Passenger Safety Research Articles

Effects of Anthropomorphic Driving Vehicles on Traffic Flow

Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people’s trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people’s acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehicles to explore their impact on traffic efficiency. The result shows that, compared to non-automated vehicles, both autonomous vehicles and anthropomorphic driving vehicles can improve traffic efficiency during congested periods, increase traffic volume per unit time, reduce the total travel time and time loss, and have a higher average speed. Compared to autonomous vehicles, anthropomorphic driving has a shorter total travel time and a similar time loss. In terms of average speed, anthropomorphic driving performed better than autonomous driving in terms of both congested and non-congested times. In summary, compared to non-automated vehicles, autonomous driving vehicles have a positive effect on improving traffic efficiency, while, compared to autonomous driving, anthropomorphic driving has more advantages in increasing traffic efficiency and reducing traffic congestion.

A survey of pilot behavior detection methods

This research investigates the use of action and gesture detection technology to analyze pilot behavior and ensure flight safety by employing artificial intelligence. In civil aviation, pilot and passenger safety is of paramount importance ,and the pilot behavior is an important factor in safety. Thus, it is necessary to detect the pilot behavior to secure the flight process which involves the detection of multiple modalities. In the past, most researchers have focused on single-modal behavioral detection methods, primarily gestures. However, a single modality, which may also include physiological data like EEG, is not a good and comprehensive understanding of a series of scenarios. Therefore, this paper proposes using multimodal data fusion to integrate behavior detection data from multiple sources. This paper summarizes and outlines the main methods of pilot behavior detection, such as cockpit voice, gesture detection and text information, and look forward to the possibility of multimodal data fusion of these there modalities data

A high‐altitude thermal infrared method for estimating moose abundance and demography in Rocky Mountain National Park, USA

Resource managers require accurate estimates of large herbivore abundance and demography to maintain ecological integrity. Common methods to count these species, including observations from low altitude helicopter flights, may conflict with other protected area management objectives and struggle to produce precise estimates for more cryptic species. To address these issues, we evaluated thermal infrared (TIR) assisted counts of Shiras moose Alces alces shirasi in a temperate montane ecosystem of Colorado, USA. We binned the 694.7 km2 study area into two strata, depicting higher and lower moose habitat suitability. Sixty transects (each 536 m wide by 5 km long) were randomly selected and flown from fixed wing aircraft flying at altitudes centered around ~ 610 m (2000 ft) above ground level (AGL) in July 2019 and again in July 2020. We determined abundance and demographic information using double‐observer line distance sampling with estimates produced using n‐mixture models. Detection probability of groups on the line, and of individuals within a group, conditional on the group being observed, were high. Mean moose density estimates across the study area were consistent from year to year, with reasonable confidence. We estimated 0.215 moose km‐2 (HDI 0.145, 0.286) in 2019 and 0.207 moose km‐2 (HDI 0.144, 0.276) in 2020. Within‐stratum estimates varied from year to year, likely an effect of the definition of suitable habitat and transect classifcation. Estimated ratios of bulls per cow and calves per cow fell within expected ranges for Shiras moose in Colorado but did vary across years. Thermal clutter created some impacts to TIR video quality. Our study indicates that this approach can reliably estimate moose densities in forested and topographically complex environments, while maintaining greater aircraft altitude above ground level when compared with traditional aerial survey methods. This reduced disturbance to wildlife, impacts to wilderness and improved aviator and aircraft passenger safety.

Perceived risk and acceptance of automated vehicles users to unexpected hazard situations in real driving conditions

ABSTRACT The decision-making systems in highly automated vehicles use artificial intelligence (AI) techniques to replicate human-like behaviour in driving. However, when studying the acceptance of such vehicles, analysing the human factor is essential if their deployment is to be a success. This paper presents an analysis, based on real driving conditions, of perceived risk and occupant acceptance of an SAE level 4 automated vehicle (where the user adopts only the role of a passenger) in two unforeseen situations involving risk, one in which the life of a pedestrian may be endangered and the other in which the life of the user himself may be endangered. To assess perceived risk, pupil diameter, a physiological variable closely related to emotional arousal, was measured. The study involved 40 participants. The study revealed that perceived risk is significantly higher when the user's personal safety is compromised, reinforcing the principle of prioritising passenger safety in autonomous vehicle design. Key findings include an inverse correlation between perceived risk and driving experience, and no evidence suggesting that prior automated vehicle experience modifies future usage expectations. The research emphasises the critical role of human-centered design in AI-driven decision-making systems, highlighting the importance of understanding psychological and physiological responses to autonomous technology.

Landscape lexical diversity for readability and safety mitigation on the commuter line bilingual information board

This study examines lexical diversity and readability of bilingual information boards on the Commuter Line Tanah Abang-Rangkasbitung in Indonesia, with a focus on their impact on passenger safety. It is motivated by observed discrepancies in translations, which could compromise clarity and effectiveness of public signage. The primary objective is to analyze lexical diversity and assess the readability of these translations using theoretical frameworks from Nida and Taber (2003), Newmark (1988), and Venuti (2012). The study employs a qualitative descriptive research design with Spradley’s Ethnographical Approach, focusing on the Green Ring Route, which encompasses 19 CL stations. Data were collected through direct observation, photographic documentation, and field notes and analyzed using domain, taxonomy, componential, and cultural theme. The findings reveal significant variation in readability levels, influenced by the translation techniques used. High readability was found in translations employing Literal Translation and Established Equivalent techniques, while medium and low readability were associated with Borrowing, Modulation, and Amplification techniques. These findings underscore the need for consistent, culturally relevant translation practices to ensure effective communication and passenger safety. The study concludes by recommending regular usability testing, feedback integration, and use of technology to enhance the accessibility and effectiveness of bilingual signage in public transportation.

Signalling Solution for Railway Diamond Crossing using Weight Sensor for Passenger Safety

Signalling Solution for Railway Diamond Crossing using Weight Sensor for Passenger Safety

Cybersecurity in Aviation: A Case-Based Approach to Preparedness

With interconnected systems governing everything from flight management to passenger services, cybersecurity has emerged as a critical concern. This paper presents a comprehensive case-based review of cybersecurity preparedness in the aviation industry. Through an analysis of notable incidents such as data breaches, system outages, and cyber-attacks, the study explores the vulnerabilities inherent in aviation systems. It examines the impact of these incidents on passenger safety, operational continuity, regulatory compliance, and economic stability. Furthermore, the paper evaluates the effectiveness of existing cybersecurity measures and identifies areas for improvement. Drawing insights from real-world cases, the study aims to provide valuable lessons and recommendations to enhance cybersecurity preparedness in the aviation sector. By delving into the complexities of cybersecurity challenges faced by the industry, this review seeks to contribute to a deeper understanding of the evolving landscape of cybersecurity in aviation and the imperative for proactive measures to safeguard critical infrastructure and ensure the safety, security, and reliability of air travel in an increasingly digitalized world.

Child Passenger Safety in Indonesia

Current Indonesian road safety laws, particularly Law No. 22 of 2009 on Traffic and Road Transport, lack specific provisions for the use of child restraint systems, which significantly reduces child passenger safety. Unlike many developed countries with strict regulations that mandate child-specific safety equipment, Indonesian regulations currently mandate only standard seatbelts designed for adults. This inadequacy poses a substantial risk to children because of their physical vulnerability. This study examines the urgency of specific regulations regarding child safety restraints for child passengers in Indonesia. This research employs a normative legal approach to analyze Indonesia's existing legislation alongside international practices. The findings underscore those countries such as the United States, the United Kingdom, and Australia have effectively lowered child accident rates through mandatory child safety restraints and public awareness campaigns. The study concludes that adopting similar practices in Indonesia could enhance child passenger safety, reduce injury rates, and improve overall road safety for young passengers. The implementation of specific child restraint laws and public awareness initiatives are recommended to ensure comprehensive legal protection for child passengers in Indonesia.

Changes in child restraint practices in Shenzhen, China three years after the enactment of local legislation: two population-based cross-sectional surveys.

The enactment of child restraint systems (CRSs) legislation is highly effective in increasing CRS practices. However, evidence from low- and middle-income countries is still lacking. This study aimed to assess the changes in CRS practices in Shenzhen, China following the implementation of CRS legislation. Data from two cross-sectional surveys conducted in community health service centres and kindergartens 1 year before and 3 years after the enactment of mandatory CRS legislation in 2015 were used to assess the changes in CRS practices in Shenzhen, China. Temporal changes in CRS practices were investigated, and logistic regression models were performed to examine the differences in CRS practices 3 years after the legislation compared with the period before the legislation. The proportion of CRS possession and use increased from 27.8% (1047/3768, 95% CI: 26.4% to 29.3%) to 72.6% (4900/6748, 95% CI: 71.5% to 73.7%) and from 22.9% (864/3768, 95% CI: 21.6% to 24.3%) to 56.3% (3800/6748, 95% CI: 55.1% to 57.5%), respectively, with a decrease of appropriate CRS use from 75.9% (656/864, 95% CI: 72.9% to 78.7%) to 69.7% (2649/3800, 95% CI: 68.2% to 71.2%) after the implementation of CRS legislation. The findings indicate a significant improvement in CRS possession and use in Shenzhen, 3 years after the enactment of mandatory CRS legislation. Further efforts to update the local legislation to provide specific guidelines for appropriate CRS use and implement targeted multifaceted interventions are needed to increase optimal CRS practices for better child passenger safety in Shenzhen.

Crashworthiness topology optimisation of a crash box to improve passive safety during a frontal impact

AbstractThis paper focuses on the application of topology optimisation algorithms to improve the crashworthiness of heavy passenger vehicles, in frontal impact conditions. The objective is to find the optimal arrangement of material to minimise compliance, satisfying a volume constraint. Ultimately, it is intended to analyse the influence of this component in the energy absorption capability of the vehicle structure during a crash. Crashworthiness design plays a crucial role in the automotive industry, particularly in enhancing passenger safety. It aims to develop structures that can absorb more energy while minimising intrusion, to maintain the driver’s survival space. If so, crashworthiness design deals with conflicting objectives, and optimisation methods can be used to find a compromise between these parameters. The application of topological optimisation in the context of vehicle structure crashworthiness is still limited, mainly due to the high computational costs associated with crash simulations that deem these approaches impractical. To tackle this difficulty, this study performed the optimisation process on a single component, a crash box, employing optimisation algorithms built within a Matlab code, that iteratively interfaces with Abaqus® where the non-linear crash simulation is performed. Afterwards, the optimised component was incorporated into the coach chassis baseline and tested using an already established finite element model on VPS/PamCrash®, simulating a frontal impact, according to the ECE R29 regulation. The application of this methodology demonstrated that evolutionary algorithms can effectively be applied for topology optimisation under crashworthiness conditions, generating an optimised crash box that improves the crashworthiness metrics of the coach baseline structure.

A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas.

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer-Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin-destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures.

Fault diagnosis of elevator systems based on multidomain feature extraction and SHAP feature selection

In smart buildings, elevator faults may affect the traveling efficiency and the safety of passengers. It is important to find a quick and accurate method for fault diagnosis to minimize disruption time. Most existing fault diagnosis methods are for gear systems, which ignore the characteristics of elevators and need adaptations before applying them to the elevators. This study presents a novel multidomain feature extraction method for elevator fault diagnosis. It calculates the time domain features and wavelet packet energy and then extracts the elevator frequency features. These multidomain features are utilized as the input of a support vector machine to diagnose elevator faults. Subsequently, SHapley Additive exPlanations is used to select features to remove redundant features without reducing diagnosis accuracy. Comparative experiments show that multidomain feature extraction based on triaxial data improves diagnosis accuracy. Additionally, the elevator frequency feature extraction method further enhances diagnosis accuracy. In addition, experiments demonstrate that SHapley Additive exPlanations feature selection can be effectively applied to remove redundant features in different fault diagnoses, which improves the training efficiency and reduces the risk of overfitting. Practical applications In this paper, we present a practical solution for elevator fault diagnosis in intelligent buildings. By the proposed method, maintainers can find elevator faults based on objective evidence rather than intuitive feeling. We utilize a phone instead of the vulnerable and expensive equipment to collect and analyze signals, which makes the cost for this solution inexpensive and propagable. Thus, this solution can be applied to most practical elevator maintenance to reduce the elevator downtime.

Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation.

Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks. To address this issue, we propose an advanced artificial intelligence (AI) solution for identifying unsafe behaviours in public transport. The proposed approach employs deep learning action recognition models and utilises technologies like NVIDIA DeepStream SDK, Amazon Web Services (AWS) DirectConnect, local edge computing server, ONNXRuntime and MQTT to accelerate the end-to-end pipeline. The solution captures video streams from remote train stations closed circuit television (CCTV) networks, processes the data in the cloud, applies the action recognition model, and transmits the results to a live web application. A temporal pyramid network (TPN) action recognition model was trained on a newly curated video dataset mixing open-source resources and live simulated trials to identify the unsafe behaviours. The base model was able to achieve a validation accuracy of 93% when trained using open-source dataset samples and was improved to 97% when live simulated dataset was included during the training. The developed AI system was deployed at Wollongong Train Station (NSW, Australia) and showcased impressive accuracy in detecting violence incidents during an 8-week test period, achieving a reliable false-positive (FP) rate of 23%. While the AI correctly identified 30 true-positive incidents, there were 6 cases of false negatives (FNs) where violence incidents were missed during the rainy weather suggesting more data in the training dataset related to bad weather. The AI model's continuous retraining capability ensures its adaptability to various real-world scenarios, making it a valuable tool for enhancing safety and the overall passenger experience in public transport settings.

Development of a Scaled Down Test-Rig for Wheel–Rail Contact Thermal Experiments Using Optical Sensors

Abstract Rail wheel contact measurement is crucial for several reasons. First and foremost, it directly affects the safety of passengers, crew, and the general public. Accurate measurements help identify irregularities in wheel-rail contact, such as wheel defects, wear, or track anomalies, which can lead to derailments or accidents if left unaddressed. An inventive technique for measuring the temperatures at rail wheel contact at various speeds is presented in this research. The novel approach uses a 1:5 scaled-down test rig model of a wheel and rail with Fiber Bragg Grating (FBG) sensor to combine the experimental and finite element analysis simulation to determine the rail wheel contact temperature. By employing the various data acquisition and data analysis techniques rail wheel contact temperature at different speeds ranging between 10kmph to 40kmph was determined 1538.735 nm to 1538.831nm with centre wavelength of 1538.438 nm. The results illustrate the possibilities of the downsized test rig with experimental observations at varying speeds by examining the benefits of FBG sensors over traditional sensors. The experimental results are used to determine the equivalent wavelength shift. Fibre Bragg Grating (FBG) sensor design and simulation are done with the Grating MOD optical tool. For this temperature range and Bragg's wavelength of 1538.438 nm, the sensitivity of Fiber Bragg Grating is observed to be 13.6pm/°c.

Maintenance in aviation enabled by Industry 4.0 technologies: exploring the current research trends using a topic modeling approach

PurposeSince passenger safety is critical, aviation maintenance is essential. Aviation maintenance management is changing due to Industry 4.0 (I4.0). According to earlier research, I4.0 technologies improve aircraft manufacturing efficiency and responsiveness through automation, predictive maintenance and process self-optimization. Thus, this study examines I4.0 research and aircraft maintenance's potential interaction.Design/methodology/approachUsing a text-mining methodology, this paper looks at the state of the art in aviation maintenance research in the I4.0 era. We used the topic modeling approach and Latent Dirichlet Allocation (LDA) technique to analyze the abstracts and indexed keywords of 929 research articles on the intersection of aviation maintenance and I4.0, subsequently clustering them into eight topics.FindingsWe have mapped out the emerging research trends at the intersection of “aviation maintenance” and “I4.0 technologies”, and presented suggestions for theoretical frameworks, applied frameworks and future lines of inquiry. This paper makes a theoretical contribution to the systematization of literature on I4.0 technologies in aviation maintenance. It provides valuable insight for managers by exploring the implications and opportunities that arise in light of recent innovations brought by I4.0 in aviation maintenance.Originality/valueThis study focuses on the use of Industry 4.0 technologies in aircraft maintenance processes, contributing to the growing research on digital technology in maintenance and maintenance, repair and overhaul (MRO). Furthermore, the study's analysis of the LDA topic model provides valuable insights for future research on using I4.0 technologies to investigate specific areas of application in the context of digital maintenance.

PERLINDUNGAN HUKUM TERHADAP KERUSAKAN DAN KEHILANGAN BARANG DALAM PENGANGKUTAN UDARA

Basically all items can be included in checked baggage on an airplane. However, some airlines place restrictions on passenger items that can be carried in aircraft luggage for the comfort and safety of passengers. Items that are prohibited from checked baggage are valuables. These items can be carried by passengers in checked baggage if approved by the airline. This research aims to determine the legal protection for air transport consumers in the event of loss of goods in aircraft baggage and to determine the suitability of the Jakarta District Court decision in Case Number 172/PDT.G/2009/PN.JKT.PST with the law on the protection of air transport consumers for lost goods. -items in airplane luggage. This research uses a type of normative juridical research. The approaches used in this research are the statutory approach, conceptual approach and case approach. The data sources used are primary legal materials, secondary legal materials and tertiary legal materials. In this research, the author used data collection techniques using library research and analyzed the data deductively, namely drawing conclusions from a general problem regarding the concrete problems faced. The judge's decision does not provide justice for consumers where consumer losses are the responsibility of business actors. Judges in judicial institutions should be able to emphasize the losses experienced by consumers in order to provide protection for the interests of each individual by forcing business actors to carry out their obligations by passing decisions fairly and in accordance with the function of the law.

The Influence of Occupant Characteristics, Seat Positioning, and Pre-Crash Maneuvers on Front Passenger Safety Performance.

Crash avoidance vehicle maneuvers are known to influence occupant posture and kinematics which consequently may influence injury risks in the event of a crash. In this work, a generic buck vehicle finite element (FE) model was developed which included the vehicle interior and the front passenger airbag (PAB). Seat position and occupant characteristics including anthropometry, sex, and age were varied in a design of experiments. Two pre-crash maneuvers representing 1) a generic 1g braking and 2) turning-and-braking scenarios were simulated. Rigid-body human models with active joints (GHBMCsi-pre models) obtained by morphing a 50th male model to selected anthropometries were used in pre-crash simulations. The kinematics data of belted GHBMCsi-pre models at the end of the pre-crash phase were transferred using a developed switch algorithm to the corresponding morphed GHBMC occupant simplified (OS) models to predict occupant injury risks. During both pre-crash maneuvers, the occupant's head and thorax moved forward towards the dashboard. Therefore, the head and thorax contacted the PAB earlier, leading to lower head accelerations when the pre-crash phase was considered. Overall, it was concluded that pre-crash braking decreased the severity of injury sustained by the passenger. Seat track position and seat recline angle showed the highest influence on the head injury criteria (HIC). The brain injury criteria (BrIC), and neck injury criteria (Nij) were most sensitive to pre-crash maneuver type, seat recline angle, and occupant size.

An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection

Safety is crucial in the railway industry because railways transport millions of passengers and employees daily, making it paramount to prevent injuries and fatalities. In order to guarantee passenger safety, computer vision, unmanned aerial vehicles (UAV), and artificial intelligence will be essential tools in the near future for routinely evaluating the railway environment. An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection (UAV-RSOD) comprises high-resolution images captured by UAVs over various obstacles within railroad scenes, enabling automatic railroad extraction and obstacle detection. The dataset includes 315 raw images, along with 630 labeled and 630 masked images for railroad semantic segmentation. The dataset consists of 315 original images captured by the UAV for object detection and obstacle detection. To increase dataset diversity for training purposes, we applied data augmentation techniques, which expanded the dataset to 2002 augmented and annotated images for obstacle detection cover six different classes of obstacles on railroad lines. Additionally, we provide the original 315 images along with a script for augmentation, allowing users to generate their own augmented data as needed, offering a more sustainable and customizable option. Each image in the dataset is accurately annotated with bounding boxes and labeled under six categories, including person, boulder, barrel, branch, jerry can, and iron rod. This comprehensive classification and detailed annotation make the dataset an essential tool for researchers and developers working on computer vision applications in the railroad domain.

From bricks to bytes: AI-based airport digital transformation in practice

Air travel is one of the most developed and advanced forms of transport, the success of which is reflected in a high level of innovation and the safety and comfort of passengers. In the context of long-term growth in air travel, constant cost and capacity pressures at airports and rapid advancements in innovative technologies, numerous opportunities arise. The greatest risk for any airport would be to neglect digital transformation. Technological improvements based on innovative, sustainably oriented technologies have significant potential. This paper discusses how to address current challenges at airports using evolving technologies and artificial intelligence (AI). It examines the dynamics of ground handling operations and highlights the transformative impact of technology and innovation. Unlike landside operations, many airside processes at most airports remain manual and inefficient. The paper introduces an advanced solution for monitoring aircraft ground handling, enabling proactive management of flight delays and resource shortages due to changes in flight schedules. This tool offers insights into potential efficiency gains by using a system of cameras and AI for the automatic recognition of the start and end of aircraft turnaround processes, without human intervention in the process itself.

Short-term Subway Passenger Flow Prediction based on LSTM and ARIMA Model

With the acceleration of urbanization, urban traffic problems have become increasingly severe. To alleviate urban traffic congestion, the development of public transportation has been greatly emphasized. As a convenient and rapid means of urban travel, the metro has become the preferred choice for citizens, leading to significant passenger flow pressure. Therefore, it is crucial to forecast passenger flow in advance. This paper utilizes passenger flow data from Chengdu Metro Line 1 from May to September 2019 to conduct a study. LSTM and ARIMA models were constructed to simulate the actual passenger flow data. The prediction performance of both models shows that the trends simulated by these models fit the original data well, verifying their applicability in passenger flow forecasting. Furthermore, it was found that the LSTM model outperformed the ARIMA model among the constructed models in this paper. Accurate metro passenger flow forecasting can help relevant departments manage the metro system more effectively, thereby improving passenger travel efficiency and safety.