[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

LSTM-based traffic flow prediction with missing data

In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI) With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters Most of the researches focus on historical data in forecasting traffic congestion However, a few articles made real-time traffic congestion prediction This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised

/pdf/a-review-of-traffic-congestion-prediction-using-artificial-3guq1d3ymb.pdf

A Review of Traffic Congestion Prediction Using Artificial Intelligence

Department of Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, India Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, India School of Computer Science and Engineering, SCE, Taylor's University, Subang Jaya, 47500, Malaysia School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Department of IT, National Engineering College, Kovilpatti, India Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience.

/pdf/design-and-implementation-of-an-ml-and-iot-based-adaptive-17hb3gto.pdf

Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities

Directed against the shortcoming of the vulnerability assessment based on complex network theory for urban road network traffic systems, a state vulnerability assessment method, considering the influence of congestion, is constructed by using a cloud model to describe the randomness and uncertainty characteristics of risks First, based on complex network theory, the primary index assessment system of road network vulnerability is introduced Second, to describe the congestion states of roads, the cloud model theory is introduced to characterize the congestion features of road sections After that, a state vulnerability identification method based on congestion cloud charts is constructed Finally, on the basis of topological mapping of road network in Nan’an District in Chongqing, experiments and analyses are carried out in light of the actual congestion delay index dataset provided by AutoNavi Maps to verify the effectiveness and rationality of our proposed scheme The experimental results show that the assessment method of state vulnerability can better describe the overall operational state and vulnerable road sections for road network

An Assessment Method for Traffic State Vulnerability Based on a Cloud Model for Urban Road Network Traffic Systems

In vehicular environment, Location Based Services (LBS) are widely deployed to provide a service or a piece of information that is related to the location of a driver. Though a series of technologies, such as anonymity, mix zone or private information retrieval, can be exploited to protect the drivers position information, the data proprietorship is always ignored. In addition, since the embedded systems like Onboard Unit are generally recourse-constrained, efficient schemes are urgent for now. In this paper, we presented a novel oblivious transfer protocol based on Number Theory Research Unit Encryption and structured a security LBS scheme in terms of it. Comparing with Jannati and Bahrak’s protocol, it is concluded that our scheme is more efficient and practical.

Efficient LBS Security-Preserving Based on NTRU Oblivious Transfer

The early trajectory prediction of micro unmanned aerial vehicles (micro-UAVs) with random behavior intentions facilitates the elimination of potential safety hazards. However, due to the property of a small radar cross-section (RCS), the backscattered radar signals from micro-UAVs may be submerged under strong background clutters, leading to distorted tracking and false prediction. To this end, this paper presents a spatial-temporal integrated framework (STIF) for end-to-end micro-UAV trajectory tracking and prediction based on a four-dimensional (4D) multiple-input-multiple-output (MIMO) radar. Especially, to obtain accurate trajectories in low signal-to-noise ratio (SNR) conditions, the target detection and tracking are considered to be interdependent and addressed jointly in this work, rather than treating them as two separate processes in conventional methods. The advantage is that with the assistance of tracking, all consecutive spatial information encoded in raw radar streams can be incorporated to enhance the continuous detection performance, avoiding information loss using only one single scan. Subsequently, to accommodate high maneuvering scenarios, an intention-aware end-to-end transformer-based prediction framework is presented to simultaneously discover both spatial and temporal dependencies hiding in long-term estimated trajectories. Consequently, a 4D frequency modulated continuous wave (FMCW) radar is utilized to evaluate the proposed system. Numerous simulation and experimental results indicate that STIF outperforms competing state-of-the-art methods and achieve superior prediction performance with the accuracy of 0.3851m in low SNR conditions. 

STIF: A Spatial-Temporal Integrated Framework for End-to-End Micro-UAV Trajectory Tracking and Prediction with 4D MIMO Radar

With the rapid development of vehicular networking and intelligence, more interfaces are adopted by cars to interact with the external world. Accordingly, this also brings enormous security risks, which are potentially catastrophic due to communication loopholes. Since the Controller Area Network (CAN) is critical to the transmission of commands among vehicular components, it has become a prime target for hacker research and attack. Considering that the CAN bus is commonly used and its protocol is always flawed, how to efficiently detect the intrusions against it has become an evitable problem. In this paper, we presented an intrusion detection system that can be rapidly deployed inside the vehicle. Aiming at achieving the goal of real-time detection, we devised a feature extraction algorithm with low complexity and thoroughly exploited its advantages via a GRU-based lightweight neural network. The experiment was physically conducted on in-vehicle embedded devices using publicly available datasets. Experiment results illustrated that our intrusion detection system could be rapidly deployed with high classification and real-time performance. Moreover, we also discussed how an intrusion detection system could work with OTA services to improve the intelligence of vehicular operating systems and prevent potential attacks. 

/pdf/a-gru-based-lightweight-system-for-can-intrusion-detection-2ajkgcr4.pdf

A GRU-Based Lightweight System for CAN Intrusion Detection in Real Time

The communication topologies between vehicles in a platoon substantially impact the platoon’s stability. This study provides a distributed linear feedback control law that considers time-varying delay with guaranteed internal and string stability of the platoon system under various communication topologies. Firstly, the vehicle dynamics linearized model was derived using the precise feedback linearization technology. Different types of communication topologies, such as vehicle-to-vehicle communication and sensor-based communication, were described using directed graphs. Secondly, the linear feedback control law was designed to establish the stable zone of the linear controller gain under the effect of different communication topologies using directed graphs and the Routh-Hurwitz stability theorem. The Lyapunov-Razumikhin theorem determines the upper bound of the time-varying delay of various communication topologies. Additionally, the string stability of leader-predecessor following topology was studied, and the results were combined with the internal stability to determine the upper bound of time-varying delay. Finally, the results were verified by conducting two numerical simulations.

Design and Analysis of Longitudinal Controller for the Platoon With Time-Varying Delay

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