Relevant bibliographies by topics / Road condition monitoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Road condition monitoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Road condition monitoring"

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Radopoulou, Stefania C., and Ioannis Brilakis. "Improving Road Asset Condition Monitoring." Transportation Research Procedia 14 (2016): 3004–12. http://dx.doi.org/10.1016/j.trpro.2016.05.436.

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Linton, Michael A., and Liping Fu. "Winter Road Surface Condition Monitoring." Transportation Research Record: Journal of the Transportation Research Board 2482, no. 1 (January 2015): 46–56. http://dx.doi.org/10.3141/2482-07.

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Oladele, Adewole S. "Evaluation and Analysis of Botswana Gravel Road Condition for District Transportation Networks Monitoring." Applied Mechanics and Materials 505-506 (January 2014): 740–44. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.740.

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Gravel roads are majorly affected by deterioration which manifests as loss of gravel materials due to traffic and environmental conditions. District Road Maintenance Managers are faced with competing investment demands to maintain gravel road networks in the best condition due to inadequate intelligent techniques of evaluating the roadway performance. The aim of evaluating the road network performance is to reduce the rate of deterioration so that maintenance interventions could be extended. Road condition data is a precursor for road monitoring and is collected on a periodic basis by road authorities to assist in transportation planning. The primary objective of this paper was to evaluate and analysis the trend of Botswana gravel road condition which best captures the effects of gravel road condition influencing factors. This was achieved by carrying out exploratory statistical analysis. Gravel road condition data were collected through the Botswana Roads Department covering 2002, 2005 and 2008 for Botswana district gravel road networks. The variables required for the analysis were clustered and pre-processed to determine their suitability. The analysis results gave a broad overview of the extent to which gravel road condition trends lend credence to their usefulness in district transportation networks monitoring in Botswana.
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Marciniuk, Karolina, Maciej Blaszke, and Bożena Kostek. "Acoustic Road Monitoring." MATEC Web of Conferences 231 (2018): 05002. http://dx.doi.org/10.1051/matecconf/201823105002.

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The subject of this research is showing the performance of an automatic acoustic road monitoring system proposed by the authors. The main goal of the study is describing road traffic by means of an acoustic representation and testing effectiveness of traffic flow sensors. Evaluation metrics of the road conditions such as velocity of the traffic flow, its structure and weather condition are presented along with acoustic descriptors derived from the audio signal analysis. Accuracy of emergency vehicles pass by detection based on acoustic monitoring is also briefly described.
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Åstrand, Max, Erik Jakobsson, Martin Lindfors, and John Svensson. "A system for underground road condition monitoring." International Journal of Mining Science and Technology 30, no. 3 (May 2020): 405–11. http://dx.doi.org/10.1016/j.ijmst.2020.04.006.

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Roberts, Ronald, Gaspare Giancontieri, Laura Inzerillo, and Gaetano Di Mino. "Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning." Applied Sciences 10, no. 1 (January 1, 2020): 319. http://dx.doi.org/10.3390/app10010319.

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Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.
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Kunz, Bethany K., Nicholas S. Green, Janice L. Albers, Mark L. Wildhaber, and Edward E. Little. "Use of Real-Time Dust Monitoring and Surface Condition to Evaluate Success of Unpaved Road Treatments." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 52 (October 9, 2018): 195–204. http://dx.doi.org/10.1177/0361198118799167.

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Fugitive dust from unpaved roads creates human health hazards, degrades road surfaces, and increases the cost of road maintenance. As a result, many different chemical treatments are applied to unpaved roads in an attempt to control dust and stabilize the wearing course. However, investigations of the effectiveness of these treatments have often been poorly planned or executed. The objective of this study was to use a combination of real-time dust monitoring and objective road condition evaluations to assess the success of two chemical treatments for a period of 19 months post-application, to provide quantitative information in support of road management decisions. Dust production from road sections treated with calcium chloride-based durablend-C™ or the synthetic fluid EnviroKleen® was monitored on five dates using a vehicle-mounted particulate matter meter. Both products reduced dust by up to 99% relative to an untreated control section during the monitoring period, and quantitative data from the meter were consistent with qualitative observations of dust conditions. Linear models of dust production indicated that road treatment and humidity explained 69% of the variation in dust over time. Road sections treated with either product developed less rutting and fewer potholes than the untreated control. Overall, the combination of real-time dust monitoring and surface condition evaluation was an effective approach for generating quantitative data on endpoints of interest to road managers.
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Dong, Dapeng, and Zili Li. "Smartphone Sensing of Road Surface Condition and Defect Detection." Sensors 21, no. 16 (August 12, 2021): 5433. http://dx.doi.org/10.3390/s21165433.

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Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.
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Boucetta, Zakaria, Abdelaziz Fazziki, and Mohamed Adnani. "A Deep-Learning-Based Road Deterioration Notification and Road Condition Monitoring Framework." International Journal of Intelligent Engineering and Systems 14, no. 3 (June 30, 2021): 503–15. http://dx.doi.org/10.22266/ijies2021.0630.42.

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Ardian, Muhammad, Sahala Ruben A., and Reza Ardhianto. "HAUL ROAD CONDITION MONITORING USING SENSORS AND GNSS DATA." Prosiding Temu Profesi Tahunan PERHAPI 1, no. 1 (March 29, 2020): 293–304. http://dx.doi.org/10.36986/ptptp.v1i1.73.

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ABSTRAK Salah satu faktor agar mendapatkan performa alat angkut yang baik adalah dengan kondisi jalan angkut yang baik. Oleh karena itu, perlu diantisipasi dengan cara melakukan rekayasa engineering terhadap hal yang berpotensi menimbulkan kondisi jalan yang tidak baik. Dalam pembuatan prototype aplikasi ini digunakan data sensor strut pressure & data GNSS (Global Navigation Satellite System) sebagai teknologi untuk melakukan pengawasan terhadap kondisi jalan. Penggunaan sistem pengawasan jalan tambang menggunakan data sensor strut pressure dan data GNSS diharapkan dapat membantu meningkatkan produktivitas & efektivitas dalam pengambilan keputusan terkait dengan kegiatan pengawasan dan pemeliharaan kondisi jalan angkut di area tambang Pit Batu Hijau. Kata Kunci : Jalan Angkut, Fleet Management System, Sensor Strut Pressure, Global Navigation Satellite System, Analisa Data Geospasial ABSTRACT One of factor to get the good performed of hauling equipment is good condition of hauling road. Because of this, need to be prevent with engineering method about the things that potential can impact the poor haul road condition. The simulation was performed with using sensor and GNSS (Global Navigation Satellite System) Data as a technology about monitoring haul road condition. Utilizing haul road monitoring system using data sensor and GNSS expect can help to improve the productivity & effectivity for interpretation the good decision about monitoring activity and maintain Haul road condition at Pit Batu Hijau.Key Words : Haul Road, Fleet Management System, Sensor Strut Pressure, Global Navigation Satellite System, Geospatial Data Analyst
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Dissertations / Theses on the topic "Road condition monitoring"

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Hu, Liuqing. "Calibrating Smartphones for Monitoring Road Condition on Paved and Unpaved Roads." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28859.

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Transportation agencies report the localization of roadway anomalies that could cause serious hazards to the traveling public. However, the high cost and limitations of present technical prevent scaling the road monitoring to all roadways. Especially the unpaved road, because of the complexity of unpaved road. Using smartphone application as road condition data collection tool offer an attractive alternative because of its potential to monitor all roadways in real time and its low cost. However, the sensor sensitivity and sampling frequency of different smartphones may vary significantly, which challenge the confidence of using smartphones for actual pavement condition assessment applications. This study tends to solve this challenge by calibrating different smartphones using two different calibrating methods including calibrating towards reference or average road roughness.
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Beitelmal, Jamal A. "Development of appropriate technology road condition monitoring system." Thesis, University of Newcastle Upon Tyne, 1999. http://hdl.handle.net/10443/533.

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This dissertation is concerned with the principles of pavement management systems and their applications in western and developing countries. The first part of the dissertation deals with the principles of pavement engineering and the role of the different layers in order to gain the required knowledge in highway pavement components, which will allow a cost-effective repair related to each specific defect. The second part deals with the existing systems for monitoring pavement condition and evaluatest heir benefit in assessingh ighway condition. The study shows the main problems usually militate against using the sophisticated technology in monitoring highway condition and implementing maintenance management systems in some cities in developing countries. In addition to the problems inherent in cities in developing countries, the city of Benghazi in Libya has special factors which have developed as a result of UN sanctions which were imposed in 1992. Therefore, the city of Benghazi has been selected as a case study for this particular research since it is a typical example of most cities in developing countries in terms of size, population and in ten-ns of lack of maintenance resources and skilled labour (Benghazi might have been so well resourcedth at it would no longer fall into the categoryo f developingc ity but for the sanction). The objectives of the study are attained through conclusions which indicate that establishing a pavement maintenance strategy in the city of Benghazi based on any or some of the sophisticated technology in road condition monitoring is not appropriate. This conclusion is tested by manufacturing a unique prototype measuring machine and using it in pilot monitoring exercises in the cities of Newcastle and Sunderland. The results of these pilot exercises are analysed to evaluate the benefit which such appropriate technology equipment can bring to the issue of monitoring of pavement condition in cities in developing countries having problems similar to those that prevail in Benghazi. The prototype equipment developed in this study is unique in that it is purely mechanical and uses no electronics in monitoring road condition. Moreover, all parts of the machine are fabricated from materials available in most cities in developing countries and therefore such machines could be easily maintained locally. The prototype described in this study is not only relevant to road monitoring but points the way towards the development of similar equipment in many engineering situations in developing countries. This research study points engineers in similar conditions in the direction that the Author thinks they should follow in applying their engineering abilities in developing countries.
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Khashayar, Hojjati Emami. "Human-centered Reliability Assessment and Condition Monitoring in Road Transportation Systems." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32126.

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The risk analysis process involving information acquisition, modeling, analysis, and decision steps result in system design improvement. To allow an accurate and active system risk assessment in road transportation, this study identifies the contributing factors in reliability of road transportation systems and develops the systematic and stochastic methodologies and mathematical models. The developed models and methodologies aim to assess the reliability and risk of drivers interacting with the today’s typical vehicles equipped with Advanced Drivers Assistance System (ADAS) and Passive Safety Systems (PSS) with any degree of complexity and availability of such systems. The research further examines and addresses the specific needs of such vulnerable users and perhaps risk to others on roads including older drivers, younger drivers and pedestrians. The research presents the conditions monitoring concepts as in-vehicle tools for live assessment of risk state of drivers built on the methodologies and models developed in the studies. The necessity for availability of good data and specific databases for purpose of risk assessment in road transportation is then highlighted and stressed. The complete procedure for accident investigation and data collection is developed and presented in the research and a conceptual model for a typical human centered reliability databases in road transportation is also developed. The research is novel and innovative and expected to pave the way for improvement and development of new risk mitigating systems and better assessment and monitoring of the safety of users on roads and with the capability of information sharing resulting in saving many lives worldwide.
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Hu, Yazhe. "Degenerate Near-planar Road Surface 3D Reconstruction and Automatic Defects Detection." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98671.

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This dissertation presents an approach to reconstruct degenerate near-planar road surface in three-dimensional (3D) while automatically detect road defects. Three techniques are developed in this dissertation to establish the proposed approach. The first technique is proposed to reconstruct the degenerate near-planar road surface into 3D from one camera. Unlike the traditional Structure from Motion (SfM) technique which has the degeneracy issue for near-planar object 3D reconstruction, the uniqueness of the proposed technique lies in the use of near-planar characteristics of surfaces in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem using only two images. Following the accuracy-enhanced 3D reconstructed road surface, the second technique automatically detects and estimates road surface defects. As the 3D surface is inversely solved from 2D road images, the detection is achieved by jointly identifying irregularities from the 3D road surfaces and the corresponding image information, while clustering road defects and obstacles using a mean-shift algorithm with flat kernel to estimate the depth, size, and location of the defects. To enhance the physics-driven automatic detection reliability, the third technique proposes and incorporates a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from supervised learning approaches which need labeled training images, the road anomaly detection network is trained by road surface images that are automatically labeled based on the reconstructed 3D surface information. In order to collect clear road surface images on the public road, a road surface monitoring system is designed and integrated for the road surface image capturing and visualization. The proposed approach is evaluated in both simulated environment and through real-world experiments. The parametric study of the proposed approach shows the small error of the 3D road surface reconstruction influenced by different variables such as the image noise, camera orientation, and the vertical movement of the camera in a controlled simulation environment. The comparison with traditional SfM technique and the numerical results of the proposed reconstruction using real-world road surface images then indicate that the proposed approach effectively reconstructs high quality near-planar road surface while automatically detects road defects with high precision, accuracy, and recall rates without the degenerate issue.
Doctor of Philosophy
Road is one of the key infrastructures for ground transportation. A good road surface condition can benefit mainly on three aspects: 1. Avoiding the potential traffic accident caused by road surface defects, such as potholes. 2. Reducing the damage to the vehicle initiated by the bad road surface condition. 3. Improving the driving and riding comfort on a healthy road surface. With all the benefits mentioned above, it is important to examine and check the road surface quality frequently and efficiently to make sure that the road surface is in a healthy condition. In order to detect any road surface defects on public road in time, this dissertation proposes three techniques to tackle the road surface defects detection problem: First, a near-planar road surface three-dimensional (3D) reconstruction technique is proposed. Unlike traditional 3D reconstruction technique, the proposed technique solves the degenerate issue for road surface 3D reconstruction from two images. The degenerate issue appears when the object reconstructed has near-planar surfaces. Second, after getting the accuracy-enhanced 3D road surface reconstruction, this dissertation proposes an automatic defects detection technique using both the 3D reconstructed road surface and the road surface image information. Although physics-based detection using 3D reconstruction and 2D images are reliable and explainable, it needs more time to process these data. To speed up the road surface defects detection task, the third contribution is a technique that proposes a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from traditional neural network-based detection techniques, the proposed combines the 3D road information with the CNN output to jointly determine the road surface defects region. All the proposed techniques are evaluated using both the simulation and real-world experiments. Results show the efficacy and efficiency of the proposed techniques in this dissertation.
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Brunken, Hauke [Verfasser], Clemens [Akademischer Betreuer] Gühmann, Olaf [Gutachter] Hellwich, and Uwe [Gutachter] Stilla. "Stereo vision-based road condition monitoring / Hauke Brunken ; Gutachter: Olaf Hellwich, Uwe Stilla ; Betreuer: Clemens Gühmann." Berlin : Universitätsverlag der TU Berlin, 2021. http://nbn-resolving.de/urn:nbn:de:101:1-2021092901575243428483.

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Axelsson, Tobias. "Using supervised learning algorithms to model the behavior of Road Weather Information System sensors." Thesis, Luleå tekniska universitet, Datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69972.

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Trafikverket, the agency in charge of state road maintenance in Sweden, have a number of so-called Road Weather Information Systems (RWIS). The main purpose of the stations is to provide winter road maintenance workers with information to decide when roads need to be plowed and/or salted. Each RWIS have a number of sensors which make road weather-related measurements every 30 minutes. One of the sensors is dug into the road which can cause traffic disturbances and be costly for Trafikverket. Other RWIS sensors fail occasionally. This project aims at modelling a set of RWIS sensors using supervised machine learning algorithms. The sensors that are of interest to model are: Optic Eye, Track Ice Road Sensor (TIRS) and DST111. Optic Eye measures precipitation type and precipitation amount. Both TIRS and DST111 measure road surface temperature. The difference between TIRS and DST111 is that the former is dug into the road, and DST111 measures road surface temperature from a distance via infrared laser. Any supervised learning algorithm trained to model a given measurement made by a sensor, may only train on measurements made by the other sensors as input features. Measurements made by TIRS may not be used as input in modelling other sensors, since it is desired to see if TIRS can be removed. The following input features may also be used for training: road friction, road surface condition and timestamp. Scikit-learn was used as machine learning software in this project. An experimental approach was chosen to achieve the project results: A pre-determined set of supervised algorithms were compared using different amount of top relevant input features and different hyperparameter settings. Prior to achieving the results, a data preparation process was conducted. Observations with suspected or definitive errors were removed in this process. During the data preparation process, the timestamp feature was transformed into two new features: month and hour. The results in this project show that precipitation type was best modelled using Classification And Regression Tree (CART) on Scikit-learn default settings, achieving a performance score of Macro-F1test = 0.46 and accuracy = 0.84 using road surface condition, road friction, DST111 road surface temperature, hour and month as input features. Precipitation amount was best modelled using k-Nearest Neighbor (kNN); with k = 64 and road friction used as the only input feature, a performance score of MSEtest = 0.31 was attained. TIRS road surface temperature was best modelled with Multi-Layer Perceptron (MLP) using 64 hidden nodes and DST111 road surface temperature, road surface condition, road friction, month, hour and precipitation type as input features, with which a performance score of MSEtest = 0.88 was achieved. DST111 road surface temperature was best modelled using Random forest on Scikit-learn default settings with road surface condition, road friction, month, precipitation type and hour as input features, achieving a performance score of MSEtest = 10.16.
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Steckenrider, John J. "Multi-Bayesian Approach to Stochastic Feature Recognition in the Context of Road Crack Detection and Classification." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/81752.

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This thesis introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. The approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level classification. Unlike many conventional methods, these features’ uncertainties are characterized so that test data can be correctively cast into the feature space with probability distribution functions that can be integrated over class decision boundaries created by a quadratic Bayesian classifier. The proposed approach is specifically formulated for road crack detection and characterization, which is one of the potential applications. For test images assessed with this technique, ground truth was estimated accurately and consistently with effective Bayesian correction, showing a 33% improvement in recall rate over standard classification. Application to road cracks demonstrated successful detection and classification in a practical domain. The proposed approach is extremely effective in characterizing highly probabilistic features in noisy environments when several correlated observations are available either from multiple sensors or from data sequentially obtained by a single sensor.
Master of Science
Humans have an outstanding ability to understand things about the world around them. We learn from our youngest years how to make sense of things and perceive our environment even when it is not easy. To do this, we inherently think in terms of probabilities, updating our belief as we gain new information. The methods introduced here allow an autonomous system to think similarly, by applying a fairly common probabilistic technique to the task of perception and classification. In particular, road cracks are observed and classified using these methods, in order to develop an autonomous road condition monitoring system. The results of this research are promising; cracks are identified and correctly categorized with 92% accuracy, and the additional “intelligence” of the system leads to a 33% improvement in road crack assessment. These methods could be applied in a variety of contexts as the leading edge of robotics research seeks to develop more robust and human-like ways of perceiving the world.
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Zurita, Millán Daniel. "Contributions to industrial process condition forecasting applied to copper rod manufacturing process." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461087.

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Ensuring reliability and robustness of operation is one of the main concerns in industrial anufacturing processes , dueto the ever-increasing demand for improvements over the cost and quality ofthe processes outcome. In this regard , a deviation from the nominal operating behaviours implies a divergence from the optimal condition specification, anda misalignment from the nominal product quality, causing a critica! loss of potential earnings . lndeed, since a decade ago, the industrial sector has been carried out a significant effort
Asegurar la fiabilidad y la robustez es uno de los principales objetivos en la monitorización de los procesos industriales, ya que estos cada vez se encuentran sometidos a demandas de producción más elevadas a la vez que se deben bajar costes de fabricación manteniendo la calidad del producto final. En este sentido, una desviación de la operación del proceso implica una divergencia de los parámetros óptimos preestablecidos, lo que conlleva a una desviación respecto la calidad nominal del producto final, causando así un rechazo de dicho producto y una perdida en costes para la empresa. De hecho, tanto es así, que desde hace más de una década el sector industrial ha dedicado un esfuerzo considerable a la implantación de metodologías de monitorización inteligente. Dichos métodos son capaces extraer información respecto a la condición de las diferentes maquinarias y procesos involucrados en el proceso de fabricación. No obstante, esta información extraída corresponde al estado actual del proceso. Por lo que obtener información respecto a la condición futura de dicho proceso representa una mejora significativa para poder ganar tiempo de respuesta para la detección y corrección de desviaciones en la operación de dicho proceso. Por lo tanto, la combinación del conocimiento futuro del comportamiento del proceso con la consecuente evaluación de la condición del mismo, es un objetivo a cumplir para la definición de las nuevas generaciones de sistemas de monitorización de procesos industriales. En este sentido, la presente tesis tiene como objetivo la propuesta de metodologías para evaluar la condición, actual y futura, de procesos industriales. Dicha metodología debe estimar la condición de forma fiable y con una alta resolución. Por lo tanto, en esta tesis se pretende extraer la información de la condición futura a partir de un modelado, basado en series temporales, de las señales críticas del proceso, para después, en base a enfoques no lineales de preservación de la topología, fusionar dichas señales proyectadas a futuro para conocer la condición. El rendimiento y la bondad de las metodologías propuestas en la tesis han sido validadas mediante su aplicación en un proceso industrial real, concretamente, con datos de una planta de fabricación de alambrón de cobre.
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Omer, Raqib. "An Automatic Image Recognition System for Winter Road Condition Monitoring." Thesis, 2011. http://hdl.handle.net/10012/5799.

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Municipalities and contractors in Canada and other parts of the world rely on road surface condition information during and after a snow storm to optimize maintenance operations and planning. With an ever increasing demand for safer and more sustainable road network there is an ever increasing demand for more reliable, accurate and up-to-date road surface condition information while working with the limited available resources. Such high dependence on road condition information is driving more and more attention towards analyzing the reliability of current technology as well as developing new and more innovative methods for monitoring road surface condition. This research provides an overview of the various road condition monitoring technologies in use today. A new machine vision based mobile road surface condition monitoring system is proposed which has the potential to produce high spatial and temporal coverage. The proposed approach uses multiple models calibrated according to local pavement color and environmental conditions potentially providing better accuracy compared to a single model for all conditions. Once fully developed, this system could potentially provide intermediate data between the more reliable xed monitoring stations, enabling the authorities with a wider coverage without a heavy extra cost. The up to date information could be used to better plan maintenance strategies and thus minimizing salt use and maintenance costs.
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Jang, Jinwoo. "Development of Data Analytics and Modeling Tools for Civil Infrastructure Condition Monitoring Applications." Thesis, 2016. https://doi.org/10.7916/D82N52HN.

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This dissertation focuses on the development of data analytics approaches to two distinct important condition monitoring applications in civil infrastructure: structural health monitoring and road surface monitoring. In the first part, measured vibration responses of a major long-span bridge are used to identify its modal properties. Variations in natural frequencies over a daily cycle have been observed with measured data, which are probably due to environmental effects such as temperature and traffic. With a focus on understanding the relationships between natural frequencies and temperatures, a controlled simulation-based study is conducted with the use of a full-scale finite element (FE) model and four regression models. In addition to the temperature effect study, the identified modal properties and the FE model are used to explore both deterministic and probabilistic model updating approaches. In the deterministic approach (sensitivity-based model updating), the regularization technique is applied to deal with a trade-off between natural frequency and mode shape agreements. Specific nonlinear constraints on mode shape agreements are suggested here. Their capabilities to adjust mode shape agreements are validated with the FE model. To the best of the author's knowledge, the sensitivity-based clustering technique, which enables one to determine efficient updating parameters based on a sensitivity analysis, has not previously been applied to any civil structure. Therefore, this technique is adapted and applied to a full-scale bridge model for the first time to highlight its capability and robustness to select physically meaningful updating parameters based on the sensitivity of natural frequencies with respect to both mass and stiffness-related physical parameters. Efficient and physically meaningful updating parameters are determined by the sensitivity-based clustering technique, resulting in an updated model that has a better agreement with measured data sets. When it comes to the probabilistic approach, the application of Bayesian model updating to large-scale civil structures based on real data is very rare and challenging due to the high level of uncertainties associated with the complexity of a large-scale model and variations in natural frequencies and mode shapes identified from real measured data. In this dissertation, the full-scale FE model is updated via the Bayesian model updating framework in an effort to explore the applicability of Bayesian model updating to a more complex and realistic problem. Uncertainties of updating parameters, uncertainty reductions due to information provided by data sets, and uncertainty propagations to modal properties of the FE model are estimated based on generated posterior samples. In the second part of this dissertation, a new innovative framework is developed to collect pavement distress data via multiple vehicles. Vehicle vibration responses are used to detect isolated pavement distress and rough road surfaces. GPS positioning data are used to localize identified road conditions. A real-time local data logging algorithm is developed to increase the efficiency of data logging in each vehicle client. Supervised machine learning algorithms are implemented to classify measured dynamic responses into three categories. Since data are collected from multiple vehicles, the trajectory clustering algorithm is introduced to integrate various trajectories to provide a compact format of information about road surface conditions. The suggested framework is tested and evaluated in real road networks.
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Books on the topic "Road condition monitoring"

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Perchanok, M. S. Evaluation of a video system for remote monitoring of winter road surface conditions. Downsview, Ont: Research and Development Branch, Ministry of Transportation, 1994.

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Book chapters on the topic "Road condition monitoring"

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Kutila, Matti, Pasi Pyykönen, Johan Casselgren, and Patrik Jonsson. "Road Condition Monitoring." In Computer Vision and Imaging in Intelligent Transportation Systems, 375–97. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118971666.ch15.

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Haddar, Maroua, Fathi Djmal, Riadh Chaari, S. Caglar Baslamisli, Fakher Chaari, and Mohamed Haddar. "Adaptive On-Line Estimation of Road Profile in Semi-active Suspension." In Applied Condition Monitoring, 144–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_15.

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Capasso, Clemente, Moncef Hammadi, Stanislao Patalano, Ruixian Renaud, and Ottorino Veneri. "RFLP Approach in the Designing of Power-Trains for Road Electric Vehicles." In Applied Condition Monitoring, 249–58. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14532-7_26.

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Ben Hassen, Dorra, Mariem Miladi, Mohamed Slim Abbes, S. Caglar Baslamisli, Fakher Chaari, and Mohamed Haddar. "Effect of Non-linear Suspension on the Recognition of the Road Disturbance." In Applied Condition Monitoring, 65–74. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_7.

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Ben Hassen, Dorra, Mariem Miladi, Mohamed Slim Abbes, S. Caglar Baslamisli, Fakher Chaari, and Mohamed Haddar. "Estimation of Road Disturbance for a Non Linear Half Car Model Using the Independent Component Analysis." In Applied Condition Monitoring, 96–103. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96181-1_9.

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Kassem, Diana, and Carlos Arce-Lopera. "Road-Condition Monitoring and Classification for Smart Cities." In Advances in Intelligent Systems and Computing, 437–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51328-3_60.

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Mohamed, Adham, Mohamed Mostafa M. Fouad, Esraa Elhariri, Nashwa El-Bendary, Hossam M. Zawbaa, Mohamed Tahoun, and Aboul Ella Hassanien. "RoadMonitor: An Intelligent Road Surface Condition Monitoring System." In Advances in Intelligent Systems and Computing, 377–87. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11310-4_33.

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Perttunen, Mikko, Oleksiy Mazhelis, Fengyu Cong, Mikko Kauppila, Teemu Leppänen, Jouni Kantola, Jussi Collin, et al. "Distributed Road Surface Condition Monitoring Using Mobile Phones." In Ubiquitous Intelligence and Computing, 64–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23641-9_8.

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Campos, Jaime, Mirka Kans, and Lars Håkansson. "Information System Requirements Elicitation for Gravel Road Maintenance – A Stakeholder Mapping Approach." In Advances in Asset Management and Condition Monitoring, 377–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57745-2_32.

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Sujatha, K., P. Vijai Babu, A. Ganesan, N. P. G. Bhavani, P. Sinthia, V. Srividhya, and S. Ponmagal. "Cloud Computing for Image Based Condition Monitoring of Road Surface." In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018, 1400–1406. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03146-6_164.

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Conference papers on the topic "Road condition monitoring"

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SASI PRIYA, S., S. Rajarajeshwari, K. Sowmiya, and P. Vinesha. "Road Traffic Condition Monitoring using Deep Learning." In 2020 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2020. http://dx.doi.org/10.1109/icict48043.2020.9112408.

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Li, Kang, James A. Misener, and Karl Hedrick. "On-Board Road Condition Monitoring System Using Slip-Based Tire-Road Friction Estimation and Wheel Speed Signal Analysis." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14102.

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This paper presents an on-board road condition monitoring system developed for the safety application in Vehicle Infrastructure Integration (VII) project. The system equipped on the so-called probe vehicle is able to continuously evaluate road surface in terms of slipperiness and coarseness. Road surface is classified into four grades using stock mobile sensors and GPS speed-based measurements. The task of distinguishing slippery extents of road surfaces was treated as a "pattern-recognition" problem based on experimental results such that road surfaces can be classified into three slip levels, normal (μmax ≥0.5), slippery (0.3≥μmax <0.5), and very slippery (μmax <0.3) provided enough excitation. To distinguish rough road surfaces like gravel roads from normal asphalt roads, a separate classifier making use of a filterbank for analyzing wheel speed signal was implemented. Experimental results demonstrate the feasibility of this road condition monitoring system for detecting slippery and rough road surfaces in close to real-time. Once a slippery road condition is detected by the probe vehicle, a warning message with accurate GPS position can be transmitted from the probe vehicle to road side equipment (RSE) and further be relayed to following vehicles as well as traffic management center (TMC) via Dedicated Short Range Communication (DSRC); hence the safety of road users can be improved with the aid of this cooperative or VII active safety system.
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Ito, Kenta, Go Hirakawa, Goshi Sato, and Yoshitaka Shibata. "SDN Based Road Condition Monitoring System for ITS." In 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). IEEE, 2015. http://dx.doi.org/10.1109/bwcca.2015.110.

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Mori, Teppei, Tomonori Ohiro, Yasushi Hanatsuka, and Tomoyuki Higuchi. "Data-Driven Road Condition Forecasting with High Spatial Resolution: Utilizing Tire-Centric Road Condition Monitoring Technology." In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020. http://dx.doi.org/10.1109/itsc45102.2020.9294592.

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Zyabirov, I. M., and A. I. Zyabirov. "METHOD FOR MONITORING THE PARAMETERS OF THE TECH-NICAL CONDITION OF THE TRANSMISSION OF A TRACK." In Innovative technologies in road transport. Voronezh State University of Forestry and Technologies named after G.F. Morozov, Voronezh, Russia, 2021. http://dx.doi.org/10.34220/itrt2021_10-15.

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Jokela, Maria, Matti Kutila, and Long Le. "Road condition monitoring system based on a stereo camera." In 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2009. http://dx.doi.org/10.1109/iccp.2009.5284724.

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Kortmann, Felix, Julin Horstkotter, Alexander Warnecke, Nicolas Meier, Jens Heger, Burkhardt Funk, and Paul Drews. "Live Demonstration: Passive Sensor Setup for Road Condition Monitoring." In 2020 IEEE SENSORS. IEEE, 2020. http://dx.doi.org/10.1109/sensors47125.2020.9278776.

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Jang, Jinwoo, Andrew W. Smyth, Yong Yang, and Dave Cavalcanti. "Road surface condition monitoring via multiple sensor-equipped vehicles." In IEEE INFOCOM 2015 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2015. http://dx.doi.org/10.1109/infcomw.2015.7179334.

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Pawlenka, Tomas, and Jaromir Skuta. "Road Condition Monitoring with Use of MEMS Based Unit." In 2020 21th International Carpathian Control Conference (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc49264.2020.9257232.

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Baruah, Barnana, and Subhasish Dhal. "A Secure and Privacy-Preserved Road Condition Monitoring System." In 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2020. http://dx.doi.org/10.1109/comsnets48256.2020.9027482.

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Reports on the topic "Road condition monitoring"

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Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, July 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
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Li, Howell, Enrique Saldivar-Carranza, Jijo K. Mathew, Woosung Kim, Jairaj Desai, Timothy Wells, and Darcy M. Bullock. Extraction of Vehicle CAN Bus Data for Roadway Condition Monitoring. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317212.

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Obtaining timely information across the state roadway network is important for monitoring the condition of the roads and operating characteristics of traffic. One of the most significant challenges in winter roadway maintenance is identifying emerging or deteriorating conditions before significant crashes occur. For instance, almost all modern vehicles have accelerometers, anti-lock brake (ABS) and traction control systems. This data can be read from the Controller Area Network (CAN) of the vehicle, and combined with GPS coordinates and cellular connectivity, can provide valuable on-the-ground sampling of vehicle dynamics at the onset of a storm. We are rapidly entering an era where this vehicle data can provide an agency with opportunities to more effectively manage their systems than traditional procedures that rely on fixed infrastructure sensors and telephone reports. This data could also reduce the density of roadway weather information systems (RWIS), similar to how probe vehicle data has reduced the need for micro loop or side fire sensors for collecting traffic speeds.
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Desai, Jairaj, Jijo K. Mathew, Woosung Kim, Mingmin Liu, Howell Li, Jeffrey D. Brooks, and Darcy M. Bullock. Dashboards for Real-time Monitoring of Winter Operations Activities and After-action Assessment. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317252.

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The Indiana Department of Transportation (INDOT) operates a fleet of nearly 1100 snowplows and spends up to $60M annually on snow removal and de-icing as part of their winter operation maintenance activities. Systematically allocating resources and optimizing material application rates can potentially save revenue that can be reallocated for other roadway maintenance operations. Modern snowplows are beginning to be equipped with a variety of Mobile Road Weather Information Sensors (MARWIS) which can provide a host of analytical data characterizing on-the-ground conditions during periods of wintry precipitation. Traffic speeds fused with road conditions and precipitation data from weather stations provide a uniquely detailed look at the progression of a winter event and the performance of the fleet. This research uses a combination of traffic speeds, MARWIS and North American Land Data Assimilation System (NLDAS) data to develop real-time dashboards characterizing the impact of precipitation and pavement surface temperature on mobility. Twenty heavy snow events were identified for the state of Indiana from November 2018 through April 2019. Two particular instances, that impacted 182 miles and 231 miles of interstate at their peaks occurred in January and March, respectively, and were used as a case study for this paper. The dashboards proposed in this paper may prove to be particularly useful for agencies in tracking fleet activity through a winter storm, helping in resource allocation and scheduling and forecasting resource needs.
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Cooper, Christopher, Jacob McDonald, and Eric Starkey. Wadeable stream habitat monitoring at Congaree National Park: 2018 baseline report. National Park Service, June 2021. http://dx.doi.org/10.36967/nrr-2286621.

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The Southeast Coast Network (SECN) Wadeable Stream Habitat Monitoring Protocol collects data to give park resource managers insight into the status of and trends in stream and near-channel habitat conditions (McDonald et al. 2018a). Wadeable stream monitoring is currently implemented at the five SECN inland parks with wadeable streams. These parks include Horseshoe Bend National Military Park (HOBE), Kennesaw Mountain National Battlefield Park (KEMO), Ocmulgee Mounds National Historical Park (OCMU), Chattahoochee River National Recreation Area (CHAT), and Congaree National Park (CONG). Streams at Congaree National Park chosen for monitoring were specifically targeted for management interest (e.g., upstream development and land use change, visitor use of streams as canoe trails, and potential social walking trail erosion) or to provide a context for similar-sized stream(s) within the park or network (McDonald and Starkey 2018a). The objectives of the SECN wadeable stream habitat monitoring protocol are to: Determine status of upstream watershed characteristics (basin morphology) and trends in land cover that may affect stream habitat, Determine the status of and trends in benthic and near-channel habitat in selected wadeable stream reaches (e.g., bed sediment, geomorphic channel units, and large woody debris), Determine the status of and trends in cross-sectional morphology, longitudinal gradient, and sinuosity of selected wadeable stream reaches. Between June 11 and 14, 2018, data were collected at Congaree National Park to characterize the in-stream and near-channel habitat within stream reaches on Cedar Creek (CONG001, CONG002, and CONG003) and McKenzie Creek (CONG004). These data, along with the analysis of remotely sensed geographic information system (GIS) data, are presented in this report to describe and compare the watershed-, reach-, and transect-scale characteristics of these four stream reaches to each other and to selected similar-sized stream reaches at Ocmulgee Mounds National Historical Park, Kennesaw Mountain National Battlefield Park, and Chattahoochee National Recreation Area. Surveyed stream reaches at Congaree NP were compared to those previously surveyed in other parks in order to provide regional context and aid in interpretation of results. edar Creek’s watershed (CONG001, CONG002, and CONG003) drains nearly 200 square kilometers (77.22 square miles [mi2]) of the Congaree River Valley Terrace complex and upper Coastal Plain to the north of the park (Shelley 2007a, 2007b). Cedar Creek’s watershed has low slope and is covered mainly by forests and grasslands. Cedar Creek is designated an “Outstanding Resource Water” by the state of South Carolina (S.C. Code Regs. 61–68 [2014] and S.C. Code Regs. 61–69 [2012]) from the boundary of the park downstream to Wise Lake. Cedar Creek ‘upstream’ (CONG001) is located just downstream (south) of the park’s Bannister Bridge canoe landing, which is located off Old Bluff Road and south of the confluence with Meyers Creek. Cedar Creek ‘middle’ and Cedar Creek ‘downstream’ (CONG002 and CONG003, respectively) are located downstream of Cedar Creek ‘upstream’ where Cedar Creek flows into the relatively flat backswamp of the Congaree River flood plain. Based on the geomorphic and land cover characteristics of the watershed, monitored reaches on Cedar Creek are likely to flood often and drain slowly. Flooding is more likely at Cedar Creek ‘middle’ and Cedar Creek ‘downstream’ than at Cedar Creek ‘upstream.’ This is due to the higher (relative to CONG001) connectivity between the channels of the lower reaches and their out-of-channel areas. Based on bed sediment characteristics, the heterogeneity of geomorphic channel units (GCUs) within each reach, and the abundance of large woody debris (LWD), in-stream habitat within each of the surveyed reaches on Cedar Creek (CONG001–003) was classified as ‘fair to good.’ Although, there is extensive evidence of animal activity...
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Petrie, John, Yan Qi, Mark Cornwell, Md Al Adib Sarker, Pranesh Biswas, Sen Du, and Xianming Shi. Design of Living Barriers to Reduce the Impacts of Snowdrifts on Illinois Freeways. Illinois Center for Transportation, November 2020. http://dx.doi.org/10.36501/0197-9191/20-019.

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Blowing snow accounts for a large part of Illinois Department of Transportation’s total winter maintenance expenditures. This project aims to develop recommendations on the design and placement of living snow fences (LSFs) to minimize snowdrift on Illinois highways. The research team examined historical IDOT data for resource expenditures, conducted a literature review and survey of northern agencies, developed and validated a numerical model, field tested selected LSFs, and used a model to assist LSF design. Field testing revealed that the proper snow fence setback distance should consider the local prevailing winter weather conditions, and snow fences within the right-of-way could still be beneficial to agencies. A series of numerical simulations of flow around porous fences were performed using Flow-3D, a computational fluid dynamics software. The results of the simulations of the validated model were employed to develop design guidelines for siting LSFs on flat terrain and for those with mild slopes (< 15° from horizontal). Guidance is provided for determining fence setback, wind characteristics, fence orientation, as well as fence height and porosity. Fences comprised of multiple rows are also addressed. For sites with embankments with steeper slopes, guidelines are provided that include a fence at the base and one or more fence on the embankment. The design procedure can use the available right-of-way at a site to determine the appropriate fence characteristics (e.g., height and porosity) to prevent snow deposition on the road. The procedure developed in this work provides an alternative that uses available setback to design the fence. This approach does not consider snow transport over an entire season and may be less effective in years with several large snowfall events, very large single events, or a sequence of small events with little snowmelt in between. However, this procedure is expected to be effective for more frequent snowfall events such as those that occurred over the field-monitoring period. Recommendations were made to facilitate the implementation of research results by IDOT. The recommendations include a proposed process flow for establishing LSFs for Illinois highways, LSF siting and design guidelines (along with a list of suitable plant species for LSFs), as well as other implementation considerations and identified research needs.
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Kwon, Jaymin, Yushin Ahn, and Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization. Mineta Transportation Institute, August 2021. http://dx.doi.org/10.31979/mti.2021.2010.

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To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry. This project provides consolidated data-driven transportation information to the public including: 1. Transportation-related particle pollution data 2. Spatial analyses of geocoded vehicle emissions 3. Neighborhood characterization for the built environment such as cities, buildings, roads, parks, walkways, etc.
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