Road environment data processing device, method, electronic device and storage medium

Abstract

The present application provides a road environment data processing device, method, electronic device and storage medium, including: a data acquisition module, a database module, and a prediction module; the database module uses a simulation scheme to obtain the hourly human equivalent physiological temperature PET value at the pedestrian height of any path, and establishes a data set of the road environment and its corresponding human equivalent physiological temperature PET; and obtains the travel cumulative prospect value of any simulation scheme based on travel time and travel thermal comfort as variables, and establishes a data set of the path and the corresponding travel cumulative prospect value; the prediction module obtains the travel thermal comfort data of the corresponding path based on the road environment and its corresponding human equivalent physiological temperature PET data set, predicts the pedestrian path selection situation; and optimizes the urban paths with low frequency of use in a certain place. The present application can predict the travel path selection behavior of travelers under different thermal environment conditions, and provides a theoretical basis for the planning of urban slow-moving systems.

Description

Technical Field

[0001] The present invention belongs to the field of environmental data processing, and specifically relates to a data processing device, method, electronic device and storage medium for coupling travel time data and environmental thermal comfort data, so as to predict the walking path selection decision results of travelers and propose an urban road environment optimization strategy based on this. Background Art

[0002] Guiding residents to adopt more low-carbon, healthy and environmentally friendly walking is one of the important solutions to reduce carbon emissions from road traffic. In order to increase the probability of residents choosing walking, in addition to seeking a change in the concept of low-carbon and environmentally friendly travel for the people, it is also necessary to increase the advantages of urban sidewalks and explore the key factors that increase the probability of urban sidewalks being chosen.

[0003] For the decision-making problem of travel path selection, most researchers are currently concentrated in the field of transportation, often taking travel time and travel costs as the main influencing factors of pedestrian path decision-making. In the issue of walking traffic, only the influencing factor of travel time is considered, and the impact of travel length and travel time reliability on walking path selection decisions is explored. In fact, walking is not affected by travel costs, and because the walking speed is relatively stable, the reliability of travel time is guaranteed to a certain extent. At this time, the thermal comfort level of the travel path environment will have a more obvious impact on the pedestrian walking path decision-making problem.

[0004] The existing construction of urban road environment mainly stays at regulating the outdoor thermal environment by means of factors such as the layout of street trees and road materials, and lacks research on the behavioral choices of the main users of sidewalks. At present, the impact mechanism of the thermal comfort of the travel path environment on pedestrian walking path decisions is still unclear, and there is a lack of pedestrian walking path decision-making model that couples travel time and path environment thermal comfort, which restricts the possibility of optimizing the urban road thermal environment from the perspective of pedestrian behavior choice. In order to improve the probability of residents choosing to walk, it is necessary to increase the advantages of urban sidewalks, explore the key factors that increase the probability of urban sidewalks being selected, and build a pedestrian walking path decision-making model that couples travel time and path environment thermal comfort. These are technical difficulties that need to be solved urgently.

[0005] Based on this, a data processing device that couples travel time data and environmental thermal comfort data and can predict travelers' walking path selection decisions urgently needs to be developed. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present application proposes a road environment data processing device, method, electronic device and storage medium that couples travel time data and environmental thermal comfort data.

[0007] The technical solution adopted by this application to solve the above technical problems is:

[0008] A road environment data processing device couples travel time data and environmental thermal comfort data to predict the walking path selection decision results of travelers, including:

[0009] The data acquisition module is used to select locations with large differences in path environment in the area to be studied to arrange measurement points, and respectively obtain: road thermal environment parameters at pedestrian height, travel time data corresponding to any path in the area to be studied, thermal sensation of pedestrians at any of the measurement points, factors affecting travel path selection behavior, data on any path selection, and weights of travel thermal comfort and travel time;

[0010] The database module interacts with the data acquisition module to model the research area based on the ENVI-met computational fluid dynamics software, design a multivariate simulation scheme, and use the simulation scheme to obtain the hourly human equivalent physiological temperature PET value at the pedestrian height of any path, and establish a data set of road environment and its corresponding human equivalent physiological temperature value PET; and design a multi-path scenario working condition hypothesis questionnaire with travel time and travel thermal comfort as variables to obtain the path selection probability of pedestrians under various working conditions, and establish a data set of the working condition selection situation and the corresponding travel cumulative prospect value corresponding to each working condition; the data set of the road environment and its corresponding human equivalent physiological temperature value PET and the data set of the assumed path working condition selection and the corresponding travel cumulative prospect value are combined to obtain the cumulative prospect value data set of pedestrian travel selection under different road environments;

[0011] The prediction module interacts with the database module to obtain the travel thermal comfort data of the corresponding path for the urban road network with determined OD points according to the road environment and the PET data set, and predicts the pedestrian path selection situation by applying the pedestrian walking path selection decision model considering travel thermal comfort based on the cumulative prospect theory; and for urban paths with low usage frequency, the thermal comfort value of the road section is improved by improving the street tree pattern, street tree morphology, etc. to improve the cumulative prospect value of the path for optimization.

[0012] The acquisition of the travel time data includes:

[0013] For any trip with a certain starting point and arrival point, there are infinite paths between the starting point and the end point, but only one path is selected for each trip, which is assumed to be the path ;

[0014] Set Path Depend on Road sections and It consists of an intersection with traffic lights;

[0015] When travelers travel at speed When walking at a constant speed on any section of road, there are:

[0016] ;

[0017] In the formula, For path Upper section The sum of walking time; For path The road section Length; is the walking speed of the travelers;

[0018] set up is the number of red lights actually encountered, then the waiting time at any intersection when encountering a red light There is the following distribution , for Path Encountered How long do you wait at a red light? for Path Encountered The probability of a red light; assuming that the duration of the complete red light and green light at the intersection are , , and we get at the intersection Probability of encountering a red light , Green light probability as follows:

[0019] ;

[0020] ;

[0021] Then there is Path of intersections Meet The probability of a red light can be written as:

[0022] ;

[0023] Then there is Path of intersections Meet The waiting time for a red light is:

[0024] ;

[0025] exist Time spent traveling on the route Can be Path walking time and Path stop time Composition, expressed as:

[0026] ;

[0027] In the formula, for Total travel time of the route ; for The total waiting time of the trip along the path; For path Upper section Length of walking; For path on The total waiting time at red light intersections.

[0028] The acquisition of the environmental thermal comfort data includes:

[0029] Decomposing the path into sections with different thermal environments according to differences in road environments, and determining the thermal environment of the measuring points by means of fixed meteorological stations, thereby estimating the thermal environment of any section;

[0030] All road sections are combined and averaged to obtain the environmental thermal comfort data.

[0031] The method for acquiring environmental thermal comfort data comprises:

[0032] The human equivalent physiological temperature PET is selected as the indicator for measuring thermal comfort, and the average thermal radiation temperature per hour is obtained by the following formula :

[0033] ;

[0034] In the formula, is the black globe temperature; is the wind speed; is the air temperature; is the average emissivity of the black sphere; is the diameter of the black ball;

[0035] Combination Thermal comfort of travel on road sections and section length , get the overall path thermal comfort mean : ;

[0036] In the formula, For path Environmental thermal comfort data; For path Middle section Environmental thermal comfort data;

[0037] Based on the travel thermal comfort PET interval, the path By time interval Divide into intervals, then for the time interval For travelers traveling at any time, the travel thermal comfort at that time exists In this case, the time interval is expressed as : ;

[0038] In the formula, For the appearance of The duration of thermal comfort in each situation; It is the start time and end time of the travel time data interval.

[0039] The pedestrian walking path selection decision model considering travel thermal comfort based on cumulative prospect theory includes:

[0040] The travel time data and the environmental thermal comfort data are normalized by using the range method:

[0041] ;

[0042] ;

[0043] In the formula, is the normalized travel time data parameter; is the normalized environmental thermal comfort data parameter; The shortest travel time; is the longest trip duration; The minimum environmental thermal comfort data is the human equivalent physiological temperature; is the maximum equivalent physiological temperature of a human body during travel;

[0044] Then the generalized travel cost function is:

[0045] ;

[0046] In the formula, for The generalized travel cost of the path; for The weight of travel time data for each type of traveler, The weight of the environmental thermal comfort data of this type of travelers.

[0047] The weight ratio is allocated with the help of the Lister scale, which sets five levels. The travel time and travel thermal comfort under the five levels are allocated in descending order of importance: 5, 4, 3, 2, and 1 points, as shown in Table 1. Travelers make choices based on their own circumstances.

[0048] Table 1 Division of Lex scale intervals based on travel time and travel thermal comfort sensitivity

[0049] More emphasis on travel time More emphasis on travel time Both are equally important Pay more attention to thermal comfort when traveling Pay more attention to thermal comfort when traveling Travel time 5 4 3 2 1 Thermal comfort when travelling 1 2 3 4 5

[0050] Travel thermal comfort conversion weight The calculation formula is:

[0051]

[0052] In the formula, ——The probability of choosing the degree of importance attached to travel time and travel thermal comfort. The larger the i, the more importance is attached to travel time. .

[0053] The travel time weight is 1, and the calculation result is the sensitivity weight relative to travel time. It is customary to record the total weight as 1. The two weights are converted in proportion to obtain the travel time weight. and travel thermal comfort weight 1- .

[0054] The travel thermal comfort data of the corresponding path is obtained based on the road environment and the PET data set, and the pedestrian walking path selection decision model considering travel thermal comfort based on the cumulative prospect theory is used to predict the pedestrian path selection situation, including:

[0055] Obtain road environment data of any city with the help of OSM;

[0056] The cumulative prospect value dataset of pedestrian travel choices under different road environments is used to quantify the correlation between plant structure, layout, road width, material and PET in the urban road environment, and obtain the travel thermal comfort of each path;

[0057] Combined with the cumulative prospect theory travel path choice model, the travel paths of urban residents are predicted.

[0058] The cumulative prospect theory travel route selection model is combined to predict the travel routes of urban residents, including:

[0059] Set the travel cost of the reference point to ,when When , it is believed that the traveler gains benefits. When , the traveler thinks it is a loss, and constructs the value function:

[0060]

[0061] In the formula, is the generalized travel cost; , All are sensitivity coefficients; is the loss aversion coefficient; is the generalized travel cost of path k;

[0062] Construct a subjective probability function of the probability of occurrence of all real travel outcomes that travelers know when traveling:

[0063] In terms of returns, the subjective probability function for:

[0064] ;

[0065] In case of loss, the subjective probability function for:

[0066] ;

[0067] In the formula, The probability of different generalized travel costs faced by travelers when choosing different routes; , are all parameters of the subjective probability function;

[0068] Solve the decision problem of pedestrian path selection based on cumulative prospect theory:

[0069] set up is a natural finite state set; let is the set of all results;

[0070] When solving the decision problem of pedestrian path selection, the foreground By probability distribution Given, the cumulative prospect value CV of the path is:

[0071] ;

[0072] And there are:

[0073] ;

[0074] ;

[0075] In the formula, and The cumulative prospect values ​​of the travelers representing the gain part and the loss part respectively; is the probability weight function;

[0076] The path with the largest total cumulative prospect value is used as the walking path selection decision result of the traveler;

[0077] The total cumulative prospect value CV (ΔC K ,P ks ) = CV+ (ΔC K , P ks )+ CV - (ΔC K , P ks );

[0078] Among them, CV + (ΔC K , P ks ) represents the cumulative positive prospect value of the K path; CV - (ΔC K , P ks ) represents the cumulative negative prospect value of the K path; ΔC K represents the difference between the actual travel cost of path K and the reference point; P ks Represents the objective probability of scenario S occurring on the K path.

[0079] The urban paths with low usage frequency are optimized by improving the street tree pattern and street tree form, improving the thermal comfort value of the road section to enhance the cumulative prospect value of the path, including:

[0080] According to the multivariate simulation scheme, the quantitative effects of different plant structures, layouts, road widths and materials on the thermal environment of the path are obtained, and the pedestrian path selection situation is predicted according to the cumulative prospect theory travel path selection model, and a street tree pattern optimization strategy that can effectively improve the probability of pedestrian travel path selection is obtained.

[0081] A data processing method based on the above road environment data processing device:

[0082] Select locations with large differences in path environment in the area to be studied to arrange measurement points, and obtain: road thermal environment parameters at pedestrian height, travel time data corresponding to any path in the area to be studied, thermal sensation of pedestrians at any of the measurement points, factors affecting travel path selection behavior, data on any path selection, and weights of travel thermal comfort and travel time;

[0083] Relying on ENVI-met computational fluid dynamics software to model the study area, a multivariate simulation scheme is designed, and the simulation scheme is used to obtain the hourly human equivalent physiological temperature PET value at the pedestrian height of any path, and a data set of road environment and its corresponding human equivalent physiological temperature PET value is established; and a multi-path scenario working condition hypothesis questionnaire is designed with travel time and travel thermal comfort as variables to obtain the path selection probability of pedestrians under various working conditions, and the corresponding cumulative prospect value calculated for each working condition is established. The data set of the working condition selection and the corresponding travel cumulative prospect value is established; the data set of the road environment and its corresponding human equivalent physiological temperature PET value and the data set of the assumed path working condition selection and the corresponding travel cumulative prospect value are combined to obtain the cumulative prospect value data set of pedestrian travel selection under different road environments;

[0084] For the urban road network with determined OD points, the travel thermal comfort data of the corresponding paths are obtained based on the road environment and the PET data set, and the pedestrian walking path selection decision model considering travel thermal comfort based on the cumulative prospect theory is used to predict the pedestrian path selection situation; and for urban paths with low frequency of use, the thermal comfort value of the road section is improved by improving the pattern and morphology of street trees to enhance the cumulative prospect value of the path for optimization.

[0085] An electronic device for walking path selection decision-making, comprising:

[0086] Storage medium for storing computer programs;

[0087] The processing unit exchanges data with the storage medium, and is used to execute the computer program through the processing unit to perform the steps of the method as described above when making a walking path selection decision.

[0088] The beneficial effects brought by this application are:

[0089] The data processing device for coupling travel time data and environmental thermal comfort data described in the present application obtains, through the data acquisition module, the thermal environment parameters of the road at the height of the pedestrian, the travel time corresponding to any path in the area to be studied, the thermal sensation of the pedestrian for any of the measuring points, the influencing factors of the travel path selection behavior, and the data of any path selection situation; then, according to the influencing factors of the travel path selection behavior, the factor weight ratios of travelers with different travel purposes are allocated, and the thermal comfort data of the travelers and the travel time data are coupled to construct a generalized travel cost function, and based on the cumulative prospect theory model, the impact of travel thermal comfort on pedestrian path selection is quantitatively analyzed. A walking path selection decision model based on the cumulative prospect theory that couples travel time and environmental thermal comfort is constructed. The model can predict the travel path selection behavior of travelers under different thermal environment conditions, and provide a new interpretation perspective for slow traffic travel scenarios where travelers are irrational and make path selections in uncertain travel environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a block diagram of the road environment data processing device described in this application.

[0091] Figure 2 It is a flow chart of the operation of the road environment data processing device described in this application.

[0092] Figure 3 yes Figure 2 Block diagram of the walking path choice decision model.

[0093] Figure 4 yes Figure 2 The logical block diagram of the prediction module.

[0094] Figure 5 It is an evolutionary logic framework diagram of travel route selection behavior based on travel time and thermal comfort.

[0095] Figure 6 It is a schematic diagram of the design case of diversified street tree three-dimensional form, spacing, spatial distribution and road width paving material parameters. DETAILED DESCRIPTION

[0096] The technical solution described in this application is further described below in conjunction with the accompanying drawings. Specific embodiment 1:

[0098] This application provides an embodiment:

[0099] like Figure 1-3A road environment data processing device that couples travel time data and environmental thermal comfort data is used to predict the decision results of travelers' walking path selection, including: a data acquisition module 100, a database module 200, and a prediction module 300; wherein the data acquisition module 100 is used to select locations with large differences in path environment in the area to be studied to arrange measurement points, and respectively obtain: road thermal environment parameters at pedestrian height, travel time corresponding to any path in the area to be studied, thermal sensation of pedestrians for any of the measurement points, factors affecting travel path selection behavior, and any path selection situation data, and also includes weights of travel thermal comfort and travel time; the database module 200 interacts with the data acquisition module 100 for data modeling of the study area based on ENVI-met computational fluid dynamics software, designing a multivariate simulation scheme, using the simulation scheme to obtain the hourly human equivalent physiological temperature PET value at pedestrian height on any path, and establishing a road environment and its corresponding human equivalent physiological temperature PET value. Data set; and design a multi-path scenario working condition hypothesis questionnaire with travel time and travel thermal comfort as variables to obtain the path selection probability of pedestrians under various working conditions, and establish a data set of the working condition selection situation and the corresponding travel cumulative prospect value corresponding to each working condition; collect the data set of the road environment and its corresponding human equivalent physiological temperature value PET and the content of the data set of the assumed path working condition selection and the corresponding travel cumulative prospect value to obtain the pedestrian travel selection cumulative prospect value data set under different road environments; the prediction module 300 interacts with the database module for data, and is used to determine the urban road network of the OD point, obtain the travel thermal comfort data of the corresponding path according to the road environment and the PET data set, and apply the pedestrian walking path selection decision model based on the cumulative prospect theory considering travel thermal comfort to predict the pedestrian path selection situation; and for urban paths with low usage frequency, improve the thermal comfort value of the road section by improving the street tree pattern, street tree morphology, etc. to improve the path cumulative prospect value for optimization.

[0100] In the database module 200, a multi-road condition SP questionnaire survey is designed, and travel time and travel thermal comfort are used as variables for permutation and combination to design travel conditions. The impact thresholds of the two factors of travel thermal comfort and travel time on travel route selection are sorted and quantified according to the questionnaire survey results, and a data set of each road condition and its corresponding cumulative prospect value is established.

[0101] like Figure 4In the prediction module 300, based on the case area to be studied, the travel OD point is selected with a walking distance of 2 kilometers as the limit distance, the road network information is counted and simplified to plan the travel path, the urban road environment data is obtained based on OSM, and the travel thermal comfort data of the corresponding path is obtained based on the road environment and the PET data set, and the pedestrian walking path selection decision model based on the cumulative prospect theory considering travel thermal comfort is used to predict the pedestrian path selection situation, and for the urban greenway with low frequency of use, a road optimization strategy that can effectively improve the probability of pedestrian travel path selection is proposed based on the data set; specifically, by simplifying the urban road network of the selected OD point in the urban area, the road environment of each city is obtained with the help of OSM, and the different plant structures, layouts, road widths, materials and PETs are quantified by the data set in the database module 200 (the data set of the road environment and its corresponding thermal value PET and the data set of the path and the corresponding travel cumulative prospect value), and the travel thermal comfort of each path is calculated. Combined with the travel path selection model, the travel path of urban residents is predicted. Not only does the thermal comfort of the road environment be considered from the perspective of road environment creation, but also the path selection psychology of travelers is emphasized in combination with actual road usage scenarios. The prospect value of travel path selection behavior is taken as the final measurement indicator, thus proposing a road environment optimization strategy for infrequently used urban greenways to effectively increase the utilization rate of urban roads.

[0102] The multivariable simulation scheme includes: one or more of the following: the shape and layout of roadside trees, the width of the road and the paving materials. Specific embodiment 2:

[0104] This application also provides an embodiment:

[0105] The road environment data processing device for coupling travel time data and environmental thermal comfort data described in Example 1 uses the following data processing method when making walking path selection decision predictions, such as Figure 2 , specifically including the following steps:

[0106] S1. Conduct field research on the experimental paths in the selected experimental area, select locations with large differences in path environment to arrange measurement points to obtain road thermal environment parameters at pedestrian height; obtain the length of each path section and the duration of traffic lights; distribute questionnaires to investigate pedestrians' thermal sensations at each measurement point, factors affecting travel path selection behavior, and the selection of each path.

[0107] Specifically, the thermal environment parameters measured on the spot mainly include: air temperature, relative humidity, black globe temperature, and wind speed; secondly, the estimation of travel time is based on the route composition obtained by field investigation on the one hand, and the walking speed of different travelers is obtained through literature research on the other hand; the questionnaire survey mainly includes pedestrian thermal comfort survey and travel behavior selection survey. In addition to the pedestrian thermal sensation survey, the pedestrian thermal comfort survey also needs to conduct statistics on the status and clothing of pedestrians to obtain data such as clothing thermal resistance and exercise intensity.

[0108] S2. Analyze the factors affecting travel path selection behavior, calculate the factor weight ratio distribution of travelers with different travel purposes, and on this basis, couple travel time and environmental thermal comfort to establish a generalized travel cost function; combine the existing cumulative prospect theory travel path selection model considering travel time, replace the single travel time variable with generalized travel cost, and develop a pedestrian walking path selection decision model method based on cumulative prospect theory considering travel thermal comfort.

[0109] S21. Use Likert scale to conduct factor weight analysis and assign weight ratios to each factor; the construction of the generalized travel cost function of travelers includes the construction of travel time cost and travel thermal comfort cost. First, for a trip with a starting point O and an arrival point D, there are infinite paths between O and D, but only one path is selected for each trip. The path is assumed to be . Then in The time spent on the path can be given by Path walking time and Path stop time If each road is broken down into several sections and several intersections, then we can assume that Path by Road sections and It consists of an intersection with traffic lights.

[0110] Path walking time Can be regarded as The sum of walking time on a road segment, when the traveler travels at a speed of When walking at a constant speed on each road section, the walking time of the road section is the length of the road section divided by the walking speed, which is:

[0111] (1)

[0112] In the formula, On the path The sum of the walking time of the road segments, that is Path walking time, ; for On the path The length of the road segment, ; is the walking speed of the traveler, Path stop time It can be regarded as the sum of the waiting time when encountering a red light at each intersection with a traffic light. The total waiting time of the path can be written as the event :way The total time that a person stops waiting for a red light at an intersection; if the number of intersections is limited, then the event is a finite set of events and can be used in It is described by the number of red lights encountered on the path. Assume that the event exists Situation: is the number of red lights encountered, ,and is an integer, i.e., the event There is the following distribution , for Path Encountered How long do you wait at a red light? for Path Encountered To simplify the problem of encountering a traffic light, this embodiment assumes that when a traveler encounters a red light, he needs to stop for the entire red light duration, and when he encounters a green light, the stop time is 0, and the probability of encountering a traffic light at each intersection is independently distributed. Assume that the duration of the complete red light and green light at the intersection is , That is, we can conclude that at the intersection Probability of encountering a red light , Green light probability as follows:

[0113] ; (2)

[0114] ; (3)

[0115] Then there is Intersection Meet on the path The probability of a red light can be written as:

[0116] (4)

[0117] Then there is Intersection Meet on the path The waiting time for a red light can be written as:

[0118] (5)

[0119] exist Time spent traveling on the route Can be Path walking time and Path stop time Composition, therefore, can be expressed as the following formula:

[0120] (6)

[0121] In the formula, The total travel time of the route, ; The total waiting time for the trip on the path, ; for On the path The walking time of the route segment, ; On the path The total waiting time at red light intersections, ; for The number of red lights encountered during a trip on the path, .

[0122] There are many indicators of thermal comfort when traveling. This embodiment selects the human equivalent physiological temperature PET as an indicator to measure thermal comfort. The calculation of PET is based on the measured thermal environment parameters of hourly average air temperature, relative humidity, black globe temperature, and wind speed. The hourly average thermal radiation temperature is calculated by substituting the hourly average air temperature, wind speed, and black globe temperature into formula (7): , the PET value is calculated by inputting the parameters of thermal radiation temperature, air temperature, relative humidity and wind speed with the help of Rayman software.

[0123] (7)

[0124] In the formula, is the black ball temperature, °C, is the wind speed, ; is the air temperature, °C; is the average emissivity of the black sphere, ; is the diameter of the black ball, .

[0125] Due to the differences in road environment along the path, the thermal comfort of different sections is different. Thermal comfort of travel on road sections and section length , calculate the overall path thermal comfort mean .

[0126] (8)

[0127] In the formula, : Thermal comfort of the route, ℃; : path Thermal comfort of travel on the road section, ℃;

[0128] In a certain time period Any time within On the path Travel, the event B: Always on the path Obviously, the thermal comfort of travel at any time during a certain period of time is a collection of possible results of thermal comfort levels. Then the event B has the following distribution: ,in To determine the time interval Any time within Thermal comfort when traveling, This is the probability of this thermal comfort situation occurring.

[0129] Assumption Path The time interval can be divided into Divide into intervals, then for a certain time interval And for travelers who may travel at any time, the thermal comfort of travel at a certain time exists In this case, the time interval is expressed as :

[0130] (9)

[0131] In the formula, For the appearance of The duration of thermal comfort in each case, : The start time and end time of the travel time interval.

[0132] The travel cost of travelers is coupled with travel time and travel thermal comfort to construct a generalized travel cost function for travelers, which is defined as the total travel time and travel thermal comfort cost required for travelers to travel. However, for different types of travelers, the importance of travel thermal comfort and travel time in a travel behavior is different, and their influence on travelers' travel path selection is also different.

[0133] Travel time and travel thermal comfort are parameters of different natures. In order to unify the relationship between the two, the range method is used here to normalize them:

[0134] (10)

[0135] (11)

[0136] In the formula, is the normalized travel time parameter, with a value range of 0-1; is the normalized travel thermal comfort parameter, with a value range of 0-1; The shortest travel time, ; : The longest trip duration, ; : Minimum travel thermal comfort human equivalent physiological temperature, ℃; Maximum equivalent physiological temperature of human body during travel, ℃.

[0137] (12)

[0138] In the formula, for The generalized travel cost of the path; for The weight of travel time for each type of traveler, The travel thermal comfort weight of the type of travelers; , which can be obtained from survey data of different types of travelers.

[0139] S22. Based on the cumulative prospect theory, the profit and loss of a route selection decision made by a travel decision maker does not depend on the actual generalized travel cost of this route selection, but on the difference between the actual and expected. The expected generalized travel cost is the reference point for measuring profit and loss. When actually traveling, the travel time and the thermal comfort of the route are usually uncertain. After determining the O point and D point of the trip, the traveler will preset the expected generalized travel cost of this trip based on his travel experience and route information, and hope that the generalized travel cost will be lower during the actual trip. However, due to the uncertainty of the route, the generalized travel cost of the traveler's travel may be larger or smaller than the expected cost. At this time, the expected generalized travel cost is the reference point for the traveler's travel.

[0140] Since different types of travelers have different travel needs, this embodiment sets different reference points for different types of travelers. The mean of the generalized travel cost is set as the reference point for research, and the model setting for the reference point of different types of travelers is:

[0141] (13)

[0142] In the formula, express Type of traveler, express path.

[0143] S23. Based on the concept of generalized travel cost, the reference point is set as In the cumulative prospect theory, the carrier of value is not the final state of value, but the change of utility value, that is, utility is not determined by the generalized travel cost of actual travel. decision, but depends on the difference between the actual and the expected, that is, when When , it is believed that the traveler gains benefits. When the traveler thinks it is a loss, construct the value function

[0144] (14)

[0145] In the formula, , (0 1,0 1), , The larger the value, the greater the sensitivity of the travelers to the risk of gain and loss, but commuters are more sensitive to the risk of loss; is the loss aversion coefficient, It is always true under any conditions and represents the degree of loss aversion of travelers. According to Kahneman's research, = , =2.25 is more appropriate.

[0146] S24. It is impossible for travelers to know the probability of all real travel outcomes when traveling, that is, travelers make uncertain decisions when choosing routes. When travelers process uncertain travel information, they often deviate from the objective probability, that is, they form the subjective probability function of travelers. Based on the subjective probability function form proposed by Tversky, the calculation formula of the subjective probability function is as follows:

[0147] In terms of returns, the subjective probability function The calculation formula is:

[0148] (15)

[0149] In case of loss, the subjective probability function The calculation formula is:

[0150] (16)

[0151] In the formula, It is the objective probability of a certain result occurring, that is, the probability of different generalized travel costs faced by travelers when choosing different paths. , is the parameter of the subjective probability function, which was calibrated by Kahneman through experiments. , .

[0152] S25. For the decision-making problem of pedestrian path selection, the following definition is given based on the cumulative prospect theory:

[0153] ① Set It is a natural finite state set, representing the set of events that may occur on the travel path. Each trip has only one event and the event is unknown to the travel decision maker.

[0154] ②Set is the set of all results, representing the result after the traveler chooses the path, and assuming There is a neutral result 0, and the remaining results are divided into positive and negative numbers according to gains and losses.

[0155] For an uncertain prospect Can be seen as arrive The mapping function, that is, for any There is only one , so that the function To define cumulativeness, sort the results for each prospect in ascending order. Separately , Foreground use( ) to describe, when Will produce results when it occurs , if and only if When .when hour, , hour, At this time, you can The middle part is recorded as , similarly, the negative part is recorded as .

[0156] In this travel path decision problem, the prospect By probability distribution Given, it can be considered as a probabilistic or risky prospect According to the cumulative prospect theory, the cumulative prospect value CV of the path is:

[0157] (17)

[0158] And there are:

[0159] (18)

[0160] (19)

[0161] In the formula, and The cumulative prospect values ​​of the travelers representing the gain part and the loss part respectively; is the probability weight function, which is the comprehensive value of the subjective probability function. , the probability weight function associated with the outcome can be interpreted as the marginal value of the respective time, that is, the probability weight associated with the payoff outcome is "the outcome is at least "As good" and "The results are definitely better than The difference in volume between the two events "good" and "bad", and the same is true for the loss result. Then the probability weight function can be expressed as:

[0162] (20)

[0163] (twenty one)

[0164] (twenty two)

[0165] S3. Relying on ENVI-met computational fluid dynamics software to model the study area, design multivariate simulation schemes such as the shape and layout of street trees, road width and paving materials, simulate the hourly human equivalent physiological temperature PET values ​​at the pedestrian height of each path, and establish a data set of road environment and its corresponding human equivalent physiological temperature PET values; design a multi-road scenario combination SP path choice questionnaire with travel time and travel thermal comfort as variables, calculate the travel cumulative prospect value of each scheme, and establish a data set of each road condition and the corresponding travel cumulative prospect value; combine the contents of the two data sets to obtain a data set of cumulative prospect values ​​of pedestrian travel choices under different road environments.

[0166] Furthermore, the S3 step is as follows: modeling the study area based on ENVI-met computational fluid dynamics software, designing multivariate simulation schemes such as the shape and layout of street trees, road width and paving materials, obtaining the hourly equivalent physiological temperature of the human body at the pedestrian height of each path, and obtaining the correlation data set between each road environment variable and the thermal comfort index PET; selecting appropriate travel time and travel thermal comfort interval and designing an SP questionnaire with multiple operating scenario assumptions through permutations and combinations, and establishing a path selection behavior data set for each road condition and the cumulative prospect value of travel based on the travel choice situation and the calculation results of the cumulative prospect value of each road condition; and using the data set as the parameter set for the area to be studied.

[0167] S4. Use OSM to obtain urban road environment data, and obtain travel thermal comfort data of corresponding paths based on road environment and PET datasets. Apply the pedestrian walking path selection decision model based on cumulative prospect theory considering travel thermal comfort to predict pedestrian path selection, and propose road environment optimization strategies for urban roads with low usage frequency that can effectively improve the probability of pedestrian travel path selection.

[0168] By analyzing the simulation results, we can obtain the quantitative effects of different plant structures, layouts, road widths, and materials on the thermal environment of the path. The pedestrian path selection decision model based on the cumulative prospect theory considering travel thermal comfort is used to predict pedestrian path selection under different design schemes, and a street tree pattern optimization strategy that can effectively improve the probability of pedestrian path selection is proposed.

[0169] The device described in this embodiment uses computational fluid dynamics software to simulate cases with different parameters such as street tree morphological parameters and spatial layout, and fully simulates the path thermal environment within the ten hours of the main travel period from 8:00 to 18:00, specifically and clearly reflecting the differences in the changes in the thermal environment under different cases, and can predict and evaluate the pedestrian path selection under different street tree pattern design schemes, and proposes a street tree pattern optimization strategy that can effectively improve the probability of pedestrian travel path selection, which provides a theoretical basis for the planning of urban slow-moving systems and also provides a reference for improving the probability of green travel for urban residents and improving the living space of urban residents. Specific embodiment 3:

[0171] This application is implemented based on the device described in Specific Example 1, and the specific contents are as follows:

[0172] The path from a certain community in a university town to a certain subway station is taken as the research area. The specific steps are as follows:

[0173] Step 1: Conduct field research on the experimental paths in the selected experimental area, and select locations with large differences in path environment to arrange measurement points to obtain road thermal environment parameters at pedestrian height. The thermal environment parameters measured on site mainly include: air temperature, relative humidity, black ball temperature, and wind speed. The length of each path section and the duration of traffic lights are known, and the walking speed of different travelers is obtained through literature research; the questionnaire survey mainly includes pedestrian thermal comfort survey and travel behavior selection survey. In addition to the pedestrian thermal sensation survey, the pedestrian thermal comfort survey also needs to conduct statistics on the status and clothing of pedestrians, and obtain data such as clothing thermal resistance and exercise intensity. The travel behavior selection survey needs to investigate the traveler's travel purpose, the factors affecting travel path selection behavior, and the selection of each path.

[0174] Step 2: Simplify the travel time into the walking time and the waiting time at the signal light, and apply the formulas (1)-(6) in the specific embodiment 1. Apply the formulas (7)-(9) in the specific embodiment 1 for the calculation of the thermal comfort of the travel environment. Analyze the factors affecting the travel path selection behavior, and calculate the weight ratio distribution of the factors for travelers with different travel purposes. The corresponding weights are the weights in the formula (12) in the specific embodiment 1. On this basis, the travel time and the thermal comfort of the environment are coupled to establish a generalized travel cost function, which is the formulas (10)-(12) in the specific embodiment 1.

[0175] Step 3: Combining the existing cumulative prospect theory travel path selection model considering travel time, replacing the single travel time variable with generalized travel cost, and developing a pedestrian walking path selection decision model method based on cumulative prospect theory considering travel thermal comfort, and the evolutionary logic of pedestrian travel path selection behavior.

[0176] like Figure 5 Combined with the discrete distribution of travel time and travel thermal comfort on the path, the generalized travel cost that may be incurred on each path can be listed and its change value compared with the reference point can be obtained. , and a matrix of the results and their corresponding objective probabilities can be listed ( , ). The value function on each path is obtained by formula (14) in specific embodiment 1; the subjective probability function value is obtained by formulas (15)-(16) in specific embodiment 1, the cumulative probability weight is calculated by formulas (20)-(22) in specific embodiment 1, and the cumulative travel prospect value is calculated by formulas (17)-(19) in specific embodiment 1.

[0177] Step 3: Relying on ENVI-met computational fluid dynamics software to model the study area, design multivariable simulation schemes such as street tree morphology and layout, road width and paving materials, and verify the accuracy of the ENVI-met model based on the measured path thermal environment parameter data and perform debugging and optimization.

[0178] Step 4: Based on the debugged ENVI-met software, with road width, road paving materials, plant three-dimensional spatial morphological parameters, street tree spacing, and street tree layout as independent variables, the control variable method is used to design multivariate simulation conditions. In order to further comprehensively consider the differential effects of urban road plant three-dimensional morphology and spatial layout on the road environment, combined with the actual plant characteristics in the humid and hot climate zone, 10 different plant three-dimensional morphological parameter schemes are designed based on street tree height and crown width; based on the recommended plant spacing width proposed in the existing road specifications and the actual survey results of street tree spacing in the study area, a comprehensive parametric design scheme for street tree spacing is proposed: 6m, 8m, 10m; on this basis, the street tree spatial layout is designed, including four groups of conditions: double row parallel, double row staggered, single row on the south side, and single row on the north side of street trees; according to the survey of the study area, the sidewalk road width design scheme is designed as 4m and 6m, and the road paving materials are concrete and asphalt. The specific diversified parameter scheme design is as follows: Figure 6 shown.

[0179] Step 5: The multivariable scheme simulates the hourly calculation results of air temperature, relative humidity, wind speed and all-weather solar radiation at pedestrian height on each path, and calculates the hourly equivalent human body temperature value, and explores the quantitative impact of different plant structures, layouts, road widths, and materials on the path thermal environment. The pedestrian walking path selection decision model based on the cumulative prospect theory considering travel thermal comfort is used to predict the pedestrian path selection under different design schemes. Travelers are more likely to choose paths with large cumulative prospect values. Based on this, a street tree pattern optimization strategy that can effectively improve the probability of pedestrian travel path selection is proposed.

[0180] In summary, this application is based on field research to learn about the path travel time and thermal environment conditions; then the equivalent physiological temperature of the human body is selected as the thermal comfort index coupled with the travel time, and the weight index is assigned with the help of questionnaire survey data to construct a generalized travel cost function for travelers. Based on the existing cumulative prospect theory model of travel path selection that only considers the travel time of pedestrians, the generalized travel cost is used to replace the single travel time variable, and a pedestrian walking path selection decision model method considering travel thermal comfort based on cumulative prospect theory is developed. With the help of the ENVI-met computational fluid dynamics model, numerical modeling and dynamic simulation are carried out on the selected measured case area, and a multivariate road environment simulation scheme is designed with road width, street tree height, street tree spacing, road paving material, and street tree arrangement as independent variables, and a data set of road environment and its corresponding thermal comfort index PET is established; based on the SP path selection questionnaire survey, the path selection of urban residents under multiple road condition scenarios is obtained, and the threshold of the influence of the two factors on the travel path selection is calculated, and a data set of each road condition and its corresponding travel cumulative prospect value is established. Subsequently, based on the urban case area, the OD points of travel were selected, the road network information of the OD points was counted and simplified, the urban road environment information was obtained based on OSM, the road environment and the corresponding PET data set were used to obtain the corresponding thermal comfort value, and then the cumulative prospect value of each path was calculated to predict the walking travel path of urban residents, and a targeted optimization strategy was proposed for urban roads with low selection probability based on the data set. The invention constructs a generalized travel cost function by coupling the thermal comfort of travel pedestrians and travel time, quantifies the impact of travel thermal comfort on pedestrian path selection, and predicts and evaluates the pedestrian path selection under different street tree pattern design schemes, and proposes a street tree pattern optimization strategy that can effectively improve the probability of pedestrian travel path selection, providing a theoretical basis for the planning of urban slow-moving systems, and providing a reference for improving the probability of green travel for urban residents and improving the living space of urban residents. Specific embodiment 4:

[0182] This application also provides an embodiment:

[0183] An electronic device for making walking path selection decisions, comprising: a storage medium and a processing unit; wherein the storage medium is used to store a computer program; the processing unit exchanges data with the storage medium, and is used to execute the computer program through the processing unit when making walking path selection decisions, and perform the steps of the method described in specific embodiment 2.

[0184] In the above-mentioned electronic device, the storage medium is preferably a storage device such as a mobile hard disk, a solid-state hard disk, or a USB flash drive; the processing unit is preferably a CPU. The above-mentioned CPU can perform various appropriate actions and processes according to the program stored in the storage medium. The electronic device also includes the following peripherals, including input parts such as a keyboard and a mouse, and may also include output parts such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; in particular, according to the embodiments disclosed in the present application, Figure 2 The described processes can all be implemented as computer software programs.

[0185] This application also provides an embodiment:

[0186] A computer-readable storage medium: the computer-readable storage medium stores a computer program; when the computer program is running, the steps of the method described in specific embodiment 2 are executed.

[0187] In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0188] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily change or replace within the technical scope disclosed in the present application, which belongs to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A road environment data processing device, which couples travel time data and environmental thermal comfort data, and is used to predict the walking path selection decision results of travelers, characterized in that: include: The data acquisition module is used to arrange measuring points in the area to be studied, and respectively obtain: road thermal environment parameters at pedestrian height, travel time data corresponding to any path in the area to be studied, thermal sensation of pedestrians at any measuring point, factors affecting travel path selection behavior, data on any path selection, and weights of travel thermal comfort and travel time; The database module interacts with the data acquisition module to model the research area based on the ENVI-met computational fluid dynamics software, design a multivariate simulation scheme, use the simulation scheme to obtain the hourly human equivalent physiological temperature PET value at the pedestrian height of any path, and establish a data set of road environment and its corresponding human equivalent physiological temperature value PET; and design a multi-path scenario working condition hypothesis questionnaire with travel time and travel thermal comfort as variables to obtain the path selection probability of pedestrians under various working conditions, and establish a data set of any working condition selection situation and the corresponding travel cumulative prospect value in correspondence with the cumulative prospect value calculated for each working condition; the data set of the road environment and its corresponding human equivalent physiological temperature value PET and the data set of the assumed path working condition selection and the corresponding travel cumulative prospect value are collected to obtain the cumulative prospect value data set of pedestrian travel selection under different road environments; the prediction module interacts with the database module to obtain the travel thermal comfort data of the corresponding path for the urban road network with the determined OD point according to the road environment and the PET data set, and predict the pedestrian path selection situation using the pedestrian walking path selection decision model based on the cumulative prospect theory considering travel thermal comfort; In addition, for urban paths with low usage frequency, the thermal comfort value of the road section is improved by improving the pattern and shape of street trees to enhance the cumulative prospect value of the path for optimization; The acquisition of the travel time data includes: for any trip with a determined starting point and arrival point, there are infinite paths between the starting point and the end point, but only one path is selected for each trip, and the path is assumed to be the path ; Set path Depend on Road sections and There are intersections with traffic lights; when travelers travel at a speed When walking at a constant speed on any section of road, there are: ; In the formula, For path Upper section The sum of walking time; For path The road section Length; is the walking speed of the travelers; set up is the number of red lights actually encountered, then the waiting time at any intersection when encountering a red light has the following distribution , for Path Encountered How long do you wait at a red light? for Path Encountered The probability of a red light; assuming that the duration of the complete red light and green light at the intersection are , , and we get at the intersection Probability of encountering a red light , Green light probability as follows: ; ; then there is Path of intersections Meet The probability of a red light is written as: ; then there is Path of intersections Meet The waiting time for a red light is: ;exist Time spent traveling on the route Depend on Path walking time and Path stop time Composition, expressed as: Where, for Total travel time of the route ; for The total waiting time of the trip along the path; For path Upper section Length of walking; For path on The total waiting time at a red light intersection; the travel thermal comfort data of the corresponding path is obtained based on the road environment and the PET data set, and the pedestrian walking path selection decision model considering travel thermal comfort based on the cumulative prospect theory is used to predict the pedestrian path selection, including: Obtain any urban road environment data with the help of OSM; quantify the correlation between plant structure, layout, road width, material and PET in the urban road environment through the cumulative prospect value dataset of pedestrian travel choices under different road environments, and obtain the travel thermal comfort of each path; Combined with the cumulative prospect theory travel path selection model, the travel path of urban residents is predicted; the combined cumulative prospect theory travel path selection model is used to predict the travel path of urban residents, including: Set the travel cost of the reference point to ,when When , it is believed that the traveler gains benefits. When , the traveler thinks it is a loss, and constructs the value function: ； In the formula, is the generalized travel cost; , All are sensitivity coefficients; is the loss aversion coefficient; is the generalized travel cost of path k; Construct a subjective probability function of the probability of occurrence of all real travel outcomes that travelers know when traveling: In terms of returns, the subjective probability function for: ； In case of loss, the subjective probability function for: ； In the formula, The probability of different generalized travel costs faced by travelers when choosing different routes; , are all parameters of the subjective probability function; Solve the decision problem of pedestrian path selection based on cumulative prospect theory: set up is a natural finite state set; let is the set of all results; When solving the decision problem of pedestrian path selection, the foreground By probability distribution Given, the cumulative prospect value CV of the path is: ; and: ； ； In the formula, and The cumulative prospect values ​​of the travelers representing the gain part and the loss part respectively; is the probability weight function; The path with the largest total cumulative prospect value is taken as the decision result of the traveler's walking path selection; The total cumulative prospect value CV (ΔC K ,P ks ) = CV + (ΔC K , P ks )+ CV - (ΔC K , P ks ); Among them, CV + (ΔC K , P ks ) represents the cumulative positive prospect value of the K path; CV - (ΔC K , P ks ) represents the cumulative negative prospect value of the K path; ΔC K represents the difference between the actual travel cost of path K and the reference point; P ks represents the objective probability of scenario S occurring on the K path; The acquisition of the environmental thermal comfort data includes: The selected route is decomposed into sections with different thermal environments according to the differences in road environment, and the thermal environment of the measuring point is determined by means of fixed meteorological stations, so as to estimate the thermal environment of any section; All road sections are combined and averaged to obtain the environmental thermal comfort data; The method for acquiring environmental thermal comfort data comprises: The human equivalent physiological temperature PET is selected as the indicator for measuring thermal comfort, and the average thermal radiation temperature per hour is obtained by the following formula : ； In the formula, is the black globe temperature; is the wind speed; is the air temperature; is the average emissivity of the black sphere; is the diameter of the black ball; Combination Thermal comfort of travel on road sections and section length , get the overall path thermal comfort mean : ; In the formula, For path Environmental thermal comfort data; For path Middle section Environmental thermal comfort data; Based on the travel thermal comfort PET interval, the path By time interval Divide into intervals, then for the time interval For travelers traveling at any time, the travel thermal comfort at that time exists In this case, the time interval is expressed as : ; In the formula, For the appearance of The duration of thermal comfort in each situation; It is the start time and end time of the travel time data interval.

2. The road environment data processing device according to claim 1, characterized in that: The pedestrian walking path selection decision model considering travel thermal comfort based on cumulative prospect theory includes: The travel time data and the environmental thermal comfort data are normalized by using the range method: ; ; In the formula, is the normalized travel time data parameter; is the normalized environmental thermal comfort data parameter; The shortest travel time; is the longest trip duration; The minimum environmental thermal comfort data is the human equivalent physiological temperature; is the maximum equivalent physiological temperature of the human body when traveling; then the generalized travel cost function is: ; In the formula, for The generalized travel cost of the path; for The weight of travel time data for each type of traveler, The weight of the environmental thermal comfort data of this type of travelers.

3. The road environment data processing device according to claim 1, characterized in that: The urban paths with low usage frequency are optimized by improving the pattern and form of street trees to improve the thermal comfort value of the road section and enhance the cumulative prospect value of the path, including: According to the multivariate simulation scheme, the quantitative impact of different plant structures, layouts, road widths and materials on the path thermal environment is obtained, and the pedestrian path selection situation is predicted according to the cumulative prospect theory travel path selection model, and an optimization strategy for the street tree pattern that can effectively improve the probability of pedestrian travel path selection is obtained.

4. A data processing method based on the road environment data processing device according to any one of claims 1 to 3, characterized in that: Measuring points are arranged in the area to be studied, and the following are obtained: thermal environment parameters of the road at the height of pedestrians, travel time data corresponding to any path in the area to be studied, thermal sensation of pedestrians at any measuring point, factors affecting travel path selection behavior, data on any path selection, and weights of travel thermal comfort and travel time; Relying on ENVI-met computational fluid dynamics software to model the study area, a multivariate simulation scheme is designed, and the multivariate simulation scheme is used to obtain the hourly human equivalent physiological temperature PET value at the pedestrian height of any path, and a data set of road environment and its corresponding human equivalent physiological temperature PET value is established; and a multi-path scenario working condition hypothesis questionnaire is designed with travel time and travel thermal comfort as variables to obtain the path selection probability of pedestrians under various working conditions, and the corresponding working condition selection situation and the corresponding travel cumulative prospect value The data set of the road environment and its corresponding human equivalent physiological temperature value PET and the data set of the assumed path working condition selection and the corresponding travel cumulative prospect value are obtained. The cumulative prospect value data set of pedestrian travel selection under different road environments is obtained; For the urban road network with determined OD points, the travel thermal comfort data of the corresponding paths are obtained according to the road environment and PET data set, and the pedestrian walking path selection decision model considering travel thermal comfort based on cumulative prospect theory is used to predict the pedestrian path selection situation. In addition, for urban paths with low usage frequency, optimization is carried out by improving the pattern and shape of street trees, improving the thermal comfort value of the road section and enhancing the cumulative prospect value of the path.

5. An electronic device for pedestrian path selection decision-making, characterized in that: include: A storage medium for storing a computer program; a processing unit for exchanging data with the storage medium, for executing the computer program through the processing unit when making a walking path selection decision, and performing the steps of the method as claimed in claim 4.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program; when the computer program is run, the steps of the method as claimed in claim 4 are executed.

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