A Ranking Method to Prioritize VFR Airports to Be Provided With Instrument Approach Procedures

The primary purpose of this work is to investigate the necessity of a more comprehensive and systematic method to prioritize airports to be provided with instrument approach and landing procedures in the Brazilian air transportation landscape. An overview of the main contributors to risks associated with the approach and landing phases is provided, covering the most important aspects of unstable approaches and CFIT events. Considering the emergence of Terrain Awareness and Alerting Systems (TAWS), the role of its contribution to safety is discussed, as well as the certification context related to the design, installation, and operation of those systems. A ranking method is developed based on the analysis of TAWS alert events in several Brazilian airports. The method results in a ranking list of airports eligible for instrument procedures and points to objective means to improve safety, accessibility, and efficiency on the flight operations to those locations

A Ranking Method to Prioritize VFR Airports to be Provided Instrument Approach Procedures

The primary purpose of this work is to investigate the necessity of a more comprehensive and systematic method to prioritize airports to be provided with instrument approaches and landing procedures in the Brazilian air transportation landscape. First, an overview of the main contributors to risks associated with the approach and landing phases is provided, covering the most critical aspects of unstable approaches and controlled flight into terrain (CFIT) events. Second, considering the emergence of Terrain Awareness and Alerting Systems (TAWS), the role of its contribution to safety is discussed and the certification context related to the design, installation, and operation of those systems. A ranking method is developed based on the analysis of TAWS alert events in several Brazilian airports. The technique results in a ranking list of airports eligible for instrument procedures and points to objective means to improve safety, accessibility, and efficiency on the flight operations to those locations

Accident Hotspots in Southern Expressway of Sri Lanka: Interpolation Evaluation using GIS

In this study, the southern expressway, which is the first and lengthiest E class highway (126 km) in Sri Lanka, was analysed for roadside accident incidences. The primary objective of this paper is to identify the best-fit interpolation techniques for the hotspots' most distinctive causes of vehicular crashes. The accident details were collected from the Police Headquarters consisting of 966 accidents that took place during the period from 2015 to 2017. To identify accident hotspots, GIS-based interpolation techniques such as Ordinary Kriging, Kernel Density Estimation (KDE), Inverse Distance Weighting (IDW), and Nearest Neighbour Interpolation methods were used. The spatial interpolation outcome of the four methods was compared based on the standard Prediction Accuracy Index (PAI). The analysis was executed using QGIS and GeoDa. Results of PAI revealed that an IDW and KDE outperformed the other two interpolation methods. The left and right lanes of the expressway, spotted with 11 and 20 hotspots, respectively, indicate the right lane was 50% more prone to accidents than the left lane. Notably, nearly 5% of the entire road stretch is estimated as accident-prone spots in both lanes. Peak accidents were recorded during afternoon and evening hours, and buses were the most active vehicle type. Uncontrolled speeding was the primary reason for more than 50% of the accidents, while unsuccessful overtake accounted for more than 20% of the accidents on the highway. The road design modifications and warning sign placements at appropriate places may be recommended as countermeasures

Early warning system of natural hazards and decrease of climat impact from aviation; ALARM funded project

Aviation safety can be jeopardised by multiple hazards arising from natural phenomena, e.g., severe weather, aerosols/gases from natural hazard, and space weather. Furthermore, there are the anthropogenic emissions and climate impact of aviation that could be reduced. To mitigate such risk and/or to decrease climate impact, tactical decision-making processes could be enhanced through the development of multihazard monitoring and Early Warning System (EWS). With this objective in mind, ALARM consortium has implemented alert products (i.e., observations, detection and data access in near realtime) and tailored product (notifications, flight level — FL contamination, risk area, and visualization of emission/risk level) related to Natural Airborne Hazard (NAH, i.e., volcanic, dust and smoke clouds) and environmental hotspots. New selective detection, nowcasting and forecasts of such risks for aviation have been implemented as part of ALARM prototype EWS. This system has two functionalities. One is to provide alerts on a global coverage using remote sensing from satellites and models (focus on NAH, space weather activity and environmental hotspots). A second focuses on detecting severe weather and exceptional SO2 conditions around a selection of few airports, on providing nowcasts and forecasts of risk conditions

Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos

We perform spatio-temporal analysis of public sentiment using geotagged photo collections. We develop a deep learning-based classifier that predicts the emotion conveyed by an image. This allows us to associate sentiment with place. We perform spatial hotspot detection and show that different emotions have distinct spatial distributions that match expectations. We also perform temporal analysis using the capture time of the photos. Our spatio-temporal hotspot detection correctly identifies emerging concentrations of specific emotions and year-by-year analyses of select locations show there are strong temporal correlations between the predicted emotions and known events.Comment: To appear in ACM SIGSPATIAL 201

Detailed Analysis and Identification of Key Factors Resulting in Motor Accidents Across the UK

Motor accidents across the globe amount to a large number of deaths every year. The collisions result in not just the personal injury to people involved but also in the loss of money to the motor insurance companies, trauma to the people involved, and added pressure on the emergency services. With the help of data analytics techniques, this project aims to identify critical factors that might contribute to the accidents. Upon investigating the temporal features and geo-spatial features of the motor accident locations, we tried to establish a correlation between the accident intensity and its key factors. For this exploratory analysis, we also considered weather conditions and daily average traffic flow data. We then trained Supervised learning models on the data to find out the best performing multi-label classification model

Detailed Analysis and Identification of Key Factors Resulting in Motor Accidents Across the UK

Motor accidents across the globe amount to a large number of deaths every year. The collisions result in not just the personal injury to people involved but also in the loss of money to the motor insurance companies, trauma to the people involved, and added pressure on the emergency services. With the help of data analytics techniques, this project aims to identify critical factors that might contribute to the accidents. Upon investigating the temporal features and geo-spatial features of the motor accident locations, we tried to establish a correlation between the accident intensity and its key factors. For this exploratory analysis, we also considered weather conditions and daily average traffic flow data. We then trained Supervised learning models on the data to find out the best performing multi-label classification model

Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model

Road infrastructure can affect the occurrence of road accidents. Therefore, identifying roadway features with high accident probability is crucial. Here, we introduce image inpainting that can assist authorities in achieving safe roadway design with minimal intervention in the current roadway structure. Image inpainting is based on inpainting safe roadway elements in a roadway image, replacing accident-prone (AP) features by using a diffusion model. After object-level segmentation, the AP features identified by the properties of accident hotspots are masked by a human operator and safe roadway elements are inpainted. With only an average time of 2 min for image inpainting, the likelihood of an image being classified as an accident hotspot drops by an average of 11.85%. In addition, safe urban spaces can be designed considering human factors of commuters such as gaze saliency. Considering this, we introduce saliency enhancement that suggests chrominance alteration for a safe road view.Comment: 9 Pages, 6 figures, 4 table