Spatio-Temporal Influence of Extreme Weather on a Taxi Market

2021 ◽  
pp. 036119812110038
Author(s):  
R. C. P. Wong ◽  
P. L. Mak ◽  
W. Y. Szeto ◽  
W. H. Yang
Keyword(s):  
Tropical Cyclone ◽  
Search Time ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Taxi Drivers ◽  
Passenger Demand ◽  
Temporal Influence ◽  
System Data ◽  
Spatio Temporal ◽  
The Government

Extreme weather conditions, strong gusts, and torrential rainfall threaten the safety of the general public and restrict people’s travel options. Most of the transportation modes are suspended because of safety reasons. Taxis are one of the only few available non-private transport modes to provide services to those who have urgent and unavoidable travel needs. This study uses global positioning system data collected from 460 Hong Kong urban taxis during nine ordinary and one tropical cyclone periods aiming to find out and explain the differences in relation to the percentage of taxis not in operation, the number of served passenger-trips, average time spent by vacant-taxi drivers finding a customer, and the percentage of taxi drivers in cross-district customer-search throughout the same 48 h duration. The findings show an inadequate level of taxi supply and a high passenger demand during the tropical-cyclone-affected period. Up to 80% of taxis were not in operation to serve the urgent and necessary trips. The average customer-search time for taxi drivers, which is anticipated inversely proportional to the demand for taxi rides, was very short (about 5 min). Policy measures are discussed and recommended to the government to improve the taxi services during extreme weather conditions.

scholarly journals A Community-Based Vulnerability Assessment of Terrestrial Flooding in the South Oropouche River Basin After Tropical Storm Bret

2018 ◽  
Vol 9 (4) ◽  
pp. 166
Author(s):  
Don Charles
Keyword(s):  
Trinidad And Tobago ◽  
River Basin ◽  
Vulnerability Assessment ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Tropical Storm ◽  
The South ◽  
Community Based ◽  
The Government ◽  
The Impact

The year 2017 had a very active season for hurricanes and extreme weather conditions. Hurricanes Harvey, Irma, and Maria did damage to several Caribbean islands. Even Trinidad and Tobago (T&T), a country which rarely experiences extreme weather conditions, was affected by Tropical Storm Bret. Tropical Storm Bret caused flooding in T&T, especially in the low lying South Oropouche River Basin.There is a dearth of research conducted in T&T about the impact of extreme weather conditions and flooding on communities and families. Thus, this study sought to conduct a community base vulnerability assessment (CBVA) of the impact of the Tropical Storm Bret induced flooding upon the residents of the South Oropouche River Basin.Primary data was collected via semi-structured interviews and questionnaires to conduct the CBVA. Furthermore, this study introduced a Modal Community Based Vulnerability Assessment Index (MCBVAI) to help determine which factors the residents South Oropouche River Basin are most vulnerable to.This study found that the most vulnerable residents were vulnerable largely to their building of structures at locations unsuitable for housing. Moreover, the most vulnerable residents also built structures that were not resilient to flooding and was elevated less than 4 feet (ft) off the ground. The appropriate policy response for the Government of the Republic of Trinidad and Tobago (GORTT) would be to i) establish building codes, ii) develop a comprehensive spatial planning strategy which prohibits people from building structures in unsuitable areas, and iii) implement disaster risk reduction programmes which focus on improving pre-event disaster preparedness, improving the national and local response, and promoting educational awareness.

scholarly journals Investment appraisal and risk assessment of protected vegetable cultivation under Leyte conditions

2017 ◽  
pp. 174-193
Author(s):  
Therese Ratilla ◽  
Moises Neil Seriño
Keyword(s):  
Open Field ◽  
Technical Assistance ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Small Scale ◽  
Vegetable Production ◽  
Strong Wind ◽  
Investment Appraisal ◽  
Protected Cultivation ◽  
The Government

Protected cropping technology has been introduced to address the inability of farmers to achieve a successful year-round vegetable production. However, small scale farmers are reluctant to adopt this technology due to huge investment costs and the risk associated with extreme weather conditions. Hence, this study was conducted in some parts of Leyte, to evaluate the profitability and assess the risk of protected and open-field cultivation during the occurrence of extreme weather conditions such as tropical cyclones and strong wind phenomena. Results show that protected cultivation generates higher yields compared to open field cultivation. In Baybay site, investment on steel-type high-strength-tunnel covered with polyethylene plastic is the most viable option as it attained the highest net present values (NPVs), benefit-cost ratios (BCRs) and internal rate of return (IRRs). It also has the earliest payback period across different climatic scenarios. At the Cabintan site, the low-tunnel-structure is the most viable when a high-end market is established. This implies that market outlet is one of the critical factors affecting profitability and pricing. Given the potential of protected cultivation in minimizing crop failures, it is recommended that the government and private sector shall extend financial and technical assistance to farmers. Investors shall be covered with crop and structure insurances as risk of crop failures and loss of capital is high during inclement weather conditions.

Online Monitoring System of Tailing Dam Seepage Line in Abominable Weather

2013 ◽  
Vol 340 ◽  
pp. 913-919 ◽  
Author(s):  
Ou Yang Wei ◽  
Wen Jian Li
Keyword(s):  
High Reliability ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Normal Operation ◽  
Time Data ◽  
The People ◽  
Tailing Dam ◽  
Pressure Meter ◽  
The Government ◽  
Data Acquisition Unit

Tailings pond is a high potential energy artificial debris flow dangerous source. Once break, it will bring great harmless. Plant the seepage pressure meter in tailing dam thin section and use lightning protection and intelligence power supply unit which can adapt to extreme weather conditions. The people in site duty room can obtain the real time data, which is acquired by data acquisition unit (MCU) and transmitted by GPRS wireless. The government administration departments and enterprise leaders can also very quickly and easily watch it through the Internet. It will provide a reliable guarantee for the normal operation of the mining enterprises. This project has a innovation point of high reliability distributed intelligent power device, which can adapt to extreme weather conditions.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

Multi-objective resilience enhancement program in smart grids during extreme weather conditions

2021 ◽  
Vol 129 ◽  
pp. 106824
Author(s):  
Rahman Ashrafi ◽  
Meysam Amirahmadi ◽  
Mohammad Tolou-Askari ◽  
Vahid Ghods
Keyword(s):  
Smart Grids ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Multi Objective

scholarly journals Extreme weather conditions and dengue outbreak in Guangdong, China: spatial heterogeneity based on climate variability

2021 ◽  
pp. 110900
Author(s):  
Jian Cheng ◽  
Hilary Bambrick ◽  
Laith Yakob ◽  
Gregor Devine ◽  
Francesca D. Frentiu ◽  
...  
Keyword(s):  
Climate Variability ◽  
Spatial Heterogeneity ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Dengue Outbreak

scholarly journals Overheating Risk and Energy Demand of Nordic Old and New Apartment Buildings during Average and Extreme Weather Conditions under a Changing Climate

2021 ◽  
Vol 11 (9) ◽  
pp. 3972
Author(s):  
Azin Velashjerdi Farahani ◽  
Juha Jokisalo ◽  
Natalia Korhonen ◽  
Kirsti Jylhä ◽  
Kimmo Ruosteenoja ◽  
...  
Keyword(s):  
Climate Change ◽  
Energy Demand ◽  
Energy Use ◽  
Residential Buildings ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Living Room ◽  
Extra Energy ◽  
Cooling Unit ◽  
Indoor Conditions

The global average air temperature is increasing as a manifestation of climate change and more intense and frequent heatwaves are expected to be associated with this rise worldwide, including northern Europe. Summertime indoor conditions in residential buildings and the health of occupants are influenced by climate change, particularly if no mechanical cooling is used. The energy use of buildings contributes to climate change through greenhouse gas emissions. It is, therefore, necessary to analyze the effects of climate change on the overheating risk and energy demand of residential buildings and to assess the efficiency of various measures to alleviate the overheating. In this study, simulations of dynamic energy and indoor conditions in a new and an old apartment building are performed using two climate scenarios for southern Finland, one for average and the other for extreme weather conditions in 2050. The evaluated measures against overheating included orientations, blinds, site shading, window properties, openable windows, the split cooling unit, and the ventilation cooling and ventilation boost. In both buildings, the overheating risk is high in the current and projected future average climate and, in particular, during exceptionally hot summers. The indoor conditions are occasionally even injurious for the health of occupants. The openable windows and ventilation cooling with ventilation boost were effective in improving the indoor conditions, during both current and future average and extreme weather conditions. However, the split cooling unit installed in the living room was the only studied solution able to completely prevent overheating in all the spaces with a fairly small amount of extra energy usage.

Potential Impacts of Extreme Heat and Bushfires on Dementia

2021 ◽  
Vol 79 (3) ◽  
pp. 969-978
Author(s):  
Taya L. Farugia ◽  
Carla Cuni-Lopez ◽  
Anthony R. White
Keyword(s):  
Rural Communities ◽  
Natural Disasters ◽  
Low Socioeconomic Status ◽  
Forest Fires ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Extreme Heat ◽  
Indigenous Populations ◽  
Future Research ◽  
Diagnosis Of Dementia

Australia often experiences natural disasters and extreme weather conditions such as: flooding, sandstorms, heatwaves, and bushfires (also known as wildfires or forest fires). The proportion of the Australian population aged 65 years and over is increasing, alongside the severity and frequency of extreme weather conditions and natural disasters. Extreme heat can affect the entire population but particularly at the extremes of life, and patients with morbidities. Frequently identified as a vulnerable demographic in natural disasters, there is limited research on older adults and their capacity to deal with extreme heat and bushfires. There is a considerable amount of literature that suggests a significant association between mental disorders such as dementia, and increased vulnerability to extreme heat. The prevalence rate for dementia is estimated at 30%by age 85 years, but there has been limited research on the effects extreme heat and bushfires have on individuals living with dementia. This review explores the differential diagnosis of dementia, the Australian climate, and the potential impact Australia’s extreme heat and bushfires have on individuals from vulnerable communities including low socioeconomic status Indigenous and Non-Indigenous populations living with dementia, in both metropolitan and rural communities. Furthermore, we investigate possible prevention strategies and provide suggestions for future research on the topic of Australian bushfires and heatwaves and their impact on people living with dementia. This paper includes recommendations to ensure rural communities have access to appropriate support services, medical treatment, awareness, and information surrounding dementia.

scholarly journals Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

2021 ◽  
Author(s):  
Haitham Baomar ◽  
Peter J. Bentley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Training Data ◽  
Aviation Industry ◽  
Control Models ◽  
Novel Approach ◽  
Network Modules ◽  
Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

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