Traffic Data Research Articles

Identification of Accident-Prone Points (Black Spots) on the Ujung Gading-Simpang Empat Road Section

The Ujung Gading-Simpang Empat road section has the highest number of accidents in West Pasaman Regency. Accidents that occur at a location range from minor injuries to death. This research aims to determine the main factors influencing accidents, accident characteristics, and black spots and find solutions to minimize accidents. Research method, the method used is a quantitative descriptive method. Driver factors are the biggest factor causing accidents with 330 cases in 2018-2022. Apart from driver factors, vehicle factors also influence traffic accidents with 75 cases in the 2018-2022 period. Analysis of the Highest Accident Rate using the Accident Equivalent Number (AEK) method, and to calculate the number of accidents and the Upper Control Limit (UCL) method to determine accident-prone locations. The data used is the 2018-2022 West Pasaman Traffic Police traffic accident report data which is grouped based on characteristics. Based on the calculation results, the Ujung Gading-Sungai Aur road section has an AEK value of 1081 and UCL 1,036, Sungai Aur-Simpang Tiga Alin with an AEK value of 697 and UCL 884m26, and Simpang Tiga Alin-Simpang Empat with an AEK value of 690 and UCL 840.77. So these three roads are declared as accident-prone areas (black spots). To minimize the number of accidents, efforts are made by law enforcement (Enforcement), education (Education), and engineering (Engineering).

IoT Based Smart Traffic Management System using Image Processing and Automated Street Lighting System

Through the integration of image processing, IoT, and automatic street lighting, this study presents a novel solution to urban traffic management. Real-time traffic data, such as vehicle counts and congestion levels, are continuously collected by smart cameras positioned at strategic intersections. A central control unit receives this data and uses sophisticated algorithms to dynamically modify traffic signal timings. Additionally, by including sensors to identify surrounding cars and pedestrians, an automatic street lighting system improves sustainability. This system helps save energy and money by optimizing energy consumption through the ability to change lighting intensity levels as needed. In general, this Internet of Things (IoT)-based smart traffic management system improves efficiency and safety for both commuters and locals by addressing congestion and fostering sustainable urban development

A Novel Hybrid Intrusion Detection Framework Leveraging Machine Learning and Flower Pollination Optimization

The exponential growth in technologies such as cloud computing, smart devices, virtualization, and the Internet of Things (IoT) has generated over four hundred zettabytes of network traffic data annually. This surge necessitates robust cybersecurity strategies to protect information from intrusions, which can result in significant financial losses. Reducing security risks requires the use of data analytics and machine learning to derive insights and make informed decisions based on network data. This study introduces the Flower Pollination Optimization (FPO) algorithm for feature selection to enhance the performance of several classifiers on the UNSW-NB15 dataset. We evaluated four classifiers: Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbors (KNN) in two scenarios: without feature selection and with FPO-based feature selection. The results demonstrate significant improvements in classifier performance with FPO, with QDA achieving the highest accuracy of 99.16%. Comparative analysis with recent studies highlights the superior performance of our approach, setting a new benchmark in intrusion detection. This research underscores the essential role of effective feature selection in improving the accuracy and reliability of Intrusion Detection Systems (IDS), particularly in IoT environments.

Analysis of Effect Overloading on the Remaining Life Pavement Plan on the Bungah Highway - Ngawen Highway Section

Bungah -Ngawen highway section is one of the accesses to the industrial area in Gresik City. This affects the vehicles that cross this road, which are dominated by heavy vehicles that distribute goods and services. These conditions can cause faster damage to the pavement which can hinder smooth transportation. This research aims to analyze the impact of vehicle loads on the remaining life of the plan on the Jalan Raya Bungah-Jalan Raya Ngawen section (STA 0+000-STA 5+500). The reason for this research is the amount of damage to the pavement. Based on a direct survey, this road has a road width of 6 m with a length of road damage of 2,15 km from the road section under review. The data used are primary data in the form of average daily traffic and secondary data, namely LHR data and vehicle load data. The calculation method used is the AASHTO 1993 Method. From the primary and secondary data obtained, traffic growth, percentage of overload, ESAL value, W18 value, and the remaining value of the road plan life can be calculated. From the calculation results, the standard ∑W18 value during the plan life is 338422516.86 to 4796885453.64, while for the overload ∑W18 value is 531674277.94 to 6849455940.63. The main trigger for pavement damage on the Jalan Raya Bungah-Jalan Raya Ngawen section is class VIb vehicles because they have the largest overload of 27,90%. The remaining life value of standard load conditions in 2024 of 57,46% decreased under overloading conditions to 33,29%, with a difference of -24,17% and the road service life will end in March 2026 or a reduction from the planned life of 10 years.

Exploring the Relationship between Data Sample Size and Traffic Flow Prediction Accuracy

Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.

Ant Colony Optimization and Beam-Ant Colony Optimization on Traveling Salesman Problem with Traffic Congestion

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that has various implications in a variety of industries. Even the purest formulation of TSP has applications on from logistics routes to microchip manufacturing, unexpectedly, it can be used on DNA sequencing with slight modification as a sub-problem. In this paper, two versions of TSP were studied, a classical TSP and the TSP containing traffic congestion data. Two state-of-the-art solution methods were used, Ant Colony Optimization (ACO) and Beam-ACO. These algorithms were hybridized with 2-Opt local search and their performances compared on the same benchmark instances. The experimental results show the efficiency of Beam-ACO compared to ACO.

Reversing accidents involving pedestrians: an epidemiological investigation and cluster analysis using a novel database derived from Swedish National Register data

Objectives In contemporary urban environments, the intersection between Vulnerable Road Users, specifically unprotected pedestrians, and motor vehicles poses a persistent challenge to road safety. Reversing accidents, characterized by vehicles moving backward, often result in severe consequences for pedestrians due to limited visibility and inherent blind spots for the driver. This paper aims to provide a thorough examination of reversing accidents involving pedestrians in Sweden between 2000–2021; shedding light on the magnitude of the problem, contributing factors, consequences, and potential mitigation strategies. Method Using a national database, reversing accidents were compiled by classifying free text descriptions of traffic accidents reported to the Swedish Traffic Accident Data Acquisition. The database was then used to describe reversing accidents and their consequences for struck pedestrians using descriptive epidemiology and cluster analysis. Results The results show that reversing accidents accounted for 12% of all pedestrians injured in a collision with a motor vehicle in Sweden during the studied period. In terms of personal characteristics, the struck pedestrians were more often female and of old or young age, whilst the drivers of the reversing vehicles were more often between 18–54 years and men. Most accidents were non-fatal, with only roughly 2% resulting in fatalities. Through a cluster analysis, four distinct accident types were identified. Two of these were identified as particularly important to prioritize in future preventative work: accidents that are characterized by occur during daylight hours in the urban environment (often low speeds) and often result in fatality or serious injury. Conclusions Due to the low speeds, reversing accidents are rarely fatal. However, they account for a large proportion of accidents with pedestrians and can be grouped into clearly distinguishable accident types that can function as templates in road safety development in Sweden.

Quantifying the effect of striking with picketing on grocery store foot traffic

Unionized workers often use striking and picketing to raise attention to their grievances, dissuade customers from patronizing a business, and pressure employers in negotiations. Despite its wide use and recognition in popular culture, the effects of picketing and striking on retail business are not well understood. Adjacent literature has used cell phone tracking and other digital geo-tagging techniques to measure the effects of factory closures, COVID-19 restrictions, and stimulus payments on store patronage and economic activity. This article provides a case study using mobile geolocation data to quantify the loss of store foot traffic due to striking with picketing by analyzing the 2022 King Soopers strike in Colorado, USA. Using the historic foot traffic data of the past two years for 118 King Soopers locations, 78 of which went on strike, two SARIMA models were trained, and their predicted foot traffic values were compared to the actual values during the strike period. This technique indicates an average 47% decrease in foot traffic for striking stores and a 14% decrease in foot traffic for nonstriking locations.

An optimized multi-layer ensemble model for airborne networks intrusion detection

In view of the characteristics of airborne networks, such as the traffic data of 1553B data bus and public networks, numerous redundant and irrelevant data, and fewer but more types of attack behaviors, a highly representative balanced data subset is generated by combining K-means with synthetic minority oversampling technique (SMOTE). A feature selection (FS) method of fast correlation-based filter combined with information gain (IG-FCBF) is proposed to filter the key characteristics with 90 % cumulative importance. Aiming at the problem that it is not easy for a single classifier to accurately classify various types of attacks, as well as the difficulty in obtaining the best prediction results by adjusting default and manual parameters, a multi-layer ensemble model based on Bayesian optimization tree-structured Parzen estimator (BO-TPE) is proposed for airborne networks intrusion detection, combining the advantages of supervised learning, stacking ensemble method and hyperparameter optimization (HPO). The experimental results show the superiority and effectiveness of the model, as well as its universality in Integrated Avionics Systems (IAS) and onboard public networks, which provides a new approach for airborne networks intrusion identification and protection.

Open set identification of malicious encrypted traffic based on multi-feature fusion

In the current network environment, an increasing amount of malicious traffic is transmitted through encrypted channels, carrying control commands and data. With the continuous development of communication protocols and applications, new types of malicious encrypted traffic are emerging, posing significant challenges for network management (e.g., traffic engineering). Therefore, accurately identifying malicious traffic in complex open network spaces has become a hot research topic in network security. In this study, we draw inspiration from channel theory in image science and innovatively convert traffic data into Red-Green-Blue (RGB) image format to achieve the fusion of multiple features. Inspired by image recognition technologies, we have designed a multi-granularity network model that integrates both global and local features, serving as our core network architecture. At the top of the model, we have equipped each known category with a unique autoencoder, using its generated manifold to replace traditional prototypes for model construction. Classification is accomplished through a scoring mechanism that evaluates category membership and by setting thresholds to achieve open set recognition of unknown categories. Relying on our self-created dataset,Malicious and Encrypted Traffic 2024 (MNET2024), we conduct a series of extensive experiments. The results demonstrate that our proposed method exhibits outstanding performance in both closed-set and open-set recognition tasks.

Traffic and transportation management data storage terminal based on Internet of Things

Traditional data storage models are inadequate in the face of the growing demand for big data in transportation and transportation management. Its poor horizontal scalability makes it difficult to deal with the growth of massive data; on the other hand, its complex management makes it challenging to achieve unified management and effective resource utilization due to the differences in equipment from different manufacturers. In order to effectively store and manage this huge amount of information, it is urgent to rely on advanced technical tools. In this context, while ensuring data security and simplifying data management, it is also necessary to meet the terminal’s demand for high real-time performance, and these factors jointly promote the continuous attention and improvement of data storage terminal performance. This paper proposes a Hadoop solution based on distributed computing. As a distributed system infrastructure, Hadoop allows users to develop distributed programs without a deep understanding of distributed details, fully using the high-speed computing and storage capabilities of Hadoop clusters, which is especially suitable for big data processing tasks on the Internet of Things (IoT) platform. The experimental results show that for a 10 GB data file, the traditional terminal (Terminal 1) can store 7.8 GB, while the Hadoop-based terminal (Terminal 2) can store 9.9 GB. For 50 GB of data files, Terminal 1 and Terminal 2 can store 40.4 GB and 49.8 GB of data respectively. These results show that Hadoop terminals have significant advantages in processing large-scale data, especially in terms of data storage efficiency, and can use storage resources more effectively to meet the high-performance requirements of traffic and transportation management for data storage terminals.

Time series forecasting using transformer neural network

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is proposed.This model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes.

Computer Networks Cybersecurity Monitoring Based on Deep Learning Model

ABSTRACTEffective cybersecurity monitoring is essential for safeguarding computer networks against evolving threats. However, the increasing scale, complexity, and data volume of modern networks pose significant challenges to traditional monitoring methods. To address these challenges, this article proposes a deep learning‐based approach for computer network cybersecurity monitoring. Leveraging the MnasNet‐LSTM model, network traffic data is classified into distinct categories, including normal traffic and cyberattacks. The model is trained using the CICIDS2017 dataset, yielding promising results with a classification accuracy of approximately 97.05% and a minimal error rate of 8.46%.

Hybrid Deep-Learning Approach with Geoffrey E. Havers-Based Loss Function and Evaluation Metric for Multilocation Traffic-Flow Forecasting

Traffic forecasting can enhance the efficiency of traffic control strategies such as routing decisions, variable speed limits, and ramp metering, resulting in a decrease in congestion, pollutants, and expenses, and an improvement in journey time predictability. Traffic forecasting, however, remains challenging because of the complex, heterogeneous, and cyclic nature of traffic data. To address this complexity, this research employs a multi-input hybrid deep self-attention network (MIHDSAN) for multilocation forecasting. The model inputs are selected using correlation analysis. New tunable loss and evaluation metrics formulations are proposed based on the traffic-modeling Geoffrey E. Havers (GEH) statistic. The proposed method was validated on two independent real-world traffic datasets from Stockton and Oakland, California. The weekly periodicity was the more relevant periodic input feature compared with daily variations; however, the daily variation was also significant for the Stockton dataset. The inclusion of weekly traffic periodicity (>95% correlated) improved the performance of the model by 3%. Adding daily periodicity was only beneficial for the Stockton dataset (91% correlated). The proposed GEH metric and its standard acceptance criterion offer both quantitative and qualitative means of evaluating the forecasts produced. The GEH loss function was consistent and outperformed current industry-standard methodologies of mean absolute error (MAE) in 80% and mean squared error (MSE) in 94% of cases. Therefore, this research presents evidence to suggest that the proposed GEH loss and evaluation functions validated in this paper become a standard criterion for traffic forecasting.

Hybrid Deep Learning Model for Traffic Flow and Speed Forecasting at Urban Junction

The hybrid deep learning model H-CNN-LSTM (Hybrid Convolution Neural Network and Long Short-Term Memory) is introduced in this paper. The model combines CNN to extract traffic flow and speed features, LSTM networks to model temporal dependencies, and employs a combined matrix and a rolling forward input method during data input, enhancing the coarse-grained extraction of traffic data and further refining the utilization of adjacent data. Our research focuses on the complex dynamic traffic conditions of Fuli East Road in Xigu District, Lanzhou City of China. Using detailed traffic video data collected during one month of evening rush hours, we predict future traffic using the H-CNN-LSTM model. By comparing it with other prediction methods, we validate the effectiveness of our approach in predicting traffic flow and speed in real-world traffic scenarios

Enhancing Deep Learning-Based City-Wide Traffic Prediction Pipelines Through Complexity Analysis

Deep learning models can effectively capture the non-linear spatiotemporal dynamics of city-wide traffic forecasting. Evidence of varying deep learning model performance between different cities, different prediction horizons, different scales, specific city regions, and during particular hours of the day abounds in the literature on deep learning-based traffic prediction, yet a unified metric to quantify the complexity of different prediction tasks does not exist. This paper proposes two metrics—model complexity (MC) and intrinsic complexity (IC). While MC quantifies the effective complexity of deep learning models for city-wide traffic prediction tasks, the IC quantifies the underlying complexity of the prediction task. Being an effective complexity metric, MC depends on the model and the data. The IC depends only on the data and is invariant to the model being used. Both metrics are validated through systematic experimentation using traffic volume data from three cities. Finally, we demonstrate how these metrics can improve the workflows for deep learning-based data-driven traffic prediction pipelines and deployment by reducing the hyperparameter search scope and comparing the effectiveness of different design pathways.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. In response to these challenges, this paper presents a Personalized Federated Learning method based on Client Reputation for traffic flow prediction. This method avoids direct sharing of original data by performing model training and information aggregation locally, thereby better protecting user privacy. We initially constructed the foundational model by integrating Graph Neural Networks (GCNs) and Gated Recurrent Units (GRUs) for federated learning. GCNs are capable of capturing the spatial relationships between nodes in a road network, thereby extracting the spatial features of traffic flow. On the other hand, GRUs has advantages at handling sequential data, effectively capturing the dynamic temporal characteristics of traffic flow. Consequently, by combining GCNs and GRUs, we are able to build a model that captures both the spatial and temporal features of traffic flow. Subsequently, we employed a federated learning framework to train the model. In the training process, we propose two evaluation algorithms to evaluate the reputation of all clients participating in federated learning. Then, different weights are customized for each client through a personalized algorithm based on the client's reputation to improve its personalization. Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.

Analysis and Justification of Data Structure Selection for Traffic Flow Management Tasks

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

Initial Insights into Teleworking’s Effect on Air Quality in Madrid City

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study analyzes the relationship between the improvement of air quality and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. This article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the influence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, and traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables, there is a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was maintained during the pandemic, except for O3, the association of which increased during this period and then decreased in the later period due to various motives. These results seem to indicate the existence of a relevant relationship between urban mobility and air quality and an especially relevant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

The frequent usage of computer networks and the Internet has made computer networks vulnerable to numerous attacks, highlighting the critical need to enhance the precision of security mechanisms. One of the most essential measures to safeguard networking resources and infrastructures is an intrusion detection system (IDS). IDSs are widely used to detect, identify, and track malicious threats. Although various machine learning algorithms have been used successfully in IDSs, they are still suffering from low prediction performances. One reason behind the low accuracy of IDSs is that existing network traffic datasets have high computational complexities that are mainly caused by redundant, incomplete, and irrelevant features. Furthermore, standalone classifiers exhibit restricted classification performance and typically fail to produce satisfactory outcomes when dealing with imbalanced, multi-category traffic data. To address these issues, we propose an efficient intrusion detection model, which is based on hybrid feature selection and stack ensemble learning. Our hybrid feature selection method, called MI-Boruta, combines mutual information (MI) as a filter method and the Boruta algorithm as a wrapper method to determine optimal features from our datasets. Then, we apply stacked ensemble learning by using random forest (RF), Catboost, and XGBoost algorithms as base learners with multilayer perceptron (MLP) as meta-learner. We test our intrusion detection model on two widely recognized benchmark datasets, namely UNSW-NB15 and CICIDS2017. We show that our proposed IDS outperforms existing IDSs in almost all performance criteria, including accuracy, recall, precision, F1-Score, false positive rate, true positive rate, and error rate.