Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds

Automatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper, we propose a data-driven AID framework that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate a robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making it feasible to implement the framework over large networks. Our study shows that such robust thresholds can improve incident detection performance significantly compared to traditional threshold determination. Second, we leverage the knowledge of the topology of the road network to construct threshold heatmaps and perform image denoising to obtain spatio-temporally denoised thresholds. We used two image denoising techniques, bilateral filtering and total variation for this purpose. Our study shows that overall AID performance can be improved significantly using bilateral filter denoising compared to the noisy thresholds or thresholds obtained using total variation denoising

Contribución al análisis de datos de sensores en el ámbito de ciudad inteligente

Este trabajo se enmarca dentro del vasto contexto de Ciudades Inteligentes, y se centra en el área de la conducción inteligente de vehículos, tanto en zonas urbanas como interurbanas, mediante la recogida de datos en tiempo real, medidos con sensores, por parte de los propios conductores, así como de datos capturados mediante simulación. El objetivo de este trabajo es doble. Por un lado, el estudio y aplicación de las diferentes técnicas y métodos de detección de valores atípicos en bases de datos multivariantes, además de una comparativa entre ellos mediante las pruebas llevadas a cabo con datos de tráfico real. Y por otro lado, establecer una relación entre las situaciones anómalas de tráfico, como puedan ser atascos o accidentes, con los valores atípicos multivariantes encontrados. La detección de valores atípicos representa una de las tareas más importantes a la hora de realizar cualquier análisis de datos, sea cual sea el dominio o área de estudio, ya que entre sus funciones primordiales se encuentra el descubrir información útil, que resulta de gran valor, y que por lo general queda oculta por la alta dimensión de los datos. Con el uso de mecanismos de detección de valores atípicos junto con métodos de clasificación supervisada, se va a poder llevar a cabo el reconocimiento de elementos de la infraestructura vial urbana como pueden ser rotondas, pasos de cebra, cruces o semáforos.This work is related to the Smart Cities context, and it focuses on the area of intelligent vehicle driving, both in urban and interurban areas, through the collection of real-time sensed data by the drivers themselves, as well as data collected in a simulator. The goal of this paper is twofold. On the one hand, the study and application of the different techniques and methods of outliers detection in multivariate databases, as well as a comparison between them through the tests carried out with real traffic data. And on the other hand, to establish a relation between anomalous traffic situations, such as traffic jams or accidents, with the multivariate outliers found. Outliers detection represents one of the most important tasks when performing any data analysis, regardless of the domain or area of study, since among its fundamental functions is to discover useful and valuable information that usually is hidden by the high dimensionality of the data. By means of using outliers detection mechanisms together with data classification methods, the recognition of elements of urban infrastructure such as roundabouts, zebra crossing or traffic lights will be carried out.Este trabajo ha sido financiado por el Ministerio de Economía, Industria y Competitividad, con la ayuda FPI: BES-2014-070462Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Natividad Martínez Madrid.- Secretario: Jesús Arias Fisteus.- Vocal: Norberto Fernández Garcí

Freeway traffic incident detection using large scale traffic data and cameras

Automatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper, a data-driven AID framework is proposed that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate a robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making it feasible to implement the framework over large networks. Our study shows that such robust thresholds can improve incident detection performance significantly compared to traditional threshold determination. Second, we leverage the knowledge of the topology of the road network to construct threshold heatmaps and perform image denoising to obtain spatio-temporally denoised thresholds. We used two image denoising techniques, bilateral filtering and total variation for this purpose. Our study shows that overall AID performance can be improved significantly using bilateral filter denoising compared to the noisy thresholds or thresholds obtained using total variation denoising. The second research objective involved detecting traffic congestion from camera images. Two modern deep learning techniques, the traditional deep convolutional neural network (DCNN) and you only look once (YOLO) models, were used to detect traffic congestion from camera images. A shallow model, support vector machine (SVM) was also used for comparison and to determine the improvements that might be obtained using costly GPU techniques. The YOLO model achieved the highest accuracy of 91.2%, followed by the DCNN model with an accuracy of 90.2%; 85% of images were correctly classified by the SVM model. Congestion regions located far away from the camera, single-lane blockages, and glare issues were found to affect the accuracy of the models. Sensitivity analysis showed that all of the algorithms were found to perform well in daytime conditions, but nighttime conditions were found to affect the accuracy of the vision system. However, for all conditions, the areas under the curve (AUCs) were found to be greater than 0.9 for the deep models. This result shows that the models performed well in challenging conditions as well. The third and final part of this study aimed at detecting traffic incidents from CCTV videos. We approached the incident detection problem using trajectory-based approach for non-congested conditions and pixel-based approach for congested conditions. Typically, incident detection from cameras has been approached using either supervised or unsupervised algorithms. A major hindrance in the application of supervised techniques for incident detection is the lack of a sufficient number of incident videos and the labor-intensive, costly annotation tasks involved in the preparation of a labeled dataset. In this study, we approached the incident detection problem using semi-supervised techniques. Maximum likelihood estimation-based contrastive pessimistic likelihood estimation (CPLE) was used for trajectory classification and identification of incident trajectories. Vehicle detection was performed using state-of-the-art deep learning-based YOLOv3, and simple online real-time tracking (SORT) was used for tracking. Results showed that CPLE-based trajectory classification outperformed the traditional semi-supervised techniques (self learning and label spreading) and its supervised counterpart by a significant margin. For pixel-based incident detection, we used a novel Histogram of Optical Flow Magnitude (HOFM) feature descriptor to detect incident vehicles using SVM classifier based on all vehicles detected by YOLOv3 object detector. We show in this study that this approach can handle both congested and non-congested conditions. However, trajectory-based approach works considerably faster (45 fps compared to 1.4 fps) and also achieves better accuracy compared to pixel-based approach for non-congested conditions. Therefore, for optimal resource usage, trajectory-based approach can be used for non-congested traffic conditions while for congested conditions, pixel-based approach can be used