort for each service level is shown in Fig. 9 As shown. The driving comfort index is characterized by the coefficient of variation. The larger the value, the greater the speed fluctuation and the worse the comfort; the closer the value is to 0, the better the driving comfort. The driving comfort corresponding to the second service level is the highest, and the driving comfort corresponding to the third and fourth service levels is second, but the gap with the second level is not too large. The driving comfort corresponding to the first service level is medium, and the driving comfort corresponding to the fifth service level is the worst, and the travel experience is the worst under the corresponding traffic conditions. Therefore, the driving comfort corresponding to the second, third and fourth service levels can be defined as high driving comfort; the driving comfort corresponding to the first service level is defined as medium driving comfort; and the driving comfort corresponding to the fifth service level is defined as low driving comfort.

[0107] The service levels are divided into five categories through fuzzy C-means clustering: low reliability and high comfort, medium reliability and high comfort, high reliability and high comfort, high reliability and medium comfort, and high reliability and low comfort.

[0108] A 10-fold cross validation was used to test the accuracy of the classification and regression tree in classifying service levels. Precision, Accuracy, Recall, and F1 Score were used for evaluation. The formulas are defined as follows. The closer the index value is to 1, the better the classification effect.

[0109]

[0110] Among them, TP means that the positive class is predicted as the positive class, FP means that the negative class is predicted as the positive class, TN means that the negative class is predicted as the negative class, and FN means that the positive class is predicted as the negative class. The evaluation of the classification and regression tree for service level classification is shown in Table 3. All indicators are around 0.95, indicating that the generated classification and regression tree has a high classification accuracy.

[0111] Table 3 Summary of evaluation indicators

[0112]

[0113] Use confusion matrix to show the classification accuracy of classification and regression tree, such as Fig.10 As shown in the figure, the darker the color of the diagonal square, the lighter the color of the other squares, the better the classification effect. Among the 39 samples in the test set, the model only misclassified two samples, indicating that the model has good classification performance.

[0114] With travel time reliability and driving comfort as predictor variables and service level categories as corresponding variables, classification and regression trees are used to learn the service level division rules. To prevent overfitting, grid search is first used to search for the optimal maximum depth of the classification and regression tree, and the optimal maximum depth is 4. The classification and regression tree is used to generate a five-category service level division decision tree, such as Fig.11 shown.

[0115] In some embodiments, the basis for different types of service levels includes:

[0116] Driving comfort ≤ 0.1459, and travel time reliability ≤ 0.8753, the service level is low reliability and high comfort;

[0117] Driving comfort ≤ 0.1459, and 0.8753 < travel time reliability ≤ 0.8865, the service level is medium reliability and high comfort;

[0118] Driving comfort ≤ 0.1459, and 0.8865 < travel time reliability ≤ 0.945, the service level is medium reliability and high comfort;

[0119] Driving comfort ≤ 0.1392, and travel time reliability > 0.945, the service level is high reliability and high comfort;

[0120] 0.1392< driving comfort ≤ 0.1459, and 0.945< travel time reliability ≤ 0.9793, the service level is high reliability and medium comfort;

[0121] 0.1392<Driving comfort≤0.1459, and travel time reliability>0.9793, the service level is high reliability and high comfort;

[0122] 0.1459<Driving comfort≤0.2252, and travel time reliability≤0.9243, the service level is medium reliability and high comfort;

[0123] 0.2252<Driving comfort≤0.2669, and travel time reliability≤0.9243, the service level is high reliability and medium comfort;

[0124] 0.1459<Driving comfort≤0.2669, and travel time reliability>0.9243, the service level is high reliability and medium comfort;

[0125] 0.2669<Driving comfort, the service level is high reliability and low comfort.

[0126] According to the above rules, different types of service levels are divided according to different thresholds of travel time reliability index and driving comfort index. As long as the reliability of road travel time and driving comfort of a certain period of time are calculated, the corresponding service level of the road can be obtained by comparing with the above classification rules.

[0127] An embodiment of the present application provides a highway service level evaluation device based on toll data. The device of the embodiment of the present application can implement the method of the above-mentioned embodiment. The above-mentioned method embodiment can be used to understand the device of the embodiment of the present application, and the description part of the embodiment of the following device can also be used to understand the method of the above-mentioned embodiment.

[0128] See also Fig.12 The highway service level evaluation device based on toll data of the embodiment of the present application includes an acquisition module, a cleaning module and an analysis module. The acquisition module is used to obtain the toll data of the target section of the highway to be evaluated; the cleaning module is used to clean the toll data to extract valid data; the analysis module is used to obtain the traffic parameters of each valid data; with the set time period as the time granularity, the travel time reliability index and the driving comfort index of each set time period in the uplink direction and the downlink direction are calculated according to the traffic parameters, and the results are plotted into a scatter plot; the travel time reliability index and the driving comfort index of each set time period in the uplink and downlink directions are clustered and analyzed by fuzzy C-means clustering, and the clustering results are used as the basis for dividing different types of service levels.

[0129] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above methods when executing the computer program.

[0130] See also Fig.13 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.13 As shown, the electronic device 600 may include: at least one processor 601 , at least one network interface 604 , a user interface 603 , a memory 605 , and at least one communication bus 602 .

[0131] The communication bus 602 is used to realize the connection and communication between these components.

[0132] The user interface 603 may include a display screen (Display) and a camera (Camera), and the optional user interface 603 may also include a standard wired interface and a wireless interface.

[0133] The network interface 604 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0134] Among them, the processor 601 may include one or more processing cores. The processor 601 uses various interfaces and lines to connect various parts of the entire electronic device 600, and executes various functions and processes data of the electronic device 600 by running or executing instructions, programs, code sets or instruction sets stored in the memory 605, and calling data stored in the memory 605. Optionally, the processor 601 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 601 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 601, and it can be implemented separately through a chip.

[0135] Among them, the memory 605 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 605 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 605 may optionally be at least one storage device located away from the aforementioned processor 601. As Fig.13 As shown, the memory 605 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program.

[0136] exist Fig.13 In the electronic device 600 shown, the user interface 603 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 601 can be used to call the application stored in the memory 605 and specifically perform the operations of any of the above method embodiments.

[0137] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0138] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods recorded in the above method embodiments.

[0139] Those skilled in the art can clearly understand that the technical solution of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0140] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0141] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0145] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk, etc., and other media that can store program codes.

[0146] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0147] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A highway service level evaluation method based on toll data, characterized in that: include: Obtain toll data for the target section of the highway to be evaluated; Cleaning the charging data to extract valid data; Acquire traffic parameters of each of the valid data; Taking a set time period as the time granularity, respectively calculating the travel time reliability index and the driving comfort index of each set time period in the upward direction and the downward direction according to the traffic parameters, and drawing the results into a scatter plot; The fuzzy C-means clustering method is used to perform cluster analysis on the travel time reliability index and driving comfort index of each set time period in both the uplink and downlink directions, and the clustering results are used as the basis for dividing different types of service levels.

2. The method according to claim 1, characterized in that The travel time reliability index is calculated by the following formula (1): R(t)=P{t<(t median +θ*t median )} (1) Among them, R(t) is the travel time reliability index, t median is the median of the vehicle travel time on the road section within a specific time period, and θ is the reliability threshold parameter; The travel time reliability index has a value between 0 and 1. The closer it is to 1, the higher the travel time reliability. When the value is 1, it means that all vehicles can complete the trip within an acceptable travel time threshold.

3. The method according to claim 2, characterized in that The travel time reliability index of each set time period in the upward direction and the downward direction is calculated according to the traffic parameters, including: The target road section is divided into several sections according to the toll gates, and the travel time reliability index of each section is calculated by formula (1); The travel time reliability index of the target road section is calculated according to the following formula (2): Among them, R represents the travel time reliability index of the target road section, R i It represents the travel time reliability index of the ith interval, and n is the number of intervals on the target road section.

4. The method according to claim 1, characterized in that The driving comfort index of the upward direction and the downward direction in each set time period is calculated according to the traffic parameters, including: The driving comfort index is used to describe the passenger's feeling about the speed change when the vehicle passes through each section of the target road section. The larger the driving comfort index, the greater the vehicle speed fluctuation and the worse the driving comfort; the smaller the driving comfort index, the smaller the vehicle speed fluctuation and the better the driving comfort; The driving comfort index is calculated by the following formula (3): Among them, C v is the driving comfort index of the target road section, v i represents the spatial average speed of the ith interval in a specific time period, It represents the mean of the spatial average vehicle speed in each section of the target road section, and N represents the number of sections in the target road section.

5. The method according to claim 1, characterized in that Cluster analysis is performed using fuzzy C-means clustering, including: The number of clusters for clustering the travel time reliability index and the driving comfort index is determined by using the elbow rule and / or the silhouette coefficient; wherein The expression of the sum of squared errors and the number of clusters is shown in formula (4). Among them, C i represents the i-th cluster, p is C i The sample points in, k is the number of clusters, m is i It is C i The mean of all samples in ; A relationship graph between the sum of squared errors and the number of clusters is drawn according to the expression, and the relationship graph between the sum of squared errors and the number of clusters presents an elbow shape, and the optimal number of clusters corresponding to the elbow is the number of clusters for clustering the travel time reliability index and the driving comfort index; The silhouette coefficient is used to determine the number of clusters, including: The expression of the silhouette coefficient of the sample point is as follows (5): Among them, S represents the silhouette coefficient, a represents the average distance between the sample point and other sample points in the same cluster, and b represents the average distance between the sample point and all sample points in the nearest cluster; The silhouette coefficient of clustering is the average of the silhouette coefficients of all sample points, and its value range is [-1,1]. The closer it is to 1, the better the clustering effect. The cluster with the largest silhouette coefficient is the number of clusters for clustering the travel time reliability index and the driving comfort index.

6. The method according to claim 1, characterized in that include: Before clustering, the travel time reliability index and driving comfort index were normalized respectively; The normalization formula is as follows (6):

7. The method according to claim 5, characterized in that The clustering results serve as the basis for different types of service levels, including: Based on the clustering results, the travel time reliability and driving comfort of each service level are classified and defined respectively; According to the definition of travel time reliability and driving comfort of the same type of service level, different types of service levels are defined separately.

8. The method according to claim 7, characterized in that The number of clusters is 5, and the travel time reliability and driving comfort of each service level are defined as follows: The travel time reliability corresponding to the 1st, 4th and 5th categories is defined as high reliability; the travel time reliability corresponding to the 3rd service level is defined as medium reliability; the travel time reliability corresponding to the 2nd service level is defined as medium reliability; The driving comfort corresponding to the second, third and fourth service levels is defined as high driving comfort; the driving comfort corresponding to the first service level is defined as medium driving comfort; and the driving comfort corresponding to the fifth service level is defined as low driving comfort.

9. The method according to claim 8, characterized in that The basis for different types of service levels includes: Driving comfort ≤ 0.1459, and travel time reliability ≤ 0.8753, the service level is low reliability and high comfort; Driving comfort ≤ 0.1459, and 0.8753 < travel time reliability ≤ 0.8865, the service level is medium reliability and high comfort; Driving comfort ≤ 0.1459, and 0.8865 < travel time reliability ≤ 0.945, the service level is medium reliability and high comfort; Driving comfort ≤ 0.1392, and travel time reliability > 0.945, the service level is high reliability and high comfort; 0.1392< driving comfort ≤ 0.1459, and 0.945< travel time reliability ≤ 0.9793, the service level is high reliability and medium comfort; 0.1392<Driving comfort≤0.1459, and travel time reliability>0.9793, the service level is high reliability and high comfort; 0.1459<Driving comfort≤0.2252, and travel time reliability≤0.9243, the service level is medium reliability and high comfort; 0.2252<Driving comfort≤0.2669, and travel time reliability≤0.9243, the service level is high reliability and medium comfort; 0.1459<Driving comfort≤0.2669, and travel time reliability>0.9243, the service level is high reliability and medium comfort; 0.2669<Driving comfort, the service level is high reliability and low comfort.

10. A highway service level evaluation device based on toll data, characterized in that: include: An acquisition module is used to acquire the toll data of the target section of the expressway to be evaluated; A cleaning module, used for cleaning the charging data to extract valid data; An analysis module, used for obtaining traffic parameters of each of the valid data; Taking the set time period as the time granularity, the travel time reliability index and the driving comfort index of each set time period in the upward direction and the downward direction are calculated respectively according to the traffic parameters, and the results are plotted into a scatter plot; the travel time reliability index and the driving comfort index of each set time period in the upward and downward directions are clustered and analyzed by fuzzy C-means clustering, and the clustering results are used as the basis for dividing different types of service levels.

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