Train Accidents Research Articles

Accident Prevention of BOXN Wagon Train

The safety and efficiency of railway freight transportation are critical to the functioning of many national economies, and India is no exception. BOXN wagons, widely used in the Indian railway system for transporting heavy goods such as coal, minerals, and other bulk materials, are essential for supporting industries across the country. However, due to their size, weight, and the volume of cargo they carry, these wagons are prone to accidents if not properly managed. Accidents involving BOXN trains can result in severe consequences, including loss of life, damage to property, and environmental degradation. One of the biggest and most significant industries in the world is the Indian goods railway sector. It is essential for the transportation of materials and goods across the nation. Indian Railways seeks to shorten commute times by boosting the average freight train speed efficiently from 22 mph to 50 mph. The adoption of new technologies is anticipated to have positive effects on the Indian goods railway sector as well. Smart Innovations, for instance, smart goods cars and digital signaling can contribute to reducing train accidents and help to increase railroad productivity using smart mobility.

Intubating Laryngeal Mask Airway for Airway Management and Blind Tracheal Intubation Through It From 360° Around a Supine Patient: A Randomized Controlled Clinical Study in a Simulated Prehospital Emergency Scenario.

Introduction In emergency situations, airway management is often given priority over other treatment methods. The preferred technique for airway management of unconscious patients is endotracheal intubation, which has become the standard of care. Intubation of the trachea not only enables controlled ventilation even for extended periods and in any position but also allows for the removal of tracheal secretions. Supraglottic airways have several advantages over endotracheal intubation, including faster insertion,less need for neuromuscular blockade, and less hemodynamic instability. They can also be used as a bridge to intubation or as a rescue device when intubation fails or is contraindicated. The aim of this randomized controlled clinical study is to simulate a prehospital emergency/disaster scenario to evaluate and study the feasibility and effectiveness of the use of intubating laryngeal mask airway (ILMA) for onsite airway management from 360 degrees around the patient's head as in such situations, there may be limited or no access behind the head of the victim. Such a scenario can be extrapolated to disaster conditions where the victims may be trapped under the rubble following a building collapse /earthquake or are trapped in a vehicular road traffic/ train accident. It may take substantial time for extrication and evacuation of such patients to a hospital and hence it may be life-saving to provide prompt and early onsite airway management from wherever access is possible around the victim. We believe that the provision of a steel handle integrated with the airway tube may provide an opportunity for successful insertion of the device from 360 degrees around the patient merely by suitably changing the way the handle is gripped, so as to allow a single-handed smooth arc-like movement of the device for insertion, irrespective of the position of the rescuer relative to the patient's head. Objectives Our objective is to study the ease and time of insertion of ILMA, the number of attempts for successful ILMA insertion, and oropharyngeal leak pressure attained from unconventional positions in a supine patient. Materials and methods This prospective, randomized, observer-blinded controlled trial included 90 patients undergoing elective surgery under general anesthesia. Patients were randomized using a chit and box system for group allocation. Groups were as follows: Group 1 (n=30) - Investigator standing on the back of the head of the patient (0°); Group 2 (n=30) - Investigator standing on the left side facing the patient (120°); Group 3 (n=30) - Investigator standing on the right side facing the patient (240°). Then ease and time of insertion of ILMA, number of attempts for successful ILMA insertion and oropharyngeal leak pressure were noted, and intergroup comparison was done. Conclusion ILMA has proved to be an effective ventilatory device and a suitable conduit for intubation in patients lying in the supine position from a conventional standard position standing behind the head of the patient, as well as non-conventional position, facing the patient at 120° or 240° from the standard position.

Railway Infrastructure Cybersecurity: An Overview

The railway infrastructure constitutes a type of operational technology (OT)-based critical infrastructure, which is expected to work 24x7, 365 days a year, and where the life expectancy of operational equipment often exceeds 30 years. In this domain, an operational anomaly compromising the OT system can cause a train accident or interrupt traffic, with potentially significant impact in terms of business as well as for passenger safety. Due to their relevance, railways are strategic assets of national interest and, consequently, targets of interest for cybercriminals and cyberwarfare activities. For instance, service interruptions may trigger ripple effects resulting in product shortages and widespread supply chain disruptions, with severe impacts for both the economy and national security. In a bid to optimise and streamline operations. the railway industry has recently started taking a series of significant steps towards digitization, with infrastructures experiencing a significant paradigm shift which, for instance, makes it possible to have centralised interlockings and Radio Block Centre (RBC) for an entire country, with geographical redundancy, ensuring the utmost availability and punctuality by moving the control logic to the cloud. Nevertheless, these developments must always be carried on within the scope of established cybersecurity standards and frameworks. This paper presents an analysis of the state of the art on railway cybersecurity, focused on the existing solutions based on the application of the CENELEC “Technical specification 50701 - Railway Application – Cybersecurity”, which is currently the latest European specification addressing railways, being designed to help suppliers, integrators, and operators to implement a cybersecurity risk assessment plan, the necessary controls, and the management of the complete system life cycle. Special attention will be paid to the conduit between the rail signal interlocking system, that controls the line signalling, and the Automatic Train Supervision (ATS) that runs in the Operational Control Centre (OCC), as this has been identified by the European Union Agency for Cybersecurity (ENISA) as one of the most critical systems identified by the operators of essential services.

Quick Response Crisis Communication Strategies Insights from Public Relations PT. KAI DAOP 2 Bandung's Train Accident Response

A crisis that occurs in a company has the potential to damage the company's reputation. However, if the crisis can be handled properly, then the crisis will not harm the company. The purpose of this research is to find out the crisis communication steps and efforts made by the Public Relations of Daop 2 Bandung in dealing with the crisis situation caused by the train accident. Researchers use a descriptive analysis approach because it is considered capable of describing something clearly and each part can be studied one by one, so that the facts of a case can be revealed transparently. Data collection techniques in the form of interviews, in this study the data source consists of primary data and secondary data. Primary data was obtained from interviews with Mr. Ayep Hanapi as Public Relations Manager of PT KAI Daop 2 Bandung. The results showed that PT KAI Public Relations has made several efforts to maintain the company's image, namely: First, pre-crisis PT KAI applies the zero accident principle by increasing employee competence and discipline as well as maintaining facilities and infrastructure. Second, PT KAI's crisis team collaborates with stakeholders, provides solutions, is responsive in serving the media by preparing accurate data and fact information. Third, post-crisis to improve the image by providing compensation to employees and accident victims and conducting evaluations to improve train safety and security in the future.

Widespread impacts to precipitation of the East Palestine Ohio train accident

On 3 February 2023, a Norfolk Southern train derailment occurred in East Palestine, Ohio. The accident and subsequent fire resulted in the emissions of large amounts of hazardous compounds to the ambient atmosphere over many days. We used precipitation chemistry measurements routinely collected by the National Atmospheric Deposition Program (NADP) to estimate the spatial extent and chemical compounds deposited as a result of the accident. Our measurements revealed a large areal impact from the Midwest through the Northeast and likely Canada, and perhaps as far south as North Carolina (portions of 16 states, 1.4 million km2). Observations showed the expected high chloride concentrations, but also unexpectedly high pH (basic) and exceptionally elevated levels of base cations exceeding 99th percentiles versus the historic record. These results were consistent with the meteorological conditions and atmospheric trajectories, and were not due to highly-concentrated low volume precipitation samples or wildfires. The robust measurements of the NADP network clearly show that the impacts of the fire were larger in scale and scope than the initial predictions, and likely due to the uplift from the fire itself entraining pollutants into the atmosphere. A more detailed evaluation of the accident and resulting fire could further refine the full impact of the atmospheric concentrations, dry and wet deposition, and the more specific extent of the spatial impact.

A novel robotic-assisted deep learning-enabled computer vision approach for nondestructive diagnosis of railway bolt faults

Abstract Railways play a vital role in the inland transportation system worldwide, and abnormal bolt components at the track joints are the main cause of train accidents. The detection and identification of faults in rail bolt components are of considerable research importance. To address this problem, numerous researchers have opted for computer vision-based methods to accomplish the detection and identification of the target, but the existing methods have poor detection performance diminished detection capabilities when the target position changes or some feature information is occluded, and the detection speed and accuracy are far from meeting the requirements of practical applications. Therefore, based on the construction of a dedicated dataset for bolt components, this paper uses the K-means dimensional clustering algorithm to re-cluster the dataset according to the target size characteristics, with the aim of reduce the bounding box regression error. At the same time, a novel loss function iteration method is proposed by incorporating an adaptive optimization algorithm, in order to improve the detection speed and ensure good convergence, and the model complexity is reduced based on deep model pruning. Finally, the optimized detection model is implemented on the robotic-assisted platform for testing, and the experimental results verify that the algorithm can quickly and accurately complete various fault diagnosis tasks of bolt components in practical applications. The main achievements of this study include the construction of a large-scale image dataset for novel rail bolt components and propelled the application of deep learning methods in vision-based rail bolt fault diagnosis.

Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models

Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying railway track defects, explicitly focusing on corrugation and squat defects. The research's uniqueness lies in its application of these specific models and the composition of a dataset collected from extensive field measurements and inspections across various railway tracks within the Track Network Maintenance Ampang Line in Malaysia. The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. The proposed methodology provides the railway industry with a powerful tool to streamline maintenance planning and prioritize defect remediation efficiently. Early defect detection can prevent potential accidents and improve safety and operational efficiency. Future studies can expand on these findings by exploring the extension of the proposed techniques to address other types of rail defects. Incorporating a diverse range of scenarios and operating conditions in the dataset could further enhance the models' performance and generalization. Real-time deployment and integration with existing maintenance systems are crucial for practical adoption. This research has strengths but acknowledges limitations. Additional evaluation metrics and a diverse dataset are essential for model performance. Leveraging deep learning models offers a reliable solution for railway maintenance, enhancing safety and efficiency. Addressing these limitations will drive proactive defect management, ensuring safe and reliable railway networks.

The relationship between freight train length and the risk of derailment.

In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost-saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013-2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway-Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100-car trains is associated with 1.11 (95% confidence interval: 1.10-1.12) times the derailment odds of running 50-car trains (or a 11% increase), even accounting for the fact that only half as many 100-car trains would need to run. For 200-car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20-1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding.

Usulan Peningkatan Kinerja Masinis Dan Asisten Masinis Kereta Api Berdasarkan Metode Fatique Index Dan PSQI

In Indonesia, almost every year train accidents occur which result in casualties and considerable material losses. The factors that cause train accidents are human resources, facilities, infrastructure and environmental factors. The method used in this study is the measurement of the level of fatigue that occurs in machinists and assistant machinists using the fatigue index calculator and for measuring the quality of sleep whether good or not using the Pitsburgh sleep quality index questionnaire. Data collection is divided into two parts, namely the collection of working time data from machinists and assistant machinists to determine the level of fatigue and the collection of data from the distribution of questionnaires to determine sleep quality. Working time data is the schedule of machinists and assistant machinists every day which will then be calculated the level of fatigue using the fatigue index calculator. For the calculation of sleep quality, the Pitsburgh sleep quality questionnaire was distributed to 62 respondents. From the results of the calculation of the fatigue index calculator for machinists and assistant machinists every day, it is found that the fatigue index value varies for each day. This can also be seen from the results of the calculation of the standard deviation for 62 respondents which ranges between the value of 6 or 7. The results of the calculation of the sleep quality index showed that 84% of machinists and assistant machinists have a good sleep quality.

A survey of train disasters and safety systems in Indian railways with a focus on signalling and telecommunications

Train is considered as one of the primary means of transport for the people across various regions in India. With a route length of around 68000 km, the Indian rail network is the fourth longest in the world and millions use its services daily. Despite having an extensive network and infrastructure, train accidents are not uncommon in India. The recent incident in Balasore district, Odisha, is the deadliest train disaster in over two decades. Nearly 295 people lost their lives and several hundred got injured in the triple train crash. This incident has brought attention to the safety measures and infrastructure of the country’s railway system. Although the Indian railway systems have seen significant development, the lack of a robust signalling system remains a major issue for such massive mishaps. This study discusses major train accidents with their causes and effects. Then, it discusses train disaster categories and the railway disaster management plan. It also provides a brief review of State-of-the-Art safety systems to prevent train accidents. Further, this study highlights the role of signalling and telecommunication systems in the safe and speedy movement of trains.

Violence-Related Injuries: The Most Common Cases in Hyderabad, Pakistan

The law influences every aspect of human activity, including medical practice. The nature of casualty is affected by geographical regions, cultures, and social values. Determining characteristics of casualty can guide health policy and can assist in managing healthcare resources. Objective: To profile the cases at casualty department of a tertiary teaching hospital. Methods: The study was conducted at the casualty department of Liaquat University Hospital Hyderabad, Pakistan and Department of Forensic Medicine & Toxicology LUMHS, Jamshoro, over a period of one year from January 2021 to December 2021. A total of 3,487 cases were recorded and categorized into violence-related, transportation-related, substance-related, and sexual assault-related injuries. Results: The majority of cases were violence-related incidents (84.65%), followed by transportation-related accidents (14.65%), substance-related injuries (3.61%), and sexual assault-related injuries (0.69%). Violence-related injuries were divided into assault cases, firearm incidents, and police torture. Transportation-related injuries were categorized as road traffic accidents and train accidents. Assault and road traffic accidents were the leading cause of violence-related and transportation-related injuries, respectively. The highest numbers of cases were observed in April, June, and May. Conclusions: A substantial portion of cases at casualty department of Liaquat University Hospital, Hyderabad is contributed by violence-related injuries

A Model of Accident Avoiding System for Trains

Abstract: Safety must be the top most priority in train running and it plays an important role in safe running of trains in our country. Already many systems are existed to avoid accidents, even though an attempt is made in this project work to study the subject of train accidents avoiding systems to enhance the technology further. The Train Collision Avoidance System (TCAS) is the major subject to detect unsafe situations arising due to over speed and train collisions in station area. The advanced accident prevention methods must be incorporated those measures under which the trains will be in constant communication with the protection systems through electronic communication systems. Although, Indian Railways have safe running devices, we have made an effort to cover few more important features like, 1 – to detect the track break in running rail track, 2 – object detection in between the tracks with auto control, and 3 – auto stop when train detects Red signal present over the track side signal post. These are the three important parameters incorporated in our project work and for live demo, a mini model train will be constructed over which all required sensors and their control circuits will be installed to prove the theme practically.

A Study of Death Due to Railway Accidents: An Autopsy Based Cross Sectional Study Conducted in a Tertiary Care Hospital

A train accident is defined as “Collision, Derailment, or any other event involving the operation of n- track equipment”. To estimate the pattern of injuries in Railway Accidents and to estimate the Cause of death and Manner of death in Railway Accidents. The present study is conducted in Department of Forensic Medicine, Government Stanley Medical College from January 2020 to December 2020 in all the cases of Railway Accidents subjected to postmortem examination. During the study period, 61 cases were subjected for autopsy, the most common age group is 20 to 40 years. The most commonly injured organ is Brain followed by Liver and most of them died within 12 to 24 hours prior to post mortem examination. The railway fatalities were more of accidental in nature, less frequently suicidal and rarely homicidal. Internal organs commonly involved Brain followed by spinal cord, liver etc., It is also found that injuries to the upper half of the body are more common when compared to that of lower half of the body. The most common cause of death was hemorrhage shock from Multiple injuries, followed by Head injury, Thoracic injury, and Abdominal injury.

RT Based Railway Gates Automation Using NI myRIO

Railway safety at level crossings remains a paramount concern due to the recurring incidence of train accidents. This study proposes an innovative solution to enhance safety by introducing an automatic gate system at unmanned crossings, replacing manual operation. The system utilizes strategically positioned proximity detectors to detect approaching trains, automating gate management. This reduces gate closure duration compared to manual operation, minimizing disruptions to road traffic. Additionally, the system reduces reliance on human labor, offering a more efficient and reliable solution. It is particularly suitable for unmanned crossings prone to higher accident probabilities, where dependable operation is crucial. By integrating infrared sensors and servo motors, the system ensures prompt gate closure upon train detection, reducing potential accidents caused by negligence. This study enhances prior research by utilizing two infrared sensors on each side of the track, improving train position tracking accuracy. The sensors are programmed using the LabVIEW platform, facilitating integration with myRIO controllers. Overall, this study presents a comprehensive approach to enhancing railway safety through innovative automation technology at unmanned level crossings. Keywords— NI myRIO, IR sensors, Servomotor, Labview.

Tapping on Collective National Trauma

Tapping on Collective National Trauma

A socio-technical systems approach of the accident analysis in Indonesian multiple train accident cases: An application of AcciMap methodology.

The number of cases of train accidents in Indonesia continues to be large in the past few years. This paper analyzes train accident cases in Indonesia using a socio-technical system approach accident model called AcciMap, developed by Jens Rasmussen. The source of data comes from train accident investigation reports from 2015 to 2021 published by the National Transportation Safety Committee. This study uses a qualitative approach with thematic analysis to identify contributing factors and their interactions within the reports. Based on the socio-technical system approach, there are roles and contributions from the five levels of the socio-technical railway transportation system in Indonesia that interact with each other in the occurrence of accidents, including the railway regulatory bodies; other organizations; management of railway service company; processes and physical actions by staff; and condition of equipment and environment. This study has shown that the AcciMap methodology can provide a comprehensive view of the problems and their interactions across the socio-technical levels that influence each other in the occurrence of train accidents in Indonesia. The results also suggest that the socio-technical system approach is expected to be applied in the accident investigation process for the related industry to get more comprehensive insights into the accident.

CHALLENGES AND OPPORTUNITIES FOR IMPROVING MANAGEMENT AND REGULATION OF THE RAIL TRANSPORT SECTOR IN INDONESIA: PERSPECTIVE FROM THE INDUSTRY 4.0 CONCEPT

Objective: The research aims to uncover the causes of train accidents in Indonesia beyond those identified by experts and proposes solutions for inclusion in the regulations and standard operating procedures of the Indonesian Railways Company. Theoretical Framework: The United Nations Economic Commission for Europe (UNECE), operating under the UN Economic and Social Council since its establishment in 1947, has introduced the Industry 4.0 concept, titled 'Increasing the Competitiveness of Transportation between Europe and Asia: European Union Rail R&I. This concept incorporates three crucial indicators known as ACT (Aging, Climate Change, and Technology), aligned with the Railway Sustainable Development Goals. Method: This research employs a qualitative approach, gathering data through in-depth interviews using purposive sampling. Interview reviews are presented in the results section. The study involved 28 participants, focusing on the cities of Palembang and Lampung, situated on the tip of the island of Sumatra, bordering the island of Java. Results and Conclusions: The study identified seven themes for inclusion: The development of Railways in Indonesia; Organizational strengths and weaknesses; Human resources; Infrastructure; Operational aspects; Causal factors in train accidents, and Future solutions and contributions to regulation. These were then simplified into three themes following UNECE guidelines. Notably, the main issues with freight trains, primarily used for coal transportation, include an inadequate number of carriages, damage to rails and train sleepers, and excessive cargo loads, leading to frequent train derailments and accidents. The law regulates general provisions for rail freight transportation, but violations are common, and sanctions are minimal due to the state-owned nature of the managing company. Research Implications: This study suggests measures to enhance regulations and Standard Operating Procedures (SOPs) for the Indonesian Railways Company, including adapting regulations and SOPs to improve the operational safety of goods trains. This involves modernizing signaling systems and addressing human errors, particularly during night shifts. Recommendations also cover optimizing single-path and double-path routes, proposing regulations for the adaptation of automatic control systems, and improving the Grade of Automation (GOA). Ensuring the safety of personnel, especially operators and machinists, is emphasized. Originality/Value: This research explores the paradigm shift within Indonesia Railways, transitioning from GOA 0 to GOA 1. Anticipated to significantly impact the safety of goods (coal) trains, this study offers profound insight into the evolution of transportation governance for freight transport by rail. Both the government and Indonesia Railways can consider integrating these findings into regulations or SOPs, despite facing challenges such as funding constraints and the high cost associated with upgrading to GOA 2 infrastructure.

Determining Smart Phone Sensing and K-Means Clustering for Accurate and Timely Railway Track Joint Fault Diagnosis

The railway track joint is an important component that connects two sections of the rail and ensures a smooth and safe operation of trains. However, the joint is also a critical point of failure that can lead to train derailments and accidents. Therefore, accurate and timely detection of joint faults is crucial for ensuring the safety and reliability of railway transportation. In this paper, we propose a novel approach for railway track joint fault diagnosis using smart phone sensing and k-means clustering. Our approach utilizes the accelerometer sensor of a smart phone to measure the vibrations and movements of a specifically developed railway shoe stick that is employed on an actual railway track for the condition monitoring of the railway tracks. More than 60000 data values are collected and are then processed and analysed using k-means clustering, a popular unsupervised machine learning technique that groups similar data points together. The K means clustering in this study forms 3 clusters as a result. The 3 clusters after being validated on the track by virtue of visual inspection are determined to be acceleration values of the healthy track, track with higher joint gap than the standardized value and super-elevated railway track joint fault(s), respectively. In addition to its high accuracy and efficiency, our approach has several advantages over traditional methods, such as low cost, easy deployment, and high scalability. Moreover, the smart phone sensing technology can be easily integrated with existing train monitoring systems, making it a useful tool for real-time joint fault diagnosis and maintenance. Overall, this study demonstrates the potential of smart phone sensing and k-means clustering for railway track joint fault diagnosis and highlights the need for further research in this field.

Effect of Split Condition and Driving Duration on Fatigue Based on Changes in Brain Wave Signals when Driving a Train Simulator

One of the causes of train accidents is driver fatigue. Train driver fatigue can be caused by sleep factors, known as split sleep. This study aims to assess the impact of split sleep on train driver fatigue. A total of 12 male participants were asked to drive a train simulator for 2.5 hours after facing two sleep conditions, namely split sleep and baseline. The split sleep condition required participants to sleep in two segments at 05.00-10.00 and 12.00-15.00, while the baseline condition was conducted in one segment at 21.00-05.00. Fatigue was measured based on changes in brain wave signals via electroencephalogram (EEG) and Swedish Occupational Fatigue Inventory (SOFI). Fatigue measurements with EEG were conducted at the 10-minute start and end of the simulation, while fatigue measurements with SOFI were conducted before and after the simulation. The results of this study showed a higher level of subjective fatigue in split sleep compared to the baseline. However, the EEG signal change data and other dimensions of SOFI dimensions showed no difference between the two sleep states. Another result was an increase in fatigue after simulation in all dimensions of the SOFI. Therefore, split sleep should not be applied by drivers because it can increase subjective fatigue. However, if split sleep needs to be applied, it is necessary to fulfill sleep quantity (7-9 hours) and improve sleep quality. In addition, the company also needs to ensure that the train driver are awake at least 15 minutes.

Design and development of artificial intelligence assisted railway gate controlling system using internet of things

A improvised solution for the Indian Railways' human level crossing controls and the attendant reliability issues they cause. In our nation, level crossings have been the scene of several train accidents. So far, there have been zero productive actions. Using an infrared (IR) detector and an audible alert, the proposed system automatically opens and closes level crossing gates based on when trains approach or depart from the crossing. However, it's possible that a car may become stuck between the pedestrian crossing barriers if they're automated. Here, an ultrasonic sensor may identify the obstruction between the crossing gates, and an Internet of Things (IoT) module would inform the train of the situation. When a train approaches or departs from a railroad crossing, infrared sensors detect its presence and alert an Arduino-UNO, which then commands the gates to open or close using a little servomotor stationed nearby. Using the Node MCU and GPS navigational technology, this system also aims to reduce latency in detecting combustion attacks within the compartment. The resulting section of this paper proves the proposed work efficiency by means of graphically illustrates the following metrics such as: human detection accuracy, gate closing efficiency, alerting system efficiency and data storage efficiency.