ProfessorArnabMajumdar

Research on air traffic control complexity is focused on the identification of complexity factors from a variety of sources. The principal sources are safety databases, simulation modelling and structured face-to-face interviews with wit air traffic controllers. Subsequent analysis of these factors assesses their impacts on airspace design and control procedures.

Airspace safety analysis research focuses on the use of aviation incident data to improve safety, precursors to incidents (including human error) and contextual factors, development of methodology for benchmarking airspace incidents globally, analysis of reliability of reporting of incidents, the development of safety indices and the assignment of target levels of safety. The research is driven mainly by the requirements of the CAAs and air navigation service providers (ANSPs) as confirmed by the international Workshop held at Imperial in May 2005. Preliminary analysis of incidents from 3 countries has already yielded important findings in terms of definitions, taxonomy, completeness of data and reliability.

Helicopter accidents are the cause of many fatalities, and their avoidance is a major area of work for civil aviation safety authorities around the World. The International Helicopter Safety Team has set itself the target of reducing the worldwide helicopter accident rate by 80% by the year 2016 when compared to 2005. Accident investigations reveal that two causes underlying helicopter accidents are crew error (i.e. human factors) and malfunction of equipment on the helicopter. Research is focused on developing frameworks for analysis of helicopter accidents from both a detailed human factors as well as equipment components failures. Furthermore, statistical analysis of such factors has led to the development of safety metrics used by civil aviation authorities.

Railways constitute a major component for most countries’ welfare and thus, every country with a rail network aims for safe and highly reliable railways. The safety of railways depends on several factors including rail traffic rules, infrastructure and rolling stock reliability, organisational safety management and human factors. Figures indicate that 80-90% of all accidents are attributed to train operator (drivers, signallers and controllers) human error and in Europe at least 75% of fatal railway accidents within the last 30 years were due to human error. There has been a significant research on the factors that affect human performance in railway operations, so as to prevent conditions related to degrade performance and to reduce the probability of human errors. However current methods, developed on the principles of Human Reliability Analysis (HRA), are based on research from other domains, including nuclear, oil and gas, and aviation. Hence, they are not suited to the rail industry and can be difficult to apply reliably to railway specific operations.

Research at the LRF TRMC has addressed the current limitations and proposed a new methodology to identify the factors that affect the performance of railway operators, and assess human performance.

In particular, this research has developed for the first time a novel and comprehensive taxonomy for railway operations, referred to as the Railway-Performance Shaping Factors (R-PSFs) taxonomy. The taxonomy was derived from a variety of sources including: extensive literature review, operators’ hierarchical task analysis, and the analysis of global accidents and incidents. Subject matter experts validated the taxonomy. Results identified 43 contributing factors, whilst further statistical analysis indicated that 12 out of 43 factors are responsible for more than 90% of total occurrences regardless of the type of network, responsibility and severity of consequences. Unlike current taxonomies, the framework developed accounts for both the influence of each individual factor and the dynamic interactive influence of the factors due to their mutual dependencies. The R-PSFs taxonomy can be used in a variety of ways by railway stakeholders:

i) to enhance the Safety Management Systems (SMS) of an organization, and

ii) as part of the training program of an organisation in order to inform and engage the railway personnel with respect to the factors that primarily affect their performance, and

iii) by investigatory authorities to obtain information about the human aspect that may have led to railway occurrences.

This research also developed, tested and validated a framework, referred to as the Human Performance (HuPeROI) to enhance safety in railway operations. Based on the 12 largest contributing factors, the HuPeROI is a new scheme to assess human performance, as function of the various R-PSFs. The HuPeROI for the first time introduced an approach to quantify the impact (weight) of each of the factors that affect human performance in order to account for all the dependencies amongst those factors. HuPeROI was developed by integrating the generic concept of two techniques, the Analytic Network Process and the Success Likelihood Index Methodology (SLIM). The former is one of the best known and widely used multi-criteria decision making techniques and was used to evaluate the influence of each RPSF on operators’ performance. SLIM was applied to rate the importance (weight) of each of the R-PSFs for different operational actions and finally to estimate the reliability index for these actions.

The HuPeROI methodology was demonstrated in a case study for three different types of railway operations: regional, high-speed and underground, and helped to define the influence of each individual factor on human performance as well as to indicate the relative likelihoods of different human errors. 

Aviation contributes to worldwide connectivity and the global economy. The rapid, widespread growth witnessed in recent years is expected to continue and many airports require expansion. However, pressure to conform to EU Air Quality Standards requires efficient ground-level aircraft operations to reduce pollutant emissions; yet quantification of this has been limited by data restrictions. Research at the LRF TRMC has used 7,090 flight data records to perform high-resolution evaluation of aircraft fuel consumption and pollutant emissions at London Heathrow airport.

Aircraft air quality impacts at airports are evaluated in three areas. Firstly, emission modelling approaches are analysed and associated errors quantified. The high-resolution approach leads to improved NO_X and CO emissions modelling accuracy by 41.4% and 19.8% respectively, relative to the least refined, existing input data approaches and the identification of operational variability that leads to optimisation for pollutant emission reduction. Secondly, methods for optimising aircraft operations using observed variability is demonstrated and results quantified. Single engine taxi-in (SET), reduces fuel consumption by 49.6%, and NO_X, CO and HC emissions by 49.3%, 46.2% and 22.1% respectively. However, optimised taxi-in operations, where the time before SET is initiated is reduced to the 25^th percentile of observed values reduces fuel consumption, NO_X, CO and HC emissions by a further 6.7%, 8.7%, 14.2% and 11.5%. Reducing the taxi-in thrust setting, with no SET, increases emissions. Reduced thrust takeoff operations reduce fuel consumption, NO_X and BC emissions by 24.1%, 42.2%, and 48.6% respectively; relative to 100% thrust takeoff. However, optimising within observed thrust setting variability, and accounting for aircraft, engine and takeoff weight (TOW), further reductions of 2.3%, 5.6% and 7.8% for fuel consumption, NO_X and BC emissions are achieved. Finally, the ability of modelled aircraft emissions to explain measured variation in local air quality is investigated. The aircraft, engine and thrust setting explain variability in measured NO_X and CO concentrations, which increase by 20% and 56% respectively due to aircraft runway activities, however other explanatory variables exist.

The limitations of the current civil aviation surveillance systems include a lack of coverage in some areas and low performance in terms of accuracy, integrity, continuity and availability particularly in high density traffic areas including airports, with a negative impact on capacity and safety. Automatic Dependent Surveillance Broadcast (ADS-B) technology has been proposed to address these limitations by enabling improved situational awareness for all stakeholders and enhanced airborne and ground surveillance, resulting in increased safety and capacity. In particular, its scalability and adaptability should facilitate its use in general aviation and in ground vehicles. This should, in principle, provide affordable, effective surveillance of all air and ground traffic, even on airport taxiways and runways, and in airspace where radar is ineffective or unavailable.

The success of the progressive implementation of ADS-B has led to numerous programmes for its introduction in other parts of the World where the operational environment is considerably different from that of Australia. However, a number of critical issues must be addressed in order to benefit from ADS-B, including the development and execution of a safety case that addresses both its introduction into legacy and new systems’ operational concepts, the latter including the Single European Sky (SES) / Single European Sky ATM Research (SESAR) and the US’ Next Generation Air Transportation System (NexGEN). This requires amongst others, a good understanding of the limitations of existing surveillance systems, ADS-B architecture and system failures and its interfaces to the existing and future ATM systems. Research on ADS-B to date has not addressed in detail the important questions of limitations of existing systems and ADS-B failure modes including their characterisation, modelling and assessment of impact. The latter is particularly important due to the sole dependency of ADS-B on GNSS for information on aircraft state and its reliance on communication technologies such as Mode-S Extended Squitter, VHF Data Link Mode-4 (VDLM4) or Universal Access Transceiver (UAT), to broadcast the surveillance information to ground-based air traffic control (ATC) and other ADS-B equipped aircraft within a specified range, all of which increase complexity and the potential for failures.

This research proposed a novel framework for the assessment of the ADS-B system performance to meet the level of safety required for ground and airborne surveillance operations. The framework integrates various methods for ADS-B performance assessment in terms of accuracy, integrity, continuity, availability and latency, and reliability assessment using probabilistic safety assessment methods; customized failure mode identification approach and fault tree analysis. Based on the framework, the research developed a failure mode register for ADS-B, identifies and quantifies the impact of a number of potential hazards for the ADS-B. Furthermore, this research identified various anomalies in the onboard GNSS system that feeds aircraft navigation information into the ADS-B system. Finally, the research mapped the ADS-B data availability and the quantified system performance to the envisioned airborne surveillance application’s requirements. The mapping exercise indicates that, the quantified ADS-B accuracy is sufficient for all applications while ADS-B integrity is insufficient to support the most stringent application: Airborne Separation (ASEP). In addition, some of the required performance parameters are unavailable from aircraft certified to DO-260 standard. Therefore, all aircraft must be certified to DO-260B standard to support the applications and perform continuous monitoring, to ensure consistency in the system performance of each aircraft. 

Airports are complex systems involving the continuous interaction of human operators with the physical infrastructure, technology and procedures to ensure the safe and efficient conduct of flights. From an operational perspective, airport surface operations (i.e. runway and taxiway operations) require the interaction of five main stakeholders (i.e. crew or pilots, air traffic control, airport operator, ground handling and regulator) both to facilitate the ground movement of aircraft and vehicles, and to maintain the surface in a working condition. The complexity of these operations makes the runway and taxiway system vulnerable and presents a risk of failure with the consequent potential for the occurrence of accidents. Therefore, the development and implementation of an effective Safety Management System (SMS) are required to ensure the highest level of safety for surface operations. A SMS is a systematic approach to managing safety based on the four cornerstones of safety policy and objectives, risk management, assurance, and safety promotion. Although the International Civil Aviation Organisation (ICAO) provides the global legislative framework for SMS, the relevant regulations are still to be established at the national level with the consequence that practical guidance on the development and implementation of SMS is rare, and reliable tools to support SMS are lacking. The consequence of this is that the current approach to surface safety management is piecemeal and not integrated. Typically, a single accident and incident type is investigated from the perspective of an individual stakeholder with the consequence that resulting proposals for safety mitigation measures are biased and limited in terms of their impact. In addition, the industry is characterised by non-standardised data collection and investigation practices, insufficient or missing definitions, differing reporting levels, and a lack of a coherent and standardised structure for efficient coding and analysis of safety data. Since these shortcomings are a major barrier to the required holistic and integrated approach to safety management, this research addresses the four cornerstones of SMS and recommends major enhancements. In particular, a framework for a holistic airport surface safety management is proposed. The framework comprises the static airport architecture, a process model of surface operations, the determination of causal factors underlying failure modes of these operations, a macroscopic scenario tool and a functional relationship model. Safety data and other data sources feed the framework and a dedicated data pre-processing strategy ensures its validity. Unlike current airport surface safety management practices, the proposed framework assesses the safety of the operations of all relevant actors. Firstly, the airport architecture is modelled and the physical and functional variability of airports defined. Secondly, a process model of surface operations is developed, which captures the tasks of the stakeholders and their interactions with physical airport surface infrastructure. This model serves as a baseline model and guides the further development of the airport SMS. To manage the safety of surface operations, the causes of accidents and incidents must be identified and their impacts understood. To do so, a reference data set combining twelve databases from airlines, airport operators, Air Navigation Service Providers (ANSPs), ground handling companies and regulators is collected. Prior to its analysis, the data is assessed for its quality, and in particular, for its internal validity (i.e. precision), external validity (i.e. accuracy) and in terms of reporting levels. A novel external data validation framework is developed and each database is rated with a data quality index (DQI). In addition, recommendations for reporting systems and safety policies are given. Subsequently, the data is analysed for causal factors across stakeholders and the contribution of the individual actors are highlighted. For example, the analysis shows that the various stakeholders capture different occurrence types and underlying causal factors, often including information that is of potential use for another party. The analysis is complemented by interviews, observations and statistical analysis, and the results are summarised in a new taxonomy. This taxonomy is applicable to all relevant stakeholders and is recommended for operational safety risk management. After the airport surface operations have been modelled and the drivers to safety identified, the results are combined, resulting in a macroscopic scenario tool which supports the management of change (i.e. safety assurance), training and education, and safety communication (i.e. safety promotion) functions of the SMS. Finally, a structured framework to assess the functional relationship between airport surface accidents / incidents and their underlying causal factors is proposed and the system is quantified in terms of safety. Compared to the state-of-the-art safety assessments that are biased and limited in terms of their impact, the holistic approach to surface safety allows modelling the safety impact of each system component, their interactions and the entire airport surface system architecture. The framework for a holistic airport surface safety management developed in this research delivers a SMS standard for airports. The standard exceeds international requirements by standardizing the two SMS core functions (safety risk management and safety assurance) and integrating safety-relevant information across all relevant stakeholders. This allows a more effective use of safety information and provides an improved overview on, and prediction of, safety risks and ultimately improves the safety level of airports and their stakeholders. Furthermore, the methodology employed in this research is flexible and could be applied to all aspects of aviation SMS and system analysis.

The Air Traffic Management (ATM) system is a complex socio-technical system that ensures safe, efficient and cost-effective air traffic movements on the ground and in the air. The current ATM system is saturated as a result of an everlasting growth in air travel demand, leading to delays and potential negative safety impacts. In order to meet future demand, current ATM modernisation initiatives in the European Union and the USA are developing a new concept of operations based on strategic holistic system optimisation. On the airport surface, this is achieved by optimising operations not only during the take-off, landing and taxiing phases, but also during the turnaround process on the apron. This requires the boundary of the ATM system to expand to include new elements, namely the apron. A key deficiency in current initiatives is that, while they focus on capacity, punctuality and cost-effectiveness of the apron, they do not address safety. This has potential negative impacts in terms of setting and prioritising safety targets.

Unlike the rest of the aviation domain, which is aircraft-centric, the concept of apron safety is much wider and in addition to aircraft safety, it also includes occupational health and safety. Recent aviation safety statistics show that aircraft accidents attributed to ground handling operations are six times more frequent than those attributed to the ATM. Additionally, the UK Health and Safety Executive (HSE) statistics show worse safety records on the apron when compared to the construction and agricultural industries. Considering the change in the ATM system boundary and the low aviation and occupational health and safety records, the airport apron has been identified in this research as a new safety-critical area of the future ATM system. Therefore, a key focus of this research is to address current deficiencies with respect to safety management on the apron, by developing a better understanding of the processes carried out on the apron and a new framework for safety assessment, as well as recommending enhancements to existing safety management practices.

In contrast to existing safety management practices that are based on a dated understanding of safety (referred to as Safety-I), which is predominantly reactive, the framework proposed in this research, for the first time, adopts a state-of-the-art proactive and predictive understanding of safety (referred to as Safety-II) for the apron. The research demonstrates for the first time that the existing linear component-based models traditionally used for modelling apron safety do not account for the system complexity. Therefore, the proposed framework develops a state-of-the-art systemic functional Total Apron Safety Management (TASM) model and a corresponding taxonomy of factors that characterise different sources of variability of ground handling services, capable of accounting for dependencies and dynamic interactions between different layers of the apron system (i.e. technological, human and organisational).

The proposed functional model and taxonomy have been applied to three case studies in retrospective, prospective and system design analysis demonstrating the multi-purposive nature of the framework, particularly important under existing financial pressures. In retrospective analysis the proposed functional model and taxonomy have shown to identify systemic factors previously not found during the occurrence investigation. In prospective analysis, a new protocol for systemic and systematic hazard analysis in complex socio-technical systems (including the apron) was developed. Furthermore, a novel conceptual framework for a safety trend analysis based on the TASM framework was developed, offering a quick, simple, cost-effective analysis of large datasets. A key advantage of the TASM framework is that it is transferable to all ground handling services carried out by Ground Service Providers (GSP), airlines and/or airports.

A holistic model of emergency evacuations in large, complex, public occupancy buildings

Evacuations are crucial for ensuring the safety of building occupants in the event of an emergency. In large, complex, public occupancy buildings (LCPOBs) these procedures are significantly more complex than the simple withdrawal of people from a building. This research developed a novel, holistic, theoretical model of emergency evacuations in LCPOBs inspired by systems safety theory. LCPOBs are integral components of complex socio-technical systems, and therefore the model describes emergency evacuations as control actions initiated in order to return the building from an unsafe state to a safe state where occupants are not at risk of harm. The emergency evacuation process itself is comprised of four aspects - the movement (of building occupants), planning and management, environmental features, and evacuee behaviour. To demonstrate its utility and applicability, the model has been employed to examine various aspects of evacuation procedures in two example LCPOBs - airport terminals, and sports stadiums. The types of emergency events initiating evacuations in these buildings were identified through a novel hazard analysis procedure, which utilised online news articles to create events databases of previous evacuations. Security and terrorism events, false alarms, and fires were found to be the most common cause of evacuations in these buildings. The management of evacuations was explored through model-based systems engineering techniques, which identified the communication methods and responsibilities of staff members managing these events. Social media posts for an active shooting event were also analysed using qualitative and machine learning methods to determine their utility for situational awareness. Finally, an experimental study on pedestrian dynamics with movement devices was conducted, which determined that walking speeds during evacuations were unaffected by evacuees dragging luggage, but those pushing pushchairs and wheelchairs will walk significantly slower.

Evacuations are crucial for ensuring the safety of building occupants in the event of an emergency. In large, complex, public occupancy buildings (LCPOBs) these procedures are significantly more complex than the simple withdrawal of people from a building. This research developed a novel, holistic, theoretical model of emergency evacuations in LCPOBs inspired by systems safety theory. LCPOBs are integral components of complex socio-technical systems, and therefore the model describes emergency evacuations as control actions initiated in order to return the building from an unsafe state to a safe state where occupants are not at risk of harm. The emergency evacuation process itself is comprised of four aspects - the movement (of building occupants), planning and management, environmental features, and evacuee behaviour. To demonstrate its utility and applicability, the model has been employed to examine various aspects of evacuation procedures in two example LCPOBs - airport terminals, and sports stadiums. The types of emergency events initiating evacuations in these buildings were identified through a novel hazard analysis procedure, which utilised online news articles to create events databases of previous evacuations. Security and terrorism events, false alarms, and fires were found to be the most common cause of evacuations in these buildings. The management of evacuations was explored through model-based systems engineering techniques, which identified the communication methods and responsibilities of staff members managing these events. Social media posts for an active shooting event were also analysed using qualitative and machine learning methods to determine their utility for situational awareness. Finally, an experimental study on pedestrian dynamics with movement devices was conducted, which determined that walking speeds during evacuations were unaffected by evacuees dragging luggage, but those pushing pushchairs and wheelchairs will walk significantly slower.