The New South Wales government has teamed up with Cisco to trial the use of AI, IoT, and edge computing technology to improve the reliability of public transport in Sydney and Newcastle. As part of the trial, Transport for NSW (TfNSW) is using IoT to enable physical objects to be "digitised" and connected to the transported network via sensors, while edge computing will be leveraged to take real-time data from connected objects to enable faster decision making. AI, meanwhile, will be used to assist with understanding data and automating the process. The technologies will be connected to several buses, ferries, and light rail vehicles in both cities, the state government said. "We've partnered with Cisco to investigate how a real-time view of vehicle supply and customer demand, and performance, can guide future network decisions, and monitor road conditions to identify where repair work is needed," Minister for Transport and Roads Rob Stokes said.
Engineers at Stanford University have built a robotic bird to understand how birds are able to fly and perch on branches. The robot has a pair of snatching talons that attach to a circular flat base; that is then attached to a quadcopter drone to fly around. To account for the size of the drone that allows it to fly, the avian robot is based on the legs of a peregrine falcon. In place of bones, the machine has a 3D-printed structure with motors and fishing line for muscles and tendons. Each leg has its own motor to move back and forth, another for grasping, and a mechanism to absorb impact energy when it lands.
Business interest in artificial intelligence (AI) has rocketed in recent years -- spending could reach $15.7 trillion by 2030, according to PwC. But there remain lingering concerns that businesses are failing to realize the full value from their investments. Ever since their emergence, AI, machine learning (ML), and data science have all been surrounded by hype. We've been promised technology that will solve our most complex challenges for us and automatically optimize everything from internal processes to customer experiences. Advances are being made every day that promise to transform virtually every aspect of our lives.
Today's urban life is out of imagination without the presence of elevators and the elevator controller algorithm has been well studied by different techniques including reinforcement learning . A glance over the references gave the impression that the majority of studies has focused on elevators installed in high-rise buildings while those in train stations are barely discussed. Elevators in train stations, however, deserve their own attention because of their obvious difference from systems in buildings. A good example is the Gare de Lyon in Paris, a station with 2 underground floors on which you find 2 different train lines' platforms respectively. From my personal experience, it usually takes quite a while to get to floor -2 from floor -1 for a train change with my baby stroller by elevator.
"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.
Now imagine a city without private cars. A city where transportation is emissions-free, largely self-driving and connected to the internet. A city where cars, as well as taxis, buses, trains and bicycles, are shared. Instead of parked cars and concrete, city streets might be filled with mini parks, markets and more. A look at how innovation and technology are transforming the way we live, work and play.
The internet of things (IoT) can help prevent crime and ensure public safety. The need for gathering and processing large volumes of data makes the application of IoT in the public sector highly impactful. Governments have the responsibility of ensuring the health, safety, and prosperity of large populations, with the help of an incredibly small supply of personnel. This makes the use of IoT in enabling government functions obvious. Using IoT in the government sector can ensure the smooth functioning of routine activities and focus on long-term, demanding projects.
Traffic forecasting is an integral part of intelligent transportation systems (ITS). Achieving a high prediction accuracy is a challenging task due to a high level of dynamics and complex spatial-temporal dependency of road networks. For this task, we propose Graph Attention-Convolution-Attention Networks (GACAN). The model uses a novel Att-Conv-Att (ACA) block which contains two graph attention layers and one spectral-based GCN layer sandwiched in between. The graph attention layers are meant to capture temporal features while the spectral-based GCN layer is meant to capture spatial features. The main novelty of the model is the integration of time series of four different time granularities: the original time series, together with hourly, daily, and weekly time series. Unlike previous work that used multi-granularity time series by handling every time series separately, GACAN combines the outcome of processing all time series after each graph attention layer. Thus, the effects of different time granularities are integrated throughout the model. We perform a series of experiments on three real-world datasets. The experimental results verify the advantage of using multi-granularity time series and that the proposed GACAN model outperforms the state-of-the-art baselines.
With the launch of this new service, all Moscow Metro stations and road networks will use Face Pay for ticket payments. This is a first in that it allows people's faces instead of their bank cards or coins as they enter one station per journey throughout city limits with Fare card providers. To enter the Moscow Metro, you need to link your photos and financial card information with Face Pay through the local subway program to be valid. Once connected, simply look at the camera when passing through the turnstile without showing Troika or phone whatsoever – all are digitally verified using facial recognition technology. The Moscow Transport Agency has gone to great lengths to ensure the privacy of its passengers.