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A minute before midnight on 21 July 2021, as passengers staggered sleepily through Manchester airport, I stood wringing my hands in the glow of a vending machine that was seven feet tall, conspicuously branded with the name of its owner – BRODERICK – and positioned like a clever trap between arrivals and the taxi rank. I opted for Doritos, keying in a three-digit code and touching my card to the reader so that the packet moved jerkily forwards, propelled by a churning plastic spiral and tipped into the well of the machine. My Doritos landed with a thwap, a sound that always brings relief to the vending enthusiast, because there hasn't been a mechanical miscue. Judged by the clock, which now read 12am, it was the UK's first vending-machine sale of the day. Nine hours later, I was sitting in a spruce office in the Manchester suburb of Wythenshawe, drinking coffee with John "Johnny Brod" Broderick, the man who owned and operated that handsome airport machine. I'd had an idea to try to capture 24 hours in the life of vending machines. With their backs against the wall of everyday existence, they tempt out such a peculiar range of emotions, from relief to frustration, condescension to childish glee. For decades I'd been a steady and unquestioning patron. I figured that by spending some time in the closer company of the machines and their keepers, by immersing myself in their history, by looking to their future, I might get to the bottom of their enduring appeal. What made entrepreneurs from the Victorian age onwards want to hawk their goods in this way? What made generations of us buy? Johnny Brod seemed a good first person to ask. Freckle-tanned, portly and quick to laugh, Broderick has a playful exterior that conceals the fiery heart of a vending fundamentalist. He is a man so invested in the roboticised transmission of snacks that, come Halloween, Johnny Brod has been known to park a machine full of sweets in his driveway, letting any costumed local kids issue their demand for treats via prodded forefinger. With his brother Peter and his father, John Sr, he runs the vending empire Broderick's Ltd, its 2,800 machines occupying some of the most sought-after corridors and crannies of the UK.
Sardar Vallabhbhai Patel International Airport (SVPIA) has introduced an indigenously developed artificial intelligence (AI) based surveillance service, Desk of Goodness, to help flyers through smart detection techniques. Desk of Goodness aims to serve passengers like senior citizens, women with infants, and passengers in need of a wheelchair. This desk is manned by goodness champions equipped with smart tabs, which keep them updated on possible sites where passengers need support. "Sardar Vallabhbhai Patel International Airport continues to improve infrastructure and services to enhance the passenger experience," said Jeet Adani, Director, Adani Airport Holdings. "AI-based video content analytics plays a crucial role in reaching out to flyers in emergencies. Analytics-based learnings will allow us to set new benchmarks in operational intelligence and increasing situational awareness, thereby improving safety, security and efficiency."
Anomalies, or outliers, can be a serious issue when training machine learning algorithms or applying statistical techniques. They are often the result of errors in measurements or exceptional system conditions and therefore do not describe the common functioning of the underlying system. Indeed, the best practice is to implement an outlier removal phase before proceeding with further analysis. In some cases, outliers can give us information about localized anomalies in the whole system; so the detection of outliers is a valuable process because of the additional information they can provide about your dataset. There are many techniques to detect and optionally remove outliers from a dataset.
"By understanding how people enjoy the Space Needle's observation decks, food and beverage experiences, and amenities, we can better provide both a safe and enjoyable experience," said Luis Quintero, senior operations manager at the Space Needle. "Through Veovo's crowd management solution, we can reduce and prevent overcrowding, while understanding trends over time will allow us to optimise our operations and resourcing." London Gatwick Airport will use Passenger Predictability solution to optimise security operations and improve passenger flow. The partnership will allow the airport to efficiently handle increasing passenger numbers and build back better for a more sustainable, passenger-centred travel experience. The AI-powered technology gives Gatwick real-time awareness of people's movement and experiences in the North and South terminal security areas.
A $1.7-billion expansion project at Los Angeles International Airport was officially unveiled Monday by local officials who expressed optimism that the facility will soon help serve a resurgence of travel demand from the yearlong pandemic slump. The new facility, named West Gates and billed as an expansion of the Tom Bradley International Terminal, holds 15 gates. The project broke ground in 2017, when international travel was surging, particularly with big-spending visitors from China. At the time, the airport was the second-busiest in the nation and was considered the West Coast gateway to the United States. The airport served more than 84 million domestic and international travelers that year, according to LAX records.
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Delta Air Lines is bringing facial recognition technology to domestic flights. Last week, the airline announced that it is launching its digital ID technology for domestic flights out of Detroit Metropolitan Wayne County Airport. Delta previously debuted the technology in 2018 for international flights.
Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they impact travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examines emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data are matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcings role in Chicagos mobility system. The goal is to investigate the systematic variations in patronage of ride-hailing. K-prototypes is utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output is a classification of the data into several clusters called prototypes. Six ridesourcing prototypes are identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses implications of the identified clusters related to affordability, equity and competition with transit.
Tesla is developing its own electric van for zipping passengers through its underground'boring' tunnels. According to a report from The Mercury News, San Bernardino County Transportation Authority will work with Tesla - and its sister drilling company Boring Company - to develop a 12-seat electric van for transporting passengers through a nearly 3-mile tunnel. The vans will be used in a recently approved connector line between Rancho Cucamonga and the Ontario International Airport. Tesla may develop an electric van capable of caring passengers between a 3-mile underground tunnel connecting Rancho Cucamonga and the Ontario International Airport. in San Bernardino County. While plans originally called for specially designed cars, the $60 million project will use the vans instead to eventually carry 1,200 passengers per day or about 10 million per year according to The Mercury News.
Like the city that hosts the Consumer Electronics Show (CES) there is a lot of noise on the show floor. Sifting through the lights, sounds and people can be an arduous task even for the most experienced CES attendees. Hidden past the North Hall of the Las Vegas Convention Center (LVCC) is a walkway to a tech oasis housed in the Westgate Hotel. This new area hosting SmartCity/IoT innovations is reminiscent of the old Eureka Park complete with folding tables and ballroom carpeting. The fact that such enterprises require their own area separate from the main halls of the LVCC and the startup pavilions of the Sands Hotel is an indication of how urbanization is being redefined by artificial intelligence.