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This work was funded by the Ministry of Science and Innovation through CDTI through the Ayudas Cervera para Centros Tecnologicos grant of the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project EGIDA under Grant CER-20191012, and in part by the Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (ERDF) funds under Project TIN2017-83494-R., Network Security Monitoring (NSM) is a popular term to refer to the detection of security incidents by monitoring the network events. An NSM system is central for the security of current networks, given the escalation in sophistication of cyberwarfare. In this paper, we review the state-of-the-art in NSM, and derive a new taxonomy of the functionalities and modules in an NSM system. This taxonomy is useful to assess current NSM deployments and tools for both researchers and practitioners. We organize a list of popular tools according to this new taxonomy, and identify challenges in the application of NSM in modern network deployments, like Software Defined Network (SDN) and Internet of Things (IoT)., Ministry of Science and Innovation through CDTI through the Ayudas Cervera para Centros Tecnologicos grant of the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project EGIDA CER-20191012, Spanish Ministry of Economy and Competitiveness, European Regional Development Fund (ERDF) funds TIN2017-83494-R

The timely and accurate detection of traffic incidents is beneficial to reduce associated economic losses and avoid secondary crashes. Inspired by the impressive success of the image classification algorithms, especially convolutional neural networks (CNNs), this study proposes a novel framework to detect highway traffic incidents by learning the traffic state as images. In such a framework, the probe vehicles equipped with the global positioning system equipment are used to obtain data. The Gramian Angular Difference Fields and Piecewise Aggregation Approximation algorithms are used to convert the link speed time series data into images. CNNs can extract the traffic features based on these images and consider an incident detection problem as a binary classification task. Further, the effectiveness of the proposed framework is evaluated by applying it to detect the traffic in a real-world environment, i.e., the Guangzhou Airport Expressway. The results illustrate that the proposed model outperforms several other algorithms with respect to almost all the performance indexes, including the detection rate with different false alarm rates and the area under the receiver operating characteristic curve.

As a component of intelligent transport systems (ITS), vehicular ad hoc network (VANET), which is a subform of manet, has been identified. It is established on the roads based on available vehicles and supporting road infrastructure, such as base stations. An accident can be defined as any activity in the environment that may be harmful to human life or dangerous to human life. In terms of early detection, and broadcast delay. VANET has shown various problems. The available technologies for incident detection and the corresponding algorithms for processing. The present problem and challenges of incident detection in VANET technology are discussed in this paper. The paper also reviews the recently proposed methods for early incident techniques and studies them.

This research paper investigates a hybrid model using logistic regression with a wavelet-based feature extraction for detecting traffic incidents. A logistic regression model is suitable when the outcome can take only a limited number of values. For traffic incident detection, the outcome is limited to only two values, the presence or absence of an incident. The logistic regression model used in this study is a generalized linear model (GLM) with a binomial response and a logit link function. This paper presents a framework to use logistic regression and wavelet-based feature extraction for traffic incident detection. It investigates the effect of preprocessing data on the performance of incident detection models. Results of this study indicate that logistic regression along with wavelet based feature extraction can be used effectively for incident detection by balancing the incident detection rate and the false alarm rate according to need. Logistic regression on raw data resulted in a maximum detection rate of 95.4% at the cost of 14.5% false alarm rate. Whereas the hybrid model achieved a maximum detection rate of 98.78% at the expense of 6.5% false alarm rate. Results indicate that the proposed approach is practical and efficient; with future improvements in the proposed technique, it will make an effective tool for traffic incident detection.

The variable speed limit (VSL) system has been implemented on many freeways and urban arterials as a link control strategy. Objectives for deploying VSL systems may include giving advance warning, stabilize and smooth traffic (i.e. harmonization). Accuracy and reliability of the messages displayed by VSL systems can “make or break” its successful implementation. The aim of this paper was to present methods for evaluating VSL systems. As VSL systems may be implemented with different control strategies, an approach for assessing different features of the system independently was adopted. In this way, systems with different control strategies can be evaluated. Methods for assessing incident detection, warning and harmonization abilities were presented. A case study on the VSL system on the Autobahn A99 near Munich, Germany was also discussed.

Video based sensor systems can play a key role in delivering data for better road planning and traffic management. Smart road technologies will largely depend on data quality and quantity in the future. Video based detection systems, being an indispensable part of intelligent traffic systems (ITS), show huge potentials as they do not only offer a flexible way of data acquisition but are also being developed at a huge pace due to recent evolutions in hardware and software technology. In order to give a better understanding on the methods and potentials of this technology, a structured review is presented which not only includes current applications but also shows future use cases by analyzing the techniques of image processing and extrapolating their results to the future requirements of traffic engineering.

This paper presents an expert system for detecting traffic congestion and incidents from real-time GPS data collected from GPS trackers or drivers’ smartphones. First, GPS traces are pre-processed and placed in the road map. Then, the system assigns to each road segment of the map a traffic state based on the speeds of the vehicles. Finally, it sends to the users traffic alerts based on a spatiotemporal analysis of the classified segments. Each traffic alert contains the affected area, a traffic state (e.g., incident, slowed traffic, blocked traffic), and the estimated velocity of vehicles in the area. The proposed system is intended to be a valuable support tool in traffic management for municipalities and citizens. The information produced by the system can be successfully employed to adopt actions for improving the city mobility, e.g., regulate vehicular traffic, or can be exploited by the users, who may spontaneously decide to modify their path in order to avoid the traffic jam. The elaboration performed by the expert system is independent of the context (urban o non-urban) and may be directly employed in several city road networks with almost no change of the system parameters, and without the need for a learning process or historical data. The experimental analysis was performed using a combination of simulated GPS data and real GPS data from the city of Pisa. The results on incidents show a detection rate of 91.6%, and an average detection time lower than 7 min. Regarding congestion, we show how the system is able to recognize different levels of congestion depending on different road use.

We present a system to detect incidents causing traffic congestion in the road network by analyzing real-time GPS data. These data are collected from tracking devices installed in the vehicles or from drivers' smartphones. After positioning the GPS coordinates on the road map, the system assigns a traffic state to each road segment based on the velocities of vehicles, and generates alerts for incident based on a spatiotemporal analysis of these states. The system is validated by using GPS data simulated in typical traffic conditions in the city of Pisa, Italy. The results show an incident detection rate of 91.6% and an average detection time shorter than 7 minutes.

In the current Intelligent Transportation Systems (ITS) traffic monitoring and incident detection are usually supported with mostly traditional and relatively slow reactivity technologies. In this paper we propose a new service, namely THOR (Traffic monitoring Hybrid ORiented service), able to combine two different wireless technologies and to provide real time information about vehicular traffic monitoring and incident detection. THOR relies on LTE (Long Term Evolution) and Dedicated Short Range Communication based VANETs (Vehicular ad-hoc NETworks) in a hybrid approach, which is compliant with ITS standards. This hybrid networking approach can be deployed today and can be ready for tomorrow VANET technology. We test THOR by simulations in a scenario with vehicle flows synthesized from real measured vehicular traffic traces. We provide an LTE load analysis and an assessment of incident detection capabilities. Our results are promising in terms of reactivity, precision and network traffic load sustainability.

One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.

This paper presents a video-based approach to detect the presence of parked vehicles in street lanes. Potential applications include the detection of illegally and double-parked vehicles in urban scenarios and incident detection on roads. The technique extracts information from low-level feature points (Harris corners) to create spatiotemporal maps that describe what is happening in the scene. The method neither relies on background subtraction nor performs any form of object tracking. The system has been evaluated using private and public data sets and has proven to be robust against common difficulties found in closed-circuit television video, such as varying illumination, camera vibration, the presence of momentary occlusion by other vehicles, and high noise levels. © 2011 IEEE., This work was supported by the Spanish Government project Movilidad y automocion en Redes de Transporte Avanzadas (MARTA) under the Consorcios Estrategicos Nacionales de Investigacion Tecnologica (CENIT) program and the Comision Interministerial Ciencia Y Tecnologia (CICYT) under Contract TEC2009-09146. The Associate Editor for this paper was R. W. Goudy.

Three terahertz (THz) GaAs/AlGaAs quantum-well photodetectors with different 1-D metal gratings are fabricated for front-incident detection of THz waves. Photocurrent spectra are acquired and compared with 45 ° incident facet samples (without grating), and peak responsivities are determined with a calibrated blackbody radiation source. The results show that these gratings can couple THz waves into detectors effectively, resulting in good detector responsivities. The modal method is employed to simulate the light coupling efficiency and the optimization conditions of the gratings. © 2011 IEEE.

The problem of traffic incident detection can be viewed as a pattern recognition problem. Neural networks are known to solve pattern recognition problems effectively, especially when there is no mathematical model. The computational complexity of neural network algorithms, however, increases exponentially with an increase in the size of the network. Furthermore, with an increase in the size of the network, the size of the training set has to be increased exponentially in order to achieve the same level of accuracy. To overcome this double exponential complexity a hybrid feature extraction algorithm and neural network architecture is created specifically for automatic detection of traffic incidents. The upsteam and downstream traffic data are first filtered by the discrete wavelet transform. Then, a linear discriminant network is used for feature extraction. Finally, the adaptive conjugate gradient learning algorithm of Adeli and Hung is used to train the network.

While there are many examples of Intelligent Transport System deployments in Poland, more attention should be paid to traffic incident management and detection on dual-carriageways and urban street networks. One of the aims of CIVITAS DYN@MO, a European Union funded project, is to use TRISTAR (an Urban Transport Management System) detection modules to detect incidents at junctions equipped with traffic signals. First part of paper provides an overview of urban incident detection methods and algorithms. Second part of paper describes how the TRISTAR system infrastructure and software are currently used for detecting incidents on urban artery sections (with higher speed limit). Because the need to detect incidents on other arteries was identified, research were undertaken that will lead to the development of algorithms for the detection of incidents on the streets equipped with traffic signals. The initial results of simulation studies (using VISSIM software) are presented in the third part of the paper. Their objective was to initially select parameters for detecting incidents at junctions equipped with traffic signals. Further research will look at a fusion of variables and possible other variables that may develop the algorithms.

International audience; Detecting dangerous situations is crucial for emergency management. Surveillance systems detect dangerous situations by analyzing crowd dynamics. This paper presents a holis-tic video-based approach for privacy-preserving crowd density estimation. Our experimental approach leverages distributed , on-board pre-processing, allowing privacy as well as the use of low-power, low-throughput wireless communications to interconnect cameras. We developed a multi-camera grid-based people counting algorithm which provides the density per cell for an overall view on the monitored area. This view comes from a merger of infrared and Kinect camera data. We describe our approach using a layered model for data aggregation and abstraction together with a work-flow model for the involved software components, focusing on their functionality. The power of our approach is illustrated through the real-world experiment that we carried out at the Schönefeld airport in the city of Berlin.

Safety on roads is of uttermost importance, especially in the context of autonomous vehicles. A critical need is to detect and communicate disruptive incidents early and effectively. In this paper we propose a system based on an off-the-shelf deep neural network architecture that is able to detect and recognize types of unsigned (non-placarded, such as traffic signs), physical (visible in images) road incidents. We develop a taxonomy for unsigned physical incidents to provide a means of organizing and grouping related incidents. After selecting eight target types of incidents, we collect a dataset of twelve thousand images gathered from publicly-available web sources. We subsequently fine-tune a convolutional neural network to recognize the eight types of road incidents. The proposed model is able to recognize incidents with a high level of accuracy (higher than 90%). We further show that while our system generalizes well across spatial context by training a classifier on geostratified data in the United Kingdom (with an accuracy of over 90%), the translation to visually less similar environments requires spatially distributed data collection. Note: this is a pre-print version of work accepted in IEEE Transactions on Intelligent Vehicles (T-IV;in press). The paper is currently in production, and the DOI link will be added soon., Comment: Preprint to T-IV paper