Types Of Traffic Data Research Articles

MRFM: A timely detection method for DDoS attacks in IoT with multidimensional reconstruction and function mapping

To address the slow response time of existing detection modules to the Internet of Things (IoT) Distributed Denial of Service (DDoS) attacks, along with their low feature differentiation and poor detection performance, we propose MRFM, a timely detection method with multidimensional reconstruction and function mapping. Firstly, we employ a queue mechanism to capture and store incoming network traffic data within a predefined time frame. Subsequently, we introduce a multidimensional reconstruction neural network model, specifically designed to reconstruct quantitative features based on their respective indices by adjusting the loss function. This process is followed by the computation of multidimensional reconstruction errors and the transformation of vectors into mapping features, thereby augmenting the disparities among various types of traffic data and promoting the similarity within the same category of traffic data. Lastly, we extract frequency information from the qualitative feature matrix using information entropy calculations, enriching the feature profile of individual traffic instances. The experimental results on two benchmark datasets show that MRFM can effectively detect different types of DDoS attacks. Notably, MRFM consistently outperforms other existing methods, exhibiting an average metric improvement of up to 9.61 %.

Identification technique of cryptomining behavior based on traffic features

Recently, the growth of blockchain technology and the economic benefits of cryptocurrencies have led to a proliferation of malicious cryptomining activities on the internet, resulting in significant losses for companies and institutions. Therefore, accurately detecting and identifying these behaviors has become essential. To address low accuracy in detecting and identifying cryptomining behaviors in encrypted traffic, a technique for identifying cryptomining behavior traffic is proposed. This technique is based on the time series characteristics of network traffic and introduces the feature of long-range dependence, and the recognition effect is not easily affected by the encryption algorithm. First, 48-dimensional features are extracted from the network traffic using statistical methods and the rescaled range method, of which 47 dimensions are statistical features and 1 dimension is a long-range dependence feature. Second, because there is much less cryptomining traffic information than normal network traffic information in the dataset, the dataset is processed using oversampling to make the two types of traffic data balanced. Finally, a random forest model is used to identify the type of traffic based on its features. Experiments demonstrate that this approach achieves good detection performance and provides an effective solution for identifying encrypted network traffic with malicious cryptomining behavior. The long-range dependence features introduced therein together with the statistical features describe a more comprehensive flow characteristics, and the preprocessing of the dataset improves the performance of the identification model.

Establishing the relationship between crashes and unsafe driver behaviors in motorway segments

Surrogate safety analysis is an alternative approach to crash-based analysis for the assessment of traffic safety. Surrogate safety analysis relies in various data types, and it is well admitted that the advancement of technology has enabled the recording of various types of traffic data (e.g., vehicle trajectories) that can be used for safety analysis. However, it is first needed to ensure that these data are correlated with crash data and so, can be used as a proxy of crash data. This study develops crash prediction models for motorway segments using harsh braking and harsh acceleration, and speeding data recorded via a smartphone app. The models indicate that harsh acceleration events are a good predictor of average crash frequency.

Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers’ clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles’ abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle’s actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations.

Short-Term Traffic Flow Prediction with Structure-Optimized Deep Belief Network

This paper proposes a structure-optimized deep belief network method for short-term traffic flow forecast, which is used to solve the problems of too simple training data in deep learning short-term traffic flow forecast and random selection of model structure construction parameters. We constructed a deep belief network short-term traffic flow forecast model that can simultaneously train three types of traffic data related to the predicted node traffic volume, enhance the spatiotemporal correlation of predictions, and overcome the shortcomings of too single training data. At the same time, we optimize the short-term traffic flow prediction model structure of the deep belief network; and use the T-PSO algorithm to optimize the hidden layer structure parameters of the prediction model. It can avoid the decrease in the practicability of the model caused by the random selection of the model structure construction parameters. The experimental results show that the method of structure-optimized deep belief network is feasible and effective, and the prediction accuracy is better than the classic deep learning prediction model.

A multi-channel anomaly detection method with feature selection and multi-scale analysis

This paper proposes a novel multi-channel network traffic anomaly detection method combined with the idea of multi-scale decomposition, feature selection and fusion. Our method includes a feature selection and fusion module, which extracts the most important features from redundancy traffic features and fuses the selected features, a multi-scale decomposition module, and a multi-channel Generalized Likelihood Ratio Test (GLRT) detection module, for anomaly detection and decision-making. The advantage of our method is that, first, it only considers the selected features and reduces the amount of computation. Second, compared with traditional anomaly detection methods that usually work on each scale independently thus mainly focused on temporally correlated traffic, our method fully explores the internal frequency–time correlations within multiple scales. It can be verified with experiments that this method performs better than other traditional methods, thus gives a new sight on the anomaly detection with different types of traffic data.

Statistical and Nature-Inspired Modeling of Vehicle Flows by Using Finite Mixtures of Simple Circular Normal Distributions

The representation, visualization, and modeling of traffic data is at the heart of intelligent transportation systems. Different types of traffic data exist, and novel ways of their accurate representation and modeling, which are useful for further analyses, simulations, and optimizations, are sought. In this work, location-specific traffic flows are represented by finite mixtures of circular normal (von Mises) statistical distributions. The parameters of the distributions are learned from empirical data by two variants of the expectation-maximization (EM) algorithm and by a nature-inspired method, differential evolution (DE). A proposed statistical model and a fitting strategy are evaluated on real-world data sets describing traffic flows in New York City. The experimental results show that the EM algorithm is able to find model parameters that correspond to input data and that are better than their analytic estimates, while DE evolves even more accurate models. The models based on circular distributions can be represented by circular plots as a novel type of visually appealing and easily interpretable fingerprints of the underlying traffic flow patterns.

A Novel Network Traffic Prediction Method Based on RMPM

The network traffic prediction plays the key role in congestion control and bandwidth allocation. A variety of traditional learning models such as artificial neural networks (ANN) have been applied in prediction. To avoid the drawbacks of traditional models for prediction, a novel robust minimax probability machine (RMPM)-based traffic prediction method is proposed in this paper. The prediction performance is tested on two different types of traffic data, Ethernet data flow and MPEG4 video flow, at the timescale 1. The experiments demonstrate that the proposed method attains satisfactory performance in prediction accuracy. Therefore, the proposed method can be used for congestion control or bandwidth allocation, to meet the user QOS requirements.

Traffic speed prediction for intelligent transportation system based on a deep feature fusion model

Currently, many types of traffic data from different advanced data collection techniques are available. Plenty of effort has been spent to take full advantage of the heterogeneous data to enhance the prediction accuracy of the model in the advanced travel information system. The objective of this study is to build a deep feature fusion model to predict space–mean–speed using heterogeneous data. The temporal and spatial features are defined as the raw input which are extracted and trained by stacked autoencoders in the first step. Then, the extracted representative features from the data are fused. Finally, prediction models can be developed to capture the correlations. Therefore, the prediction model can consider both temporal–spatial correlation and correlation of heterogeneous data. Using the real-world data, some machine learning models including artificial neural network, support vector regression, regression tree and k-nearest neighbor are implemented and compared. The best result can be obtained when deep feature fusion model and support vector regression are jointly applied. Moreover, we compare the new proposed deep feature–level fusion method with the widely used data–level fusion method. The results indicate that proposed deep feature fusion model can achieve a better performance.

Multi-view Low Rank Representation for Multi-Source Traffic Data Completion

Intelligent Transportation System (ITS) has been widely applied in major cities to relieve congestion and decrease accidents. However, the hardware failure of detectors or transformation failure of data cause data loss, which seriously decreases the performance of ITS. How to ensure the completeness of observed traffic data becomes is a current key problem. Recently, the low rank constraint which can exploit the global relation hidden in data has been successfully used in matrix completion, such as the classic robust principal component analysis (RPCA) and its variants. The spatio-temporal correlation among traffic data make traffic data contain low rank property; therefore, we naturally apply the low rank constraint on traffic data completion. In addition, most traffic detectors installed on the road can collect various types of traffic data, so-called multi-source traffic data. Due to describing the same traffic condition, these various type of traffic data usually have similar intrinsic structure. Therefore, we consider fuse these various type of traffic data to complete the missing data. In this paper, we propose multi-view low-rank representation model for multi-source data completion and provide an efficient optimization algorithm. To variety the performance of the proposed method, some traditional traffic data completion methods are compared with our method on a highway microwave dataset. The experimental results show that our proposed method is obviously superior to other state-of-the-art traffic data completion methods.

A Multiple Regression Approach for Traffic Flow Estimation

Traffic flow information is of great importance for transport planning and related research. The conventional methods of automated data collection, such as annual average daily traffic (AADT) data, are often restricted by limited installation, while the state-of-the-art sensing technologies (e.g., GPS) only reflect some types of traffic flow (e.g., taxi and bus). Complete coverage of traffic flow is still lacking, thus demanding a rigorous estimation model. Most studies dedicated to estimating the traffic flow of the entire road network rely on single to only a few properties of the road network and the results may not be promising. This paper presents an idea of integrating five topological measures and road length to estimate traffic flow based on a multiple regression approach. An empirical study in Hong Kong has been conducted with three types of traffic datasets, namely floating car, public transport route, and AADT. Six measures, namely degree, betweenness, closeness, page rank, clustering coefficient, and road length, are used for traffic flow estimation. It is found that each measure correlates differently for the three types of traffic data. Multiple regression approach is then conducted, including multiple linear regression and random forest. The results show that a combination of various topological and geometrical measures has proved to have a better performance in estimating traffic flow than that of a single measure. This paper is especially helpful for transport planners to estimate traffic flow based on correlation available but limited flow data with road network characteristics.

Visual analytics of cellular signaling data

Cellular signaling data is a type of traffic data, which contains rich spatio-temporal information. Rather than studying the trajectories of individuals, we propose a visual analytics methodology to analyze the crowd flows among a geographical network extracted from real-time cellular signaling data. We design a suite of visualization techniques to explore and reveal mobility patterns over the networks of spatiotemporal clustering. The feasibility of our approach was verified on a real real-time cellular signaling dataset in one week.

Automatic incident classification for large-scale traffic data by adaptive boosting SVM

Modern cities experience heavy traffic flows and congestions regularly across space and time. Monitoring traffic situations becomes an important challenge for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it is helpful to automatically detect and classify different traffic incidents such as severity of congestion, abnormal driving pattern, abrupt or illegal stop on road, etc. Although most TCSS are equipped with basic incident detection algorithms, they are however crude to be really useful as an automated tool for further classification. In literature, there is a lack of research for Automated Incident Classification (AIC). Therefore, a novel AIC method is proposed in this paper to tackle such challenges. In the proposed method, traffic signals are firstly extracted from captured videos and converted as spatial-temporal (ST) signals. Based on the characteristics of the ST signals, a set of realistic simulation data are generated to construct an extended large-scale traffic database to cover a variety of traffic situations. Next, a local mean filter is introduced to suppress the effect of noise and extract significant features from the ST signals. The extracted features are then associated with various types of traffic data: one normal type (inliers) and multiple abnormal types (outliers). For the classification, an adaptive boosting classifier is trained to detect outliers in traffic data automatically. Further, a Support Vector Machine (SVM) based method is adopted to train the model for identifying the categories of outliers. In short, this hybrid approach is called an Adaptive Boosting Support Vector Machines (AB-SVM) method. Experimental results show that the proposed AB-SVM method achieves a satisfied result with more than 92% classification accuracy on average.

Evaluation of detection sensitivity and count performance of advanced vehicle detection technologies at a signalized intersection

ABSTRACTThis article evaluates the stop-bar detection and count performance of three advanced vehicle detection sensors under various environmental conditions at a signalized intersection. Continuing advancements to vehicle detection technologies and improvements to their detection capabilities to overcome issues from impacting conditions necessitates testing the performance of new and upgraded sensor products to evaluate their performance and identify the most suitable products for various climates and weather conditions. The three evaluated sensors were Autoscope Encore video, Iteris Vantage Edge 2 video, and Wavetronix SmartSensor Matrix microwave sensors. The three sensors performed with high detection sensitivity during ideal environmental conditions with up to 99.9% detection accuracy levels and are suitable for traffic monitoring centers that rely on remote access to the monitored sites and the collected data. However, they were affected by some extreme adverse weather conditions, mainly daytime and nighttime snow, daytime fog, dawn lighting, and strong winds (for high mounted devices). The selection of a sensor product will depend on the type of application and the priority given to the type of traffic data being collected. Overall, the Iteris video sensor performed with the highest detection sensitivity levels, with the Wavetronix Matrix microwave sensor performing similarly under most conditions (14 of 19 evaluated conditions). Autoscope video provided the highest count accuracies and also provides a much broader data collection capability. The results of this study will help transportation agencies in selecting suitable vehicle detection sensor technologies for future installations within their jurisdiction and improved data collection.

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.

Development of the Nowcast Traffic Simulation System for Road Traffic in Urban Areas

This paper describes the development of an on-line traffic simulation system called the “Nowcast Traffic Simulation System” for road traffic in urban areas. The system can reproduce the traffic situation based on various types of traffic data provided in real time such as floating car data and traffic sensor data. The output data is used for several traffic information services, including CO₂ monitoring. In this paper, the authors explain the concept of the Nowcast Traffic Simulation System and its theoretical framework followed by an example of a demonstration experiment in Kashiwa City in Japan.

Statistical Process Control‐Based Intrusion Detection and Monitoring

Intrusion detection systems have a vital role in protecting computer networks and information systems. In this article, we applied a statistical process control (SPC)–monitoring concept to a certain type of traffic data to detect a network intrusion. We proposed an SPC‐based intrusion detection process and described it and the source and the preparation of data used in this article. We extracted sample data sets that represent various situations, calculated event intensities for each situation, and stored these sample data sets in the data repository for use in future research. This article applies SPC charting methods for intrusion detection. In particular, it uses the basic security module host audit data from the MIT Lincoln Laboratory and applies the Shewhart chart, the cumulative sum chart, and the exponential weighted moving average chart to detect a denial of service intrusion attack. The case study shows that these SPC techniques are useful for detecting and monitoring intrusions. Copyright © 2013 John Wiley & Sons, Ltd.

A Kalman Filter Approach for Exploiting Bluetooth Traffic Data When Estimating Time-Dependent OD Matrices

Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.

THE DEVELOPMENT OF THE ROUTE GUIDANCE SYSTEM AND THE REAL-TIME INTEGRATED TRAFFIC INFORMATION SYSTEM (RITIS) FOR LARGE CITIES IN INDONESIA

The paper is written based on research on 'Dynamic Origin-Destination Matrices From Real-Time Traffic Count Information'. The latest development in automatic traffic count data collection enables us to obtain the traffic count information in a real time or short- time-interval basis. For example, ATCS (Area Traffic Control System) already installed in several large cities in Indonesia, such as: DKI-Jakarta (since 1994), Bandung (since 1997), and Surabaya (since 1998) provided us the real-time or short-time-interval traffic count information for all signalised intersections. This traffic data is updated periodically in a short- time-interval basis (e.g. 5, 15, or 30 minutes time interval). This information is provided at the Traffic Control Centre (TCC) of ATCS project and can be directly and easily accessed at a very low cost through internet. This data is the main input for the short-time-interval Origin- Destination (OD) matrix estimation. Before this type of traffic data is used in the OD matrix estimation process; firstly, these data have to be processed in the Data Processing Interface (DPI). Having it processed; the traffic data will then be ready for estimating the short-time-interval OD matrices. The output of short-time-interval OD matrices together with several practical applications will be the main input for the Real-Time Integrated Traffic Information System (RITIS). This information will be stored in a Website designed specifically and informatively for the purposes of user needs (numerical and graphical). One of the most important information is the best routes from each origin zone to each destination zone which have already considered the effect of congestion. This information will be the main data for the development of the Route Guidance System (RGS) so that each driver can choose his best route through the road network. The best route information will be changed in a short-time-interval basis depending on the traffic condition. This short-time- interval traffic system information will become the public-domain information which can be directly and freely accessed through internet by the users (e.g. Planning Authorities, Traffic Authorities, Department of Public Works, Consultants, Police, drivers, radio stations, and TV stations, other related agencies, etc). Moreover, this approach can also be extended to provide the short-time-interval environmental information.