Transportation Safety Research Articles

Jerk (d(acceleration)/dt) as an operative variable in pneumatic tube transport (PTT).

Jerk, the rate of change of acceleration (d(acceleration)/dt), is a known operative variable in public transportation safety, but this term has never appeared in the literature regarding pneumatic tube transport (PTT) and specimen integrity. We investigated profiles of acceleration and jerk for 2 PTT routes within our hospital system. Acceleration data were collected for PTT for 2 routes (A, B) using an accelerometer. Acceleration vectors (a) were analyzed in terms of distributions of jerk (da/dt), and distributions of θ, the angle between successive acceleration vectors. Routes A and B had transit times of approximately 300s. Acceleration vectors (a) ranged in magnitude from 0 to 8 g. For B, a > 1.2 g comprised 29.0% of results, compared to 13.5% of results for A (ratio = 2.1). Jerk ranged from 0 to 94 g/s. For B, jerk > 0.5 g/s comprised 71.9% of results, compared to 32.5% of results for A (ratio = 2.2). θ ranged from 0 to 180 degrees. For B, θ > 5 degrees comprised 59.3% of results, compared to 26.6% of results for A (ratio = 2.2). Differences in distribution in acceleration, jerk, and θ ran in parallel as variables for comparison between 2 PTT routes. Jerk and θ are likely to be operative variables in effects of PTT.

Innovative spoke design for non-pneumatic tyres with enhanced load-bearing capacity and customizability for all vehicles

For over a century, pneumatic tyres have been a crucial part of automobiles. Recently, non-pneumatic (airless) tyres have emerged, offering innovation but facing challenges in complexity, vehicle safety, adaptability, and load-bearing capacity. This research introduces a new spoke design for non-pneumatic tyres that outperforms conventional honeycomb and diamond spokes tyres in load-bearing capacity, while being less complex and customizable for various vehicles, including public transport. The study is structured into two phases. In Phase 1, the spoke design is selected from various CAD models using SolidWorks. This involves comparing the parabola design against prior design studies and honeycomb and diamond spokes concerning total deformation, maximum shear stress, equivalent (von Mises) stress, and maximum principal elastic strain with the help of FEA software Ansys. Results show that the Parabola design surpasses the pneumatic tyres in terms of load-bearing capacity, The parabola spoke design shows a consistent vertical stiffness under varying loads, ranging from 1500 N to 4500 N, with no sudden spikes or drops indicating structural instability. The objective is to identify the spoke design that best meets the performance objectives for phase 2 analysis. In Phase 2, the material selection and analysis are conducted. The goal is to determine the optimal polyurethane durometer rating for bus tyres. In this phase, the insights from Phase 1 are applied to scale up the design and choose suitable materials, showcasing the design’s adaptability from car tyres to bus tyres. Ansys will be used for analysing the total deformation of the tyre under a force of 40,000N, simulating the extreme operating conditions for bus tyres. This paper provides valuable data on non-pneumatic tyre spoke design, material selection, deformation analysis, design optimization, and standardization, enhancing vehicle safety and performance in public transport.

Multidimensional information fusion and broad learning system-based condition recognition for energy pipeline safety

Mechanical activities near energy pipelines pose a significant threat to energy transportation safety and energy system supply. The distributed acoustic sensing (DAS) system, which is an emerging intelligent sensing technology, can help to identify threat activities outside the pipeline. However, existing research has not distinguished fine-grained threat conditions because of insufficient information utilization and real-time requirements in the DAS system dataset, resulting in a large number of false alarms and time-consuming training in practice. To address these issues, this study proposes a pipeline radial threat condition recognition model based on multidimensional information fusion and a broad learning system (MIFBLS). First, the signal is preprocessed to improve the signal-to-noise ratio and generate the original features. Then, the extracted multidimensional temporal features are dimensionally reduced through information entropy, and the time–frequency map is fused and extracted based on a pretrained deep module, thereby achieving information fusion. Finally, the BLS incremental learning strategy is adopted to enhance the updating ability of the model and reduce the burden of data increments on the model training. Comparative experiments on a real-world natural gas pipeline dataset demonstrate the effectiveness and efficiency of the proposed method, indicating the potential for real-time monitoring and intelligent identification in pipeline transportation scenarios.

Study on Design Method of Diversion Pier in Channel Entrance Area with Complex Flow Characteristics

The entrance area is the main way for ships to enter and exit the approach channel. During the navigation period, the approach channel, the entrance area and the connecting section should avoid turbulence, whirlpool and other bad flow conditions. When conditions are limited and cannot be avoided, engineering or non-engineering measures should be taken to make it harmless to ensure the efficiency and safety of shipping. Aiming at improving the navigation safety effect of the estuary area, this paper analyzes the effect of improvement measures under the actual navigable flow condition by establishing the flow information fitting model and the prediction model of diversion pier setting, and provides the design method and basis for the installation of diversion pier in the estuary area to be predicted. It enriches the model base and knowledge base of the channel entrance area with complex flow characteristics, which has important practical significance and engineering value for shipping safety and waterway management.

Weather forecast image classification based on KNN, CNN, and VGG19

Image classification is an important technology in the field of computer vision, with the main objective of categorizing input images into different classes. It is used in many areas, such as weather forecasting. The significance of weather forecast image classification spans several areas, including enhancing weather forecast accuracy, aiding agriculture, optimizing the energy sector, ensuring transportation safety, and informing urban planning and construction. In this study, the author investigates the precision and efficiency of three machine learning algorithmsConvolutional Neural Network (CNN), K-Nearest Neighbor (KNN), and Visual Geometry Group 19 (VGG19)in classifying weather forecast images. The research meticulously elucidates the methodologies underlying each algorithm, outlining their distinct architectures, training processes, and feature extraction mechanisms. The research highlights that the self-built CNN model excels in accuracy and efficiency, while VGG19 achieves higher accuracy (93%) with longer training time. In contrast, the KNN model shows shorter training time (32.50 seconds) with slightly lower accuracy (80%).

Policy evaluation and performance assessment for sustainable urbanization: a study of selected city corporations in Bangladesh

Bangladesh is going through an unprecedented period of urban growth, and its cities are having to deal with rising service, housing, and infrastructural needs. It is critical to comprehend how urban government promotes sustainable development as the nation struggles with the intricacies of urbanization. This study aims to investigate how urban governance impacts sustainable urbanization at the city corporation level in Bangladesh, focusing on Goal 11 of the Sustainable Development Agenda, which is “sustainable cities and communities.” The study selected Rajshahi and Gazipur City Corporations as case studies and employed Principal Component Analysis (PCA) and descriptive statistics methods to gather data from primary and secondary sources. The findings of the study reveal gaps in urban governance, highlighting inconsistencies between the expectations of stakeholders and the reality on the ground. Stakeholders express high expectations for housing access, infrastructure development, disaster management, transportation, and environmental sustainability, but the current state falls short in several areas. Further, the study proposes several recommendations to enhance urban governance, including fostering participatory approaches, ensuring sufficient budget allocation for infrastructure development, addressing housing challenges, enhancing women’s safety in transportation, and strengthening disaster management systems.

A new risk level identification model for aviation safety

The aviation safety reporting system (ASRS) data, which are accident investigation reports issued by the national transportation safety board (NTSB), are often used for aviation risk level identification modeling. However, ASRS data suffer from sample imbalance and inaccurate encoding of textual information. To address these issues, this paper proposes a novel risk identification model. Initially, the pre-trained sentence bidirectional encoder representations from transformers (SBERT) model is fine-tuned using the textual information in the ASRS data, improving the accuracy of the text encoding. Subsequently, a generative adversarial network (GAN) is employed to generate samples for underrepresented categories in the ASRS data, thereby addressing the sample imbalance problem. Finally, a bayesian optimization algorithm is integrated to automatically search for the hyperparameters of the risk identification model, further enhancing the model's performance in risk level identification. Experimental results demonstrate that not only does the SBERT fine-tuning substantially improve the accuracy of text information encoding, but both data augmentation and automatic hyperparameter search also significantly contribute to the performance improvement of the risk level recognition model.

Формування умов ефективного функціонування транспортної системи автотранспортного підприємства

Road transport enterprises must constantly pay attention to increasing the efficiency of the production of quality transport services, in accordance with the requirements and expectations of consumers. During the production and provision of transport services, the flow of production processes in the transport system, problems often arise in the external and internal environment of the enterprise, which require a mandatory solution. The most common problems include the following: non-compliance of technical means with the requirements and expectations of service consumers in terms of the quality of passenger service, ensuring the necessary conditions for cargo transportation; significant negative impact of the transport process on the natural environment; not fully ensuring the safety of passenger transportation and ensuring the safety of traffic on the roads; imperfection of the system, methods, mechanism of planning and monitoring of transportation processes; imperfection of regulatory and legal regulation and control of the technical condition of vehicles in the organization of transportation; the level of driver training is not always sufficient. When conducting research on the organization of transport processes and ensuring traffic safety on roads, the enterprise must use a methodical approach to evaluate the process of production of transport services, the functioning of the enterprise's production process, which must include three main stages: development of indicators and mechanisms for evaluating the flow of processes; assessment of the efficiency of the transportation process; analysis of assessment results and development of measures to improve service production processes. The main tasks of improving the processes of service production are the formation of the transport system of the enterprise, which includes: transport flows, personnel and technical support of transport, road environment in the transport zone. The transport system and all its constituent parts must be provided with means of monitoring and mechanisms of influence and improvement of the processes of production of transport services. The process of improving the functioning of the enterprise's transport system should be conducted in three important directions: monitoring the transport process, studying and analysing the service market and taking into account the requirements and expectations of consumers, using calculation methods to determine negative impacts on the process and developing measures and mechanisms to prevent them.

Kendaraan Utama dan Pendukung PKP-PK Sebagai Penunjang Keselamatan Penerbangan

Indonesia is an archipelagic country that needs transportation in order to run the economy. Air transportation is the safest transportation at the moment, because it applies very strict standard operating procedures (SOP) for its operations. One of the most important aspects of air transportation is how airport managers ensure flight safety, namely by the presence of PKP-PK units. This research aims to understand the relationship between the main and supporting PKP-PK vehicles as supporting aviation safety. By using the literature review method as a research method, using various sources from journals, articles and books as references both online and offline. The results of this literature review article are: 1) The main PKP-PK vehicles have an influence in supporting flight safety; 2) PKP-PK support vehicles have an influence in supporting flight safety. The main and supporting vehicles are crucial factors in determining the effectiveness of handling emergency situations at the airport. Continuous efforts to improve the quality and readiness of PKP-PK units are a very valuable investment in maintaining the reputation of aviation safety.

Enhancing urban cycling safety: The role of automated helmet compliance monitoring with EasyDL

With an increasing emphasis on individual safety in urban transportation, our study addresses the critical issue of bicycle helmet compliance. Helmets play an essential role in preventing cycling-related injuries, yet their usage often falls short of safety standards. We utilize Baidus EasyDL, an automated machine learning tool, to detect the use of bicycle helmets in varied urban environments. This approach leverages a large, annotated dataset dedicated to bicycle helmets, achieving high precision in identifying helmet usage. Our preliminary tests in simulated urban settings show the methods capability to enhance safety measures effectively. This tool aids in real-time monitoring and pinpointing areas where helmet usage is neglected, aiming to elevate safety consciousness among cyclists. The study demonstrates the practicality of using automated helmet detection to improve urban cycling safety, providing a user-friendly option for those without a deep programming background. Our work underscores the value of EasyDL in analyzing bicycle helmet datasets and its potential role in reinforcing essential safety practices in urban cycling scenarios.

Maximum pitting corrosion depth prediction of buried pipeline based on theory-guided machine learning

Buried pipelines are crucial for the transportation of oil and natural gas resources. However, pipeline failure accidents have frequently occurred due to corrosion. Therefore, an accurate corrosion depth prediction model is necessary for the reliable supply of energy. In this paper, a theory-guided machine learning (ML) model is developed for maximum pitting corrosion depth prediction, the engineering theory and domain knowledge are integrated into feature space to improve the model interpretability. Firstly, several new feature variables are constructed based on the interactions between independent variables. Then, feature importance of all feature variables is obtained using random forest (RF). A hybrid multi-objective grey wolf optimization (HMOGWO) is proposed to optimize the hyper-parameters of RF model, considering feature number, prediction accuracy, and stability simultaneously. Finally, a comprehensive pitting corrosion dataset is utilized for performance evaluation. The results indicate that the proposed theory-guided model can achieve high prediction accuracy and stability, the optimal feature subset can be determined using multi-objective optimization method simultaneously, which solves the problems of model interpretability and feature selection for traditional ML models with the single-objective optimizer. This study is of great significance to the transportation safety of buried pipelines.

Research on High-Pressure Water Jet Interference for Collision Prevention of Waterway Viaduct Piers: Case Study of Guangzhou Lixinsha Bridge

In this paper, with the frequent occurrence of ship–bridge collision accidents as the context and the collision accident of the Lixinsha Bridge in China as the background, the scenario of a ship impacting a pier was simulated using ANSYS-FLUENT software, and the practical application possibility of the high-pressure water jet interference (HPWJI) anti-collision method was thoroughly investigated. Through the simulation analysis, the effectiveness of a high-pressure water jet with a total flow rate of 45 m3/s in altering the navigation direction of large-tonnage (2000 t) ships and avoiding obstacles was verified. Additionally, its impact on the stress of the ship steel plates and navigation status was also explored. It was found that, with reasonable layout and parameter adjustment, the high-pressure water jet technology could effectively intervene in the ship’s navigation trajectory while ensuring the structural safety of the ship, with minimal impact on the ship’s navigation stability and passenger comfort. Furthermore, the injection angle of the high-pressure water jet had a significant impact on the deflection and deceleration of the ship. Specifically, when the water jet impacted the ship along its forward direction, it could effectively increase the ship’s deceleration and deflection time, reducing the speed from 2.55 m/s to 1.7 m/s, a decrease of approximately 33%, significantly enhancing collision prevention effectiveness. This research provides important guidance for the practical application of high-pressure water jet collision prevention technology and is of great significance for improving the safety of waterway transportation.

Design of Ship Trajectory Control Method Integrating Self Disturbance Rejection and Neural Network

To reduce the ship trajectory control difficulty, a ship trajectory control method based on active disturbance rejection control and radial basis function neural network is designed. Firstly, a separated model structure is proposed to model the ship’s navigation system, followed by the introduction of active disturbance rejection control technology to control the ship’s trajectory, and the fusion of radial basis function to improve the parameter adjustment effect. It was found that on the AIS and MSSIS datasets when the system was iterated 25 and 22 times, respectively, the fitness values of the research method were as high as 99.46 and 99.51. In addition, when the accuracy of all algorithms was 0.900, the recall rate of the research method was significantly the highest, at 0.752. When the recall rate was 0.900, the accuracy of the research method was significantly the highest, reaching 0.869. In practical applications, when there was no external interference, the heading was a square wave signal, and when the time reached 90.11, the proposed method operated with a small difference between the planned heading and the actual heading. In addition, it was found that the control effect of the research method on ship operation remains highly stable when external interference is added during ship operation. The above results indicate that the designed method has significant advantages in ship trajectory control tasks and can effectively enhance the navigation safety and stability of ships.

Effect of Grain Corridor Agreement on Grain Prices

This study investigates the impact of the Grain Corridor Agreement (GCA), particularly in the aftermath of the Russia–Ukraine conflict, on the prices of major grains (wheat, maize, and barley), pivotal for global sustenance. By delineating three significant shocks: the initiation of the conflict, the enforcement of the GCA, and Russia's subsequent withdrawal from it, we employ an Integrated GARCH (IGARCH) model to investigate the impact of the Russia–Ukraine conflict on grain prices. Our empirical findings reveal that all grain prices surged at the onset of the conflict, with barley experiencing the most pronounced increase. Additionally, volatility escalated across all grain prices during the conflict's inception, albeit subsiding upon the implementation of the GCA. Price volatility spiked initially but decreased with the GCA's enforcement. The evidence suggests that the conflict is driving up world grain prices and causing global vulnerability, and that conciliatory policies such as the GCA offer a short-term solution. However, long-term strategies should focus on reducing external dependence by reviewing agricultural policies and promoting domestic production. Moreover, policymakers are advised to consider both domestic and global market vulnerabilities when designing sound policies. Highlights International grain prices (wheat, maize and barley) spiked during the onset of the ongoing Russia–Ukraine conflict. The conflict triggered an international response to resume safe maritime humanitarian transportation of agricultural grains via GCA. We develop an empirical framework to assess the impact of the Russia–Ukraine conflict on grain prices. Empirical findings indicate that Russia–Ukraine conflict increased all grain prices.

Vortex safety when flying at an assigned flight level

Air traffic intensity between countries and within individual countries is increasing year by year. As a rule, airways follow the same routes. As a result, so-called “roads in the sky” are formed. And where there are roads, there are bumps by the time, in the form of CAT, updraughts and downdraughts and increased turbulence. Horizontal and vertical separation plays an important role in ensuring flight safety on route. Currently, a variety of regulatory documents, defining safe separation at the flight level, has been adopted. Thus, provided there is turbulence in the vortex wake, longitudinal separation is based on the arrangement of aircraft types by three categories according to their maximum certified take-off weight. Since November 2011, the Western standard of vertical separation RVSM (Reduced Vertical Separation Minimum) has been introduced in Russia. Vertical separation is the distance between the vertical flight levels on route. Previously, this distance amounted to 600 m (2000 ft), but due to the increasing intensity of air traffic, it was decided to reduce the vertical separation to 300 m (1000 ft). Hence, at the most common flight level, the vertical separation is 300 m. The question arises if this separation ensures the safety of air transportation? The fact is that the altitude of the flight level does not necessarily coincide with the actual aircraft height. Aircraft altimeters are, inherently, calibrated barometers, that calculate the altitude by the difference in pressure on the ground and in the air. To calculate the height above ground, it would be necessary to constantly input atmospheric pressure data to altimeters at each waypoint and take into consideration the waypoint altitude above the sea level. Consequently, it is customary to use standard pressure. If the same pressure values are set on the altimeter on all aircraft, then, altitude readings on the instrument at an assigned point of airspace will be similar. Therefore, from a certain moment during the climb (transition level) to a certain moment during the descent (transition level), the aircraft height is calculated according to the standard pressure. The value of the standard pressure (QNE) is the same all over the world and amounts to 760 mmHg (1013.2 hectopascals). Thus, the flight on route is controlled by an altimeter, a barometric altimeter, which is comprised into the integrated flight and navigation system. An analysis of the instrument accuracy shows that when atmospheric pressure drops, altimeter readings may differ from true reading by ±100 m. It is known that a trailing vortex forms behind a flying plane. By the time, the trailing vortex descends and may be found at another flight level. May this cause air bump at the flight level? To answer this question, the A-380 aircraft was chosen as the object of research. This is one of the largest aircraft in the world. Therefore, the study of a trailing vortex behind the A-380 at the flight level, as the most dangerous in terms of the impact of its trailing vortex on other aircraft, will allow us to understand how safe and reasonable the accepted vertical and horizontal separation is. For the study, the special computational software system, based on the discrete vortex method, was used. This complex has passed the evaluation test and the state registration.

Experimental Investigation on Thermal Runaway of Lithium-Ion Batteries under Low Pressure and Low Temperature

Understanding the thermal runaway mechanism of lithium-ion batteries under low pressure and low temperature is paramount for their application and transportation in the aviation industry. This work investigated the coupling effects of ambient pressure (100 kPa, 70 kPa, 40 kPa) and ambient temperature (−15 °C, 0 °C, 25 °C) on thermal behaviors in an altitude temperature chamber. The experimental results indicate that lowering ambient pressure and temperature could attenuate the thermal runaway intensity, which is mainly attributable to the reduction in oxygen concentration and the increase in heat loss. Such a dual effect leads to the maximum temperature decreasing from 811.9 °C to 667.5 °C, and the maximum temperature rate declines up to 2.6 times. Correspondingly, the whole thermal runaway process is deferred, the total time increases from 370 s to 503 s, and the time interval, Δt, from safety venting gains by 32.3% as the ambient pressure and temperature decrease. This work delivers an in-depth understanding of the thermal characteristics under low pressure and low temperature and provides meritorious guidance for the safety of cell transportation in aviation.

Advancing youth transportation safety in North Carolina through addressing alcohol-related motor vehicle collisions

The aim of this work is to analyze trends in youth transportation fatalities and injuries in North Carolina (NC), assess the implementation of ignition interlock devices (IIDs) in the United States and abroad, discuss policy implications for IIDs, and highlight health equity considerations related to motor vehicle collisions (MVCs). MVCs cause the highest number of unintentional injury-related deaths for children and teenagers in NC, and policymakers should pay special attention to MVCs related to alcohol consumption. IIDs are effective in reducing collision rates and recidivism for driving under the influence of alcohol (DUI). Ignition interlock device requirements have been increasingly implemented globally over the past three decades. However, the adoption of stricter IID policies after first-time DUI offenses in NC and across the U.S. is a prudent public health measure to enhance transportation safety for both adults and children. Evidence-based interventions such as IIDs must also strive to address inequities in transportation safety, and the framing of proposed policies should reflect the tenets of cultural humility.

Contraband detection method in X-ray image based on improved CenterNet network

Security inspection has been played a crucial role in ensuring the safety of railway passenger transportation. The detection of contraband in X-ray images is an important part of safety inspection work. Since the X-ray image of security inspection is a pseudo color image with a unique imaging style, and the restricted objects are mostly small targets, the accuracy of the object detection algorithm using visible light images directly is not high. This research will propose an improved CenterNet method, and design a 16 times down sampling rate backbone network, which is more suitable for detecting contraband in X-ray images. This article will introduce attention mechanisms to increase attention to effective and key areas in X-ray images, and reveal the mechanisms of spatial and channel attention in X-ray images. Furthermore, by designing an adaptive feature fusion mechanism, the drawback of the original CenterNet by using only the last layer of output features is compensated. Experiments have shown that on the SIXray dataset, mAP is improved by 3.3% compared to the original CenterNet method, and 5.5%, 2.4%, 3.6%, 10.1% compared to SSD, Yolo V3, FPN, DERT algorithms. The detection accuracy of restricted items in X-ray images is effectively improved.

Navigating the Skies: Unraveling Communication Challenges in Diverse Aviation Accidents

Effective communication is the lifeline of aviation safety, serving as the cornerstone for preventing accidents, mitigating crises, and ensuring seamless coordination among diverse stakeholders within the complex and dynamic aviation environment. The aim of this study is to provide a qualitative analysis of the 10 fatal accidents in aviation history and incidents and accidents that occurred in the past 10 years registered by the National Transportation Safety Board (NTSB). The content analysis of the reports of the fatal accidents and reports of the 48 events identified in the accident database search revealed themes such as miscommunication between flight crew members, challenges in emergent situations, and communication breakdowns with ATC. These findings align with existing literature, emphasizing the pivotal role of communication in aviation safety. The study offers recommendations to invest in CRM training, standardized phraseology, and advanced communication technologies for aviation safety. Future research directions include exploring human-machine interaction, artificial intelligence, and communication in Unmanned Aerial Systems. Moreover, grounded in these findings, airline companies can infer practical implications such as improved safety, operational efficiency, stress reduction, cost savings, regulatory compliance, and trust-building, thereby emphasizing the industry's commitment to continuous improvement in navigating the complexities of aviation communication.

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

Abstract Because of electrification conditions, key components of battery swapping systems (BSSs) for electric heavy trucks are always damaged by electric erosion, which presents challenges to the safety and efficiency of high-intensity transportation. Due to the special working conditions of a BSS, the fault diagnosis of its driving gear encounters several issues, including reciprocation motion, low and fluctuating speed, and complicated noises. To solve these problems, audio features, including Mel-frequency cepstral coefficients and Gammatone cepstral coefficients, are extracted from the vibration signals. Then, these features are utilized to construct an original dictionary. Next, based on data augmentation and dictionary learning, a robust dictionary is generated from the original dictionary. Finally, with the robust dictionary, sparse representation-based classification is integrated into AdaBoost to achieve accurate fault diagnosis for the driving gear in BSS. The effectiveness of the fault diagnosis scheme is validated based on the monitoring data of the BSS, and the accuracy of fault diagnosis is 99.17%.