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Objective: Aberrant driving behavior (ADB) decreases road safety and is particularly relevant for urban bus drivers, who are required to drive daily shifts of considerable duration. Although numerous frameworks based on human physiological features have been applied to predict ADB, the research remains at an early stage. This study used heart rate variability (HRV) parameters to establish ADB occurrence prediction models with various machine learning approaches. Methods: Twelve Taiwanese urban bus drivers were recruited for four consecutive days of naturalistic driving data collection (from their routine routes) between March and April 2020; driving behaviors and physiological signals were obtained from provided devices. Weather and traffic congestion information was determined from public data, while sleep quality and professional driving experience were self-reported. To develop the ADB prediction model, several machine learning models—logistic regression, random forest, naive Bayes, support vector machine, and gated recurrent unit (GRU)—were trained and 10-fold cross-validated by using the testing data. Results: Most drivers with ADB reported deficient sleep quality (≤80%), with significantly higher mean scores on the Karolinska Sleepiness Scale and driver behavior questionnaire subcategory of lapses and errors than drivers without ADB. Next, HRV indices significantly differed between the measurement of a pre-ADB event and a baseline. The accuracy of the GRU models ranged from 78.84% ± 1.49% to 89.57% ± 1.31%. Conclusion: Drivers with ADB tend to have inadequate sleep quality, which may increase their fatigue levels and impair driving performance. The established time-series models can be considered for ADB occurrence prediction among urban bus drivers.

Academics and the maritime industry have used the Heinrich Pyramid for decades to justify overall safety theory, risk assessments, and accident prevention strategies. Most use Heinrich’s original severity ratios (1:29:300) for accident causation development in a factory setting. However, to use the Pyramid effectively and mitigate risks/hazards, it must be calibrated to represent specific industry reality. This paper, for the first time, focuses on calibration of Heinrich’s Pyramid to maritime accident data, using databases from the Marine Accident Investigation Branch of the Department for Transport. This research clusters five years (2013–2017) of accident data, using K-Means clustering on categorical variables and severity levels of accidents, similar logic to Heinrich’s analysis. This approach and descriptive statistics provide new ratios between accident severity classifications for casualties with a ship (CS) and occupational accidents (OAs) separately. Results show that the data do not appear to fall into Heinrich’s Pyramid shape and yield a vastly different and lower ratio to that of Heinrich’s. Especially concerning was that Very Serious and Serious accidents occurred at a 1:5 ratio for CS and 4:1 for OA, very different from Heinrich’s 1:29. Although these results calculated a new ratio, it may not represent reality owing to accident reporting requirements under UK law, a lack of an agreed taxonomy of risk and hazard definitions, and likely underreporting of less severe accidents. This is proven because, in 2017, CS data became pyramid shaped, after a decrease in the number of accidents and a 17% increase in near-misses.

To understand the dynamics of an autonomous ridesharing transport mode from the perspectives of different stakeholders, a single model of such a system is essential, because this will enable policymakers and companies involved in the manufacture and operation of shared autonomous vehicles (SAVs) to develop user-centered strategies. The model needs to be based on real data, network, and traffic information and applied to real cities and situations, particularly those with complex public transportation systems. In this paper, we propose a new agent-based model for SAV deployment that enables the parametric assessment of key performance indicators from the perspective of potential SAV users, vehicle manufacturers, operators, and local authorities. This has been applied to a case study of three regions in London: central, inner, and outer. The results show there is no linear correlation between an increased ridesharing acceptance level and average trip duration. Without a fleet rebalancing algorithm, over 80% of SAVs’ energy expenditure is on picking up customers. By reducing pickup distance, SAVs could be a contender for a nonpersonal transportation system based on trip energy comparisons. The results provide a picture of future SAV systems for potential users and offer suggestions as to how operators can devise an optimal transportation strategy beyond the question of fleet size and how policymakers can improve the overall transport network and reduce its environmental impact based on energy consumption. As a result of its flexibility and parametric capability, the model can be utilized to inform any local authority how SAV services could be deployed in any city.

There have been a number of high-profile incidents in recent years in which appropriate evacuation has been necessary due to the extreme nature of the incident (e.g., terrorist attacks). Evacuation under extreme situations causes specific behavioral responses and understanding these can significantly affect the efficacy of an evacuation, potentially saving lives. This paper offers a view of the current understanding of this area of research by performing a literature review into the required areas (sociological, psychological and computer modeling). It finds that although there has been significant progress in sociological models of human behavior, there is a lack of appropriate software models for extreme emergencies that can accurately model states of fear. This point is independently established in interviews with key stakeholders in the area of human behavior and emergencies. The interviewees identify that a better model is required and provide their desired areas of progress, specifically that more data is required to develop new theories and calibrate software models.

Accident investigation and analysis are key to reinforcing and improving railway safety. Many railway accidents have been caused by degraded human performance and human error, and the tasks of train drivers and signalers have remained essentially the same. Although new technologies and equipment have gradually reduced railway operation accidents, no investigation has been conducted to investigate whether railway performance shaping factors (R-PSFs), attributed to degraded human performance, have changed or remained constant. Focusing on UK railways, this paper analyzes railway accidents involving human error for the period 1945 to 2012. The purpose of the analysis is twofold: to identify whether the number and type of factors that affect human performance and contribute to human errors have changed during this period and to assess the quality of data collected by investigation reports and to determine whether the collection of such data has evolved and improved. The analysis identifies the number of R-PSFs and their relationship to such variables as responsible personnel, immediate causes, and the time and location of the event. The contribution of those variables to the severity of an accident is calculated. Moreover, any changes in trends and patterns in the number of R-PSFs over time are explored through smoothing techniques of time series analysis. Finally, the quality of the collected data is analyzed with the data quality index. Results show that data investigation and data collection have significantly improved. However, although accident rates have decreased, the average number and type of factors that affect human performance have remained the same.

The safety of railway system operations depends on several internal and external factors. The former include rail traffic rules, infrastructure, rolling stock reliability, organizational safety culture, and human factors. The railway systems in Europe, North America, and Australasia have seen significant technological developments for improved capacity and efficiency. Europe, for instance, is implementing the European Railway Traffic Management System (ERTMS). However, transition to a more automated traffic management system requires, among other things, changes to infrastructure, rolling stock, operational procedures, or all three. Concerning operational procedures, the literature shows that train drivers, signalers, and controllers have the greatest effect on the safety of a railway network. Therefore, the reliability and safety integrity of the railway network are largely dependent on human factors, in particular, the performance of human operators. This performance in turn is affected by a number of factors broadly known as performance-shaping factors (PSFs), with deficiencies in communication accounting for more than 90% of incidents for the conventional railway system. Therefore, this study investigated the influence of ERTMS and in particular the Global System for Mobile Communications–Railway (GSM-R) on operators' performance. The study analyzed 74 accident and incident reports of railway operations before and after GSM-R implementation from several European railway organizations. The results identified the communications-related factors that affected human performance in the conventional and upgraded railway system based on the existing railway–PSF taxonomy. Finally, the results showed the positive impact of GSM-R implementation on operators' performance.

Several hazardous industries have embraced Heinrich's premise that incidents of negligible safety consequences are precursors to accidents in a statistical sense. However, in few such industries has research verified the truth of that assumption. This paper explores the relationships between accidents and reported incidents in the context of oil and gas–related offshore helicopter operations by using the accident investigation reports published by the UK Air Accident Investigation Branch and the incidents filed under the British Mandatory Occurrence Reporting Scheme between 1997 and 2010. Classification frameworks were developed to enable the independent analysis of accidents and incidents in relation to specific variables of interest. Frequencies and statistical associations that could have indicated the precursor relationship were explored. From the results of the analysis, the paper highlights potentially severe shortcomings in the assumptions underpinning incident data collection as well as the process with which incident data are generated. For example, the paper unveils the existence of sudden failures that cannot be reliably anticipated or reported and draws attention to a potentially flawed incident-reporting culture. Given the results, the paper informs stakeholders in the industry of specific initiatives to ensure that the right lessons are learned from past occurrences (e.g., through ways of collecting incident data that will not rely solely on reporters) and how these could be used to inform future interventions, for example, through the analysis of potential consequences of incidents, as a complement to the analysis of frequencies.

London's extensive rail network can present travelers with several alternative paths to reach their destinations. Since the introduction of Oyster route validators in 2009, travelers using Oyster pay-as-you-go could be charged a lower fare between the same origin and destination if they avoided traveling through central London's Zone 1. The path choice decision involves a trade-off of the price differential between paths against other attributes of the paths, particularly journey time. Data from 150 days of these journeys were analyzed to ascertain the extent to which differences in fare and journey time affected path choice during peak and off-peak periods. The analysis found that at peak times travelers would be more likely to change their journey path to avoid Zone 1 if the fare discount for doing so were increased than if the time difference were reduced, but during off-peak hours a reduced time differential would be more effective. Other environmental factors, such as overcrowding, also played a part in the path choice decision, especially during peak hours.

This paper develops a new set of European air travel demand elasticities with data from the International Civil Aviation Organization; a new database was constructed in the context of examining the role for dynamic demand management in reducing aviation carbon dioxide (CO2) emissions. The results indicate that air travel demand appears to be fairly price inelastic, with a fare elasticity of 20.29% in the short run and 20.44% in the long run. As a result, demand management strategies focused solely on increasing air fares may do little to curb demand growth. In contrast, supply restrictions in the form of reduced service frequencies may offer a more effective means of reducing aviation CO2 emissions—a 10% reduction in frequency levels is found to reduce passenger demand by 5% in the short run and 7.6% in the long run—although such restrictions can create negative economic consequences. A combination of supply restrictions and pricing may offer a more balanced solution.

Airport surface safety is acknowledged worldwide as a key area to aviation safety. However, current methods for analyzing and mitigating surface safety occurrences are piecemeal in their approach and limited in scope and perspective. This paper emphasizes the need for a holistic approach to surface safety and introduces a new methodology for the development of a holistic taxonomy for critical factors underlying airport surface safety occurrences. The taxonomy incorporates findings from the relevant literature, safety data, airport surveys, and interviews with subject matter experts. It benefits from the viewpoints of all the relevant aviation stakeholders (regulators, air navigation service providers, airport authorities, airlines, ground handling companies, accident investigation boards) and derives its robustness from the combination of a number of research methods. The application of the taxonomy to support the safety risk management function within the context of safety management systems is proposed. Application of the taxonomy to a multinational data set (North America, Europe, Oceania) verifies that the databases complement each other and that a holistic picture can only be achieved through their combined use. In addition, the analysis indicates that differences in the data sets are a function of the national air traffic system and airport infrastructure, underlying regulations, reporting system and safety culture, and the viewpoint of the aviation stakeholder that is represented. Finally, the holistic approach proposed in this paper is shown to be transferable to other areas of aviation safety.

The demand for air travel continues to grow rapidly, and by 2010, air traffic in Europe is expected to be twice the 1990 level. This increase in air traffic may adversely affect safety if appropriate measures are not taken. Regulators and Air Navigation Service Providers (ANSPs) analyze incidents to identify ways to prevent them from happening again. Incidents are rare events in airspace, and this poses problems with robust statistical testing and trend analysis. Aviation safety analysis uses the Poisson distribution where possible, but doubts remain as to its appropriateness. Incident data from ANSP were analyzed over 17 years in a logical quantitative manner using extreme value theory (EVT), a method that uses the limited amount of information available on incidents and defines a distribution that can be used to make statistical inferences. EVT has had considerable use in other fields but little in aviation. A statistical analysis and validation framework is outlined and used to test the data using Poisson, negative binomial, and EVT. The goodness-of-fit tests, as well as other statistical tests, indicate that by far the best fit of the data is achieved using EVT. Further analysis using EVT shows its efficacy as a tool for monitoring and predicting incidents, based on statistical hypothesis and the use of quantile information.

“Air traffic control (ATC) complexity” can be defined as the interaction between air traffic and the sector characteristics through which this air traffic flies. Research indicates that complexity is the prime determinant of the air traffic controller's workload. Research has attempted to define and classify complexity variables that have been used to assess the capacity of en route airspace in Europe and North America. Such variables can also be used to assist in ATC sector design by highlighting those complexity factors that can lead to high workload and potentially risky scenarios for the controller. With the use of a structured interview technique, a study analyzed these ATC complexity variables for their impact on controller workload and safety. Seventy-nine air traffic controllers were interviewed about the factors that affected their workload. These controllers were based in 14 area control centers in Europe, including those of high complexity, such as Maastricht, Netherlands, as well as in nine centers in Asia, one in Africa, and Mumbai, India, a region with major traffic growth in recent years. On the basis of these interviews, a taxonomy of more than 50 complexity variables subdivided into 11 major groupings, such as traffic mix and entry and exit points, has been developed. In addition to rating of the impact of the individual complexity factors to assess their effect on airspace, interactions between complexity variables were deduced from the interviews. These interactions enable assessment of the factors that add to layers of complexity for controllers. Such a taxonomy can assist airspace planners in designing sectors that avoid complex ATC situations. How the impact of such complexity factors can be investigated in sector design is outlined.

The New Zealand (NZ) government actively sets safety targets for its airspace, and the level of safety traditionally has been measured by accident rates. However, numerous problems have arisen from the use of this indicator. With the Heinrich pyramid of common causation of accidents and incidents, focus has turned to the use of incident data–-particularly loss-of-separation (LoS) incidents between aircraft in airspace–-for safety indicators. This paper uses LoS data from New Zealand, Australia, and the United Kingdom to develop safety indicators specific to a particular airspace. A survey of previous research on factors affecting LoS incidents led to the selection of seven variables: airspace environment, altitude, traffic profile of the pair of aircraft in conflict, presence of one aircraft in altitude transition, geometrical configuration, causal agent (i.e., last human instigator of the incident), and degree of severity or risk of collision (i.e., level of risk reached during the LoS incident). A high-level data analysis is followed by log-linear categorical analysis to determine significant interactions between variables, and, on the basis of these interactions, specific safety indicators are obtained for NZ airspace. Significant interactions include type of airspace and aircraft in altitude transition, type of airspace and conflict geometry, aircraft in altitude transition and conflict geometry, and causal agent and severity. The results can be used to develop refined country-specific safety indicators and allow for targeted methods of improving safety.

The New Zealand government takes airspace safety very seriously. The level of safety in New Zealand airspace is measured by the number of recorded airspace incidents. An airspace incident can be thought of as a failure in the chain of operations in the air traffic system when it is provided with air traffic service (ATS). Some of these incidents result in a loss of separation between aircraft, varying from slight to very serious with a significant risk of collision, known as a near collision. New Zealand's Civil Aviation Authority (CAA) identifies the causal factors for all airspace incidents with the Reason model of human error divided into three areas: active failures committed by individuals involved in the incident, local factors relating to the task and the ATS environment, and organizational factors originating in the managerial and organizational spheres of the ATS provider. On the basis of CAA's database, trends in controller-caused incidents in New Zealand airspace from 1994 through 2002 are analyzed in six categories: loss of separation, ATS coordination deficiency, near collisions, ATS clearance or instruction deficiency, ATS flight planning system deficiency, and ATS flight information deficiency. The results indicate that execution errors predominate for active failures, whereas poor concentration or lack of attention, inadequate checking, and controller workload are the dominant local factors. For the organizational factors, inadequate control and monitoring, inadequate specifications and procedures, and poor resource management dominate. These results can be used for a better understanding of controller working practices and ATS organization for enhanced airspace safety.

Air traffic in the United Kingdom has increased rapidly in the past two decades and is forecast to grow at a rate of 4.3% per year between 1998 and 2020. The failure to develop the air traffic system to cope with this growth has had undesirable consequences, for example, a rise in flight delays and near misses. Given the investment required in air traffic control systems to cater for this growth, the U.K. government in 2000 part-privatized the National Air Traffic Services (NATS), the body in charge of the United Kingdom’s airspace in a public–private partnership (PPP). The U.K. Airline Group acquired 46% of NATS and effective operational control, although the government retains a share in NATS and safety regulation is in the public sector. However, serious doubts about safety were raised during the debate on the PPP. Main issues surrounding the part-privatization of NATS were analyzed. First highlighted was the U.K. government’s provisions for the PPP and their implementation in the post-PPP NATS in six different categories: funding, new technology and project management, safety, pricing regime, international opportunities, and customer responsiveness. The September 11, 2001, terrorist attacks have had major impacts on air travel, and their consequences for NATS in the six categories are highlighted. Finally, attention was given to concerns that must be addressed to ensure the success of the partnership for NATS.

The growth in European air traffic has led to delays, inefficiencies, and attendant costs. This growth is predicted to increase. The workload of the air traffic controller is the limiting factor in airspace capacity and will remain so despite the introduction of new technology and procedures. A framework is provided for modeling airspace capacity by considering the factors that affect controller workload and then using a model of controller workload, aided by the appropriate analytical techniques, to estimate airspace capacity. The context of the current European air traffic control problems is given, airspace capacity is examined, and a scheme is outlined for estimating airspace capacity using simulation modeling. The Reorganized ATC Mathematical Simulator (RAMS) controller workload model was chosen for use in the research. The output of this simulation model is used for estimating airspace capacity, and simulation experiments undertaken using RAMS are described. The analysis of how these factors affect the air traffic controller, using a generalized linear modeling framework, is undertaken. This accounts for the spatial correlation structure of the error structure. Two alternative estimation methods are used: maximum likelihood and estimated generalized least squares. The results from the analysis, together with a workload-based capacity measure, are used to provide airspace capacity estimates.