A Fuzzy Logic-Based Assessment System and Method for Measuring Urban Space Perception Based on Walking Experience

Abstract

The invention encompasses an evaluation system that transforms uncertain and subjective environmental perception data regarding pedestrians' urban space experience into a quantitative and objective urban space perception value by processing and clarifying qualitative inputs grouped under multiple parameter categories using fuzzy logic, and a method describing the operation of this system.

Description

1 TARIFF MEASURING URBAN SPACE PERCEPTION BASED ON WALKING EXPERIENCE FUZZY LOGIC-BASED EVALUATION SYSTEM AND METHOD TECHNICAL AREA 5 The invention addresses the ambiguous and subjective environmental perception of pedestrians regarding their experience of urban space. fuzzy logic of qualitative inputs grouped under multiple parameter sets a quantitative and objective perception of urban space is achieved by processing and clarifying it through this method. an evaluation system that enables the conversion of value and a description of how this system works It includes a method that involves 10 The invention is particularly relevant to architecture, urban planning, smart city technologies, and environmental psychology. In these areas, after a walking experience carried out on an urban route, users in converting subjective and linguistic expressions reported by into a quantitative measure a complementary system and method aimed at eliminating the fundamental technical problem 15 It presents the whole picture. PREVIOUS TECHNIQUE how the environment is perceived by the user during the experience of walking in an urban space Measurement is a long-standing area of ​​research in the disciplines of architecture, urban planning, and environmental psychology. This is a key issue. When current practices in this field are examined, the perception of urban space becomes clear. Studies on its measurement are basically grouped into four main methodological categories. This is seen through: qualitative studies, discrete choice models, machine learning techniques, and Sequential methodologies. Qualitative studies, traditional data collection methods such as surveys and interviews. It is based on the tools of comparison of individual inferences and the results obtained. 25 It has significant limitations in terms of generalization. In these methods, participants The subjective perception data that it presents through linguistic expressions are often statistical averages or This is being evaluated with simple quantitative transformations such as frequency analysis, which affects perception. This leads to the loss of fundamental qualities such as uncertainty, ambiguity, and multidimensionality in its nature. This opens up the possibility of increasing the validity of the data obtained from the surveys, or the surveys are 30% or higher. Even if these approaches involve further elaboration or expansion of the sample set, It fails to overcome the methodological limitations and also imposes a burden of time and cost on the study. It increases significantly. Discrete selection models are based on the segmentation of computer-generated images. 35 It offers methodologies that increase the scalability of the study and improve image quality. 2 This method allows us to examine the impact of these changes on perception. However, this method analyzes the As the number of images increases, so does the cost of the study, and even more so... More importantly, the dynamic nature and multisensory nature of the act of walking (hearing, touch, smell, etc.) It focuses solely on visual perception, completely ignoring other sensory inputs. Machine learning and deep learning-based techniques, on the other hand, can address complex issues involving more variables. While this allows models to be investigated with relatively limited sample sizes, Studies generally focus on image segmentation and landscape analysis, again It focuses solely on the perception of visible elements. These techniques, along with other senses, integrating perceived elements holistically into the model and individual factors influencing perception subjective factors (such as age, gender, walking habits, sense of belonging to a place) and social factors 10 It shows significant shortcomings in measuring the data. Movement during the act of walking. The dynamic situation it brings about further enhances the multifaceted and subjective nature of perception. This makes it complex, and current machine learning approaches cannot encompass this holistic structure. It cannot be modeled to encompass all of these. In addition, virtual reality, augmented reality, wearable physiological sensors (EEG, MRI) (such as) and new technologies like eye tracking are also used in measuring urban spatial perception. are used. However, these techniques are not suitable for real-world or simulated experimental environments. due to the requirements, offering relatively narrow fields of study, and lack of broad representation. and has key disadvantages such as high experimental costs and low efficiency. Furthermore, these 20 Most of these methods are carried out in controlled environments under laboratory conditions, and in reality... It falls short of reflecting the natural walking experience in global conditions. The current situation... The common and most fundamental problem of these techniques is the inherent uncertainty in the perception of urban space. a quantitative measurement while preserving qualities such as multidimensionality, dynamism and subjectivity The problem is the lack of a holistic, flexible, and systematic assessment tool that can bring about transformation. 25 Specifically, the loss of meaning in perceptual data presented through linguistic or qualitative expressions. digitization without visiting the site, user-centered spatial evaluation, different Comparing pedestrian routes, monitoring perceptual changes over time, and In order to objectively evaluate the impact of design interventions on perception, existing This is not possible with applications. All these limitations and technical problems make urban space 30 its perception can be measured, compared, and is possible within the framework of scientific and engineering disciplines. This constitutes a serious obstacle to making it a predictable parameter. This invention, mathematically expressing the uncertainties and improbable judgments inherent in human perception. Based on the fuzzy logic methodology, which stands out for its ability to model, existing techniques This helps to fill these critical gaps and provide a holistic solution to all the technical problems listed above. It offers a solution. The system and method described in the invention are implemented in real-world settings. It allows for the individual measurement of walking experience, providing quantitative output from linguistic data. 3 It is able to produce, allows for the evaluation of subjective data, and is user-oriented. It offers spatial evaluation and, thanks to its flexible structure, allows different parameters to be incorporated into the system. It allows integration and works with small datasets without needing large datasets. It can even work with samples. In current practices, qualitative data collected through methods such as surveys or interviews are generally It is evaluated to a limited extent using statistical methods, machine learning or image Other techniques, such as segmentation, either require large datasets or only... By focusing on visual perception, the multisensory and dynamic experience brought about by the act of walking is presented holistically. It cannot be modeled as such. 10 Patent document CN108387233A describes a fuzzy logic-based pedestrian movement mode. The method of determination is mentioned. The invention is by an inertial navigation system. The location information for each step of the analyzed pedestrian is calculated using a unit of measurement for several continuous steps. By taking these as such, we apply a linear fitting process to them and this linear fitting is 15 It creates a fuzzy membership function based on this. This function determines the smoothness of the walkway. It determines the degree of membership to a line. The time interval between two consecutive steps. It is used as a fuzzy variable and the emitter is identified through another membership function. The state of movement is characterized by the membership of the walking path to the straight line. Membership status is used as a factor determining pedestrian movement mode, and these include 20 A comprehensive assessment is performed to determine whether the mode of movement is normal. The decision is made by evaluating whether the walkway is along the main corridor. The specific mode of movement of the pedestrian is determined. In the present invention, however, only the walking path is determined. Far beyond simply defining a mode of movement based on geometry and step timing, urban Numerous qualitative parameters that constitute the perception of space are holistically analyzed using fuzzy logic. 25 is included in the model, considering not only the pedestrian's physical movement but also how they perceive space. It is measurable. While the present invention focuses on navigation and positioning, in the present invention The perception of urban space itself is measured quantitatively. Patent document CN118428229A describes a fuzzy logic-based subway station turnstile 30. The discussion focuses on a method for simulating decision-making behavior. The invention primarily involves cellular automata. creating a pedestrian simulation model based on data, then influencing turnstile selection It identifies the variables. These variables are blurred, and a fuzzy rule base and A fuzzy inference system is being created, and finally, a turnstile selection decision is made through a defuzzification process. is being produced. As a result of this selection, 35 cellular automaton-based pedestrian simulation models are being created. It is integrated. The present invention, however, makes a very limited and specific decision, such as the selection of a turnstile. Rather than simulating behavior, the perceptual experience of the entire urban space after the walking experience. 4 It offers a comprehensive evaluation system for measuring quality. In the present invention... While a decision-making mechanism is modeled with only three input variables, the present invention uses ten. Seven different input parameters are grouped under three main categories to provide a holistic view of the multidimensional nature of perception. This is addressed in a certain way. Furthermore, the present invention involves behavioral decision-making in a simulated environment. When modeling, the current invention is based on the walking experience carried out in real spaces. 5 It works as follows. Patent document CN110345939A describes a fuzzy logic evaluation and map. The invention refers to an indoor positioning method that combines information. Primarily, making an indoor pedestrian trajectory prediction, then subjecting it to fuzzy logic evaluation 10 It uses an adaptive weighted direction angle fusion algorithm based on specific angles. whether to use only the best-case directional angle data for a given situation or It decides whether or not to activate the fusion algorithm. Activating fusion... If inserted, real-time data from the gyroscope and magnetometer will be available. Based on this, the weights are adjusted adaptively, and the magnetometer's orientation angle is 15. The walking posture is determined using this method, as well as the directional angle from the previous time step. A fusion with fixed weights after deciding whether or not to add them. This is being carried out. Finally, landmarks are identified via WiFi peak recognition. A map matching-based particle filter is applied using and particle positions is being corrected. In the present invention, the physical position and orientation of the pedestrian in indoor spaces are corrected. While the aim is to determine this in some way, the present invention aims to determine the physical position of a pedestrian. independently, quantitatively quantify his subjective perceptual outputs regarding space during his walking experience. It is measured as follows. While the present invention deals with the problem of positioning and navigation, The current invention allows the perceptual quality of urban space to be expressed with a score. Patent document CN111653125A describes a pedestrian crossing for a driverless car. The invention describes a method for determining the priority mode. It primarily concerns traffic. In pedestrian crossing sections where there is no signaling, actual vehicle drivers must avoid pedestrians. We collect data on yielding behavior and analyze this data according to specific criteria. It filters the data. The filtered data is graded and divided into combinations, and these 30 Based on graded and combined data, the moment a driver gives way to pedestrians Logical fuzzy rules are created. These fuzzy rules are then... By applying this to a driverless vehicle, depending on the vehicle's current speed and distance to the pedestrian The driver is instructed to slow down to a rate appropriate to human driving behavior, thereby giving way to pedestrians. The present invention aims to make the behavior pattern of a vehicle towards pedestrians human-like and comfortable. 35 While the intention was to introduce it, the present invention has no relation to vehicle behavior or traffic flow. It does not include [a specific measurement method], it focuses entirely on measuring the pedestrian's own internal perception of space. While the present invention models a vehicle-pedestrian interaction, the present invention also models the pedestrian's interaction with their environment. It models the cognitive and perceptual interaction between these two factors and produces a scalable numerical value. Patent document CN109871738A describes a pedestrian capable of adapting to a human-mixed environment. The text discusses a method for recognizing the intention to move. The invention involves recognizing the status of a target pedestrian. parameters and other traffic within a defined area in the environment where this pedestrian is located It retrieves the status parameters of the entities and defines the sub-regions of interest. Each A representative is designated to represent the traffic assets within each sub-region of the area of ​​interest. Later, a system was created based on difference field theory and fuzzy logic methods. Using a pedestrian movement intention recognition model, the status parameters of the target pedestrian and each of the 10 points of interest are determined. Analysis of the traffic presence representative and its status parameters in the sub-region of the area As a result, the target pedestrian's intention to move is determined. In the present invention, a pedestrian's Predicting instantaneous movement intention based on its interaction with dynamic objects in its environment. While the aim was to achieve this, the present invention focuses on a walk rather than the instantaneous intention of a pedestrian. after the completion of the route, an in-depth perceptual evaluation of that route 15 This is being done. The present invention attempts to predict the pedestrian's future instantaneous actions, The present invention retrospectively improves the quality of the entire experience following a walking experience. It presents this with an objective score. Patent document KR101526197B1 describes a time domain analysis and fuzzy logic 20 The text describes a device and method for detecting gait freezing. The invention is an accelerometer sensor that generates a three-axis acceleration signal based on the movement of a pedestrian. It includes these signals. The root mean square values ​​are calculated for each axis from these signals, such as the mean and variance of these root mean square values ​​over a specific time interval. Statistical features are extracted. These extracted time-domain features are used to form membership functions. 25 gait freezing is detected by blurring the surface and evaluating it with a fuzzy rule base. A decision is made as to whether or not the event occurred. In the present invention, in particular... Detection of a pathological gait disorder resulting from neurological disorders while aiming, the present invention is completely independent of the detection of a pathological condition, Measuring the perception of urban space during normal walking experiences of healthy individuals 30 The aim is to... While the current invention relies solely on accelerometer-based biomechanical signals, The present invention is a multi-layered system that evaluates seventeen different qualitative and subjective parameters simultaneously. It has a model. Patent document number KR101463684B1 describes a situation where a pedestrian has an abnormal gait and is 35 years old. The invention describes a method for measuring whether a pedestrian is on the left or right. Through multiple sensors placed on her feet, the ground created during walking is monitored. 6 It involves measuring reaction forces. This force is measured by each sensor. The values ​​are applied to predefined fuzzy membership functions, resulting in a first set of fuzzy values. are converted into values. This first set of fuzzy values ​​obtained is then converted into predetermined values. A second set of fuzzy logic for a large number of walk phases by applying a fuzzy logic rule. The values ​​are being calculated. This second set of fuzzy values ​​is from a previously recorded normal walk 5. By comparing the data, a decision is made as to whether the pedestrian's gait is abnormal. In the present invention, gait is monitored through force sensors implanted in the lower extremities. When evaluating biomechanical symmetry and smoothness, any body in the present invention without the need for a sensor, solely based on the subjective perceptual expression of the pedestrian after walking. An evaluation can be made based on the notifications. The current invention relates to the physiological aspects of walking. 10 while questioning its normality, the current discovery walking experience within the context of urban space It questions perceptual quality and produces scalable value. Patent document number KR101908481B1 describes an image-based pedestrian detection device and The method is described. The invention was achieved using a visible light camera and an infrared camera. 15 From the images obtained, it was determined which type of image was more advantageous for pedestrian detection. It selects adaptively using a logic-based approach. Fuzzy logic, real-time. From the incoming images, the characteristics of the visible light image and the infrared image are analyzed. by determining the more suitable image type for detection, and assigning a value to that selected image. It improves pedestrian detection performance by applying a convolutional neural network. In the present invention, a 20 Pedestrian presence detected via images captured by a vehicle or fixed camera system. While the aim is to achieve this, the present invention does not involve any image processing or camera use. It is not the subject of discussion; it is entirely based on the perceptual data reported by the pedestrian themselves. The current While the invention attempts to detect the presence of a pedestrian from the perspective of an external observer, the current invention from a first-hand perspective, the pedestrian's own internal spatial experience and perception is quantitatively measured. 25 It transforms. Patent document KR101514790B1 describes a frequency domain analysis and fuzzy logic. The text describes a device and method for detecting gait freezing. The invention uses an accelerometer sensor similar to the previous KR101526197B1, but with a time 30 It performs frequency domain analysis instead of frequency domain analysis. The triaxial acceleration obtained from the accelerometer. The signals are converted to the frequency domain, and the power densities in specific frequency bands are determined. Characteristics such as total power and peak frequency are extracted. These frequency domain characteristics... The walking freeze event is being blurred and evaluated with a fuzzy rule base. A decision is made as to whether it occurs or not. In the present invention, 35 in the frequency domain. While attempts are made to detect a pathological gait disorder through the analysis of biomechanical signals, The current invention does not require any biomechanical signal analysis and walking 7 It does not deal with pathological conditions such as freezing. The current invention is based on a limited number of sensor data. While presenting a dual decision-making mechanism based on numerous qualitative and subjective factors, the current invention Combining the parameters to obtain a continuous and scalable perception value between 0 and 100. It produces. The studies revealed that the current techniques for measuring urban spatial perception have 5 major shortcomings. It relies to a large extent on traditional qualitative methods such as surveys and interviews, and these methods serious considerations regarding comparing individual inferences and generalizing results. It appears to have limitations. Some fuzzy logic-based approaches, however, are more... Determining pedestrian movement patterns, modeling pedestrian crossing behavior, indoor environments. 10 quite narrow and specific examples such as positioning or recognizing pedestrian movement intention. these studies focus on problem areas, and these studies generally examine the physical position of the pedestrian, it analyzes its orientation, instantaneous decision-making mechanisms, or biomechanical movement parameters This is being detected. Existing fuzzy logic-based systems mostly rely on inertial navigation. Acceleration data from sensors, segmentation obtained through image processing techniques These 15 systems that holistically integrate the dynamic and multisensory experience brought about by the act of walking. inability to model, understand subjective and linguistic expressions of how the pedestrian perceives space It is unable to convert it into a quantitative measurement without causing a loss, and also the perception of urban space. influencing numerous different parameters such as walkability, perceptibility and individual factors at the same time It lacks a flexible and holistic structure that can be evaluated at the moment. This invention, identified in 20 To address these technical shortcomings and limitations encountered, the results obtained after walking experience... The qualitative perception data obtained mathematically illustrates the uncertainty inherent in the nature of human perception. an evaluation based on processing through fuzzy logic methodology that can model It presents a method describing the system and its operation. This is the subject of the present invention. the system, walkability factors of the space, perceptibility factors of the space and 25 user-related factors Seventeen different qualitative parameters, grouped under three main parameter categories, including individual factors. It transforms the input into fuzzy sets through membership functions, with each parameter Using a rule base created separately for each group, with a Mamdani-type fuzzy inference system evaluating and clarifying the numerical perception for each group separately. It produces values. In this way, the current invention avoids loss of meaning from linguistic and qualitative data. 30 It is able to produce quantitative output without requiring subjective data, and allows for the evaluation of subjective data. It offers a user-centered spatial assessment of different pedestrian routes. comparison, monitoring perceptual changes over time, and design interventions It allows for an objective evaluation of its impact, as well as for large datasets. A flexible and systematic solution tool that can work even with small samples without needing to be manually operated. 35 In this way, it overcomes all these limitations of previous techniques. 8 Ultimately, the problems mentioned above, which cannot be solved with the current technology, are related to the technical aspects. This has made it necessary to make an innovation in the field. A BRIEF DESCRIPTION OF THE INVENTION The present invention is a 5-fold improvement developed to eliminate the technical shortcomings mentioned above. A fuzzy logic-based approach to measuring urban spatial perception based on walking experience. It relates to the evaluation system and method. The main purpose of the invention is to address the most fundamental technical problem encountered in measuring urban spatial perception. subjective perception data, expressed through linguistic and qualitative terms, can be quantitatively measured. The aim is to offer a fundamental solution to the fact that it cannot be transformed. In this way, the nature of human perception is 10 The uncertainties and inconclusive judgments found are resolved using fuzzy logic methodology. It is mathematically modeled and digitized without loss of meaning, thus providing an objective and A scalable urban spatial perception value is obtained. Another aim of the invention is to overcome the limitations of existing techniques that rely only on visual perception or a limited number of applications. By moving beyond its narrow approach that focuses on a single parameter, it includes all factors that influence the perception of urban space. It is about being able to evaluate it holistically. The walkability factors of the space, the space itself... These factors fall into three main groups: perceptibility factors, user-specific factors, and... Seventeen different qualitative inputs were collected simultaneously within the same fuzzy logic framework. The multifaceted nature of the walking experience is being processed and preserved in its entirety. Another aim of the invention is to enable data-intensive methods such as machine learning or deep learning to 20 On the contrary, it is a flexible assessment tool that can operate without the need for large-scale datasets. The aim is to provide meaningful and reliable results, even with small sample sizes. This can be done, which is especially true for academic and professional settings where work needs to be done with a limited number of participants. It provides a significant advantage in professional applications. Another purpose of the invention is to enable high-resolution imaging such as virtual reality, augmented reality, or physiological sensors. without requiring costly and laboratory-dependent equipment, real world The aim is to develop a measurement system that can be applied under various conditions and in real locations. The invention, Collected solely after the walking experience, without requiring any special equipment. It operates based on survey data, thus enabling high-performance results with low experimental costs. It enables efficient field work. 30 Another aim of the invention is to objectively compare different pedestrian routes. The aim is to provide a standard and repeatable measurement method that will allow for comparison. Thanks to the numerical urban space perception value obtained between 0 and 100, different routes and times can be considered. 9 Meaningful comparisons can be made between segments or different user groups, this situation A data-driven decision support mechanism for urban planners and designers. It constitutes. Another aim of the invention is to track perceptual changes along an urban route over time and 5. Objective evaluation of the impact of design interventions on perception. The aim is to provide an opportunity. Through measurements repeated at different times along the same route, in the built environment The improvements or changes made are reflected in user perception in tangible, quantifiable ways. This can be demonstrated with data, which makes the effectiveness of planning and design decisions evidence-based. It allows for testing in this way. Another aim of the invention is that, thanks to the flexible and modular structure of the model, different research questions can be addressed. or new parameters or groups of parameters for different urban contexts into the system The goal is to ensure easy integration. The rule base of the existing fuzzy logic model. Its structure allows for the addition of new input variables or the removal of existing variables. It is designed to deliver results in a wide variety of interdisciplinary scenarios, making the invention suitable for many different types of interdisciplinary applications. This makes it possible to reuse it. 15 Another aim of the invention is to contribute to architecture, urban planning, smart city technologies, and environmental psychology. especially in all areas where user experience and perception are important. The aim is to present a methodological framework that can be applied. The developed fuzzy logic-based approach... The evaluation system is not limited to the perception of urban space, but also includes psychology and behavior. There is also a wide range of applications in the quantification of psychological processes in scientific fields. It has potential. The system that is the subject of this invention mathematically describes the uncertainty inherent in human perception. It is built on the basis of fuzzy logic methodology, which stands out for its ability to model. This Within the system, the walkability factors of the space, the perceptibility factors of the space, and Qualitative 25, grouped under three main parameter categories, including user-specific individual factors. Inputs are first transformed into fuzzy sets through membership functions. It is structured. The system in question processes the obtained fuzzy inputs for each parameter group. Mamdani-type fuzzy inference system using separately generated rule bases evaluation and then clarification process, separately for each group. It is designed to enable the generation of numerical perception values. The invention concerns 30 The method involves the operation of the fuzzy logic-based evaluation system described above. It describes all the steps involved in forming the process, and these steps are presented in a programmatic structure. This is implemented by modeling and running it in a computer environment. All the purposes mentioned above and those that will emerge from the detailed explanation below. The current invention aims to realize an urban spatial perception based on the walking experience. It is a fuzzy logic-based evaluation system for measurement, and its characteristic feature is...  A smart device that collects input data and displays the processed results,  The smart device in question is connected to the network in a way that allows it to communicate data over the network, and 5 a server configured to process the collected input data,  The server in question operates on a system that processes input data through membership functions. a fuzzification module that converts to fuzzy sets,  The fuzzy inputs generated by the aforementioned fuzzification module are fed into rule bases. A fuzzy inference module that operates using the fuzzy inference method based on, 10  Fuzzy output values ​​generated by the aforementioned fuzzy inference module a desiccant module that subjects the device to a desiccation process,  By summing the numerical values ​​generated by the aforementioned defuzzification module, the final result is obtained. a collection module that calculates the urban space perception value It includes. 15 The best way to utilize the advantages of the existing invention, together with its structure and additional elements. For it to be understood, it must be considered together with the figures explained below. BRIEF DESCRIPTION OF THE FIGURES Figure 1: Fuzzy logic for measuring urban spatial perception based on walking experience. This is a schematic representation of the inputs to the 20-based evaluation system. Figure 2: Fuzzy logic for measuring urban spatial perception based on walking experience. This is a schematic view of the self-assessment system. REFERENCE NUMBERS 1. Server 25 11. Blurring Module 12. Fuzzy Inference Module 13. Clarification Module 14. Addition Module 2. Smart Device 30 11 A. Perceived value of walkability factors A1. Pedestrian path physical quality score A2. Pedestrian accessibility score A3. Population density score A4. Motor vehicle traffic density score 5 A5. Structural element score B. Perceptibility factors, perception value B1. Imaginability score B2. Readability score. B3. Closure score 10 B4. Human scale score B5. Permeability score B6. Diversity score B7. Compatibility score B8. Original architectural identity score: 15 C. Individual factors perceived value C1. Sense of belonging score C2. Frequency score for walking for transportation purposes. C3. Frequency score for walking for sports and exercise purposes. C4. Frequency of walking for pleasure and leisure: Score 20 D. Urban spatial perception value DETAILED DESCRIPTION OF THE INVENTION This detailed explanation focuses solely on the innovation in the invention to provide a better understanding of the subject matter. It is conveyed without being limited to examples. Accordingly, in the following explanation and figures, 25 A fuzzy logic-based approach to measuring urban spatial perception based on walking experience. The evaluation system and method are explained. Figure 2, Fuzzy logic for measuring urban space perception based on walking experience. This is a schematic view of the 30-based evaluation system. Accordingly, the system: • A smart device in which input data is collected and processed results are displayed (2), • Connected to the mentioned smart device (2) in order to communicate data over the network and a server configured to process the collected input data (1), • The server mentioned (1) operates on and inputs data through membership functions. A fuzzification module that converts to fuzzy sets (11), 35 12 • Fuzzy inputs generated by the mentioned fuzzification module (11) are fed into rule bases. a fuzzy inference module that operates using the Mamdani-type fuzzy inference method based on (12), • Fuzzy output values ​​generated by the mentioned fuzzy inference module (12) a clarifying module (13) and 5 that subject to the clarifying process • By summing the numerical values ​​produced by the mentioned clarification module (13), the final a collection module that calculates the urban space perception value (14) It includes. The invention is a fuzzy method for measuring urban spatial perception based on walking experience. Logic-based evaluation system, subjective and qualitative perceptions reported by users 10 It is based on the principle of converting urban data into a quantitative measurement. The system is an urban system. Users who complete the walking experience on the route via smart device (2) It begins with the provision of qualitative input data. This input data includes the walkability of the space. factors related to perception value (A), perceptibility factors related to perception value (B) and individual user factors The factors relate to the perceived value (C) and are collected on the smart device (2). The collected 15 The qualitative input data will communicate with the aforementioned smart device (2) over the network. It is transferred to the connected server (1). Blurring is performed on the aforementioned server (1). module (11) fuzzyizes each qualitative input data transferred through the relevant membership functions. It transforms them into clusters, and this transformation process reduces the ambiguities inherent in human perception. and enables the mathematical expression of uncertain judgments. 20 The fuzzy inputs produced by the aforementioned fuzzification module (11) are on the same server (1) is processed by the fuzzy inference module (12) that runs on it. The fuzzy inference in question module (12) is based on rule bases created separately for each factor group Mamdani uses a fuzzy inference method and these rule bases are based on different inputs. 25 "if-then" type combinations determine which output values ​​they correspond to. It consists of rules. As a result of this process, the fuzzy inference module (12) mentioned above will search for the answer. Fuzzy output values ​​are produced. The intermediate values ​​produced by the mentioned fuzzy inference module (12) The fuzzy output values ​​are generated by the defuzzification module running on the aforementioned server (1). (13) is subjected to a defuzzification process. This defuzzification process removes fuzzy clusters. It enables the conversion of intermediate values ​​expressed in this form into a single numerical value, thus 30 walkability factors perception value, perceptibility factors perception value and individual factors perception The value is calculated separately. Finally, by the mentioned clarification module (13) These three separate numerical values ​​calculated are used by the summation module running on the aforementioned server (1). (14) collected the final urban space perception value (D) between 0 and 100 points. is calculated. This final calculated value is the network connection 35 via the aforementioned server (1). 13 It is sent back to the smart device (2) via this and is displayed to the user. Thus, the system offers user-centric spatial evaluation capabilities, allowing for the analysis of subjective data. It enables the conversion into a quantitative measurement without loss of meaning and allows different pedestrians Comparing their routes allows for tracking perceptual changes over time. He knows. 5 The data given as input to the smart device (2) are as follows: The walkability factors of the space constitute the perceived value (A):  The pedestrian path physical quality score (A1) is entered into the model; “physical quality level of the walking route” It is the value of the parameter (score). It was obtained from a survey of people who walked the route. is obtained. 10  Pedestrian path accessibility score (A2), entered into the model; “accessibility level of the walking route” It is the value of the parameter (score). It was obtained from a survey of people who walked the route. is obtained.  The population density score (A3) is entered into the model as; “the population density of the route walked”. It is the value of the parameter (score). This is from a survey of people who walked the route. is obtained.  Motor vehicle traffic density score (A4), entered into the model; “motor vehicle traffic density on the route walked It is the value of the "traffic density" parameter (score). This is done based on the data of people walking the route. It is obtained from a survey.  Structural element score (A5), entered into the model; “structural elements such as eaves covering, etc. of the route walked 20 This is the value of the "number of elements" parameter (score). It is calculated based on the number of people walking the route. It is obtained from a survey. Perceptibility factors constitute the perceived value (B);  The Imaginability Score (B1) is the "level of imaginability of the route walked" entered into the model. It is the value of the parameter (score). This is from a survey of 25 people who walked the route. is obtained.  Readability score (B2), entered into the model; “readability level of the route walked” It is the value of the parameter (score). It was obtained from a survey of people who walked the route. is obtained. 30  The closure score (B3) is the parameter "level of closure of the route walked" entered into the model. It is the value (of the score). It is obtained from a survey of people who walked the route. 14  The Human Scale Score (B4) is the parameter entered into the model: “human scale level of the route walked”. It is the value (of the score). It is obtained from a survey of people who walked the route.  Permeability score (B5) is the value of the “permeability level of the walked route” parameter entered into the model. It is the value (of the score). It is obtained from a survey of people who walked the route.  Diversity score (B6) is the parameter "diversity level of the route walked" entered into the model. It is the value (of the score). It is obtained from a survey of people who walked the route.  The adaptation score (B7) is the parameter (score) of "level of adaptation of the route walked" entered into the model. It is a value derived from a survey of people who walked the route.  Original architectural identity score (B8), entered into the model; “original architectural identity of the route walked This is the value of the “level” parameter (score). The survey was conducted with people who walked the route. 15 It is obtained from his work. Individual factors constitute the perceived value (C);  Sense of belonging score (C1), entered into the model; “level of sense of belonging to the route walked” 20 It is the value of the parameter (score). It was obtained from a survey of people who walked the route. is obtained.  The frequency score for walking for transportation purposes (C2) is entered into the model; “the transportation route walked”. This is the value of the parameter (score) for "frequency of use for the purpose of...". (This applies to people walking the route.) This information is obtained from a survey. 25  The frequency score for walking for sports and exercise purposes (C3) is entered into the model; “the walking route for sports” and frequency of use for exercise purposes” is the value of the parameter (score). The route is walked by... It is obtained from survey studies conducted with individuals.  The frequency score for walking for pleasure and leisure (C4) is entered into the model; “the pleasure and leisure of the walking route This is the value of the parameter (score) "frequency of use for sightseeing". 30 people walked the route. It is obtained from survey studies conducted with individuals. The steps involved in the process of implementing the system described in this invention are as follows:  From users who completed the experience of walking on an urban route, Through the aforementioned smart device (2), the perceived value of the walkability factors of the space (A), Perceptibility factors constitute the perceived value (B) and individual factors constitute the perceived value (C). Collection of qualitative input data,  The collected qualitative input data is transmitted via network connection through the aforementioned smart device (2) 5 transfer to the aforementioned server (1) via,  Through the blurring module (11) running on the aforementioned server (1), Each qualitative input data point transferred is fuzzyed using the relevant membership functions. converting into clusters,  The fuzzy inputs produced by the mentioned fuzzification module (11) are 10 Through the fuzzy inference module (12) running on the aforementioned server (1), each Fuzzy inference based on rule bases created separately for each factor group. Producing intermediate fuzzy output values ​​by processing them using this method,  The intermediate fuzzy output values ​​produced by the mentioned fuzzy inference module (12), 15 through the defuzzification module (13) running on the mentioned server (1) The perceived walkability factor value (A) of the space after being subjected to a decluttering process, Perceptibility factors perception value (B) and individual factors perception value (C) separately calculation,  The walkability factors calculated by the mentioned defuzzification module (13) are perceived. The perceived value (A), the perceptibility factors perceived value (B), and the individual factors perceived value (C) are 20. collected via the collection module (14) running on the mentioned server (1) Calculation of the final urban space perception value (D),  The calculated final urban space perception value is transmitted through the aforementioned server (1) via the network. by sending it back to the mentioned smart device (2) via the connection and to the user 25 views It includes the steps of the process. For modeling the evaluation system in a computer environment using the fuzzy logic method. MATLAB Simulink and Fuzzy Logic Tool programs were used. The first step in building the model was... Membership functions of the inputs were defined and fuzzy sets were created. 30 After the fuzzification process, the rule base was created. Each group contains... Inputs have been correlated internally to create rules. Within this context, the space... 16 There were 243 responses for walkability factors, 6561 for perceptual factors of space, and 81 for individual factors. A total of 6885 rules were written using the MATLAB Fuzzy Logic Toolbox program, each rule being created. A separate fuzzy inference system was developed for each group. The results obtained in the continuation of the method... The fuzzy output value was subjected to a defuzzification process, and urban space perception was determined for each group. The value 5 has been obtained. After calculating the urban space output value separately for each group, MATLAB Simulink System modeling was performed using the program. Walkability factors perceived value (A), The perceptibility factors perception value (B) and individual factors perception value (C) are summed to make the final result. The urban space perception value (D) (output value) is calculated between 0 and 100 points. The Mamdani type fuzzy logic method was used in model construction. Input and 10 Gaussian membership functions were used in the outputs.

Claims

17 REQUESTS 1. Fuzzy logic for measuring urban spatial perception based on walking experience. It is a base-based evaluation system, and its characteristic feature is...  A smart device in which input data is collected and processed results are displayed (2),  5 connected to the mentioned smart device (2) to communicate data over the network. and a server configured to process the collected input data (1),  The server mentioned (1) runs on and inputs data through membership functions a fuzzification module that converts to fuzzy sets (11),  Rule the fuzzy inputs produced by the mentioned fuzzification module (11). A fuzzy inference module that operates using the fuzzy inference method based on its bases. 10 (12),  Fuzzy output values ​​produced by the mentioned fuzzy inference module (12) a debridement module that subjects to debridement process (13),  By summing the numerical values ​​produced by the mentioned clarification module (13) a summation module (14) 15 that calculates the final urban space perception value (D) It includes.

2. A system that conforms to Claim 1, characterized by its ability to process fuzzy inputs based on rule bases. It includes a fuzzy inference module (12) that operates with the Mamdani type fuzzy inference method.

3. The system conforms to Claim 1 and its characteristic is that it perceives the walkability factors of the space from the input data. The values ​​(A) are composed of: pedestrian path physical quality score (A1), pedestrian path accessibility score (A2), 20 human density score (A3), motor vehicle traffic density score (A4) and structural element It includes the score (A5).

4. The system conforms to Claim 1, and its characteristic is that it detects the perceptibility factors and the perceived value from the input data. (B) comprises; imaginability score (B1), readability score (B2), closure score (B3), human scale score (B4), permeability score (B5), diversity score (B6), fit score (B7) and unique 25 It includes an architectural identity score (B8).

5. The system conforming to claim 1, its characteristic being; the individual factors perception value (C) from the input data. This includes; belonging score (C1), frequency of walking for transportation score (C2), sports and Exercise-based walking frequency score (C3), pleasure and leisure-based walking frequency score It contains (C4). 30 18 6. Fuzzy logic for measuring urban spatial perception based on walking experience. It is a base-based evaluation method, and its characteristic is...  From users who completed the experience of walking on an urban route, Through the aforementioned smart device (2), the perceived value of the walkability factors of the space (A), 5 Perceptibility factors constitute the perceived value (B) and individual factors constitute the perceived value (C). Collection of qualitative input data,  The collected qualitative input data is transmitted via the network connection through the aforementioned smart device (2). transfer to the aforementioned server (1) via,  Through the blurring module (11) running on the aforementioned server (1), 10 Each qualitative input data point transferred is fuzzyed using the relevant membership functions. converting into clusters,  The fuzzy inputs produced by the mentioned fuzzification module (11), Through the fuzzy inference module (12) running on the aforementioned server (1), each Fuzzy inference based on rule bases created separately for each factor group 15 Producing intermediate fuzzy output values ​​by processing them using this method,  The intermediate fuzzy output values ​​produced by the mentioned fuzzy inference module (12), through the defuzzification module (13) running on the mentioned server (1) The perceived walkability factor value (A) of the space after being subjected to a decluttering process, Perceivability factors perception value (B) and individual factors perception value (C) are each 20 calculation,  The walkability factors calculated by the mentioned defuzzification module (13) are perceived. value (A), perceptibility factors perception value (B) and individual factors perception value (C) collected via the collection module (14) running on the mentioned server (1) Calculation of the final urban space perception value (D), 25  The calculated final urban space perception value is transmitted through the aforementioned server (1) via the network. by sending it back to the mentioned smart device (2) via the connection and to the user display It includes the steps of the process. 19 7. The method is in accordance with claim 6 and is characterized by being processed using the Mamdani type fuzzy inference method. It involves generating intermediate fuzzy output values.

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