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Evaluating the Impacts of the Internet of Things to Reduce Runway Incursions
Published in Erick C. Jones, Supply Chain Engineering and Logistics Handbook, 2020
Samuel Innanore Okate, Erick C. Jones
In December of 2015 at Mumbai airport, an airport technician died after a fatal accident occurred where the worker was sucked into the engine of an Air India plane in preparation from being taxied out to take off (Mullen & Singh, 2015). Could this fatal accident have been avoided? This accident is an example of a runway incursion. Approximately, one runway incursion occurs each day in the United States, and the potential for a catastrophic accident is “unacceptable”, according to the FAA’s risk/severity matrix [2]. At its extreme, runway incursions have been identified to be able to cause hundreds of deaths in a single air traffic accident [2]. With these alarming statistics, it is no secret why runway safety is one of the most crucial issues in aviation safety. The likelihood for runway incursions grows exponentially as a function of air traffic growth within the U.S. National Airspace System (NAS) [2].
Economics of aviation safety and security
Published in Bijan Vasigh, Ken Fleming, Thomas Tacker, Introduction to Air Transport Economics, 2018
Bijan Vasigh, Ken Fleming, Thomas Tacker
Finally, a runway incursion refers to any occurrence on an airport runway involving an aircraft and any object or person on the ground that creates a collision hazard or results in a loss of separation with an aircraft taking off, intending to take off, landing, or intending to land (FAA, 2015). Although aviation accidents have been diminishing over the past few years, runway incursions continue to occur (Table 13.1). Runway incursions are further classified into four categories: A, B, C, and D, where A and B are considered serious. These categories are based on available reaction time, evasive or corrective action, environmental conditions, speed of the aircraft and/or vehicle, and proximity of aircraft/vehicle.2 The FAA further classifies runway incursions into three error types: Operational errors/deviations (OE/D): an event of the air traffic system where an aircraft, vehicle, equipment, or personnel encroaches upon a landing area that was delegated to another position of operation without prior approval or coordination.Pilot deviation (PD): action of a pilot that violates federal aviation regulations.Vehicle/pedestrian deviations (V/PD): involves pedestrians, vehicles, and other objects interfering with aircraft operations. These incursions are not to be misunderstood as causative but are nature of incursion occurrence (DOT, 2014).
Airport Signing: Movement Area Guidance Signs
Published in Cándida Castro, Tim Horberry, The Human Factors of Transport Signs, 2004
Kirstie Carrick, Peter Pfister, Robert Potter, Roy Ng
The FAA reports that at towered airports, runway incursions tend to be due to pilots who take off from or enter a runway despite acknowledging instructions from the tower to the contrary. Other causes are air traffic controllers allowing aircraft to get too close together or drivers or pedestrians on the tarmac who do not communicate with the tower or apparently ignore instructions from the tower (Rodgers, 2002). These conclusions do not explain why such loss of situational awareness occurs, nor are signs explicitly mentioned.
Decomposed fuzzy cost-benefit analysis and an application on ophthalmologic robot selection
Published in The Engineering Economist, 2023
Cost-benefit analysis is a widely used method in the evaluation of investment alternatives for alternative selection since it makes it possible to add many different decision factors to the analysis numerically. Guo and Xiang (2022) used CBA in photovoltaic-storage investment assessment in integrated energy systems. Liu et al. (2022) evaluated economical validation of residential solar power investment CBA approach. Sospiro et al. (2021) presented a CBA of pumped hydroelectricity storage investment in China. Chi and Bunker (2021) showed a perspective on real-life CBA and assessment frameworks for transport infrastructure investments. Jin et al. (2021) assessed the impact of CBA on financial benefit evaluation of investment projects under back propagation neural network. Mouter et al. (2021) used CBA in the context of urban mobility investments. Henke et al. (2020) proposed a combined multi-criteria and CBA evaluation processes for a new highway investment in the transport sector. Bardal (2020) presented contradictory outcomes of CBA and findings from public-investment projects. Ison (2020) analyzed runway incursion trends with implications for CBA of mitigation investments. Zhong and Wu (2020) investigated the effects of CBA under back propagation neural network on financial benefit evaluation of investment projects.
Seasonal crash prediction model for urban signalized intersections: Wisconsin southeast region
Published in Traffic Injury Prevention, 2020
Boris Claros, Madhav Chitturi, Glenn Vorhes, Andi Bill, David Noyce
The Negative Multinomial has been successfully used for modeling roadway crashes (Hauer et al. 2004, Ulfarsson and Shankar 2003, Caliendo et al. 2007) and airfield runway incursions (Claros et al. 2017). Hauer et al. (2004) developed safety models for urban four-lane undivided roadway segments with data from Washington State. The period of analysis was between 1993 and 1996, and the model prediction contained scale parameters for every year of analysis. Ulfarsson and Shankar (2003) developed models for median crossover crashes with multiyear cross-sectional roadway data from Washington State. Two approaches were compared and the results showed that the Negative Multinomial significantly outperformed the Negative Binomial in terms of fit suggesting that the Negative Multinomial was appropriate for multiyear crossover crash prediction. Caliendo et al. (2007) developed crash prediction models for multilane rural roads in Italy including rain precipitation as a predictor variable. In other modes of transportation such as aviation, Claros et al. (2017) developed runway incursion prediction models for hub airports in the United States using yearly takeoff/landing operations, runway and taxiway geometry configurations, and weather variables as predictor variables. The models were developed by runway incursion severity between 2009 and 2013 and scaled parameters for every year of analysis were provided.
An exploratory study on the effects of human, technical and operating factors on aviation safety
Published in Journal of Transportation Safety & Security, 2019
Joyce M. W. Low, Kum Khiong Yang
The air transportation industry employs technologically sophisticated equipment in its service operations. As such, there is a wealth of literature that examines the technical causes of aviation accidents. Some of these common causes that have received in-depth treatments in the recent articles include misunderstanding between control tower and pilot Hazrati (2015), in-flight loss of control (Ancel et al., 2015), runway excursion (Ju, 2011; Wagner & Banker 2014), and runway incursion (Kim & Yang, 2012; McLean & Monro, 2004; Rogerson & Lambert 2012; Rogerson, Lambert, & Johns, 2012; Schonefeld & Moller 2012; Yan & Haimes, 2010; Young & Vlek, 2009). Nonetheless, through modelling techniques of vector auto-regression and vector error correction (VEC), Harizi, Belhaiza, & Harizi (2013) found that factors for air crashes that occurred globally during 1950 to 2008 converge and are complementary and interdependent.