Aggressive Driving Behavior Detection Using Integrated I-DBSCAN and LSTM Neural Network

Little is known about the nature and scope of aggressive driving in developing countries. The objective of this study is to specifically examine the sociodemographic factors associated with aggressive driving behavior among 3-wheeler taxi drivers in Sri Lanka. Convenience samples of 3-wheeler taxi drivers from Rathnapura, Ahaliyagoda, Sri Lanka were surveyed from June to August 2006. Analyses included bivariate and multivariate logistic regression. Drivers with less than high school education were 3.5 times more likely to drive aggressively (odds ratio [OR] = 3.46; 95% confidence interval [CI] = 1.08, 11.1). Single drivers were 9 times more likely to run red lights (OR = 8.74; 95% CI = 2.18, 35.0), and being single was a major risk factor for drunk driving (OR = 4.80; 95% CI = 1.23, 18.7). Furthermore, high school completers were 4 times more likely to bribe a policeman (OR = 4.27; 95% CI = 1.23, 14.9) when caught violating the road rules. Aggressive driving and risk-taking behavior are amenable to policy initiatives, and preventive programs targeted at key groups could be used to improve road safety in Sri Lanka. This study demonstrates that aggressive driving behavior is associated with sociodemographic factors, including the level of education, marital status, and other socioeconomic factors. Hence, economic factors should be addressed to find solutions to traffic-related issues. It will be the government's and policy makers' responsibility to try and understand the economic factors behind risky road behavior and bribe-taking behavior prior to legislating or enforcing new laws.

The goal of the current study was to examine the relationship between identity traits, manliness, and aggressive driving in young male drivers. Manliness was hypothesized to mediate the relationship between identity traits and aggressive driving. It is generally agreed that there will likely be a relationship between identity features, manliness, and forceful driving in young male drivers, and that manliness will probably operate as an intermediary in that relationship. It is a cross-sectional co-relational research study. A total of 150 young male drivers made up the sample. Purposive sampling was used to gather the sample from various Lahore institutions. The Doing Masculinity Composite Scale (DMCS), the Ten-Item Personality Inventory (TIPI), and the Aggressive Driving Behavior Questionnaire (ADBQ) are among the measures. Structural Equation Modelling (SEM) was used to examine the mediating effect of masculinity between aggressive driving behavior and personality factors. Pearson product moment correlation was utilized to evaluate the relationship between the study variables. The goal of this study is to identify any potential psycho-social causes of aggressive driving in order to develop a strategy to curb this behavior while improving men's social perception.

Based on the velocity effect (VE) model, a cellular automaton (CA) model is proposed with the consideration of timid and aggressive driving behaviors. Numerical simulations are performed to investigate the characteristics of traffic flow. The flux-density curves are plotted. The results show that the maximum flux and the corresponding density decrease as the proportion of timid drivers increases, while they increase as the proportion of aggressive drivers increases. In addition, road capacity is also investigated with the consideration of various driving behaviors. The results show that only a smaller proportion of timid drivers can play an essential role on the road capacity, while it needs much more aggressive drivers to promote the road capacity.

Aggressive driving behavior is regarded as a social dysfunction that poses a major risk to traffic safety. Previous research has indicated trait mindfulness as a protective factor for maladaptive thoughts and driver anger. The current study was designed to examine the mediating role of trait driver aggressiveness on the relationship between trait mindfulness and propensity for aggressive driving behavior. A sample of 122 drivers responded to a series of online questionnaires that assessed trait mindfulness, trait driver aggressiveness, and propensity for aggressive driving behavior. Results indicated that trait mindfulness was a significant predictor of trait driver aggressiveness. Similarly, trait mindfulness had also a moderating effect on the relationship between propensity for aggressive driving and trait driver aggressiveness. This indicated that those with higher trait mindfulness scores and lower trait driver aggressiveness scores were less likely to engage in aggressive driving behavior. Both theoretical and practical implications are discussed, and directions for future research are presented.

We examined the effects of trait driving anger, aggressive stimuli, and anonymity on aggressive driving behavior in a driving simulation task. High and low driving anger participants were randomly assigned to one of four conditions: (a) anonymous vs. identifiable driver; and (b) exposure to aggressive stimuli versus nonaggressive stimuli. Participants drove more aggressively when they were anonymous (d = .28) and exposed to aggressive stimuli (d = .05). Males drove more aggressively than did females (d = .06). No main or interaction effects were found for trait driving anger. Results suggest that situational factors affecting other forms of aggression are also important in aggressive driving.

With the fast development of autonomous vehicle (AV) technology and possible popularity of AVs in the near future, a mixed-vehicle type driving environment where both AVs and their surrounding human-driving vehicles drive on the same road will exist and last for a long time. An AV measures its driving environments in real time and make control decisions to ensure driving safety. However, surrounding human-driving vehicles may conduct aggressive driving behaviors (e.g., sudden deceleration, sudden acceleration, sudden left or right lane change) in practice, which requires an AV to make correct control decisions to eliminate the effect of aggressive driving behaviors on its driving safety. In this paper, we propose a reinforcement learning based decision-making system (ReDS) which considers aggressive driving behaviors of surrounding human-driving vehicles during the decision making process. In ReDS, we firstly build a mixture density network based aggressive driving behavior detection method to detect possible aggressive driving behaviors among surrounding vehicles of an AV. We then build a reward function based on aggressive driving behavior detection results and incorporate the reward function into a reinforcement learning model to make optimal control decisions considering aggressive driving behaviors. We use a real-world traffic dataset from the United States Department of Transportation Federal Highway Administration to evaluate optimal control decision determination performance of ReDS in comparison with the state-of-the-art methods. The comparison results show that ReDS can improve optimal control decision success rate by 43% compared with existing methods, which demonstrates that ReDS has good optimal control decision determination performance.

Aggressive driving behavior or “road rage” is often viewed as a dysfunctional pattern of social behavior that poses a serious risk to public safety. In a previous study, we conducted a factor analytic study of 79 items derived from five different driving behavior scales to produce a new scale, which can be correlated to driver anger. The goal of the present research was to empirically test this scale in a controlled laboratory environment. It was hypothesized that the newly developed Aggressive Driving Behavior Questionnaire (ADBQ) would predict driving performance of aggressive drivers. The results showed a significant effect of traffic condition on lane deviations and that the composite scores co-varied significantly with lane deviations and collisions. In addition, several dependent measures were significantly correlated with the composite scores. These variables included lane deviations and collisions. This suggests that the ADBQ is a valid predictor of aggressive driving behavior in a simulated environment.

It is key index of air-jet texturing yarn quality such as air-jet texturing yarn strength and so on. It can well control air-jet texturing yarn quality by predicting yarn strength and so on. Generally, it is normal used to predict yarn strength such as Multiple Linear Regression (MLR), Support Vector Regression (SVR) and shallow Artificial Neural Network (ANN). Because the processing of air-jet texturing yarn production has time sequence, the paper proposes a new deep neural network, it is Long Short-Term Memory neural network (LSTM). It used 1800 sets of data to train LSTM, SVR and ANN. It tested LSTM, MLR, SVR and ANN with 200 sets of data. Experimental results show that LSTM neural network is the best accuracy among these four algorithms.

Blind source computer device identification from recorded VoIP calls for forensic investigation

To better solve the problem of thermal error of computerized numerical control machining equipment (CNCME), a thermal error prediction model based on the sparrow search algorithm and long short-term memory neural network (SSA-LSTMNN) is proposed. Firstly, the Fuzzy C-means clustering algorithm (FCMCA) is used to screen the key temperature-sensitive points of the CNCME. Secondly, by taking the temperature rise data of key temperature-sensitive points as input and the corresponding time thermal error data as output, we established the SSA-LSTMNN thermal error prediction model. The SSA is used to optimize the parameters of LSTMNN and make its performance play the best. Taking the VMC1060 vertical machining center as the research object, we carried out the experiment. Finally, the prediction effect of the proposed model is compared with the article swarm optimized algorithm and LSTM neural network (PSOA-LSTMNN), the LSTMNN, and the traditional recurrent neural network (TRNN) model. The results show that the average values of the predicted residual fluctuations of the SSA-LSTMNN model are all more than 44% lower than those of the other three models under different operating conditions, which has a strong practicality.

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

The long short-term memory neural network (LSTM) is a type of recurrent neural network (RNN). During the training of RNN architecture, sequential information is used and travels through the neural network from input vector to the output neurons, while the error is calculated and propagated back through the network to update the network parameters. Information in these networks incorporates loops into the hidden layer. Loops allow information to flow multi-directionally so that the hidden state signifies past information held at a given time step. Consequently, the output is dependent on the previous predictions which are already known. However, RNNs have limited capacity to bridge more than a certain number of steps. Mainly this is due to the vanishing of gradients which causes the predictions to capture the short-term dependencies as information from earlier steps decays. As more layers in RNN containing activation functions are added, the gradient of the loss function approaches zero. The LSTM neural networks (LSTM-ANNs) enable learning long-term dependencies. LSTM introduces a memory unit and gate mechanism to enable capture of the long dependencies in a sequence. Therefore, LSTM networks can selectively remember or forget information and are capable of learn thousands timesteps by structures called cell states and three gates.

Understanding how drivers behave at stop-controlled intersection is of critical importance for the control and management of an urban traffic system. It is also a critical element of consideration in the burgeoning field of smart infrastructure and connected and autonomous vehicles (CAV). A number of past efforts have been devoted to investigating the driver behavioral patterns when they pass through stop-controlled intersections. However, the majority of these studies have been limited to qualitative descriptions and analyses of driver behavior due to the unavailability of high-resolution vehicle data and sound methodology for classifying various driver behaviors. In this paper, we introduce a methodology that uses computer-vision vehicle trajectory data and unsupervised clustering techniques to classify different types of driver behaviors, infer the underlying mechanism and compare their impacts on safety. Two major types of behaviors are investigated, including vehicle stopping behavior and vehicle approaching patterns, using two clustering algorithms: a bisecting K-means algorithm for classifying stopping behavior, and the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm for classifying vehicle approaching patterns. The methodology is demonstrated using a case study involving five stop-controlled intersections in Montreal, Canada. The results from the analysis show that there exist five distinctive classes of driver behaviors representing different levels of risk in both vehicle stopping and approaching processes. This finding suggests that the proposed methodology could be applied to develop new safety surrogate measures and risk analysis methods for network screening and countermeasure analyses of stop-controlled intersections.

Language models based on Long Short-Term Memory (LSTM) neural networks have been widely applied to automatic speech recognition, natural language processing research. However, current LSTMs are trained by employing the cross-entropy loss function, which only considers the target category without considering the competing categories during the training processes. Thus, current training methods cannot fully exploit the discriminative information provided by the data labels. To tackle this problem, we propose a Large Margin Long Short-Term Memory Neural Network (LMLSTM) model in this paper. Our model employs the large margin discriminative principle as a heuristic term to navigate the convergence process during training. Our model has improved the discriminative ability of the original LSTM while maintaining the capability when generating sequential data. Our proposed large margin term was tested on the Penn Treebank corpus language modelling task. Experimental results demonstrate that the proposed LMLSTM model outperforms current LSTM models in terms of accuracy and perplexity without increasing the depth.