The average Bengalurean spends about two hours a day stuck in traffic. The sheer volume of vehicles is impossible to manage manually, through traffic policemen on ground. While Bengaluru has led the way in terms of a tech-savvy traffic police force, there is room for improvement. Artificial Intelligence and technology have revolutionised traffic management across the world but have not yet been adopted in Karnataka, which prefers to use traffic policemen in some areas and a Westernised Webster model that doesn't suit our traffic conditions in others. Civic agencies have also fallen short in terms well-designed roads, construction regulations and meeting infrastructure needs of a fast growing population, leading to congested roads, reports M.K. Ashoka.
Artificial Intelligence and Machine Learning algorithms have increasingly become an integral part of several industries. Now they are making their way to smart city initiatives, intending to automate and advance municipal activities and operations at large. Typically, a city when recognized as a smart city means that it is leveraging some kind of internet of things (IoT) and machine learning machinery to glean data from various points. A smart city has various use cases for AI-driven and IoT-enabled technology, from maintaining a healthier environment to advancing public transport and safety. By leveraging AI and machine learning algorithms, along with IoT, a city can plan for better smart traffic solutions making sure that inhabitants get from one point to another as safely and efficiently as possible.
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.
This is a sample showing how to do real-time video analytics with NVIDIA Deepstream [SDK] on a NVIDIA Jetson Nano device connected to Azure via Azure IoT Edge. Deepstream is a highly-optimized video processing pipeline, capable of running deep neural networks. It is a must-have tool whenever you have complex video analytics requirements, whether its real-time or with cascading AI models. IoT Edge gives you the possibility to run this pipeline next to your cameras, where the video data is being generated, thus lowering your bandwitch costs and enabling scenarios with poor internet connectivity or privacy concerns. With this solution, you can transform cameras into sensors to know when there is an available parking spot, a missing product on a retail store shelf, an anomaly on a solar panel, a worker approaching a hazardous zone, etc.
In late September, Beijing unveiled to the world Daxing, a glimmering $11 billion airport showcasing technologies such as robots and facial recognition scanners that many other airports worldwide are either adopting or are now considering. Daxing fits the description of what experts hail as a "smart airport." Just as a smart home is where internet-connected devices control functions like security and thermostats, smart airports use cloud-based technologies to simplify and improve services. Of course, many of the nearly 4,000 scheduled service airports across the world are still embarrassingly antiquated. The good news for aviation is that more facilities are investing, finally, to better serve airlines, suppliers, and travelers. This year, airports worldwide will spend $11.8 billion -- 68 percent more than the level three years ago -- on information technology, according to an estimate published this month by SITA (Société Internationale de Telecommunications Aeronautiques, an airline-owned tech provider). A few trends are driving the rise of smart airports. Flight volumes are increasing, so airports need better ways to process flyers. Airports need better ways to make money, too, by encouraging passengers to spend more in their shops and restaurants. Data is growing in importance. Everything happening at an airport, from where passengers are flowing to which items are selling in stores, generates data. Airports can analyze this data to spot opportunities for eking out fatter profits. They can sell the data to third-parties as well.
Trials have begun at Haneda Airport in Tokyo on next-generation self-driving electric wheelchairs to help elderly and other people get to boarding gates more easily. Japan Airlines aims to start using self-driving wheelchairs as early as the business year that starts next April. Currently, JAL offers manual wheelchairs at airports across the country. The self-driving wheelchairs JAL aims to introduce are designed to allow users to move without any escort. They automatically return to their home positions after use, making it unnecessary for workers to go and collect them.
If human or natural intelligence is not enough to address whatever problems that ail modern societies, why not turn to Artificial Intelligence? The inexorable push for all things AI is now on. Several months ago, the National Institution for Transforming India (NITI) Aayog put out a proposal costing upwards of a billion dollars to create a national infrastructure for the promotion and adoption of AI techniques to resolve a variety of issues in the fields of agriculture, health, education, urbanisation and mobility. The presumed benefits of this proposal, if adopted, would be the addition of almost a trillion dollars to India's GDP and a 1.3% net increase in the annual growth rate by the year 2035. What would an India so transformed look like?
Airport hubs increasingly are embracing technology in their operations. As part of a three-day Airport IT conference in Munich, Amadeus head of airport IT product management Holger Mattig outlined the future of airport management and said that aviation hubs will witness more use of wearables, internet of things (IoT) applications and predictive analysis in the future. Talking about how the IoT has impacted the aviation industry, Mattig said that computing devices are already exchanging data between each other. "If you look at the apron, all of the devices that go on there – the push back tractors, the de-icing elements, all of these are actually able to talk to each other and give data about every stage of activity," he said. "In terms of flight handling, we now have technologies from companies like Assaia who can make prediction through videos generated by machine learning, and technologies like geofencing, where you can manage drones and improve safety. "We have the same for indoor where there are a lot of initiatives that are used to engage with the mobile phones of passengers in events of potential disruptions." While aviation companies are increasingly using technologies such as IoT and machine learning, Mattig said that going forward, airport and airline companies will start using wearable technology to improve efficiency. He added that employees could start wearing devices such as "smart sunglasses" and "smart bracelets" to track passenger activity, and that monitoring how passengers prefer to shop, eat and spend their time in an airport could help authorities to understand consumer behaviour. "Airports must start to build what I would call airport-centric visible analytics by implementing CRM solutions with the aim to look at the profile of passengers.
In Swedish airports, new technologies are being tested and installed to drive innovation and improve the passengers' experience. Within this digitally-lead era, airports across the world are experimenting with different technologies to determine the best way to secure innovative growth and optimise processes. To gain a perspective of the digital approach within airports across Sweden, International Airport Review spoke to Karin Gylin, Head of Innovation at Swedavia AB, during Airport IT & Security 2019 regarding how new technological applications are benefiting passenger service. In December 2018, we implemented the chatbot. It's been working very well and has been well received by passengers.
Lyon gets a new autonomous shuttle service. Operated via Navya electric driverless vehicles in cooperation with Sytral and Keolis, the open-road experiment will be fully integrated within Lyon's public transport network. Another pilot involving a shuttle supplied by the French company is taking place in Merano (Italy) where for one week the vehicle will be running on a short route closed to car traffic. Recently, a demonstration on standard-lenght autonomous bus has been held in Gothenburg thanks to a cooperation between Volvo Buses and Keolis. The new autonomous bus pilot has been launched on Friday 22th November and see Navya as a provider of two autonomous shuttles.