Traffic Supervision Research Articles

Penegakan Hukum Terhadap Penyalahgunaan Tanda Nomor Kendaraan Roda Dua Dalam Kasus Tilang Elektronik

The enforcement of legal measures against the misuse of registration plates on two-wheeled vehicles has become a critical concern in Indonesia's traffic system. This issue is exacerbated by the increasing number of violations leading to erroneous electronic fines (e-tickets), undermining public trust in the legal system. According to 2023 data from the Indonesian National Police, over 500 cases of counterfeit license plate usage resulted in misdirected e-tickets. This study examines the enforcement of laws addressing the misuse of vehicle registration plates under Law No. 22 of 2009 on Road Traffic and Transportation. It focuses on identifying weaknesses in the current legal framework and ensuring the application of justice in cases of misallocated fines. Employing a normative juridical approach, this research analyzes existing regulations and evaluates relevant cases. The findings indicate that such misuse contravenes Articles 280 and 288 of Law No. 22 of 2009, which stipulate compliance with technical specifications and legality in vehicle registration. However, enforcement efforts face challenges such as inadequate monitoring tools and the lack of integrated vehicle data systems. This study recommends enhancing the data verification processes in e-ticketing systems and fostering stronger collaboration between law enforcement and related agencies to address these issues. Such measures aim to improve legal accountability and ensure equitable traffic supervision. By tackling these systemic shortcomings, the research contributes to restoring public confidence in traffic law enforcement and promoting a more effective legal framework for road safety in Indonesia.

Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data

The monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by the insufficient density of monitoring points, significantly reduce their usability. Therefore, it is important to reconstruct the target trajectories. Existing methods for the reconstruction of target trajectories often rely on topological data and convert trajectory reconstruction into a trajectory matching problem. Such methods heavily rely on topological data and cannot reconstruct trajectories in free space. To address this issue, we proposed a trajectory reconstruction method, named Prob-Attn, which does not rely on topological data and can accurately reconstruct target trajectories in free space. This method can be divided into two steps: first, a spatial trajectory construction module is proposed to determine the spatial trajectories of targets. Then, based on the reconstructed spatial trajectory of the target, this paper proposes a time series prediction model based on historical trajectories and an attention mechanism, which considers the impact of the target’s activity cycle and the surrounding status to predict the time series inside the trajectory. Finally, the proposed method is evaluated on real automatic vehicle detection datasets collected in Chongqing, China. The experimental results show that, compared with traditional methods, the proposed method can reconstruct the spatiotemporal trajectory of the target more accurately. The reconstructed trajectory data can be used for critical applications such as the intent and behavior analysis of key targets in national airspace and ground areas, providing valuable insights into security and safety.

An Intelligent Decision-Making Approach for Multi-Ship Traffic Conflict Mitigation from the Perspective of Maritime Surveillance

Potential multi-ship conflict situations in coastal or near-shore port areas have always been one of the important factors affecting ship navigation safety and a key target of maritime traffic regulatory authorities. In recent years, with the continuous development and integration of various emerging technologies in the maritime field, maritime traffic supervision has also shown a trend of intelligent and autonomous development. The traditional supervision method dominated by human experience is evolving towards data and model-driven practices. In order to solve the problem of ship navigation safety supervision under multi-ship conflict scenarios, it is urgent to build an intelligent conflict mitigation decision-making model. Therefore, this paper designs a novel risk mitigation decision-making model for multi-ship conflict scenarios from the perspective of maritime supervision. The model proposed in this paper first extracts high-density ship clusters based on AIS (Automatic Identification System) data and uses the MCD (Mean Core Density) and PRM (Proportion of Relative Motion) as feature indicators to further mine potential multi-ship conflict scenarios. Finally, a global optimization decision-making model is constructed to effectively mitigate conflict risks. Experimental verification shows that the intelligent decision-making model for the mitigation of maritime traffic conflict proposed in this paper can autonomously identify conflict scenarios and make reasonable decisions in real time. It can effectively ensure the navigation safety of ships in multi-ship conflict scenarios and further improve the supervision level of maritime departments.

Enhancing the correlation between azimuth and Doppler frequency for speed estimation of nearby tangential targets

With the development of intelligent transportation systems, traffic supervision radar with wide coverage plays a crucial role in traffic management and vehicle-road coordination. The correlation between Doppler frequency and azimuth has been widely validated in wide coverage traffic supervision radar for high-precision velocity measurement. However, angular glint and noise of the nearby targets lead to a decrease in correlation between the azimuth and Doppler frequency, which negatively impacts the accuracy of velocity estimation. Currently, adopting separate filtering strategies for target azimuth and Doppler frequency has limited performance in enhancing correlation. This paper presents a joint observation model for azimuth and Doppler frequency to achieve the extraction of interrelated components from subspaces, which improves the accuracy of velocity measurement. The effectiveness of this approach is validated using data obtained from X-band and Ku-band sensors.

A Review of Power System False Data Attack Detection Technology Based on Big Data

As power big data plays an increasingly important role in the operation, maintenance, and management of power systems, complex and covert false data attacks pose a serious threat to the safe and stable operation of the power system. This article first explores the characteristics of new power systems, and the challenges posed by false data attacks. The application of big data technology in power production optimization, energy consumption analysis, and user service improvement is then investigated. The article classifies typical attacks against the four stages of power big data systems in detail and analyzes the characteristics of the attack types. It comprehensively summarizes the attack detection technologies used in the four key stages of power big data, including state estimation, machine learning, and data-driven attack detection methods in the data collection stage; clock synchronization monitoring and defense strategies in the data transmission stage; data processing and analysis, data integrity verification and protection measures of blockchain technology in the third stage; and traffic supervision, statistics and elastic computing measures in the control and response stage. Finally, the limitations of attack detection mechanisms are proposed and discussed from three dimensions: research problems, existing solutions, and future research directions. It aims to provide useful references and inspiration for researchers in power big data security to promote technological progress in the safe and stable operation of power systems.

Integrating machine learning techniques with IoT sensors and connected vehicles to enable real-time traffic monitoring and Adaptive signal control systems

This research investigates machine learning algorithms to traffic monitoring and adaptive signal control coordinated in the context of the Internet of things sensors and connected vehicles for real-time traffic supervision. It measures the efficiency of machine learning algorithms, simulates adaptive control strategies for signal systems, and addresses the challenges of deploying signal system changes. The performance metrics suggest better traffic flow, leading to a decrease in traffic congestion. Further study should be made towards parametrization of algorithms, improvement of communication standards and training of integration models.

Security in Optical Wireless Communication-Based Vehicular Ad Hoc Networks Using Signature and Certificate Revocation

A vehicular ad hoc network is a capable method of making possible road well-being, traffic supervision, as well as information distribution for driving users and travelers. One definitive objective in the blueprint of this type of network is to refuse to accept a variety of malevolent mistreatment and security assaults. The paper analyzed the existing procedure, which has covered the ways of offering security duties and conserving user solitude in the context of wireless access in vehicular environments (WAVE) applications. To make the standards more realistic, it considers techniques of certificate revocation and conditional privacy preservation. These techniques are found to be the most promising ones in vehicular ad hoc networks (VANET). Therefore, a group of narrative security methods is proposed for the safe implementation of the techniques of certificate revocation and conditional privacy preservation. For enhancement of security, the paper proposes that the message should be encapsulated with a proper certificate and be duly signed. For faster communication, we propose the use of optical wireless communication (OWC) between an onboard unit (OBU) and a roadside unit (RSU).

Modeling and application of ship traffic conflicts in nearshore waters with heavy traffic

Ensuring the safety of ship navigation is essential to support the sustainable development of the shipping industry, and maritime safety analysis has thus received much attention in the field of shipping. Traditionally, the safety evaluation is mostly performed with the help of accident data, which is thus limited by the insufficient amount of accident data and affects the evaluation effect. In order to overcome this limitation, a ship traffic conflict model is established, which consists of three sub-models: multi-risk ship domain model, ship traffic conflict preliminary screening model and ship traffic conflict detection model. The multi-risk ship domain model is the key foundation, combining the traditional ship domain model with the water traffic conflict severity model to fully consider the severity of ship traffic conflict. To validate the proposed model, three ships in the study waters are selected as the own ships, and the severity of traffic conflicts formed with their target ships within a certain conflict time range are analyzed separately. Then, the conflict datasets obtained from the training of the ship traffic conflict model are used to analyze the traffic conflict situation in the heavily trafficked waters near Tianjin Port. Specifically, the spatial distribution characteristics are obtained using the kernel density estimation (KDE) method and the water grid method, and the temporal distribution characteristics are verified with the help of the accident statistics method. The proposed ship conflict model can provide technical support for further research in the field of ship traffic conflict, and the experimental analysis results are of great significance for strengthening maritime traffic supervision.

Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with bayesian optimization

Uncertainty prediction of vessel trajectory is essential to enhance maritime situational awareness and traffic safety. Traditional approaches for trajectory prediction face challenges in accurately quantifying uncertainties, thereby limiting effectiveness in decision-making. To address this challenge, we propose a hybrid interval prediction frame of vessel trajectory using the lower and upper bound estimation (LUBE) and attention-modified long short-term memory (LSTM) network with bayesian optimization (BO). Firstly, trajectory data is preprocessed to solve the scale irregularity. Then, a novel trajectory interval prediction model for perceiving the prediction uncertainties is designed based on an advanced attention-modified LSTM with interval prediction capability. Meanwhile, a supervised training strategy with differentiating the interval widths of latitude and longitude is put forward to devise sample labels for training the model. Additionally, a new prediction interval-based objective function is proposed considering the target of maximizing coverage and minimizing width of trajectory interval. The BO algorithm optimizes the weighted parameters in the LSTM network by minimizing the objective function value. Finally, cases from two water areas are implemented to test and verify the proposed method. The results illustrate the superiority of the proposed approach in (1) outperforming other methods used for comparison in both coverage probability and width criteria of prediction interval. (2) quantifying the prediction uncertainty and improving the reliability of trajectory predictor. (3) performing anomaly detection tasks using visualized trajectory prediction intervals. The research can help maritime traffic participants obtain more reliable trajectory data, which supports making more reasonable traffic supervision decisions.

Identifying critical urban intersections from a fine-grained spatio-temporal perspective

With the rising travel demand of crowds, urban road networks are under increasing pressure, which makes them progressively more fragile. Accurate identification of critical intersections is essential for urban management, resource allocation and traffic supervision. However, existing intersection evaluation methods fail to fully exploit transportation infrastructure, travel behavior and their synergistic relationships, resulting in coarse-grained and low-dynamic evaluation results. In this study, we develop an urban Intersection Evaluation framework from a Fine-grained Spatio-Temporal perspective (IE-FST). The framework is divided into three parts: fine-grained extraction of intersection information, multi-perspective assessment of node importance, and dynamic perception of spatio-temporal patterns in importance ranking. Specifically, IE-FST upgrades the intersection evaluation scale to turn-level, proposes the concept of Turning Sub-Node (TSN) and constructs a refined TSN topology network. Then, by finely extracting the network properties and mobility patterns of TSNs and mining the association patterns among TSNs, IE-FST proposes a Critical Fine-grained node Identification (CFI) algorithm to achieve a refined and dynamic evaluation of urban intersections. Finally, IE-FST analyzes the temporal characteristics and spatial distribution of TSN importance rankings, and provides traffic management measures for different types of TSNs to improve urban transport efficiency. Experiments are performed in Wuhan, China, and the results indicate that the proposed IE-FST framework achieves an accurate perception of urban intersections with high spatial and temporal resolution. This study promotes a better understanding of the synergistic relationships between crowd travel movements and urban transportation infrastructure, and provides theoretical support for infrastructure siting, refined traffic supervision, and rational resource allocation, thus contributing to the resilient and sustainable development of transportation systems and society.

Research on License Plate Recognition Algorithm in Dark-light Environment Based on Deep Learning

The advent of intelligent transportation technology has witnessed substantial advancements in license plate recognition technology, thereby assuming a crucial role in contemporary traffic monitoring. Nevertheless, conventional license plate recognition technology is inherently constrained by lighting conditions, severely impeding its nocturnal application. Given the escalating demand for license plate recognition technology in nighttime traffic surveillance, traffic violation documentation, and parking facilities, there exists a significant scholarly impetus to investigate nighttime license plate recognition technology. This study primarily focuses on nocturnal license plate recognition and employs deep learning techniques to execute comprehensive license plate recognition tasks. By leveraging this approach, the accuracy of license plate recognition in low-light environments can be substantially enhanced, thereby fortifying nocturnal traffic supervision and augmenting vehicular safety levels.

The Impact of Roadkill on Cervid Populations in Lithuania

Cervid roadkill, including moose, red deer and roe deer, can pose a risk to drivers and are frequently registered. However, the roadkill influence on overall cervid populations is not fully known, especially by roadkill that are not officially registered. The aim of this study was to assess the impact of cervid roadkill on population abundance, evaluating (i) the proportion of ungulate–vehicle collisions not registered by official bodies, (ii) the number of roadkill in relation to hunted animals and (iii) the proportion of roadkill that occurs in forest habitat. The number of unreported roadkill was calculated based on a roadkill index assessed during 3815 registration sessions on main and national roads in Lithuania from 2002–2022. During this period, 373 moose, 712 red deer and 9179 roe deer roadkill were unreported, correlating to 13.8%, 95.8% and 31.1% of those registered by the Traffic Supervision Service. In conclusion, 39.5%, 17.5% and 20.1% of roadkill were registered on roads through forests. Moose roadkill amounted to a figure corresponding to 10% of those hunted, with the same figure for red deer being 1.8%, neither adding much mortality. At 16.5%–16.6%, the figure for roe deer might be important.

Ship Anomalous Behavior Detection Using Clustering and Deep Recurrent Neural Network

In this study, we propose a real-time ship anomaly detection method driven by Automatic Identification System (AIS) data. The method uses ship trajectory clustering classes as a normal model and a deep learning algorithm as an anomaly detection tool. The method is divided into three main steps: (1) quality maintenance of the original AIS data, (2) extraction of normal ship trajectory clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), in which a segmented improved Dynamic Time Warping (DTW) algorithm is used to measure the degree of trajectory similarity, (3) the clustering results are used as a normative model to train a Bi-directional Gated Recurrent Unit (BiGRU) recurrent neural network, which is used as a trajectory predictor to achieve real-time ship anomaly detection. Experiments were conducted using real AIS data from the port of Tianjin, China. The experimental results are manifold. Firstly, the data pre-processing process effectively improves the quality of raw AIS data. Secondly, the ship trajectory clustering model can accurately classify the traffic flow of different modes in the sea area. Moreover, the trajectory prediction result of the BiGRU model has the smallest error with the actual ship trajectory and has a better trajectory prediction performance compared with the Long Short-Term Memory Network model (LSTM) and Gated Recurrent Unit (GRU). In the final anomaly detection experiment, the detection accuracy and timeliness of the BiGRU model are also higher than LSTM and GRU. Therefore, the proposed method can achieve effective and timely detection of ship anomalous behaviors in terms of position, heading and speed during ship navigation, which provides insight to enhance the intelligence of marine traffic supervision and improve the safety of marine navigation.

Guagnzhou Transport Sector Carbon Emission Roadmap Study

According to the relevant requirements of achieving carbon peak in 2030,different policy scenarios are set to simulate the future energy consumption demand and CO2 Emission Trend of Guangzhou's transportation field under different scenarios, and analyze the direction and path of low-carbon development in Guangzhou.The results show that in order to achieve carbon peak by 2030, it is necessary to establish a long-term mechanism of green development: first, deepen the adjustment of transport structure;Second, guide social green and low-carbon travel; Third, continuously promote the application of new energy; Fourth, build green transportation infrastructure; Fifth, improve the green traffic supervision system.

Complete trajectory extraction for moving targets in traffic scenes that considers multi-level semantic features

Complex traffic scenes are susceptible to the occlusion of moving targets and blurred target images, resulting in incomplete extraction of target trajectories within the field of view (FOV). To address this issue, we proposed a complete trajectory extraction algorithm for moving targets (TraEA) that considers the multi-level semantic features of traffic scenes following the FOV of single-camera surveillance videos. TraEA utilizes semantic information from the multi-level scene structure, such as image pixel features, geometric features, as well as temporal and spatial relationships between targets within each keyframe image. The semantic information helps extract and optimize the complete trajectory of a target, to assemble the target’s moving trajectory with improved integrity. An experiment with real-world traffic videos collected in Beijing, China validated the proposed algorithm and showed that TraEA improved the completeness, accuracy and robustness of target trajectory extraction. Traffic trajectories from the experiment can facilitate traffic supervision by analyzing the moving behaviors of individual targets. While the proposed TraEA algorithm is applicable to extract trajectories from videos in other domains, such as animal movements, potential privacy infringement and abusive applications warrant cautions of ethical considerations.

A Feasibility Study of a Traffic Supervision System Based on 5G Communication.

At present, autonomous driving vehicles are designed in an ego-vehicle manner. The vehicles gather information from their on-board sensors, build an environment model from it and plan their movement based on this model. Mobile network connections are used for non-mission-critical tasks and maintenance only. In this paper, we propose a connected autonomous driving system, where self-driving vehicles exchange data with a so-called road supervisor. All vehicles under supervision provide their current position, velocity and other valuable data. Using the received information, the supervisor provides a recommended trajectory for every vehicle, coordinated with all other vehicles. Since the supervisor has a much better overview of the situation on the road, more elaborate decisions, compared to each individual autonomous vehicle planning for itself, are possible. Experiments show that our approach works efficiently and safely when running our road supervisor on top of a popular traffic simulator. Furthermore, we show the feasibility of offloading the trajectory planning task into the network when using ultra-low-latency 5G networks.

A Maritime Traffic Network Mining Method Based on Massive Trajectory Data

Intelligent ships are the future direction of maritime transportation. Route design and route planning of intelligent ships require high-precision, real-time maritime traffic network information, which changes dynamically as the traffic environment changes. At present, there is a lack of high-precision and accurate information extraction methods for maritime traffic networks. Based on the massive trajectory data of vessels, the adaptive waypoint extraction model (ANPG) is proposed to extract the critical waypoints on the traffic network, and the improved kernel density estimation method (KDE-T) is constructed to mine the spatial–temporal characteristics of marine lanes. Then, an automatic traffic network generation model (NNCM), based on the pix2pix network, is put forward to reconstruct the maritime traffic network. NNCM has been tested on the historical trajectory data of Humen waters and Dongping waters in China, the experimental results show that the NNCM model improves the extraction accuracy by 13% and 33% compared to the geometric analysis method and density clustering method. It is of great significance to improve the navigation accuracy of intelligent ships. This method can also provide important technical support for waterway design and monitoring and maritime traffic supervision.

Towards traffic supervision in 6G: a graph neural network-based encrypted malicious traffic detection method

As an important direction for the evolution of next-generation wireless communication technology, 6G will comprehensively promote the wave of economic and social digitization. Services carried by 6G network will rely heavily on the sharing and processing of massive amounts of data between entities, data security is therefore of great importance. Currently, most network applications utilize SSL/TLS protocols to ensure the confidentiality and security of network communications, while encryption mechanism also brings huge challenges to network security supervision. Though encrypted malicious traffic detection in traditional networks has become a research hotspot, existing technologies cannot be directly applied in 6G networks. In a 6G network with massive, instant and unlimited communications between heterogeneous terminals, network communication behavior patterns are much more diversified, which makes the boundary between normal traffic and malicious traffic more blurred in 6G networks than in traditional networks. Existing studies either analyze encrypted traffic in isolation or aggregation, while they all ignore the rich correlations among encrypted traffic. To this end, we propose an encrypted malicious traffic detection framework based on the graph neural network towards the network security problem of future 6G networks, ET-RSGAT. First, considering the characteristics of super high speed and super large connection of 6G network, we design a simple feature extraction method of encrypted traffic: extracting the TLS handshake raw bytes and TLS record length sequence for one single encrypted session. Second, in view of the correlations of large numbers of heterogeneous terminals and the coexistence of multi-source heterogeneous data communication in 6G networks, we analyze the correlations between encrypted sessions from 2 aspects, which are service correlations and communication behavior correlations. Then we construct an encrypted traffic graph, named ETG. On the basis of ETG, we introduce a graph attention network to utilize the correlations between encrypted sessions to enrich the feature representation of nodes. With rich representation, we build the detection model based on a multi-layer perceptron to identify threats. Considering that the simulation environment of 6G networks is immature, we deploy a variety of heterogeneous terminal nodes and run various network services to simulate the 6G communication scenario, and design related experiments for the interconnection of many heterogeneous terminals in 6G networks. The evaluation and experimental results show that our method can obtain satisfactory detection results in both traditional network and simulated environment datasets.

Information fusion structure of VTS sensors for multi-target tracking of vessels

Information fusion using VTS sensors of AIS, radar and camera are of great significance to the waterborne traffic supervision. Firstly, this paper invites shore-based CCTV cameras into detection and location of vessel targets combining with bounding boxes generated by deep-learning based detectors. Besides, this paper compares information fusion structure of central-level and track-level in simulated waterborne traffic scenario. Finally, this paper introduces a track selection method for sensors with large false alarm rate to obtain tracks with better performance on both fusion structure when fully considering strengths and weaknesses of all kinds of VTS sensors.

Designing the Calibration Process of Weigh-In-Motion Systems

Weigh-In-Motion (WIM) systems provide information on the state of road traffic and are used in activities undertaken as part of traffic supervision and management, enforcement of applicable regulations, and in the design of road infrastructure. The further development of such systems is aimed at increasing their measurement accuracy, operational reliability, and their resistance to disturbing environmental factors. Increasing the accuracy of measurement can be achieved both through actions taken in the hardware layer (technology of load sensors, the number of sensors and their arrangement, technology used in the construction of the pavement, selection of the system location), as well as by implementing better system calibration algorithms and algorithms for pre-processing measurement data. In this paper, we focus on the issue of WIM system calibration. We believe that through the correct selection of the calibration algorithm, it is possible to significantly increase the accuracy of vehicle weighing in WIM systems, from a practical point of view. The simulation and experimental studies we conducted confirmed this hypothesis.