Safety Of Transportation Systems Research Articles

Decoding Fluid Flow Characteristics Through Distributed Acoustic Sensing: A Novel Approach

Flow characteristic monitoring includes parameters such as flow regime, fluid characteristic frequency, and flow rate, which are crucial for optimizing production and ensuring the safety of oil and gas transportation systems. Existing fluid monitoring technologies, such as various flow meters, often face limitations in providing distributed and real-time monitoring data. In contrast, distributed acoustic sensing offers a spatial resolution of 1 m with high frequency sampling capability, allowing for long-term, multi-point dynamic monitoring of fluid migration characteristics. We developed an indoor physical simulation pipeline loop to assess the feasibility of using distributed acoustic sensing for monitoring flow migration characteristics. The experiment collected signal characteristics under different conditions, including background noise, single gas-phase flow, single liquid-phase flow, and gas–liquid two-phase flow. In the frequency–power spectral density analysis, single gas-phase flow signals are concentrated at lower frequencies, single liquid-phase flow displays noticeable spikes over a broader frequency range, and gas–liquid two-phase flow covers the widest frequency range with stronger amplitude signals. Autocorrelation analysis shows larger oscillations for gas–liquid two-phase flow, smoother signals for gas-phase flow, and more turbulent signals for liquid-phase flow. By examining root mean square energy changes, flow rates can be qualitatively estimated, revealing a positive correlation between energy and flow velocity. Finally, the study discussed the limitations of the experimental setup and proposed improvements and advanced research directions of distributed acoustic sensing in fluid monitoring.

Impact of socioeconomic determinants on road traffic accidents in Tunisia: insights from the dynamic ARDL and machine learning approaches

ABSTRACT Road traffic accidents are responsible for 1.35 million deaths, and road safety is the part of sustainable development goals, which aims to provide a safe, accessible, affordable, and sustainable transport system by 2030. Despite significant efforts made by the Tunisia government to reduce traffic accidents, there is still a need to understand the root causes of these accidents. This study examines the relationship between road traffic fatalities, economic growth, total mileage of highways, private vehicle ownership, and urbanization in Tunisia from 2000 to 2023. This study employs novel estimation techniques, including dynamic autoregressive distributed lag (DARDL) simulations and Kernel-based Regularized Least Squares (KRLS), to capture the counterfactual shocks in road traffic fatalities. The DARDL results revealed that 1% increase in economic growth, urbanization, and private car ownership leads to a 0.316%, 0.371% and 0.347% rise in road traffic accidents in the long-run, respectively. The KRLS results indicate that economic growth and private vehicle ownership have a positive marginal effect on road traffic fatalities. Specifically, an increase in private vehicle ownership consistently raises traffic fatalities across all percentiles. In contrast, the effect of total highway mileage is initially positive, leading to an increase in accidents, but turns negative in the later stages. Additionally, the frequency domain causality results show that economic growth granger causes road accident for frequencies corresponding to long-term (0.93–1.00) and medium-term (1.03–1.50) horizons. Urbanization granger causes road accident in the medium term (1.69 to 1.97) and short term (2.01 to 2.63). Private vehicle Granger-causes road accident for frequencies between 0.00 and 0.89 in the long run and between 1.03 and 1.73 in the medium term. The governments should include traffic education as part of syllabus from primary to higher studies. Government should create awareness about the loss due to road crashes.

Improved Variational Mode Decomposition in Pipeline Leakage Detection at the Oil Gas Chemical Terminals Based on Distributed Optical Fiber Acoustic Sensing System

Leakage in oil and gas transportation pipelines is a critical issue that often leads to severe hazardous accidents at oil and gas chemical terminals, resulting in devastating consequences such as ocean environmental pollution, significant property damage, and personal injuries. To mitigate these risks, timely detection and precise localization of pipeline leaks are of paramount importance. This paper employs a distributed fiber optic sensing system to collect pipeline leakage signals and processes these signals using the traditional variational mode decomposition (VMD) algorithm. While traditional VMD methods require manual parameter setting, which can lead to suboptimal decomposition results if parameters are incorrectly chosen, our proposed method introduces an improved particle swarm optimization algorithm to automatically determine the optimal parameters. Furthermore, we integrate VMD with fuzzy dispersion entropy to effectively select and reconstruct intrinsic mode functions, significantly enhancing the denoising performance. Our results demonstrate that this approach can achieve a signal-to-noise ratio of up to 24.15 dB and reduce the mean square error to as low as 0.0027, showcasing its superior capability in noise reduction. Additionally, this paper proposes a novel threshold setting technique that addresses the limitations of traditional methods, which often rely on instantaneous values and are prone to false alarms. This innovative approach significantly reduces the false alarm rate in gas pipeline leakage detection, ensuring higher detection accuracy and reliability. The proposed method not only advances the technical capabilities of pipeline leakage monitoring but also offers strong practical applicability, making it a valuable tool for enhancing the safety and efficiency of oil and gas transportation systems.

Assessing Safety Performance of Complete Streets Projects

Complete Streets (CS) are defined as streets that accommodate all types of users, regardless of ability, safely and equitably allowing for the presence of pedestrians, bicyclists, transit users, and vehicle drivers to share the roadway. Several agencies have developed CS policies as a vital strategy to create more inclusive and accessible environments for all road users. CS are an efficient way to support the implementation of a multimodal transportation system, providing alternatives to car-oriented roadway designs. The Kentucky Transportation Cabinet recently developed the Complete Streets, Roads, and Highways Manual, aiming to implement a safe and equitable transportation system throughout the state. However, there is a need to evaluate the benefits of CS regarding their safety performance. This study aims to present crash data and summary statistics for CS projects that have been completed in Kentucky. The methodology involves a comparative analysis of safety data collected before and after the implementation of these projects. The results reveal that CS can be an effective approach to improve safety for all road users, including vulnerable and motor vehicle users. The findings also contribute to the existing knowledge on CS, offering insights into their impact on safety performance. Given that transportation agencies continue to prioritize sustainable and inclusive transportation solutions, the outcomes of this study will provide practical guidance for urban planners, policymakers, and transportation engineers seeking evidence-based solutions for creating safer roads.

Application of deep reinforcement learning under biomechanical load optimization in warehouse site selection and material transportation path

Path optimization of logistics and transportation systems has traditionally focused on the balance between efficiency and cost, but there is a lack of systematic research on the biomechanical load of transport personnel, which leads to fatigue accumulation and increased safety hazards. To fill this gap, this paper proposes a path optimization method based on deep reinforcement learning (DRL) based on biomechanical theory, aiming to combine biomechanical load management of transport personnel with logistics efficiency improvement. Firstly, a biomechanical load assessment system for transport personnel during long-distance driving is established using human kinematics and dynamics models, with quantitative indicators including muscle fatigue index, joint load and driving posture stability. Secondly, a national logistics transportation network is constructed based on a graph theory model, with transportation distance, time and biomechanical load as constraints for multi-objective optimization, and a Deep Q Network (DQN) is designed for path planning optimization. The calculation of fatigue index is combined with driving time, road section characteristics and individual biomechanical characteristics, and verified by biomechanical simulation tools. In order to improve the optimization efficiency, the simulated annealing algorithm is used to preliminarily screen the paths, and the DRL model is combined to achieve dynamic adjustment. The experimental results show that this method significantly reduces the biomechanical load of transport personnel in nationwide logistics scheduling (the fatigue index is controlled below 0.12), and at the same time reduces the accident rate caused by fatigue (reduced by 40%), and the transportation efficiency is superior to traditional research. The research results not only deepen the application of biomechanical theory in the field of long-distance transportation, but also provide theoretical support and technical reference for building a safe, efficient and intelligent logistics and transportation system, and promote the integrated development of biomechanics and artificial intelligence in complex engineering problems.

Artificial Intelligence in Traffic Management: A Review of Applications and Impact on Transportation Systems

Artificial Intelligence (AI) has become a transformative force in traffic management, offering innovative solutions to address congestion, safety, and efficiency in transportation systems. This literature review examines recent advancements in AI applications within traffic management, focusing on methodologies such as machine learning, computer vision, and optimization techniques. The study also evaluates the impacts of these technologies on transportation systems, including their benefits and challenges. Key applications such as traffic prediction, signal control, autonomous vehicles, and incident management are discussed. The review concludes by identifying research gaps and proposing directions for future studies

Dynamic Stability Analysis and Optimization of Multi-Vehicle Systems in Heterogeneous Connected Traffic.

This study investigates the stability and performance of mixed-traffic flows consisting of human-driven vehicles (HDVs), connected autonomous vehicles (CAVs), autonomous vehicles (AVs), and connected human-driven vehicles (CHVs). Recognizing the complexity introduced by multi-vehicle interactions in such heterogeneous traffic, a refined CAV car-following model that integrates multi-vehicle state information, including headway, weighted velocity differences, weighted acceleration, and optimal velocity memory effects from both front and rear vehicles, is introduced. Through theoretical analysis of the model's linear and nonlinear stability, the key parameters that enhance flow stability in mixed environments are determined. Numerical simulations across braking, start-up, and ring road scenarios validate the proposed model's efficacy, demonstrating that it can effectively suppress traffic congestion and reduce oscillations, thereby improving traffic flow stability. This work offers valuable insights into the behavior of connected vehicles within mixed traffic and highlights the potential for CAV-based strategies to enhance both safety and efficiency in future transportation systems.

Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model

Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines. These findings show how strong and successful our improved method is in precisely spotting drivers, thereby providing a useful instrument for safe and quick transportation systems.

Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis

This paper addresses the challenge of improving decision-making capabilities and safety in autonomous vehicles (AVs) using Agent-Based Modelling (ABM). The study evaluates ABM’s effect on Advanced Driver Assistance Systems (ADASs) in challenging driving situations, like lane merging, by incorporating it into a simulation framework designed for autonomous vehicles. Identifying emergent behaviours that enhance safety and efficiency, verifying the efficacy of ABM in AV decision-making, and investigating the function of hardware acceleration to enable practical application in ADASs are some of the major achievements. According to the simulation results, ABM can greatly improve AV performance, providing a practical and scalable means of enhancing safety in future transportation systems.

Multi-Agent Reinforcement Learning for Efficient Resource Allocation in Internet of Vehicles

The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional limitations in system capacity, precipitate significant challenges in allocating wireless resources for vehicular networks. In addressing these challenges, this study formulates the resource allocation issue as a multi-agent deep reinforcement learning scenario and introduces a novel multi-agent actor-critic framework. This framework incorporates a prioritized experience replay mechanism focused on distributed execution, which facilitates decentralized computing by structuring the training processes and defining specific reward functions, thus optimizing resource allocation. Furthermore, the framework prioritizes empirical data during the training phase based on the temporal difference error (TD error), selectively updating the network with high-priority data at each sampling point. This strategy not only accelerates model convergence but also enhances the learning efficacy. The empirical validations confirm that our algorithm augments the total capacity of vehicle-to-infrastructure (V2I) links by 9.36% and the success rate of vehicle-to-vehicle (V2V) transmissions by 6.74% compared with a benchmark algorithm.

Real-time lane detection for autonomous vehicles using YOLOV5 Segmentation Model

ABSTRACT The increasing prominence of autonomous vehicles underscores the critical need for precise and prompt lane detection to ensure safe and reliable transportation. This paper aims to enhance autonomous vehicle performance by developing an advanced real-time lane detection system using the YOLOV5 Segmentation Large Model. The methodology utilises LabelMe for dataset annotation and employs rigorous pre-processing techniques such as resizing, augmentation, noise addition, greyscale conversion, enhanced exploration size, and image rotation. YOLOv5 architecture is extensively employed for in-lane instance segmentation. The proposed model achieves high performance with a pixel accuracy of 89%, a processing speed of 48 frames per second, and mAP@0.5 scores of 0.885 for the bounding box and 0.731 for the mask. These results demonstrate the model’s effectiveness in various conditions. This paper introduces an innovative approach to autonomous vehicle lane detection, leveraging the YOLOV5 Segmentation Large Model for unparalleled precision and recall rates. This paper makes a significant contribution to the advancement of autonomous vehicle technology, emphasising the critical role of reliable and timely lane detection in strengthening the dependability and safety of transportation systems.

Assessing Public Transport Convenience for Achieving SDG 11.2 in Tangerang Municipality

A reliable urban transport system is essential for achieving sustainable cities and communities. The United Nations' Sustainable Development Goal (SDG) 11.2 targets universal access to safe, affordable, accessible, and sustainable transport systems by 2030. This study evaluates SDG 11.2 performance in Tangerang Municipality, examining progress toward this target. Using spatial analysis methods—such as buffer analysis, network and isochrone analysis, and spatial autocorrelation in GIS—this research assesses transit quality and accessibility. Data were collected through purposive sampling, analyzed using an online field survey, and assessed via a Likert scale. The findings reveal that only 13.85% to 22.47% of Tangerang’s population has convenient access to public transport, with coverage limited to 17.56% of the city’s area. A dispersed yet positive spatial autocorrelation exists between public transport patterns and population density, with a Moran's I value of 0.076. However, 36 out of 104 subdistricts remain underserved. Pedestrian convenience scored the lowest due to inadequate pedestrian infrastructure and sidewalk encroachment by street vendors. If no action is taken to address these issues, accessibility could decline from 22.4% to 16.51% by 2032, assuming a 2.6% annual population growth rate. Achieving an 85% public transport share by 2032 would require an annual improvement rate of 5.6% from 2020 onward. The study recommends an integrated public transport service expansion, enhanced transit quality, improved transport data collection, and public awareness campaigns to accelerate SDG 11.2 progress in Tangerang.

Анализ применения технологий Индустрии 4.0 в интеллектуальных транспортных системах

The intensive development of digital technologies, their active implementation in the field of transport and logistics necessitates the study of their impact on the transport system, in particular on the level of its intellectualization. This will allow us to determine which technologies of Industry 4.0 need to be implemented first of all in order to achieve the best parameters for the development of intelligent transport systems. In this regard, the paper analyzes various tech-nologies of Industry 4.0, identifies possible ways of their application to solve problems in the context of the functioning of intelligent transport systems and their elements. Based on the analysis, possible parameters of the impact of technologies on the development of intelligent transport systems are identified, which include physical and environmental safety of transport, information security, speed and accuracy of decision-making, physical safety of transport, resource efficiency, convenience and comfort, the presence of an entertainment component, etc. Based on the compari-son of the identified parameters with specific types of digital technologies, it is determined which technologies have a greater impact on the intellectualization of transport systems. These technologies include the Internet of Things, augmented reality AR and cloud solutions. The parameters that are more susceptible to the effects of digital technologies are also determined: the safety and efficiency of transport systems. The results obtained are of practical importance for the development of strategic solutions in the implementation of the concept of Industry 4.0 in the field of transport and logistics.

Dynamic Road Anomaly Detection: Harnessing Smartphone Accelerometer Data with Incremental Concept Drift Detection and Classification.

Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification. As a result, there is a need for novel approaches that can adapt to changing data environments and perform effective classification without relying on pre-existing training data. This study introduces a novel, real-time road condition monitoring technique harnessing smartphone sensor data, addressing the limitations of pre-trained models that lack adaptability in dynamic environments. A hybrid anomaly detection method, combining unsupervised and supervised learning, is proposed to effectively manage concept drift, demonstrating a significant improvement in accuracy and robustness with a 96% success rate. The findings underscore the potential of incremental learning to enhance model responsiveness and efficiency in distinguishing various road anomalies, offering a promising direction for future transportation safety and resource optimization strategies.

Ergonomic Evaluation in Public Transport Particularly in Public Transportation with an Anthropometric Approach in Tegal City

Public transportation plays an important role in facilitating portability, especially in urban areas. In Tegal City, public transportation is an important mode of transportation for its citizens. However, ergonomic assessments of public transport vehicles are often neglected, resulting in potential discomfort and welfare issues for passengers and drivers. This research evaluates the ergonomic design of public transportation using an anthropometric approach to adapt vehicle dimensions to the user's physical needs, such as seating, legroom and accessibility. The results of the analysis provide recommendations for improvements, including increased ventilation, seat adjustments and safety features, to create a more comfortable and safer transportation system. By collecting and analyzing anthropometric information from agent testing results on clients in Tegal City, this research asks questions to identify the imbalance between the physical size of public transportation and the needs of its users. It is hoped that these findings will suggest changes to the plan that increase comfort, reduce fatigue, and improve general well-being. It will also contribute to a broader understanding of how ergonomic standards can be linked to open transport frameworks to improve client experience and safety.

Application of vibration signals as independent and alternative information channels to support the reliability of positioning and prediction systems for transport means movement

One of the most important aspects affecting both the safety and reliability of transport systems is the issue of proper positioning of moving transport means in the context of tracking their location while moving within the transport network. Therefore, this article presents the issue of the importance of information for the operational reliability of a selected subsystem related to the detection of a rail vehicle before a railway-road crossing, which is a critical infrastructure element and a particularly dangerous element in the transport network. The article presents an original and own vibration method for predicting the movement of a transport means, using the example of early identification of approaching rail vehicles. Redundant information can be significant for the operational reliability of safety systems in transport. In this aspect, reliable and continuous, and above all, independent information flow is crucial, even at the cost of system redundancy.

Towards 6G vehicular networks: Vision, technologies, and open challenges

Towards 6G vehicular networks: Vision, technologies, and open challenges

These aren’t the droids you’re looking for: artificial intelligence and safety in future transport systems

These aren’t the droids you’re looking for: artificial intelligence and safety in future transport systems

Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems.

Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

ABSTRACTVehicular ad hoc networks (VANETs) in portable broadband networks are a revolutionary concept with enormous potential for developing safe and efficient transportation systems. Because VANETs are open networks that require regular information sharing, it might be difficult to ensure the security of data delivered through VANETs as well as driver privacy. This paper proposes a blockchain technology that supports trusted routing and deep learning for traffic prevention and security enhancement in VANETs. Initially, the proposed Feature Attention‐based Extended Convolutional Capsule Network (FA_ECCN) model predicts the driver's behaviors such as normal, drowsy, distracted, fatigued, aggressive, and impaired. Next, the Binary Fire Hawks‐based Optimized Link State Routing Protocol (BFH_OLSRP) is used to route traffic after trust values have been assessed. Furthermore, Binary Fire Hawks Optimization (BFHO) determines the best routing path based on criteria such as link stability and node stability degree. Finally, blockchain storage is supported by the Interplanetary File System (IPFS) technology to improve the security of VANET data. Additionally, the validation process is established by using Delegated Practical Byzantine Fault Tolerance (DPBFT). As a result, the proposed study employs the blockchain system to securely send data to neighboring vehicles via trust‐based routing, thereby accurately predicting the driver's behavior. The proposed method achieves a better outcome in terms of latency, packet delivery ratio (PDR), overhead packets, throughputs, end‐to‐end delay, transmission overhead, and computational cost. According to simulation results and efficiency evaluation, the proposed approach outperforms existing approaches and enhances vehicle communication security in an effective manner.