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Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing

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Abstract

Road surface monitoring is an important problem in providing smooth road infrastructure to the commuters. The key to road condition monitoring is to detect road potholes and bumps, which affect the driving comfort and transport safety. This paper presents a smartphone based sensing and crowdsourcing technique to detect the road surface conditions. The in-built sensors of the smartphone like accelerometer and GPS¹ have been used to observe the road conditions. It has been observed that several techniques in the past have been proposed using these sensors. Such techniques either use fixed threshold values which are road or vehicle condition dependent or use machine learning based classified training which requires intensive and continuous training. The motivation of our work is to improve classification accuracy of detecting road surface conditions using DTW² technique which has not been researched on data based on motion sensors. The main features of DTW is its ability to automatically cope with time deformations and different speeds associated with time data, its simplicity is to be used in resource constrained devices such as smartphones and also the simplicity in its training procedure which is must as fast as compared to techniques such as SVM,³ HMM⁴ and ANN.⁵ Our technique shows better accuracy and efficiency with detection rate of 88.66% and 88.89% for potholes and bumps respectively, when compared with the existing techniques with the use of the proposed technique, prioritization of the road repair and maintenance can be decided based on real-time data and facts.

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... The Euler angles, applied only to the acceleration data in the preprocessing step, provide a means of representing the three-dimensional spatial orientation of any reference frame as a composition of three elementary rotations. The reference orientation can be taken as an initial orientation from which the coordinate system rotates to reach its actual orientation [62]. Thus, these formulas reorient the raw data using the provided angles. ...
... These dependencies are related to the vehicle's driving mode, where the main dependency factor we identified was the longitudinal speed [14,17,25,36,40,41,48,62]. The vehicle speed has two implications. ...
... In the first preprocessing step, few studies have used axis reorientation between coordinate systems, mapping the data from the sensor reference frame to vehicle reference frame. Among those who used them, most applied formulas based on Euler angles [39,50,62,67,68]. The angles were calculated in different ways: in [50,62,67] the initial reference frame was established as stationary state ( x = 0m/s 2 , y = 0m/s 2 e z = 9.81m/s 2 ), from which the mapping angles were calculated. ...
Article
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In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle's movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state; and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications. Available for free at: https://link.springer.com/article/10.1007/s42979-020-00275-z
... These algorithms are convenient and fast to implement but do not have satisfactory accuracy due to the noises [8]. Besides, traditional methods and classifiers are found hard to be generalized for various road surfaces [9]. Thus, we need a solution to address these challenges. ...
... Singh et al. [9] proposed a Smart Patrolling system for road surface monitoring, which uses multiple smartphones via crowdsourcing. The central server is used to analyze, filtering, and aggregate the collected data from smartphones. ...
Conference Paper
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Smartphones play an important role in our lives, which makes them a good sensor for perceiving our environment. Therefore, many applications have emerged using mobile sensors to solve different problems related to activity recognition, health monitoring, transportation, etc. One of the intriguing issues in transportation is mapping our road network's quality, road types, or discover unmapped roads in our road network, which is very costly to maintain and to examine. In this paper, we propose a methodology to recognize different road types by using accelerometer data of smartphones. The approach is based on DeepSense neural network with customised preprocessing and feature engineering steps. In addition, we compared our method performance against Convolutional Neural Network, Fully-connected Neural Network, Support Vector Machine, and RandomForest classifier. Our approach outperformed all four methods, and it was capable of distinguishing three road types (asphalt roads, stone roads, and off-roads). Source Code & Dataset: https://github.com/simonwu53/RoadSurfaceRecognition
... The article [20] proposes a method of measuring the roughness of the surface of paths for pedestrians and cyclists based on the global positioning system (GPS) and accelerometer sensors in bicycle smartphones. The article [21] presents the technique of detection and crowdsourcing with the use of a smartphone to detect the condition of the road surface, the aim of which was to improve the classification accuracy of road surface condition detection using DTW (dynamic time warping). It was determined that DTW shows high accuracy and performance due to the possibility of comparing two time-dependent series of data, which may vary in speed. ...
... The vertical linear vibrations of the body of a moving vehicle are then used to assess the condition of the road surface. These systems have the potential to provide the data necessary to estimate the road surface unevenness, i.e., the international roughness index (IRI) [21,23]. To determine the IRI value, the value of vertical displacement (resulting from pits, bumps and humps on the road) is used [24]. ...
Article
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On the basis of road tests, the authors assessed the feasibility of the vehicle body acceleration values for the purposes of assessing road surface characteristics in terms of its roughness. Short-term Fourier Transform (STFT) was used for the analysis of the recorded signal. The spectra obtained in successive frequency bands demonstrate the amplitudes originating from the natural vibrations of the rolling wheel and forces resulting from the interaction with the road roughness. The article focuses on the relationships between the road roughness and the ratios of individual amplitudes in a specific frequency band of the vehicle body acceleration values. Amplitude values derived on the basis of successive windows were averaged for analogous, arbitrarily assumed local frequency bands. The value characterizing the road surface condition provided the information regarding the mean amplitude value in specific frequency ranges depending on the instantaneous velocity of the car body and the condition of the road surface on which it was moving. In cases where the road was free of any visible roughness, the obtained mean amplitude value in the analyzed spectrum window, for the adopted vehicle velocity range from 50 km h to 100 km/h, did not exceed 0.02 m/s2. It was also demonstrated that the road surface roughness leads to an increase in the mean amplitude value from 0.07 m/s2 to 0.16 m/s2.
... Each driving style produces different impacts on the sampled signals, according to the applied traction and direction and the way the vehicle interacts with the environment. The main factor to be considered is the longitudinal speed [10,14,19,27,30,31,37,47], which impacts on the amplitude of the sampled signals and their distribution over time. Therefore, depending on the applied speed, the signals from the same road segment may have a greater or lesser amplitude and more or less samples, but it must be recognized in the same way. ...
... After selection, the data were transformed to suit the inputs of the pattern classification techniques. For Classical Machine Learning methods, data features were extracted using the statistical methods Standard Deviation, Mean and Variance for inertial sensors signals, and Mean for speed, which are the methods most commonly used in related studies to extract high-level vibration-based features on data from inertial sensor signals [4][5][6][7]21,29,30,39,40,42,47]. To analyze the influence of the number of samples, fixed windows of 100, 150, 200, 250 and 300 samples were used, considering 100 the minimum size to have enough information, and 300 the maximum size so that the delay of the first classification is not greater than 3 s. ...
Article
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The demand for several sources of situational data from the traffic environment has intensified in recent years, through the development of applications in intelligent transport systems (ITS), such as autonomous vehicles and advanced driver assistance systems. Among these situational data, the road surface type classification is one of the most important and can be used throughout the ITS domain. However, in order to have a wide application, the development of a safe and reliable model is necessary. Therefore, in addition to the application of safe technology, the model developed must operate correctly in different vehicles, with different driving styles and in different environments in which vehicles can travel to. For this purpose, in this work we collect nine datasets with contextual variations using inertial sensors, represented by accelerometers and gyroscopes. These data were produced in three different vehicles, with three different drivers, in three different environments in which there are three different surface types, in addition to variations in conservation state and presence of obstacles and anomalies, such as speed bumps and potholes. After a pre-processing step, these data were used in 34 different computational models for road surface type classification, employing both Classical Machine Learning and Deep Learning techniques. Through several experiments, we analyze the learning and generalization capacity of each technique. The best model developed was a CNN-based deep neural network, which obtained validation accuracy of 93.17%, classifying surfaces between segments of dirt, cobblestone or asphalt roads.
... The system was implemented and showed a detection accuracy of 18 out of 20 speed bumps. Singh et al. (2017) presented a smartphone-based sensing and crowdsourcing technique to detect road surface conditions. The in-built sensors of the smartphone (accelerometer and GPS) were used to observe the road conditions. ...
... As shown in Table 1, the researchers used several methods to identify a road anomaly as a pothole, speed bump or other road conditions. The Mohan et al. (2008) technique is more traditional than the others, relying upon fixed thresholds, whereas the methods used by Bhoraskar et al (2012), Singh et al., (2017), Varona et al. (2019) and Kyriakou et al. (2018Kyriakou et al. ( , 2019 rely upon more contemporary methods such as machine learning technique (SVM, DTW, CNN, long short-term memory networks, reservoir computing models, ANN, and bagged trees classification models) which require extensive training. ...
Article
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Pavement surface condition is an essential metric in providing quality and safe road infrastructure to the commuters. One of the keys to roadway condition monitoring is the detection and classification of roadway speed bumps which affect driving comfort and transport safety. The paper presents a data-driven framework and related field studies on the use of supervised machine learning and smartphone sensor technology for the detection and georeferencing of speed bumps. The study proposes a low-cost and automated method to obtain up-to-date information about speed bumps, with the use of smartphones mounted on vehicles. The proposed methodology is based on readily available and accurate technologies, it can be utilized in crowd-sourced applications for pavement management systems (PMS) and geographical information system (GIS) implementations, and it has already been field-tested for the detection and classification of cracks, rutting, ravelling, patches and potholes, exhibiting accuracy levels higher than 90%. The smartphone-based data collection and speed-bump detection algorithms discussed in this paper are complemented with robust regression analysis and Random Under Sampling (RUS) Boosted trees classification models. Ongoing work will further investigate the automated measurement of the geometric properties of the detected bumps and their compliance with regulatory requirements.
... Moving sensors, instead, exploit the potential of crowd computing and measurement, since they are represented by personal (i.e., smartphones) or driver-based sensors (i.e., sensors on vehicles) for collecting data during driving. The elaboration of their information can provide huge quantities of data (big data) in real-time, allowing an effective continuous evaluation of asset condition (see 6,9,[20][21][22][23][24][25][26][27]. ...
Article
Especially in urban contexts, the detection of damages in road infrastructures is crucial for their management and efficiency. This entails complex measurement chains. Several solutions were already developed and applied (e.g., high speed monitoring systems or sensor‐based systems), but the emerging smart city and smart road paradigms call for innovations in these fields. A possible solution could be the development of smart, non‐intrusive, and sustainable sensing systems able to perform continuous monitoring, allowing automatic and timely generation of alarms and proper maintenance strategies from Road Management Systems (RMSs). Consequently, this study aims to demonstrate the feasibility of integrating miniaturized sensing systems with high‐speed monitoring systems, to obtain innovative RMSs. In this regard, a specific web‐based platform, the core of a Smart RMS, properly defined to acquire, correlate, and exploit data from several sources was developed. To this end, high‐performance monitoring systems, an innovative sensing system (i.e., based on miniaturized devices and on feature‐ and signature‐based methods), and Micro‐electromechanical systems (i.e., smartphones accelerometers) were used. A new set of indicators has been set up and partly validated in the pursuit of setting out a new paradigm where Pavement Management Systems are supposed to become urban‐oriented and less expensive. Results show that combining traditional and innovative solutions allows providing a comprehensive overview of both the structural condition and the performance of road infrastructures. This information could be used to exponentially improve the efficiency of current RMSs in forecasting the occurrence of damages and in scheduling more effective and sustainable management interventions.
... Methods used for the detection of road-surface damage can be classified into: (a) identification of vehicular vibration types; (b) measurements using laser irradiation of the road surface; and (c) image recognition [6]. Vibration-based detection methods measure the vibration of a vehicle during motion using an attached three-axis or vertical accelerometer [7][8][9][10][11][12][13][14][15][16][17]. This approach requires several filtering steps to extract only the vibrations caused by the damage to the road surface. ...
Article
Full-text available
Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.
... En la práctica, es difícil de lograrlo, dado que en el tablero del vehículo no existen superficies planas y, muchas veces, los teléfonos se colocan de manera vertical. Entonces, para corregir la colocación del vehículo, los datos recolectados deben reorientarse virtualmente, utilizando el método de ángulos de Euler (Singh et al., 2017). ...
Article
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Los baches son un problema común en el pavimentos deteriorados. Estos desniveles disminuyen la comodidad de conducción y pueden llegar a causar siniestros viales. El geolocalizar los baches y su grado severidad permitirán a los usuarios ajustar su velocidad y trayectoria en la vía deteriorada. Además, las entidades del estado pueden planificar las intervenciones de mantenimiento en los sitios con más deterioro. Esto se podría resolverse utilizando sensores como los que tienen los teléfonos celulares o el Video VBOX Lite, que tiene mayor precisión. La información recolectada por estos equipos por sí sola, no permite la geolocalización del bache o determinar su severidad, es necesario entender, procesar y evaluar esos datos para lograrlo. Por lo tanto, el objetivo de este estudio es proponer un procedimiento para detectar baches y su severidad usando el Video VBOX Lite y dos teléfonos inteligentes. Para ello, la recolección de datos se hizo en dos fases. En la fase 1, se recolectaron los baches de manera manual (posición, profundidad y diámetro). Mientras que en la fase 2, se recolectaron los datos de los sensores de los equipos colocados en un vehículo liviano. Se circuló a velocidades entre 20 a 50 km/h. Basado en estos datos, se propuso dos procedimientos uno para el Video VBOX Lite y otro para los teléfonos celulares. Las precisión de los procedimientos llegaron a detectar entre 71-90% de los baches. Este procedimiento se puede adaptar como crowdsourcing para generar datos de las redes viales locales
... Whatever system is used to evaluate pavement roughness (through direct or indirect methods), this should be integrated at least with a high-precision global positioning system (GPS) receiver to allow a correct localization and positioning of the measurements on the road [26][27][28][29][30][31][32]. The essential measurement systems necessary for the ride evaluation (threeaxial accelerometer and GPS module), are already available in the modern smartphones where they are suitably integrated and synchronized [33][34][35][36][37][38][39]. For this reason, smartphones have been recently proposed to evaluate road condition over the world using apps with different approaches. ...
Preprint
Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, an Inertial Measurement Unit is proposed by using a low-cost three-axis Micro-Electro-Mechanical Systems accelerometer and a GPS instrument, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to monitor indirectly the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was considered. Considering 21 km of roads, with different levels of pavement decay, validation measures made using the proposed IMU, another pre-assembled IMU, and a Road Surface Profiler were performed. Therefore, comparisons between awz determined with accelerations measured on the two different IMU are made; in addition, also correlations between awz, International Roughness Index (IRI), and Ride Number (RN) were performed. The results were shown very good correlations between the awz calculated with the proposed IMU and ones in the other IMU. In addition, the correlations between awz and IRI and RN were showed promising results, considering the use and the costs of the proposed IMU as a reliable method to assess the pavements decay in road networks where the use of traditional systems is difficult and/or not cheap.
... Under the research referred to in Singh et al. (2017), the smart-patrolling system was proposed to be used in the roads of the city of Chandigarh to assess the condition of road pavement. This solution is based on a combination of measurement data from multiple streams, namely smartphones using crowdsourcing, followed by an analysis of the data obtained by dynamic time warping (DTW). ...
Article
Full-text available
The purpose of the paper is to analyse the effectiveness of a solution known as road condition tool (RCT) based on data crowdsourcing from smartphones users in the transport system. The tool developed by the author of the paper, enabling identification and assessment of road pavement defects by analysing the dynamics of vehicle motion in the road network. Transport system users equipped with a smartphone with the RCT mobile application on board record data of linear accelerations, speed, and vehicle location, and then, without any intervention, send them to the RCT server database in an aggregated form. The aggregated data are processed in the combined time and location criterion, and the road pavement condition assessment index is estimated for fixed 10 m long measuring sections. The measuring sections correspond to the sections of roads defined in the pavement management systems (PMS) used by municipal road infrastructure administration bodies. Both the research in question and the results obtained by the method proposed for purposes of the road pavement condition assessment were compared with a set of reference data of the road infrastructure administration body which conducted surveys using highly specialised measuring equipment. The results of this comparison, performed using binary classifiers, confirm the potential RCT solution proposed by the author. This solution makes it possible to global monitor the road infrastructure condition on a continuous basis via numerous users of the transport system, which guarantees that such an assessment is kept up to date.
... Dynamic Time Warping (DTW) is a time series similarity measure. From its origins in speech recognition [1], it has spread to a broad spectrum of further uses, recent examples of which include gesture recognition [2], signature verification [3], shape matching [4], road surface monitoring [5], neuroscience [6] and medical diagnosis [7]. DTW measures similarity by summing pairwise-distances between points in two series. ...
Preprint
Full-text available
Dynamic Time Warping (DTW) is a popular similarity measure for aligning and comparing time series. Due to DTW's high computation time, lower bounds are often employed to screen poor matches. Many alternative lower bounds have been proposed, providing a range of different trade-offs between tightness and computational efficiency. LB Keogh provides a useful trade-off in many applications. Two recent lower bounds, LB Improved and LB Enhanced, are substantially tighter than LB Keogh. All three have the same worst case computational complexity - linear with respect to series length and constant with respect to window size. We present four new DTW lower bounds in the same complexity class. LB Petitjean is substantially tighter than LB Improved, with only modest additional computational overhead. LB Webb is more efficient than LB Improved, while often providing a tighter bound. LB Webb is always tighter than LB Keogh. The parameter free LB Webb is usually tighter than LB Enhanced. A parameterized variant, LB Webb Enhanced, is always tighter than LB Enhanced. A further variant, LB Webb*, is useful for some constrained distance functions. In extensive experiments, LB Webb proves to be very effective for nearest neighbor search.
... Again, an accelerometer stream data is used to detect road-surface roughness. Besides, a timeseries approach by means of the well-known algorithm Dynamic Time Warping for the detection of bumps or potholes is described in [28]. ...
Article
Full-text available
The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.
... Several efforts have been made in this research field and numerous studies can be found on it. Some of these have focused on the development of algorithms for detecting individual road anomalies (especially, potholes) starting from the measurement of vertical accelerations [35][36][37][38][39][40][41][42]. Others, on the other hand, have undertaken to create algorithms capable of converting vertical acceleration measurements to pavement performance indicators. ...
Article
Full-text available
One of the criteria adopted by the Word Bank with the aim of defining the economic level of a country is represented by the condition of the road pavements. To ensure adequate road pavement quality, road authorities should be continuously monitoring and repair the detected anomalies. To fast solve problems associated with poor quality of road surface such as comfort or safety, the presence of distress must be detected quickly. The high-performance pavement distress detection, such as those base on the image processing or on the laser scanning, is very expensive and does not allow to the road administration to conduct the appropriate monitoring campaigns. To solve these problems, the paper describes the pave box methodology, an innovative and immediately operational distress detection approach based on the exploitation of data collected by the black boxes located inside the vehicles that routinely pass on the road network. Data processing and the algorithms used in the post-processing evaluation of the vertical acceleration were compared with existing visual surveys procedures such as PCI. Two different indices have been proposed to detect and classify both the local damages and the global condition of the entire road. Pave box provides a robust evaluation of the pavement condition that allows to detect all the severe distress and not less than 70% of the minor damages on the pavement surface. The proposal is characterized by low time and cost consumption and it represents an effective tool for road authorities.
... The problem of calculating road quality with usage of crowdsourcing and smartphones has been discussed earlier [3,4], however, most researchers are discussing detection of single road artefacts [5], e.g. potholes or speed bumps using different methods [6,7]. ...
Article
In this paper, the authors are showing a calculation of the road quality index called Simple Road Quality Index (SRQI) using the weight provided by the amateur drivers to best possibly rate their comfort on driving on that road. The index is calculated from acceleration data acquired by the smartphone application and is aggregated in a crowdsourcing system for the classification of road quality using the fuzzy membership function. The paper shows that the proposed index correctly shows road quality changes over time and may be used as a way to mark roads to be avoided or needs to be repaired. The numerical experiment was based on the same street in Lublin, Poland, in 2015-2021 and is correctly showing that the quality of analyzed roads deteriorated over time, especially in the winter season.
... Zhao et al. [10] used a genetic algorithm to analyze the acceleration and angular velocity data obtained only from mobile phone devices to estimate the road smoothness and pothole location. Existing studies mostly utilize bicycles bound to smart phones to collect road vibration data, and the processing process mostly uses the time domain acceleration amplitude to judge, missing the impact of different vibration frequencies on road service performance [11][12][13][14]. ...
Article
Full-text available
Road surface monitoring is a significant issue in providing smooth road infrastructure for vehicles, and the key to road condition monitoring is to detect road potholes that affect driving comfort and transportation safety. This paper presents a simple, efficient, and accurate way to evaluate road service performance based on the acquisition of road vibration data by vibration sensors installed in vehicles. Inspired by the discrete fast Fourier transform, the vibration acceleration is processed, and the RMS value of vibration acceleration at 1/2 octave is calculated, after which the road vibration level is calculated. The vibration level is optimized according to the human body's sensitivity to different frequencies of vibration, resulting in road service performance indicators that can reflect the human body's real feelings. According to the road service performance index values on the road grading, combined with GPS data on the electronic map color block labeling, the results obtained for the road condition warning, road maintenance, driver route selection have an important significance.
... Each driving style has different impacts on the sampled signals, according to the traction and direction applied, and the way the vehicle interacts with the external environment. The main factor is the longitudinal speed [20], [23]- [29], which affects the amplitude of the sampled signals, their time distribution and the number of samples per road segment. d) Environmental Properties: relate to the characteristics of the environment external to the vehicle. ...
Conference Paper
Full-text available
The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road surface type classification is one of the most important and can be used in the entire ITS domain. For its widespread application, it is necessary to employ a robust technology for the generation of raw data and to develop of a reliable and stable model to process these data in order to produce the classification. The developed model must operate correctly in different vehicles, under different driving styles and in different environments in which a vehicle can travel. In this work we employ inertial sensors, represented by accelerometers and gyroscopes, which are a safe, non-polluting, and low-cost alternative, ideal for large-scale use. We collect nine datasets with contextual variations, including three different vehicles, with three different drivers, in three different environments, in which there are three different road surface types, in addition to variations in the conservation state and presence of anomalies and obstacles such as potholes and speed bumps. After data collection, these data were used in experiments to evaluate various aspects, such as the influence of the vehicle data collection point, the analysis domain, the model input features, and the data window. Afterwards we evaluated the learning and generalization capacity of the models for unknown contexts. In a third step, the data were used in three Deep Neural Network (DNN) models: LSTM-based, GRU-based, and CNN-based. Through a multi-aspect and multi-contextual analysis, we considered the CNN-based model as the best one, which obtained an average accuracy between the data collection placements of 94.27% for learning and 92.70% for validation, classifying the road surface between asphalt, cobblestone or dirt road segments.
... DTW is a pattern-matching algorithm for measuring the similarity between two temporal sequences, which may vary in time and space [12]. Singh et al. [16] utilized accelerometer sensor data to detect road anomalies and distinguish specific types of anomalies using the DTW technique. Reference templates of potholes and bumps were manually derived from accelerometer data and stored in a template database. ...
Article
Full-text available
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
... Amazon's Mechanical Turk is an online marketplace for finding human resources to solve human intelligence tasks (HIT) for compensation. Waze is a crowdsourcing traffic and navigation application designed for people all over the world to share real-time traffic and road information (Singh, Bansal, Sofat, & Aggarwal, 2017). ...
Article
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The accelerated spread of fake news via the internet and social media such as Facebook and Twitter have created a debate concerning the credibility of sources online. Assessing the credibility of these sources is generally a complex task and cannot solely rely on computer-based algorithms as evaluation still requires human intelligence. The research question guiding this article deals with the conceptualization of a theoretically anchored concept of a participatory and co-creative medium for evaluation of sources online. The concept-driven design research methodology was applied to address the research question, which consisted of seven activities that unify design and theory. The result of this article is a proposed concept that aims to support the assessment of the credibility of sources online using crowdsourcing as an approach for evaluation. The practical implications of the proposed concept could be to constrain the spread of fake news, strengthen online democratic discourse, and potentially improve the quality of online information.
... Accuracy of the proposed road artefact detection systems differ: Nericell [16] uses thresholding with 5-10% of false positive rate. The SmartPatrolling [17] using Dynamic Time Warping is providing 88.89% and 88.66% of accuracy on speed breakers (speed bumps) and potholes respectively. Another techniques accuracy is ranging from 91.43% in the case of the rough road [18], to 94% when Artificial Neural Networks and similar techniques are used [19]. ...
... Whatever system is used to evaluate pavement roughness (through direct or indirect methods), this should be integrated at least with a high-precision global positioning system (GPS) receiver to allow a correct localization and positioning of the measurements on the road [26][27][28][29][30][31][32]. The essential measurement systems necessary for the ride evaluation (threeaxial accelerometer and GPS module), are already available in the modern smartphones where they are suitably integrated and synchronized [33][34][35][36][37][38][39]. For this reason, smartphones have been recently proposed to evaluate road condition over the world using apps with different approaches. ...
Article
Full-text available
Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system (GPS) module, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to indirectly monitor the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was used. Considering 21 km of roads with different levels of pavement decay, validation measurements were performed using the novel sensor, a high performance inertial based navigation sensor, and a road surface profiler. Therefore, comparisons between awz determined with accelerations measured on the two different inertial ensors are made; in addition, also correlations between awz, and typical pavementindicators such as international roughness index, and ride number were also performed. The results showed very good correlations between the awz values calculated with the two inertial devices (R2 = 0.98). In addition, the correlations between awz values and the typical pavement indices showed promising results (R2 = 0.83–0.90). The proposed sensor may be assumed as a reliable and easy-to-install method to assess the pavement conditions in urban road networks, since the use of traditional systems is difficult and/or expensive.
... As an autonomous driving system, the patrolling system is gaining more popularity road detection, pedestrian detection, sound detection, are becoming more to the center of scholars' attention (Xu et al., 2010;Shi et al., 2016;Singh et al., 2017). ...
Article
The volume and availability of data in the Intelligent Transportation System (ITS) result in the need for data-driven approaches. Big Data algorithms are applied to further enhance the intelligence of the applications in the transportation field. Applying Big Data algorithms has increasingly received attention in both the academic and industrial fields of ITS. Big Data algorithms in ITS have a wide range of applications including but not limited to signal recognition, object detection, traffic flow prediction, travel time planning, travel route planning and safety of vehicle and road. This survey aims to provide a bibliography, a comprehensive review of the application of ITS and a review of most recognized models with Big Data used in the context of ITS. 586 papers are reviewed over the period 1997–2019. This study provides a deep insight into applications of Big Data algorithms in ITS, revealing different areas of those applications and integrates models and applications. The result of the study identifies research gaps and direction for the future.
... organizações em suas tomadas de decisão (GUO; JIANG, 2018; VIANNA; GRAEML; PEINADO, 2018).Entre as vantagens do modelo de crowdsourcing está a facilidade no alcance dos indivíduos, considerando que a participação ocorre em plataformas digitais acessíveis por dispositivos smartphone, em muitos casos(SINGH et al., 2017). A facilidade de participar utilizando dispositivos que se encontram à mão também contribui para aumentar a possibilidade desses indivíduos se engajarem por motivações não financeiras, reduzindo o custo envolvido(HOWE, 2006;GRAEML, 2018). ...
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O presente trabalho conduziu uma survey para capturar a importância percebida por discentes de Instituições de Ensino Superior brasileiras, públicas e privadas, sobre os fatores motivacionais que influenciam sua participação em avaliações institucionais, atividade observada à luz do conceito de crowdsourcing. Participaram da pesquisa 383 discentes, de três estados brasileiros e 25 cursos. Foi desenvolvida uma análise fatorial exploratória que definiu oito fatores motivacionais relevantes estatisticamente presentes nas respostas. Em seguida foi conduzida uma comparação entre as médias dos fatores e um teste paramétrico, com o objetivo de verificar a presença, ou não, de diferença da percepção dos fatores motivacionais entre os discentes das IES públicas e os discentes das IES privadas. Também foi conduzida uma análise de conteúdo de 93 respostas a uma questão aberta de resposta opcional, que fazia parte da mesma survey. O fator motivacional “relacionamento/socialização” emergiu como o mais relevante para os discentes de IES privadas, sendo que o fator motivacional “compartilhamento de conhecimento/altruísmo” também se destacou tanto entre os discentes de IES privadas quanto públicas. A análise das respostas à questão aberta evidenciou que os discentes relacionam a avaliação institucional a uma avaliação dos docentes, mais do que à avaliação da instituição.
... • A 3D laser scanning approach [4,5], based on reflected laser pulses to generate accurate surface samples. These samples are then compared to base samples to detect anomalies in the road surface; • A Vision approach [6][7][8][9], based on image processing analysis of already captured images by a camera/video system, which are then processed to automatically detect road anomalies; • A Vibration approach [10][11][12][13][14][15][16][17][18], based on sensing vehicles' vibrations captured by motion sensors like accelerometers and gyroscopes. In reality, a vehicle has different vibration behaviors according to the type of road anomaly. ...
Article
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The need to use roads is vital. In reality, smooth asphalt roads help people in their daily lives by saving time, avoiding traffic, and preserving the means of transportation. Recently, road anomaly detection using smartphone sensors such as accelerometers, gyroscopes, and GPS has become an important topic in the field of Intelligent Transportation Systems (ITS). In this context, many solutions have been proposed using Static Sliding Window (SSW), which is based on fixed window length. However, in the real world, the window length of the anomaly changes according to the speed value and the anomaly width, which is considered as a major drawback of SSW. In this paper, we propose a new technique called Dynamic Sliding Window (DSW), which aims to improve the quality of road anomaly detection by preprocessing the accelerometer signal. The proposed technique is applied to the same dataset and under the same conditions as the SSW. To cover all scenarios, thirty different virtual roads and several types of anomalies (speed bumps, metal bumps, and potholes) were used as training and test data. The resulting outputs of the DSW and SSW have been used by seven heuristic algorithms proposed by previous researchers and seven classifiers based on twelve feature detectors. The obtained results using the proposed DSW have been compared to those obtained using the SSW to demonstrate the efficiency of the former. Indeed, based on the comparison, the proposed DSW has proven its potential to outperform all previous road anomaly detection methods.
... For instance, the authors in [44] employed accelerometer sensors to record the vehicle vibration while driving and used machine learning to assess the road pavement conditions based on the collected data. Likewise, Singh et al. [45] collected the data using the inbuilt sensors of the smartphones (accelerometer and Global Positioning System) and used Dynamic Time Warping (DTW) technique for detecting road surface conditions. The results presented in the study show that the technique DTW worked faster than the machine learning techniques Support Vector Machines and Artificial Neural Networks. ...
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Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses usability of Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26,620 images collected from multiple countries (India, Japan, and the Czech Republic) using smartphones. Thirdly, it proposes models capable of detecting and classifying road damages in more than one country. Lastly, the study provides recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification. A part of the proposed dataset was utilized for Global Road Damage Detection Challenge’2020 and can be accessed at (https://github.com/sekilab/RoadDamageDetector/).
... The threshold-based techniques have fixed threshold values which may change at different types of road surfaces. In order to overcome this limitation of thresholding based techniques, Singh et al. (2017) proposed a method that uses dynamic time warping (DTW) for potholes and speed bump detection and classification. The method is superior to the threshold-based techniques and machine learning models like HMM, SV, and ANN, etc. that need to be trained for achieving comparable classification accuracy. ...
... The threshold-based techniques have fixed threshold values which may change at different types of road surfaces. In order to overcome this limitation of thresholding based techniques, Singh et al. (2017) proposed a method that uses dynamic time warping (DTW) for potholes and speed bump detection and classification. The method is superior to the threshold-based techniques and machine learning models like HMM, SV, and ANN, etc. that need to be trained for achieving comparable classification accuracy. ...
... Dynamic Time Warping (DTW) is a distance measure for time series. First developed for speech recognition [2,3], it has been adopted across a broad spectrum of applications including gesture recognition [4], signature verification [5], shape matching [6], road surface monitoring [7], neuroscience [8] and medical diagnosis [9]. ...
Preprint
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Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDTW) variants, are time series distances with a wide range of applications. They minimize the cost of non-linear alignments between series. CDTW and WDTW have been introduced because DTW is too permissive in its alignments. However, CDTW uses a crude step function, allowing unconstrained flexibility within the window, and none beyond it. WDTW's multiplicative weight is relative to the distances between aligned points along a warped path, rather than being a direct function of the amount of warping that is introduced. In this paper, we introduce Amerced Dynamic Time Warping (ADTW), a new, intuitive, DTW variant that penalizes the act of warping by a fixed additive cost. Like CDTW and WDTW, ADTW constrains the amount of warping. However, it avoids both abrupt discontinuities in the amount of warping allowed and the limitations of a multiplicative penalty. We formally introduce ADTW, prove some of its properties, and discuss its parameterization. We show on a simple example how it can be parameterized to achieve an intuitive outcome, and demonstrate its usefulness on a standard time series classification benchmark. We provide a demonstration application in C++.
... Smartphones are the reference devices for people-centric sensing, however, this paradigm can be extended to include other forms of people-centric sensing, such as vehicular sensing. Vehicular sensing exploits the large set of sensors embedded in vehicles, the personal smartphones of the drivers and passengers, and also ad-hoc sensors (e.g., for environmental monitoring ( [HFZC16]) installed on vehicles to collect data at urban scale ( [A07], [EGHN08], [SBS17], [YDL17]). ...
Preprint
The cyber-physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards a data-centric Internet where the human user becomes more central than ever. We argue that this will profoundly impact primarily on the way data should be handled in the Next Generation Internet. It will require a radical change of the Internet data-management paradigm, from the current platform-centric to a human-centric model. In this paper we present a new paradigm for Internet data management that we name Internet of People (IoP) because it embeds human behavior models in its algorithms. To this end, IoP algorithms exploit quantitative models of the humans' individual and social behavior, from sociology, anthropology, psychology, economics, physics. IoP is not a replacement of the current Internet networking infrastructure, but it exploits legacy Internet services as (reliable) primitives to achieve end-to-end connectivity on a global-scale. In this opinion paper, we first discuss the key features of the IoP paradigm along with the underlying research issues and challenges. Then, we present emerging data-management paradigms that are anticipating IoP.
... The estimated error was in the range of 5.08% to 61.93%. In [24], the authors used accelerometer sensor data to detect anomalies using the DTW method. The method produced an accuracy in the range of 88% and was not sensitive to speed. ...
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A well developed and maintained highway infrastructure is essential for the economic and social prosperity of modern societies. Highway maintenance poses significant challenges pertaining to the ever-increasing ongoing traffic, insufficient budget allocations and lack of resources. Road potholes detection and timely repair is a major contributing factor to sustaining a safe and resilient critical road infrastructure. Current pothole detection methods require laborious manual inspection of roads and lack in terms of accuracy and inference speed. This paper proposes a novel application of Convolutional Neural Networks on accelerometer data for pothole detection. Data is collected using an iOS smartphone installed on the dashboard of a car, running a dedicated application. The experimental results show that the proposed CNN approach has a significant advantage over the existing solutions, with respect to accuracy and computational complexity in pothole detection.
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A well-maintained road network is a crucial factor for sustainable urban development. Over the past few years, researchers have proposed smartphone-based crowdsourced applications as a low-cost effective solution to acquire frequent road surface quality updates. One of the main limitations faced by these applications is that the collected values exhibit significant variations over the conditions under which the road data was collected. This study is an attempt to develop a road roughness monitoring platform using passenger cars that can produce accurate results while reducing the effect of these conditions such as the car type, smartphone model, or its placement. The developed system consists of several features including automatic journey detection, freedom to use any smartphone in any position with or without an active internet connection when collecting data, converging values collected from different sources, and visualizing them in a virtual map. A set of field tests were carried out to evaluate the proposed system based on the road condition, passenger car type, smartphone model, and smartphone placement inside the vehicle. The results show that the proposed solution is effective in predicting accurate values after reducing the effect of these varying factors.
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Advanced models of Artificial Intelligence enable systems of IoT to work with great flexibility to the needs of users. In this article we present our developed IoT system for driving support by the use of type-2 fuzzy logic control module. We have developed the IoT system to collect the data about driving conditions and evaluate them adjusting to the needs of user. Applied module of fuzzy logic of the second type was used in analysis of accelerometers signals to flexibly adjust to uncertainty of evaluation of driving expectations of each driver. Our developed system was tested in different cars by driving on various roads and results show excellent efficiency.
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The use of smartphones to collect roughness measurements or ride quality has become popular in recent years, owing to the potential for substantial cost savings in data collection. Hurdles to widespread adoption of these techniques include the quality, uncertainty, and variability of the measurements. The objective of this paper is to evaluate the multifactor effects of collecting ride quality measurements from smartphones and how crowdsourcing using measurements from a population of different vehicles can be used to overcome or mitigate the effects of less resolute and more variable measurements. This investigation was carried out using two experiments. First, a full factorial design of experiment (DOE) was developed to evaluate the multifactor effects on ride quality measurements using the average rectified slope (ARS). Second, a custom DOE with the objective of analyzing the ARS from a population of vehicles from different classifications was carried out. The results from the first experiment suggested that individual factors contribute to statistical differences in ARS measurements. However, the second experiment showed that when looking into a population of vehicles with randomly sampled factors, the ARS measurements converge, and the statistical analysis showed no significance. This approach can be successfully implemented using a crowdsourcing approach where the focus is to analyze a population of vehicles instead of individual measurements.
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The prevalence of smartphones among vehicle drivers presents exciting opportunities in assessing pavement roughness in a more efficient and cost-effective manner, compared with using conventional instruments. This paper describes the body of knowledge in smartphone-based roughness assessment, reports knowledge gaps and casts light on future research directions. First, a systematic literature search found 192 academic publications in relevant fields. These works were critically reviewed with regard to sensor selection, pre-processing methods, and assessment algorithms. Special attention was given to practical factors that are expected to affect the accuracy and robustness of smartphone-based methods, including data collection speed, vehicle type, smartphone specifications and mounting configuration. Findings from this research are expected to provide a thorough understanding of the potentials and limitations of smartphone-based roughness assessment methods and inform future research and practices in this domain.
Chapter
The foundational technologies for smart cities, such as the proliferation of wireless connectivity among people, mobile devices, and infrastructure, offer a wealth of opportunities for improving the management of public roads. This chapter presents a survey of emerging datasets for urban traffic monitoring. This discussion begins by drawing comparisons with pavement management systems, an area of roadway management that was one of the earliest areas where wide-scale data collection initiated. Traffic monitoring is following a similar development path toward pervasive data collection, but the task is more challenging because of the highly dynamic nature of traffic. Historically, traffic data consisted mainly of counting data with limited deployments of speed sensors. More recently, new datasets have emerged that are transforming the ability of transportation agencies to manage highway systems. In traffic signal systems, the arrival of high-resolution data has provided a means of evaluating the quality of traffic control in richer detail than was before possible. Meanwhile, vehicle tracking has become commonplace with use of automatic vehicle identification (AVI) and automatic vehicle location (AVL), and several tools using such data are now used by many agencies. Another currently emergent type of data consists of vehicle sensor information collected through vehicle telematics. Auto manufacturers have already begun to deploy the equipment in new vehicles and are currently working on aggregating data from these. In future, automated vehicles are likely to require even more intense use of sensors so that the vehicles, their users, and other intelligent systems can react to emerging situations more effectively.
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Analysis data is necessary for bicycle planning, but is hardly available in many municipal-ities. GPS data of cyclists can close this data gap. Existing data sets and research ap-proaches have so far failed to provide evidence of representativeness for the population of cyclists in the respective study area. Moreover, this is often discussed as a weak point of previous work. In order to examine the question of the representativeness of GPS data sets for the Ger-man region, the present study analyses the cycling behaviour of cyclists in the Dresden area. The basis of the analysis is a GPS data set of 200 test persons collected within the framework of the research project "RadVerS", which contains 5,300 individual routes in the study area of the city of Dresden and allows insights into their cycling behaviour. The collected data was processed with different methods, e.g. trips with other traffic modes were removed and trips were divided into individual routes. The route data was then en-riched with data from the traffic network of the study area and statistically analysed. The influence of individual behavioural parameters was evaluated both descriptively and by means of a generalised linear model.
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Road surface hazards affect the driving safety and comfort of road users. Recently, smartphones and mobile devices equipped with motion sensors such as accelerometers and gyroscope sensors have attracted researchers’ attention for the development of low-cost approaches for road surface monitoring. However, processing smartphone sensors to monitor road surface conditions is technically challenging due to dissimilar sensor properties, different smartphone placement, and also different vehicle mechanical properties. This study aimed to develop a hybrid method using threshold based and Machine Learning approaches for near real-time detection and classification of road surface anomalies using smartphone sensor data with higher-level accuracy. The proposed algorithm has self-adapting and self-updating capabilities to adapt itself to any type of smartphone and the dynamic behaviors of various vehicles and road surface conditions. A prototype is developed using MATLAB and ArcGIS to perform sensor data analysis, geocoding, geo-visualizing, and data querying for performance evaluation.
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With the wide availability of smartphone sensing and the Internet connections, mobile crowd sourcing (MCS) has become a promising paradigm for collecting opinions and providing services to the citizens. In the smart city context, crowd data, both in the form of their opinions and in the form of sensing data from their smartphones are very useful for scalable monitoring of resource demand and planning. Since these devices are portable, it is carried by almost every citizen, thus making them ubiquitous. However, the main bottleneck of such crowd intelligence-based services is data validation as opinions can be biased, influenced by some factors not relevant to the problem in focus. So, in this paper, a data validation framework utilizing machine learning techniques for mobile crowdsourcing applications is proposed. It is focused on MCS-based road monitoring applications. The idea of crowdsourced urban area road monitoring application(CURMA) presented in the paper is implemented and results show the crucial need for such data validation frameworks.
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The development of numerical methods and information technologies is occurring at a rapid pace. Hence, the main research question addressed by this work is whether ethics is required for artificial intelligence (AI), and thus (1) ‘Are the existing documents regulating the ethics of AI sufficient?’ and (2) ‘Is there a need to develop new regulations? If so, in what areas?’ The presented investigations involved the use of literature review method, case study, analysis, and synthesis. Based on the analysis of scientific documents and articles, as well as examples related to the use of artificial intelligence, the main and detailed research questions have been answered in the affirmative. Nonetheless, there is a pressing need to develop documentation regulating: military AI use, AI applications in social networks, robot ethics, AI in the automotive industry, or ecology. The presented case studies indicate other unregulated areas: AI applications in medicine or legal provisions on specific issues. The lack of an AI code of ethics may hinder the development of new applications for intelligent devices in the future.
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Dynamic Time Warping (DTW) is a popular similarity measure for aligning and comparing time series. Due to DTW’s high computation time, lower bounds are often employed to screen poor matches. Many alternative lower bounds have been proposed, providing a range of different trade-offs between tightness and computational efficiency. LB_KEOGH provides a useful trade-off in many applications. Two recent lower bounds, LB_IMPROVED and LB_ENHANCED, are substantially tighter than LB_KEOGH. All three have the same worst case computational complexity—linear with respect to series length and constant with respect to window size. We present four new DTW lower bounds in the same complexity class. LB_PETITJEAN is substantially tighter than LB_IMPROVED, with only modest additional computational overhead. LB_WEBB is more efficient than LB_IMPROVED, while often providing a tighter bound. LB_WEBB is always tighter than LB_KEOGH. The parameter free LB_WEBB is usually tighter than LB_ENHANCED. A parameterized variant, LB_Webb_Enhanced, is always tighter than LB_ENHANCED. A further variant, LB_WEBB*, is useful for some constrained distance functions. In extensive experiments, LB_WEBB proves to be very effective for nearest neighbor search.
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IEEE 802.11p is an established standard for Wireless Access in Vehicular Environment (WAVE) to support Intelligent Transportation System (ITS), which is one of the most imperative Vehicular Ad hoc Network (VANET) applications. VANET Vehicle-to-Everything (V2X) communications require On-Board Unit (OBU) and On-Board Diagnostic (OBD) systems to be installed in vehicles. Such systems enhance driver safety by generating in-vehicle alerts but require advanced transportation infrastructure and are high-priced. Cost-effective smartphone-based V2X communication systems have been developed for improving driver and pedestrian safety by generating collision forewarning, traffic congestion alert, road anomalies alert, etc., to prevent road crashes. In this paper, we propose to further reduce the number of accidents by augmenting driver’s awareness about driving behaviors of neighboring drivers and forthcoming unfavorable road conditions. The system creates a vehicular network based on smartphones using Wi-Fi and detects driver behavior and road condition. Novelty of the proposed system lies in disseminating road status and driver behavior (detected considering complete context) to alert the driver in advance using smartphone Wi-Fi instead of special ITS communication infrastructure. Furthermore, an algorithm to ignore invalid beacons that do not contribute to situational awareness has been proposed. Implementation and testing of the system has been done in real time using two vehicles. For large-scale implementation, SUMO and NS-3 simulations have been used. The results indicate the efficacy of the proposed system with successful message dissemination up to 130 m. The proposed system aims at providing the flavor of ITS to developing countries in a cost-effective manner using a mobile smartphone.
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Vibration-based pavement condition (roughness and obvious anomalies) monitoring has been expanding in road engineering. However, the indistinctive transverse cracking has hardly been considered. Therefore, a vehicle-based novel method is proposed for detecting the transverse cracking through signal processing techniques and support vector machine (SVM). The vibration signals of the car traveling on the transverse-cracked and the crack-free sections were subjected to signal processing in time domain, frequency domain and wavelet domain, aiming to find indices that can discriminate vibration signal between the cracked and uncracked section. These indices were used to form 8 SVM models. The model with the highest accuracy and F1-measure was preferred, consisting of features including vehicle speed, range, relative standard deviation, maximum Fourier coefficient, and wavelet coefficient. Therefore, a crack and crack-free classifier was developed. Then its feasibility was investigated by 2292 pavement sections. The detection accuracy and F1-measure are 97.25% and 85.25%, respectively. The cracking detection approach proposed in this paper and the smartphone-based detection method for IRI and other distress may form a comprehensive pavement condition survey system.
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Urban traffic mobile crowd sensing (Urban Traffic MCS) has emerged as a new effective paradigm of sensing and collecting data by means of vehicles equipped with various sensors in urban areas. In an Urban Traffic MCS system, the utility directly reflects the effectiveness of the sensing results, and it is essential to maximize the utility of the collected data. Some studies have shown that utility can be effectively improved by optimizing the selection of sensing nodes. However, most previous research has considered only the coverage and critical links of the road network while neglecting the spatiotemporal characteristics of the traffic flow, although the latter are essential for node selection optimization and significantly impact the utility of Urban Traffic MCS. Therefore, most existing methods are not suitable for Urban Traffic MCS systems. In this paper, a novel node optimization model based on the spatiotemporal characteristics of the road network is proposed. First, we introduce the Urban Traffic MCS system, and dynamic accessibility is introduced to describe the spatiotemporal characteristics of the whole road network. Then, the utility function for Urban Traffic MCS is redefined based on the effective coverage and dynamic accessibility to consider both the topological structure of the road network and the dynamic changes in traffic flow. On this basis, a node selection method with the aim of maximizing the utility of Urban Traffic MCS is proposed. Finally, the results of simulation experiments show that the node selection method in this paper can effectively achieve increased utility for an Urban Traffic MCS system.
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The monitoring of road surface conditions plays a key role in ensuring safety and comfort to the various road users, from pedestrians to drivers. Furthermore, having information on infrastructure quality allows road managers to guarantee an adequate maintenance. These data can be given and used at the same time by users, by means of mobile devices, widespread in Italy and in the World. The main aim of this paper is to realize a simple application, which can be installed on several devices (smartphone/tablet), that allows to use sensors piggybacked on them in order to monitor road surface quality. Experimental tests are carried out on urban roads of Calabria (Italy) to validate and test the mobile application. In particular, the accelerometer is used for detecting surface conditions, in terms of potholes and bumps. The Global Positioning System (GPS) is employed to know in real time the location of vehicles and of road surface anomalies. Five different devices were used, all placed in a test vehicle in three different placement conditions. For the validation process, only one device was used, completely bound in an utilitarian car. The algorithm developed to detect road bumps and potholes is based on the analysis of the acceleration signal in terms of high-energy events; three filters are applied on the original signal. Moreover, verification of the rate of false detections and undetected road anomalies is planned, using georeferenced photos that allow the correct localization on the map and the assessment of the correspondence between the elements, detected with the accelerometer, and real road conditions. The results obtained show that our application could be used as an useful automated sensing system for road quality.
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The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.
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This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastruc- ture using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of train- ing data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully de- tect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.
Conference Paper
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We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
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The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a non-linear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection. The technique has flourished, particularly in the last three years, and has been applied to a variety of problems in various disciplines. In spite of DTW's great success, there are still several persistent "myths" about it. These myths have caused confusion and led to much wasted research effort. In this work, we will dispel these myths with the most comprehensive set of time series experiments ever conducted.
Article
The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures. We evaluate uWave using a large gesture library with over 4000 samples for eight gesture patterns collected from eight users over one month. uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. We also present applications of uWave in gesture-based user authentication and interaction with 3D mobile user interfaces. In particular, we report a series of user studies that evaluates the feasibility and usability of lightweight user authentication. Our evaluation shows both the strength and limitations of gesture-based user authentication.
Conference Paper
Monitoring road and traffic conditions in a city is a problem widely studied. Several methods have been proposed towards addressing this problem. Several proposed techniques require dedicated hardware such as GPS devices and accelerometers in vehicles [7][15][8] or cameras on roadside and near traffic signals [13]. All such methods are expensive in terms of monetary cost and human effort required. We propose Wolverine1 - a non-intrusive method that uses sensors present on smartphones. We extend a prior study [12] to improve the algorithm based on using accelerometer, GPS and magnetometer sensor readings for traffic and road conditions detection. We are specifically interested in identifying braking events - frequent braking indicates congested traffic conditions - and bumps on the roads to characterize the type of road. We evaluate the effectiveness of the proposed method based on experiments conducted on the roads in Mumbai, with promising results.
Conference Paper
Dynamic Time Warping (DTW) is a kind of simple and efficient method in speech identification. The human pronounces with variable pitch period at different time. Speech parameter extraction with fixed-length window will cause error discrimination to some extent because of different signal period in windows. Sliding windows with variable length is to select sliding windows with different length according to the variation of the pitch period of the speech signal in order that each window contains speech signals with identical period.Speech segmentation via sliding windows with variable length can eliminate the errors caused by the difference of speech signals contained in each window. Experimental results verify the improvement on the precision of speech identification.
Gap Trap: A pothole detection and reporting system utilizing mobile devices
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