Infrastructure & Services

ANA starts testing autonomous bus operation at Haneda Airport

The Japan Times

ANA Holdings Inc., the operator of All Nippon Airways Co., said Wednesday it has started testing a semi-autonomous bus that will transport passengers and staff working at Tokyo's Haneda Airport. The company will conduct the test with the electric bus capable of carrying 57 passengers on a 1.9-kilometer route through the end of this month, aiming to start trial operation later in the year. The vehicle, with level-3 automation, allows drivers to turn their attention away from driving and engage in different tasks. "As the Tokyo Olympics are approaching, we hope more passengers from around the world will see our latest technology," ANA Senior Executive Vice President Shinzo Shimizu said in a ceremony at the airport. In 2018, the number of passengers who arrived at and departed from the airport increased 2.1 percent to 85 million, according to Japan Airport Terminal Co. which manages the Haneda Airport facilities.

Passenger waiting for flight takes over airport screen to play video games

FOX News

No word on whether or not the passenger made it to the next level. A passenger waiting for a flight at an Oregon airport needed a bit more screen space for his video game so he plugged his Playstation 4 into a computer screen that had been displaying a map of the airport. Kara Simonds, a spokeswoman for the Port of Portland, told KXL-AM radio in an on-air interview that Portland International Airport staff asked the man to stop gaming on the public map display. He asked if he could finish his game. They said no, and the situation resolved peacefully.

Using Machine Learning and Satellite Imagery for Street Address Generation


Researchers from Facebook and MIT Labs have proposed a new methodology that uses machine learning and satellite imagery to generate street addresses in areas of the world where individual buildings don't have a unique address. The methodology divides the street addressing into two processes. The first process is segmentation. During segmentation, road pixels are identified using deep learning from 0.5 meter resolution satellite images. The second part of segmentation involves developing the road network from these identified pixels.

Tomography of the London Underground: a Scalable Model for Origin-Destination Data

Neural Information Processing Systems

The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focussing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London Underground network, where a tap-in/tap-out system tracks the start/exit time and location of all journeys in a day.

British Airways To Launch Guide Robots At London Heathrow Airport


British Airways is experimenting with a new tool for guiding passengers through its massive London Heathrow hub: guide robots. Starting in 2020, the flag carrier of the United Kingdom will deploy an array of autonomous robots in Terminal 5 of its London Heathrow base to help guide passengers through the airport and answer basic questions. The problem is harder to solve than it may initially sound. Getting around Heathrow requires deep knowledge of the dozens of storefronts, duty-free shops and lounges in the terminals as well as the ability to navigate through multiple floors and throngs of passengers who may not always be paying attention to their surroundings. To help guide passengers, the new robots will not only have to know where they are at all times but also be able to navigate through the airport without getting lost or running into travelers.

Airlines avoid Iran and Iraq airspace

The Japan Times

PARIS – Several international airlines said Wednesday they would avoid Iranian and Iraqi airspace after Tehran fired ballistic missiles at bases housing U.S. troops in Iraq. Lufthansa and its Austrian Airlines unit nonetheless decided to maintain flights to the Iranian capital, Tehran, this week, a statement said. Iran launched more than 20 missiles at bases housing U.S. troops in the early hours, officials in Washington and Tehran said. Iran's supreme leader called the attacks a "slap in the face" after a U.S. drone strike killed Iranian military commander Qassem Soleimani near Baghdad international airport last week. In Germany, Lufthansa said it had halted overflights of Iran and Iraq until further notice.

Solar-powered electric tricycle unveiled at CES 2020 can squeeze into tight parking spots

Daily Mail - Science & tech

Samsung has shown off an 8K QKED bezel-less TV that is 99 per cent screen and ultra-thin – only 15mm. Fellow South Korean rival LG has its own set of OLED TVs that double as'a piece of art' thanks to an outer edge that mimics a picture frame and the ability to display HD art pieces when not in use. Sony unveiled a concept connected car loaded with sensors and technology from its audio/visual business as part of its own push into mobility. Panasonic had as part of its CES showcase a miniature, battery-powered prototype fire engine that can transport the same level of equipment as a full-sized fire engine but at a fraction of the cost and energy. Lenovo has showcased its foldable PC with a 13.3-inch screen that it says is more durable than Samsung's Galaxy Fold.

IBM AI used with e-bikes to modify cyclist bad behaviour


Cyclists, whether they be food delivery riders or MAMILs, are infamous for doing everything they can to conserve their hard-won speed, even if it means running a red light or careering into the way of pedestrians on the footpath. But new work from IBM Research Australia and RMIT's Exertion Games Lab, however, is looking to avoid tiresome stops or dangerous behaviour by using artificial intelligence (AI) to catch the'green wave' of traffic signals. It's well known many cyclists jump traffic signals or make legally questionable deviations to maintain momentum getting from A to B. If you're an underpaid international student under Dickensian food delivery conditions, there's simply no other way. That could be about to change. In a project dubbed'Ari the e-bike,' the researchers used traffic data and'green wave' modelling from VicRoads and internet of things (IoT) technologies to help the rider regulate their speed to match cycles of green traffic lights.

FAA proposes rule change to force identification of Colorado, Nebraska drones

FOX News

Fox News Flash top headlines for Dec. 31 are here. Check out what's clicking on One day after Colorado and western Nebraska counties reported a series of mysterious, nocturnal drone flights, the Federal Aviation Administration is promoting a rule change last week that requires most drones to be identifiable remotely, a report said Monday. The rule change, announced Thursday, have been in works for more than a year, FAA spokesman Ian Gregor said in an email to the Denver Post Monday. Under the legislation, law enforcement, federal security agencies and the FAA would be allowed to identify drones flying through their jurisdiction, the FAA said.

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control Artificial Intelligence

Adaptive signal control system (ASCS) is the most advanced t raffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time in order to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adj ust the signal phasing and timings for different traffic movements at a signalized intersection with A SCS. The accuracy of the queue length prediction model varies based on the many factors, such as th e stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and satur ated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to devel op short-term queue length prediction models for signalized intersections that can be leveraged by adapt ive traffic signal control systems using four variations of Grey systems: (i) the first order single variab le Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst mo del (GVM), and (iv) GVM with Fourier error corrections (EGVM). The efficacy of the Grey models is th at they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike stat istical models. We have conducted a case study using queue length data from five intersections with ad aptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models w ere compared with linear, nonlinear time series models, and long short-term memory (LSTM) neura l network. Based on our analyses, we found that EGVM reduces the prediction error over closest co mpeting models (i.e., LSTM and Additive Autoregressive (AAR) time series models) in predicting ave rage and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error (R MSE), and 51% and 50%, respectively, in terms of Mean Absolute Error (MAE).