Information Flow Model Research Articles

The conductor model of consciousness, our neuromorphic twins, and the human-AI deal

AbstractCritics of Artificial Intelligence (AI) posit that artificial agents cannot achieve consciousness even in principle, because they lack certain necessary pre-conditions present in biological agents. Here we highlight arguments from a neuroscientific and neuromorphic engineering perspective as to why such a strict denial of consciousness in artificial agents is not compelling. Based on the construction of a co-evolving neuromorphic twin, we argue that the differences between a developing biological and artificial brain are not fundamental and are vanishing with progress in neuromorphic architecture designs mimicking the human blueprint. To characterise this blueprint, we propose the Conductor Model of Consciousness (CMoC) that builds on neuronal implementations of an external and internal world model, while gating and labelling information flows. An extended turing test lists functional and neuronal correlates of biological consciousness that are captured by the CMoC. These correlates provide the grounding for how biological or artificial agents learn to distinguish between sensory activity generated from outside or inside of the brain, how the perception of these activities can itself be learned, and how the information flow for learning an internal world model is orchestrated by a cortical meta-instance, which we call the conductor. Perception comes with the distinction of sensory and affective components, with the affective component linking to ethical questions that are inherent in our multidimensional model of consciousness. Recognizing the existence of a blueprint for a possible artificial consciousness encompasses functional, neuronal and ethical dimensions, begging the question: How should we behave towards agents that are akin to us in the inner workings of their brains? We sketch a human-AI deal, balancing the growing cognitive abilities of artificial agents, and the possibility to relieve them from suffering of negative affects, with a protection for the rights of humans.

Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.

This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irreversibility. Specifically, the system's dynamics, influenced by the off-diagonal part of the differential covariance, exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical findings indicate that the EC matrix's outgoing strengths correlate with the flow described by the differential cross covariance, while incoming strengths are primarily driven by zero-lag covariance, emphasizing conditional independence over directionality.

Sound, Touch, Form, Taste, and Smell as Pathways for Meditation – A Review of Classical and Neuroscience Texts

Meditation has emerged as a useful tool to address various mental health issues and hence has become a fertile area of interdisciplinary research. Contemporary research is focused on evaluating the effects of meditation using different neural correlates and also on developing theoretical models to explain different meditative states. The value of these neuroscientific studies can be enhanced by integrating them with time-tested philosophical framework of classical meditation practices. This work attempts to review and summarize philosophical underpinnings of meditation with the help of hierarchical meditative states discussed in Patañjali’s Yoga darśana and an information flow model proposed based on Sāṅkhya and Yoga darśana. Sense perception as a vehicle to achieve higher meditative states is the focus of this review. Usage of gross sense objects via sense pathways of sound, touch, form, smell and taste to establish in Vitarka state and then moving to Vicāra state by replacing them with subtle sense object has been discussed in detail. A brief discussion on fundamentals of sense transduction has been discussed, which explains how sense stimulation in gross world gets converted to subtle neuro-electrical signals via multiple ionic processes. A summary of various ayurvedic, classical and contemporary yogic practices related to each sense pathway has also been presented.

Register transfer level hardware design information flow modeling and security verification method

Information flow analysis can effectively model the security behavior and security properties of hardware design. However, the existing gate level information flow analysis methods cannot deal with large-scale designs due to computing power and verification effectiveness, and the register transfer level (RTL) information flow analysis methods require formal languages to rewrite hardware designs. This paper proposes a RTL hardware design information flow modeling and security verification method. Based on the RTL functional model, this method develops an information flow tracking logical model to model security behavior and security properties of RTL hardware designs from the perspective of information flow. This method can be integrated into EDA flows and uses EDA testing and verification tools to capture security property violations and detect security vulnerabilities based on non-interference security policy. The results on experiments with Trust-Hub hardware Trojan benchmarks show that the proposed method can effectively detect hardware Trojans.

BiTGNN: Prediction of drug–target interactions based on bidirectional transformer and graph neural network on heterogeneous graph

Drug–target interaction (DTI) is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery. However, the traditional bio-experimental process of drug–target interaction identification requires a large investment of time and labor. To address this challenge, graph neural network (GNN) approaches in deep learning are becoming a prominent trend in the field of DTI research, which is characterized by multimodal processing of data, feature learning and interpretability in DTI. Nevertheless, some methods are still limited by homogeneous graphs and single features. To address the problems, we mechanistically analyze graph convolutional neural networks (GCNs) and graph attentional neural networks (GATs) to propose a new model for the prediction of drug–target interactions using graph neural networks named BiTGNN [Bidirectional Transformer (Bi-Transformer)–graph neural network]. The method first establishes drug–target pairs through the pseudo-position specificity scoring matrix (PsePSSM) and drug fingerprint data, and constructs a heterogeneous network by utilizing the relationship between the drug and the target. Then, the computational extraction of drug and target attributes is performed using GCNs and GATs for the purpose of model information flow extension and graph information enhancement. We collect interaction data using the proposed Bi-Transformer architecture, in which we design a bidirectional cross-attention mechanism for calculating the effects of drug–target interactions for realistic biological interaction simulations. Finally, a feed-forward neural network is used to obtain the feature matrices of the drug and the target, and DTI prediction is performed by fusing the two feature matrices. The Enzyme, Ion Channel (IC), G Protein-coupled Receptor (GPCR) and Nuclear Receptor (NR) datasets are used in the experiments, and compared with several existing mainstream models, our model outperforms in Area Under the ROC Curve (AUC), Specificity, Accuracy and the metric Area Under the Precision–Recall Curve (AUPR).

An information flow field model for human–machine interaction

PurposeAbout 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation.Design/methodology/approachThe performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics.FindingsThe results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors.Originality/valueThe novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

A Contour Detection Method Based on the Projective Coding Model of the Visual Cortex Information Flow

Based on the information flow partition projection characteristics of P-type and M-type ganglion cells (referred to as P cells and M cells) and the neural sparse coding mechanism, this paper proposed a new method for image contour detection. First, we considered the difference between M cells and P cells in detail sensitivity and the information transmission of different visual signals. The parallel visual pathway was constructed to simulate the pre-level characteristics of the V1 layer to obtain the primary contour response. Then, we introduced the orientation sensitivity and stimulus response difference of the visual receptive field to construct the visual information difference enhancement model. In consideration of the visual attention mechanism, we proposed an adaptive size sparse coding network model that simulates the pre-level characteristics of the V4 layer to intelligently focus the target contour features. At the same time, de-redundancy was performed to obtain the fine feature image. Finally, the hierarchical information feedback fusion was built, and the fine feature image was used to correct the primary contour response to obtain complete contour detection results. Taking the BSDS500 dataset as experimental objects, the results showed that the proposed method exhibits an effective trade-off between contour extraction and texture suppression.

Asset Information Model Management-Based GIS/BIM Integration in Facility Management Contract

Achieving efficiency success status inside an organization’s built environment and obtaining a positive return on investments need robust and comprehensive asset management and maintenance processes based on the efficiency of contract information documents within the built asset lifecycle. This paper aims to highlight the appropriate interactive approach for construction projects to build the information flow scope of asset facility management contracts based on GIS (Geographical Information System) and BIM (Building Information Modeling) integration processes and sustainability standards, and project as-built contractual documents to support owners and stakeholders with the intent of improving asset management processes. Expert interviews and contract information flow types in several facility management processes conducted in both local and international facility management organizations were used to assist the information flow scope method. The study classified and built significant integrated information and data flow models for a case study to serve as contract guidelines, including efficiency performance measures and indicators for monitoring procedures, technical evaluation, and financial issues in order to provide high-performance service quality in facility management applications.

Information Flow and Optimization Model in Counselor-Student Interaction in Higher Education Civics

Abstract With the continuous development of the information society, the information flow model makes the counselor able to transfer some civic and political knowledge to the students more quickly, while the students can get the latest information more feverishly. This paper, recognizing the impact of information flow on civics education in colleges and universities, introduces an enhanced information flow model. It specifically builds an information flow and optimization model using network information technology and information analysis. The article employs an open Civics class at a university as an empirical research object, delving into the proposed information flow model to examine the real impact of Civics teaching on teacher-student interaction. The analysis of students’ learning of the six Civics objectives reveals an improvement in their overall literacy, as evidenced by the quantitative scores of the pre-test and post-test, which are 3.051 and 3.999, respectively, with a 0.948 difference in value pursuit. The analysis of classroom organizational differences reveals that the experimental group has a behavior management score of M = 5.62, while the control group has a score of M = 4.73. This indicates that the teachers of civics courses in the experimental group are capable of providing clear and forward-looking instruction in behavior management, ensuring that students’ behavior management is in place.

An Information Flow Model of Online Mediation: Jeopardizing Privacy and Autonomy in The Shadow of Innovation

An Information Flow Model of Online Mediation: Jeopardizing Privacy and Autonomy in The Shadow of Innovation

Analysis of the Integration Teaching Mode of Traditional Music Elements and College Piano under the View of Big Data

Abstract In this paper, the fusion teaching model was constructed, and the clustering of music elements was statistically calculated by calculating the intra-class distance of music elements. The feature vector of big data clustering is extracted, the traditional music element data is partitioned using linear FM signal, and the spatial matrix of traditional music element data is obtained after initializing the clustering center. To form the information flow model of big data time series, the phase space reconstruction analysis method is used to process piano data in nonlinear mapping. To achieve the objective function after clustering, adjust the weights within the fitness function, and then output the optimal program of the integrated teaching model. The results show that the post-test scores of students in the experimental group are higher than those of students in the control group, and the scores of tuning and composition have been improved by 2 and 2.5 points to reach the full score of 10 compared with those of the control group, which demonstrates the validity and feasibility of the fusion teaching mode of traditional music elements and college piano.

Multi-Party Conversation Modeling for Emotion Recognition

Multi-party conversation modeling plays a vital role in emotion recognition in conversation (ERC). Aside from the intra- and inter-speaker dependencies between different speakers, the difficulty also lies in the fact that each conversation may contain several to many utterances that compose a long text sequence. In this article, we present two approaches to effective multi-party conversation modeling. First, to encode long sequences and capture long-range dependency between utterances, we introduce a dialog-oriented language model, DialogXL, with enhanced memory to store longer conversation sequences and dialog-aware self-attention to deal with multi-party dependencies. Second, we present a directed acyclic neural network, namely DAG-ERC, to encode the utterances with a directed acyclic graph (DAG) to better capture the intrinsic structure within a conversation. DAG-ERC combines the advantages of recurrent models and graph models and provides a more intuitive way to model information flow between sequential utterances. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed for comparison, and empirical results demonstrate the superiority of the two models in multi-party conversation modeling.

MODELING AND PROCESSING OF INFORMATION FLOWS IN THE EDUCATIONAL PROCESS OF MEDICAL STUDENTS USING MIND MAPS

The fundamental principle of medical education today should be the formation of a highly qualified competitive medical professional who can conduct innovative activities and has the skills of continuous professional development, as well as increasing the transparency and clarity of the educational process for students and building confidence in achieving effective results. The creation of a modern innovative educational environment involves ensuring optimal conditions for the development and self-development of a student, which can be created, among other things, through the use of modern digital resources by both lecturers and students. Thus, the task of digitalizing the educational process of medical students is currently relevant. The purpose of this study is to model and process the information flows of the educational process of medical students.
 Digitalization and visualization of the educational process at the Department of Histology of National Pirogov Memorial Medical University (Vinnytsya, Ukraine) by developing mind maps revolutionizes education, improves the teaching process, significantly increases the efficiency, effectiveness and quality of the educational process through the development of cognitive, productive, reproductive thinking in students, compliance with the intellectual capabilities of higher education students, increasing the level of involvement of students, developing digital competence in students, and so on.
 The modeling and processing of information flows of the educational process of medical students has shown that students' knowledge is largely determined by the characteristics of information sources, and in order to maximize the coincidence of information flows of information sources and information flows learned by students, it is worthwhile and expedient to digitalize and visualize educational content using mind maps. It has been empirically confirmed that the use of mind maps in the study of the educational component "Histology" has increased the coincidence of information flows of information sources and information flows learned by students from one quarter to three quarters.

Method of information technology for structure analysis of urban network fire-rescue units

The subject of this study is the process of analyzing the structure of the network of fire-rescue units of the city in the context of optimizing their spatial distribution. The purpose of this work is to increase the objectivity of decisions made while forming a city network of fire-rescue units by creating an information technology (IT) method for analysis of its structure based on the use of spatially distributed data. Objectives: to find ways to improve the level of fire safety, analyze existing approaches to the formation of a network of fire-rescue units, considering the peculiarities of building and organizing a network, and adapt the classical problem of placement; to propose a method for solving it to minimize the distance between the fire-rescue unit and the possible place of fire while ensuring maximum coverage of the territory by fire service; and to develop IT structure for its implementation based on the information flow model of the process of analyzing the fire-rescue units network using a geospatial approach. The following results were obtained. The study of the classical location problem and its adaptation to real problems arising from the analysis of the urban network of fire-rescue units made it possible to represent it as a set of independent complete bipartite graphs. To search the location of network nodes while solving an adapted problem, an IT method is developed, which, based on the p-median model, combines the author’s methodology for studying information processes and methods of geospatial analysis. Summarizing the requirements of the current legislation, a set of input and output IT data and a set of operations have been formed. The representation of the IT structure in the form of a data flow diagram explains how the set of factors is processed and generalized when making decisions on the creation and / or improvement of the existing city fire department. Conclusions. The results of the bibliographic search confirm the need to consider the spatial features of the area where it is planned to create a fire-rescue unit, as well as the spatial configuration of the urban network of existing fire stations, to evaluate its effectiveness using an integrated indicator. This requires the development of specialized methods focused on the use of geo-information systems for their implementation in decision support systems. Scientific and methodological support for IT has been developed, which gives local authorities a tool for analyzing fire safety in the city to create and / or improve the existing fire protection. An experiment to study the capabilities of the proposed method based on volunteered geographic information on Kharkiv city showed the effectiveness of its use for solving classical problems of placement, considering the accepted restrictions on the spatial availability of fire-rescue units. At the same time, additional opportunities appear in the formation of options for improving the network of fire-rescue units, considering their spatial distribution, workload, accessibility, and the resulting areas of coverage / non-coverage by the fire service. For example, the fire service coverage area of the existing structure of fire stations has been assessed. During the regulated time, it reaches 70% of the Kharkiv city area, and depending on the real road traffic, it can vary from 64.61% to 73.44%. It is illustrated that the creation of two additional fire and rescue units in the northern and southern parts of Kharkiv will increase the coverage area by approximately 5%, on average increasing it to 75.1%

Abstract A054: Leveraging the power of Hope Leaders for enhancing diversity in clinical trials via a bi-directional cancer health information model

Abstract Introduction: Despite increasing recognition of the need for diversity in clinical trials, participation from racial and ethnic minorities, particularly from low socioeconomic communities, remains dismally low. This underrepresentation hinders the generalizability of trial results and perpetuates health disparities. Low socioeconomic environments, characterized by economic instability, restricted healthcare access, food scarcity, and deficient educational resources, exacerbate these challenges. Residents of such communities often lean on "Hope Leaders" - trusted community figures - as a strategy to navigate through these adversities. Recognizing the pivotal role of these Hope Leaders, we advocate for a community-centered bi-directional cancer health information flow model, which positions these Leaders at the core of community engagement endeavors. This model fosters a bi-directional health information exchange that synergizes Hope Leaders with the scientific community, thereby promoting improved health equity within clinical intervention research. The implementation of this model commences with a pre-engagement phase, a respectful approach where we familiarize ourselves with the community and its dynamics, without imposing any specific demands or expectations. Approach: The University of Illinois Cancer Center has had 6 community engagement specialists that have engaged with community members through numerous pre-engagement events that encompass activities ranging from mental health, cancer prevention, survivorship events, education, health fairs and community meetings. Based on these insights, we established a dedicated working group to identify hope leaders and to create targeted engagement strategies for specific areas of our catchment region through the bi-directional cancer health information flow model. These strategies are tailored to address the unique challenges and barriers these communities face regarding enrollment in cancer clinical trials. Additionally, the community-driven health communication model was implemented to identify Hope Leaders and engage them with cancer researchers as a part of our phase I activities for this model. Conclusion: This innovative approach aims to leverage the unique position of Hope Leaders in underserved communities to amplify community voices, improve engagement, and ultimately, increase minority representation in cancer clinical trials. The model provides a promising strategy to address the persistent disparity in clinical trial participation, facilitating more equitable and inclusive research outcomes. Citation Format: Joseph Dufraine, Yamile Molina, Jeanette Gonzalez, Maria Olivero, Theresa Mobley, Saria Lofton, Dedra Reis. Leveraging the power of Hope Leaders for enhancing diversity in clinical trials via a bi-directional cancer health information model [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A054.

Modeling information flow from multispectral remote sensing images to land use and land cover maps for understanding classification mechanism

ABSTRACT Information on Land Use and Land Cover Map (LULCM) is essential for environment and socioeconomic applications. Such maps are generally derived from Multispectral Remote Sensing Images (MRSI) via classification. The classification process can be described as information flow from images to maps through a trained classifier. Characterizing the information flow is essential for understanding the classification mechanism, providing solutions that address such theoretical issues as “what is the maximum number of classes that can be classified from a given MRSI?” and “how much information gain can be obtained?” Consequently, two interesting questions naturally arise, i.e. (i) How can we characterize the information flow? and (ii) What is the mathematical form of the information flow? To answer these two questions, this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM. This hypothesis is then supported by kinetic-theory-based experiments. Thereafter, upon such an entropy, a generalized Jarzynski equation is formulated to mathematically model the information flow, which contains such parameters as thermodynamic entropy of MRSI, thermodynamic entropy of LULCM, weighted F1-score (classification accuracy), and total number of classes. This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers. This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification, opening a new door for constructing domain knowledge.

Hardware/software security co-verification and vulnerability detection: An information flow perspective

Security vulnerabilities provide attackers unauthorized access to critical resources and effective attack surfaces to compromise a system. Security verification is an emerging technique for detecting and locating such threats. However, existing security verification methods are typically restricted within the hardware or software boundary and incapable of meeting cross-layer verification requirements due to the differences in design semantics and the lack of a security model that fits both hardware and software. We attempt to address such a limitation from the perspective of information flow analysis and propose a hardware/software security co-verification method, which can check information flow security properties on fine-grained hardware information flow models. The proposed method can pinpoint security vulnerabilities by capturing information flow security property violations under clues of malicious information flows. Our information flow security model and properties are described using standard hardware design and verification languages, which allows our method to be seamlessly integrated with electronics design automation flows. Experimental results using RISC-V hardware/software designs show that the proposed method detects software, hardware and system-level security vulnerabilities, effectively.

Agroforestry Biomass Recovery Supply Chain Management: A More Efficient Information Flow Model Based on a Web Platform

Background: With the increasing societal focus on sustainability and the critical need for innovative energy solutions, this research emphasizes the undervalued biomass originating from waste products of agroforestry activities. The traditional practice of disposing of these waste products through open-air burning has led to environmental challenges and a tragic loss of their inherent energy potential. Methods: This study adopts a multifaceted approach, integrating literature reviews, expert interviews from both the academic and professional sectors, and surveys. The central focus is on understanding supply chain inefficiencies and communication gaps that contribute to waste and addressing them through the Lean philosophy, renowned for its waste reduction benefits. Results: Our research culminated in the development of a unique information management model based on a web application. Additionally, the study provides a theoretical groundwork for an application that backs the proposed model. Conclusions: The presented strategy and web-based model offer promising avenues for managing waste products from agroforestry activities more sustainably and efficiently. This approach not only addresses the environmental issues arising from waste disposal but also taps into the significant energy potential these waste products hold.

IO-YOLOv5: Improved Pig Detection under Various Illuminations and Heavy Occlusion

Accurate detection and counting of live pigs are integral to scientific breeding and production in intelligent agriculture. However, existing pig counting methods are challenged by heavy occlusion and varying illumination conditions. To overcome these challenges, we proposed IO-YOLOv5 (Illumination-Occlusion YOLOv5), an improved network that expands on the YOLOv5 framework with three key contributions. Firstly, we introduced the Simple Attention Receptive Field Block (SARFB) module to expand the receptive field and give greater weight to important features at different levels. The Ghost Spatial Pyramid Pooling Fast Cross Stage Partial Connections (GSPPFC) module was also introduced to enhance model feature reuse and information flow. Secondly, we optimized the loss function by using Varifocal Loss to improve the model’s learning ability on high-quality and challenging samples. Thirdly, we proposed a public dataset consisting of 1270 images and 15,672 pig labels. Experiments demonstrated that IO-YOLOv5 achieved a mean average precision (mAP) of 90.8% and a precision of 86.4%, surpassing the baseline model by 2.2% and 3.7% respectively. By using a model ensemble and test time augmentation, we further improved the mAP to 92.6%, which is a 4% improvement over the baseline model. Extensive experiments showed that IO-YOLOv5 exhibits excellent performance in pig recognition, particularly under heavy occlusion and various illuminations. These results provide a strong foundation for pig recognition in complex breeding environments.

Дистанційне зондування як ефективний інструмент проєктування доріг

The subject of this study is the process of engineering and geodetic surveys for developing road projects. The purpose of this article is to improve the efficiency of the process of engineering and geodetic research in road design by implementing remote data from various sources and their systematization and algorithmization of use. The task is to analyze the preparatory and field stages of the process to determine possible ways of increasing its efficiency. Focusing on information flows, to develop an information model of the process of engineering and geodetic surveys and the corresponding scientific and methodological support for road design to introduce advanced technologies for collecting data about the terrain, which reduce the time of surveys and increase the accuracy of the results obtained. For this, the following methods are used: system and structural analysis, set theory, and DFD modeling. Scientific results have been obtained. The requirements of the current legislation regarding searches and the best experience of their conduct have been systematized. A set-theoretic model of information flows of the process of engineering and geodetic surveys is proposed, with the involvement of structural modeling approaches that make it possible to combine remote data from different sources and consider the dynamics of their changes and the logic of interaction. The IDEF0 model, which considers remote sensing data as a source of accurate and up-to-date information, has been developed, and the data flow diagram algorithmizes the mechanism of their combination to obtain the necessary topographical and geodetic materials. The developed scientific and methodical support for information support of road design creates a structure of information technology for engineering and geodetic surveys. Its experimental use has confirmed the reduction of the time for performing geodetic works while simultaneously increasing the accuracy of the obtained geometric and geodetic parameters, which are necessary for the formation of objective conclusions when forming the estimate of the road project, determining the scope of works, etc. Conclusions. The results of the bibliographic search confirmed that the effectiveness of the topographic-geodetic survey procedure in road design is complicated by the large size of the geographical areas to be surveyed, complex engineering and geological conditions, or the presence of restrictions on the duration of work and financial resources. This leads to the need to practically implement into the project-research organizations new approaches based on highly productive methods of collecting information about the area. The scientific and methodological provision of information support for road design has been developed, in particular, the structure of the information technology of engineering and geodetic surveys. Its experimental use has confirmed the reduction of the time required to perform searches and the improvement of the accuracy of the obtained geometric and geodetic parameters of the area where the implementation of the road project is planned.