People
doc. Ing. Pavel Hrubeš, Ph.D.

2026, 2026 Smart City Symposium Prague (SCSP), IEEE eXplore), p. 1-3), ISBN 979-8-3315-8260-9, ISSN 2831-5618

Accurate identification of hazardous road segments is essential for improving road safety and prioritising preventive interventions. This study proposes a neural network-based framework for predicting hazardous road segments by leveraging detailed geometrical properties of roads, including lane number, lane width, and curvature. The proposed framework was applied to the D1 highway in the Czech Republic, using geospatial data and reported road accidents from 2024. The road geometry was divided into 10 m sectors, from which sequences of 300 m were generated to form input for a Long Short-Term Memory (LSTM) network. The model was trained using a weighted binary cross-entropy loss function to address class imbalance, where hazardous segments represented only 29.2 % of samples. The trained model demonstrated strong predictive performance, achieving a weighted F1 score of 0.895 and a macro F1 score of 0.906, which effectively distinguished between hazardous and non-hazardous highway segments. The results demonstrate that integrating detailed road attributes within an LSTM-based framework can effectively capture spatial dependencies and non-linear relationships associated with accident occurrence. The framework provides a scalable approach for identifying high-risk segments across various road types, offering valuable support for data-driven decision-making in transportation safety management.

Proceedings paper from a prestigious conference (Scopus)

2026, Data Quality Matters - Best Practices for Integrity and Assurance, London, IntechOpen Limited), ISBN 978-1-83634-985-3

The chapter describes the design and application of a methodology for evaluating the quality of traffic data. It emphasizes its importance for the entire transport sector and presents specific procedures and tools that make it possible to detect low-quality data. This increases user trust and improves the effectiveness of traffic management. The methodology is based on the concept of data governance. The introduction describes the current state, initial conditions, and the general ten-step process for data quality assessment. The evaluation itself is divided into syntactic and semantic checks. This includes statistical methods for detecting anomalies in the data, allowing the identification of errors or unusual traffic situations. The main part of the chapter focuses on the application of the methodology to traffic data. It provides a detailed description of the design, implementation, and use of different quality rules, along with their logical aggregation. This enables hierarchical evaluation of data quality, from individual traffic lanes to the entire road network, both at a detailed and comprehensive level of quality assessment. The functionality of the methodology and all partial procedures is illustrated by applying it and all proposed quality rules to real data from a selected section of the D11 highway in the Czech Republic in 2023. The results of the evaluation and their interpretation are presented. The types of anomalies detected in traffic volume and average speed data are also discussed. It demonstrates that automated rule-based systems can significantly improve the reliability and usability of traffic data.

Chapter in a book with transnational scope

2026, European Transport Research Review, 18 (1), p. 1-13), ISSN 1867-0717

This study investigates the forecasting of daily road traffic accidents using time series models, through a controlled comparative evaluation of baseline SARIMA and Hybrid SARIMA-LTSM frameworks. We employ a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to capture linear and seasonal patterns in accident data from 2010 to 2023. To address potential non-linearities, we extend the approach by modelling SARIMA residuals using Recurrent Neural Networks (RNNs), including bidirectional RNN (BiRNN) and Long Short-Term Memory (LSTM) variants, to develop a hybrid SARIMA-RNN framework. The hybrid model integrates SARIMA forecasts with RNNbased residual predictions as a methodological extension whose empirical performance is rigorously evaluated against the baseline. Within residual-domain model selection, the unidirectional LSTM achieved the lowest RMSE (0.184) and MAPE (1.0966%) among tested architectures, confirming its effectiveness in capturing temporal dependencies in the log-residual space. A hybrid SARIMA-LSTM model was also evaluated to enhance residual prediction; however, the one-step-ahead rolling-origin strategy with a 7-day sliding window did not improve accuracy relative to the baseline SARIMA, yielding only minor increases in RMSE and MAPE.

Article in a journal indexed in SCI Expanded

2026, International Symposium on Applied Geoinformatics 2026, Ostrava, VŠB - Technical University of Ostrava), p. 1-12), ISBN 978-80-248-4869-3

Road safety studies often rely on predefined road segments of varying lengths, which can introduce spatial inconsistency and bias in statistical modelling. This paper proposes a GISbased framework that uses dynamically generated fixed-length road segments before and after each accident location to evaluate the influence of road geometry and temporal factors on multi-vehicle crash occurrence. The approach ensures a spatially consistent and localised assessment of highway conditions at each crash site. The methodology is demonstrated using detailed police-reported accident data from the D1 highway in the Czech Republic, covering January to December 2025, combined with a national road network dataset. For each accident, local geometric attributes, including road width, number of lanes, and curvature, were derived from a 300 m dynamically extracted highway segment centred on the projected crash location. A binomial generalised linear model with a logit link function was applied to estimate the probability of multi-vehicle accidents as a function of these geometric factors and temporal variables, including time of day, month, and day of week. Results show that wider road segments and daytime periods, particularly morning and afternoon hours, significantly increase the likelihood of multi-vehicle crashes, whereas higher lane counts and weekend periods are associated with lower risk. This framework provides a transferable, spatially robust tool for localised road safety assessment and infrastructure planning.

Proceedings paper from a local conference in a foreign language

2025, Transactions on Transport Sciences, 16 (SI SCSP conference), p. 16-22), ISSN 1802-9876

The paper focuses on the practical application of procedures for the design and creation of a complex system of data quality rules in transportation domain. The aim of the research was to verify the feasibility of implementing all relevant practices of the data governance concept for the specific needs of transportation data and to present possible ways of quality evaluation of these data. The paper describes the proposed requirements, tools and methodology for developing a complex approach to data governance and data quality evaluations. The functionality and benefits of the proposed procedures and their real implementation have been verified by applying the pro cedures on real data from selected profiles of the D11 motorway in the Czech Republic from the year 2023. The discussed results demonstrate the functionality of the proposed solution and allow for future largescale application of the verified procedure across the full geographic coverage of the data. It is also possible to apply the procedures and rules to other data sources or data from other types of roads including urban traffic.

Article in a journal indexed in the Scopus database in a foreign language

2025, European Transport Research Review, 17 (1), ISSN 1867-0717

The paper addresses the task of improving the quality of traffic data by combining semantic analysis and statistical modeling. The study focuses on dynamic traffic variables, specifically speed and volume of traffic flow. The results are presented based on semantic quality control outputs for dynamic traffic variables. The semantic analysis aimed to find and label segments of time series waveforms that fell outside the expected value range at specific locations and times. This approach allowed for a more detailed analysis of specific cases, enhancing our understanding of traffic dynamics. We employed a Generalized Additive Model (GAM) to detect non-standard data segments successfully. Real traffic data from the motorway in the Czech Republic verified the effectiveness of the chosen statistical approach.

Article in a journal indexed in SCI Expanded

2025, Konference GIS Ostrava 2025, Ostrava, Vysoká škola báňská - Technická univerzita Ostrava), p. 1-1), ISBN 978-80-248-4795-5, ISSN 1213-239X

Předmětem příspěvku je poukázat na možnosti efektivní integrace dat v kontextu informačního managementu s cílem podpory a vzniku komplexního doplňku informačního modelu vystavěného prostředí, který je zároveň kompatibilní nejen s informačními systémy staveb, ale také informačními modely veřejné správy. Celé řešení se zaměřuje především na dopravní a technickou infrastrukturu a dále na inovativní přístupy informačního modelování ve vystavěném prostředí a jejich vzájemnému propojení.

Abstract in the proceedings of a local conference in Czech

2025, 2025 Smart City Symposium Prague - IEEE proceedings, New York, IEEE Press), ISBN 979-8-3315-2550-7, ISSN 2691-3666

In today's globalized society, the transportation of hazardous goods plays an important role in economic processes. With this growing trend, the transportation of hazardous materials brings a new challenge in terms of responsibility for ensuring safety and minimizing risks during transportation. The theoretical part of this scientific article describes the model by Ebrahimi and Tadic, which examines accident rates on a given edge of a graph, and how this research further develops by adding necessary variables. In the practical part, fleet data obtained from the real operations of transportation companies will be presented, providing a comprehensive overview of the movements of transport units. These data are analyzed in the study in relation to accidents involving transport units with a weight exceeding 7.5 tons in the Czech Republic. This data allows for the investigation not only of the probability of accidents involving units with a weight over 7.5 tons but also the impact and severity of these accidents. Additionally, a modified D-R routing model will be introduced.

Proceedings paper from a prestigious conference

2025, 2025 Smart City Symposium Prague - IEEE proceedings, New York, IEEE Press), ISBN 979-8-3315-2550-7, ISSN 2691-3666

Quantitative assessments of spatial and temporal distributions of street lighting across larger areas are difficult to make and express in such a form that would be appropriate as model inputs. This paper examines the density of street lighting places based on Earth Observing (EO) data in four urban areas of the Czech Republic. Previously processed GIS data of the night-time lights and planar point patterns of street lighting places were utilised. First, spatial measures of central tendency and dispersion were used to investigate the centrality and directionality of the point patterns. Second, density-based methods were conducted to assess the local and global densities of the patterns and measure the tendency to depart from Complete Spatial Randomness (CSR). Finally, the dependence of the point densities on the night-time light radiance was investigated by quadrat counting. The results of the spatial measures confirm strong directional patterns in smaller areas with sparse street lighting networks. Conditional Monte Carlo tests confirm that the point patterns deviate from CRS. Lastly, the density estimations based on night-time light radiance revealed that the higher density of points is accompanied by higher values of the radiance.

Proceedings paper from a prestigious conference

2025, Transportation Research Procedia, Amsterdam, Elsevier B.V.), p. 742-749), ISSN 2352-1457

Decreased ambient lighting conditions are one of the main factors that contribute to the higher probability of more serious road crashes or even road crash fatalities. These conditions are determined not only by weather conditions and natural light sources, however, by artificial light sources such as street lighting. Reliable data on street lighting remain scarce and it is difficult to make them as the model input, especially over large areas and for long periods of time. This paper investigates the performance of VIIRS Night-Time Lights (NTLs) as an open data source to capture the spatio-temporal variations of night-time road crashes from January 2017 to December 2022 in two study areas of the Czech Republic. The research methodology applies simple regression models to investigate the relationship between night-time crashes and radiance. Both regression models, the Poisson Generalised Linear Model (GLM) and the hurdle Poisson model, had mainly statistically significant estimates of regression parameters indicating the importance of the radiance.

Proceedings paper from a prestigious conference (Scopus)

2025, 2025 Smart City Symposium Prague - IEEE proceedings, New York, IEEE Press), p. 1-6), ISBN 979-8-3315-2551-4, ISSN 2831-5618

In the Czech Republic, floating car data currently serve as a supplementary source of information on traffic conditions. This data source may become advantageous in the future, particularly due to its availability and independence from fixed infrastructure. Although this data includes several parameters related to congestion, its validity has not been sufficiently documented. This article focuses on analyzing the relationship between vehicle speed, travel time, and congestion detection to verify the quality and reliability of this data. Through statistical analyses, consistent correlations between speed parameters and travel time were identified, confirming their suitability for further traffic modeling. On the other hand, parameters related to congestion detection exhibit significant variability, indicating the need for more in-depth analysis and the application of more sophisticated modeling methods.

Proceedings paper from a prestigious conference

2024, Transportation Research Procedia, Amsterdam, Elsevier B.V.), p. 619-626), ISSN 2352-1465

This paper presents a method for assessing the quality of floating car data and estimating emissions of the road network. The authors use a fundamental diagram from real probe vehicles to determine the categorization of the road network. These data are complemented by the widely used emission factors of the COPERT model to investigate the quality of the floating vehicle data to use them for the determination of emissions on individual road classes with a detailed spatiotemporal resolution. From the same data, an estimate of the emission rate of the categories is determined and it is assessed whether similar differences are exhibited. The hypothesis is that the quality of data is quite sufficient on highways and high-class roads, but unusable and insufficient on low-class roads. The results show that differences in emission estimates can be observed for the road categories of the highway network and low-class road network. For some pollutants, the urban network can also be identified. The offered categorization could lead to further possibilities of using floating vehicle data, such as the assumption of the possibility to detect atypical traffic phenomena (traffic jams, accidents, or impassable roads due to bad weather conditions).

Proceedings paper from a prestigious conference (Scopus)

2024, Neural Network World, 34 (4), p. 203-218), ISSN 1210-0552

The primary objective of the presented research is to enhance an existing data quality control application by integrating advanced anomaly detection mechanisms based on generalized additive models. This approach targets time- series traffic data, where traditional methods may fall short in identifying complex, non-linear patterns of anomalies. In collaboration with Simplity s.r.o., we are extending their current data quality assessment tool to incorporate generalized additive models, providing a more robust and dynamic solution for monitoring and ensuring the reliability of traffic datasets. The integration of these models aims to improve the accuracy of anomaly detection, leading to more effective data management in transport systems and contributing to higher standards of data quality in the field of traffic informatics.

Article in a journal indexed in SCI Expanded

2024, PROMET - Traffic&Transportation, 36 (6), p. 988-1005), ISSN 1848-4069

This paper attempts to determine the role of street lighting in the spatial clustering of night-time crashes involving pedestrians in the Republic of Croatia. Five-year (2018–2022) night-time pedestrian crash data were used in conditions with and without street lighting. First, distance-based statistical methods were used to assess the spatial clustering and deviations from complete spatial randomness (CRS) of the crash patterns. Second, the global Moran’s I analysis was conducted to investigate a degree of spatial autocorrelation of the annual crash counts aggregated in 21 counties of Croatia. Finally, the local indicators of spatial association (LISA) were used to identify the locations of the crash count hotspots. The results of the ANND analysis confirm a significant clustering of crashes for both street lighting conditions. However, different global Moran’s I values for both conditions were obtained with a high and statistically significant positive value for the crash counts without street lighting. Local Moran's I analysis reveals that the High-High (H-H) county clusters are located in coastal regions of Croatia, while the Low-Low (L-L) county clusters appear in the East continental part, next to Slavonia. The results suggest that inadequate lighting conditions have an impact on the clustering of pedestrian crashes at night.

Article in a journal indexed in SCI Expanded

2024

Zpráva o činnosti ČVUT FD v roce 2023 při řešení projektu Nástroje kvality dat pro zabezpečení systémové spolehlivosti dopravně informačních center.

Research report in Czech

2024, 2024 Smart City Symposium Prague - IEEE PROCEEDINGS, New York, IEEE Press), ISBN 979-8-3503-6096-7, ISSN 2831-5618

Within the activities of planning, organizing, or regulating traffic, a considerable amount of various traffic data is actively used. For responsible data-based decision-making, the quality of the utilized data is crucial. However, defining the specifics of this quality and establishing procedures for its control is often ambiguous. The primary objective of the research was to review methods for ensuring or managing data quality within the transportation environment. Building on the collected information, a practical data governance approach was delineated to be applied to specific transportation data. Simultaneously, data from detectors and floating vehicles on the highway network in the Czech Republic were analyzed for the same reason. The results show that the chosen data governance approaches are applicable to transportation data, even in the task of evaluating data quality in the detail of individual dimensions. In the context of semantic data quality control, it is necessary to explore additional suitable tools, such as the use of statistical models, to reliably distinguish real errors or otherwise reduced data quality from genuinely truthful values, such as traffic conditions.

Proceedings paper from a prestigious conference

2024

Finální dokument O2 Specifikace legislativního a institucionálního rámce pro využívání modelování vystavěného prostředí shrnuje aktivity přípravy a realizace při zpracování studie proveditelnosti na vybraném příkladu užití informačního modelu vystavěného prostředí s vyjádřením finančních i nefinančních přínosů a nákladů. Studie proveditelnosti bude realizována na vybraném reálném území (vystavěném prostředí), v lokalitě U Kunratického lesa – OC Westfield Chodov“. Hlavním milníkem v rámci zpracování Studie proveditelnosti bude vlastní povolení stavebního záměru formou sloučeného územního a stavebního řízení, přičemž hlavním záměrem je stavba přechodu pro chodce se SSZ a s ní související stavba multifunkčního přestupního terminálu.

Peer-reviewed research report in Czech

2024

Výsledek č. 1 kód: O - Využití modelování vystavěného prostředí pro podporu agend veřejné správy a soukromého sektoru je složen z částí stanovení a výběr rozhodujících aktivit, které související s modelováním vystaveného prostředí, přehledu aktuálních strategií a probíhajících aktivit a projektů na úrovni ČR a EU, přehledu zahraničních projektů zabývajících se tématem modelování vystavěného prostředí pro účely veřejné správy, přehledu relevantních koncepcí a pravidel architektury eGovernmentu ČR, přehledu aktuálních a připravovaných právních předpisů a jejich návazností, zejména v oblasti informačního modelování staveb (BIM) a NIPI a přehledu norem a standardů.

Peer-reviewed research report in Czech

2023, Neural Network World, 33 (5), p. 375-396), ISSN 1210-0552

Night-time light (NTL) radiance has a great potential in analyses of dynamic changes in patterns of human activities, and socio-economic and demographic factors. However, most of those analyses are focused on factors at global scales such as the population size, gross domestic product, electric power consumption, fossil fuel carbon dioxide emission etc. In this study we investigate the relationships between three urban lighting indicators and monthly averaged NTL radiance obtained from NASA’s Black Marble monthly NTL composites for 4 study areas in the Czech Republic at local scale. The Pearson correlation analysis was used to identify a strength of the correlations between the indicators and radiance at near-nadir for two different snow conditions. The results from the correlation show that radiance has a strong positive correlation with the number of streetlighting points and their total nominal power, while for the average mast height there were observed moderate correlation coefficients. However, the areas with larger scales have higher correlation coefficients. Moreover, we found that the correlation coefficients are higher for snow-covered condition radiances. Generalized linear (GL) regression analysis was used to examine an association between the radiance and selected indicators. Owing to the excess zeros and overdispersion in the data, the zero-inflated regression performs better than the GL regression. Results from the regression analysis evince a statistically significant relationship between the radiance and selected indicators.

Article in a journal indexed in SCI Expanded

2023, European Transport (93), ISSN 1825-3997

Data from floating vehicles is a modern technology and can be another source of data. There is a free data source available in the Czech Republic, which is relatively new. The addressed source of data from floating vehicles covers the whole Czech Republic, which is a promising source for future use e.g. in transport planning in logistics, estimation of travel times and other related issues. For this reason, it is appropriate to examine the qualitative parameters of the data to see if they characterize the traffic stream. The present paper deals with the size of the processed data. Furthermore, the paper compares the data quality and coverage. January data for four subsequent years was used. The period of the COVID19 pandemic, when traffic declined, was included. Finally, data from selected highways are compared and the period covered is evaluated.

Article in a journal indexed in the Scopus database

2023, 2023 Smart City Symposium Prague, New York, IEEE Press), ISBN 979-8-3503-2162-3, ISSN 2691-3666

Floating car data (FCD) is a beneficial consequence of the modern telematics technologies in the logistics and automotive industry, and can thus be an additional source of transportation data about current traffic flows on the road network. There is a state-provided free source of this data available in the Czech Republic, which is relatively new and gradually being introduced for use by the public and private sectors. This data source appears to be very promising for nationwide coverage; however, due to its novelty, it is necessary and advisable to investigate its qualitative parameters and assess whether it actually adequately characterizes vehicle traffic flows. The present paper deals with the characteristics and quality of processed FCD outputs before and during the covid-19 pandemic, for the possibility of capturing and comparing significant changes in the behaviour of road users. The paper describes the impact of the associated pandemic measures on traffic in the country in general, which can be detected during FCD processing, as well as the impact on the quality of FCDs as such. It also tests the hypothesis that although the quality of the data has been reduced as a result of the pandemic, the data still have sufficient predictive value. The paper describes the specifics of the data in the spatial and temporal domain analyzed and makes recommendations for future work for use in models and predictions.

Proceedings paper from a prestigious conference (Scopus)

2021, 2021 Smart City Symposium Prague, Piscataway, IEEE Signal Processing Society), p. 1-6), ISBN 978-1-6654-1524-8, ISSN 2691-3666

Modern telematics systems are based on data collected from real operation. The article works with a new source of traffic data, which is freely available to the public. Linking traffic data with spatio-temporal significance also allows us to examine data from the perspective of user behavior. From the principle of data collection, the data already have their specific assumed behaviors, which are described in the article. It is desirable to further investigate these behaviors, to what extent they are affected and whether they can be considered as parameters of the entire traffic flow

Proceedings paper from a prestigious conference

2021, 27th ITS World Congress, Brussels, ERTICO - ITS Europe), p. 1702-1713)

In the Czech Republic, there is a new, freely available source of data on the speeds of traffic flows on the road network, based on the movement of floating vehicles. This opens up new possibilities for community and commercial use, as well as research, especially in assessing the qualitative characteristics of this resource and the possibilities and areas of application of its use. It is a unique source of traffic data covering the entire country and it is necessary to examine its reliability in time and space resolution. In the future, this source could be used directly to influence and manage the optimization of the transport network operation, so it is necessary to subject it to a detailed qualitative examination. In addition to these qualitative analyzes, the paper also deals with similar research and the presentation of the user interface. Finally, possible uses and other directions of research are discussed.

Proceedings paper from an international conference

2021, 2021 The 9th International Conference on Information Technology: IoT and Smart City (ICIT 2021), New York, ACM), ISBN 978-1-4503-8497-1

Today, traffic management is based on traffic data, which must be high quality and reliable. The article deals with a new data source that is available in the Czech Republic. Thanks to this dataset, it is possible to get an overview of a wide road network throughout the whole area of the country. The authors use a broader view of the road network to study the behavior of drivers of floating vehicles in an accident on the main road. The high data coverage promises the ability to monitor traffic spills in the event of an emergency. These data can be used in the future to improve the overview of the current state of traffic and road management.

Proceedings paper from a prestigious conference (Scopus)

2021, Prostorové služby pro Smart City a Smart Region, Ostrava, VYSOKÁ ŠKOLA BÁŇSKÁ-TECHNICKÁ UNIVERZITA OSTRAVA), ISBN 978-80-248-4508-1, ISSN 1213-239X

The modern trend in the "Smart" areas is the collection of data to influence, control, optimize or predict general processes, including transport. In the Czech Republic, model data on the speed of road traffic flow based on the movement of floating vehicles, as it is called, have been available since last year. It is a new source of data on the traffic situation with nationwide coverage. These data are therefore published not only on traditional main roads but also on roads that have not been and are not equipped with any detection technology. Thanks to this model, it is possible to monitor and analyze the speed of the traffic flow and its development over time for a large part of the transport network throughout the Czech Republic. The article deals with the use of this data, their spatial significance, and processing in GIS.

Proceedings paper from an international conference in a foreign language

2020, GARI INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH, Colombo, GARI Publisher), p. 86-93), ISBN 978-955-7153-00-1, ISSN 2659-2193

Progress in the option of transport data collection allows users to collect data using floating vehicles. This data provides real-time information about the road network. In the Czech Republic, these data were published in a pilot project last year. The article uses this new data source. Data can be spatially displayed using GIS software. Analysis options can be used and drivers' choice when the main road is closed can be detected.

Proceedings paper from an international conference

2020

Research report in Czech

2020

Research report in Czech

2020, Young Transportation Engineers Conference 2020, Praha, Fakulta dopravní), p. 119-125), ISBN 978-80-01-06793-2

Digitization of the transport environment and the use of new data sources is a much-discussed topic today. FCD (Floating Car Data) systems, so-called floating vehicles, make it possible to obtain data of traffic parameters directly from the road network without the need to install stationary sensors. The article deals with the use of this new data source in the Czech Republic, financed from public sources, and made available by the Directorate of Roads and Motorways. This data can be displayed in the road network model using geographic information programs and further analyzed.

Proceedings paper from an international conference in a foreign language

2019, 2019 Smart City Symposium Prague, New York, IEEE Press), p. 1-5), ISBN 978-1-7281-0497-3

This paper presents a new approach to pavement rehabilitation using a 3D measuring. The proposed methodology of the pavement rehabilitation overcomes the limitation of the traditional methods of the pavement rehabilitation which cannot achieved the required accuracy. The 3D measuring technology eliminates human faults and improves the total quality of the pavement rehabilitation. The pavement rehabilitation is performed in the concept of Industry 4.0 which is part of Smart City owing to the 3D measuring.

Proceedings paper from a prestigious conference

2018, Proceedings of 36th International Communications Satellite Systems Conference (ICSSC), FGM Events), ISSN 2573-6124

The basic concept of Industry 4.0 is virtualization of reality - creating a digital image of real world and optimizing and planning processes in this virtual environment. The aim is to find the best solutions in a broad relationship with other processes and their realization in the real world. In the construction industry, Industry 4.0 is only at the beginning phases of utilization, especially the area of a highwayroad reconstruction, which provides an extremely high potential for process optimization. This was confirmed, for example, in the ASFALT project: Advance Galileo Navigation System for ASPHALT's fleet machines (ID: 247976, Funded under: FP7-TRANSPORT). This paper introduces the methodology of complex robotic and automation process of the pavement repair according to the industry 4.0 principles, which uses Exact Street DNA navigation, patented hybrid GNSS based technology for precise millimetre navigation of road milling machines. The DNA navigation achieves millimetre height accuracy using an inexpensive GNSS receivers. The pavement repair process often takes place in urban canyons areas, which is why the 3D data is enhanced by GNSS Galileo and EGNOS.

Proceedings paper from a prestigious conference (Scopus)