In all areas – efficiency and effectiveness, revenue generation, safety and security – AI has tremendous potential to deliver positive change if used correctly, details Ian Law, Chief Information Officer of San Francisco International Airport. As hubs of intense operational activity involving thousands of inter-dependent tasks, airports are ideal candidates for new technologies that improve the smooth flow of people, planes and bags. Artificial intelligence (AI) could be a game-changer for airports. However, without some (human) intelligent forethought, it also risks being a costly disappointment. The real value of AI will only come from a sector-wide focused collaboration, from which AI's cornerstone role tackling the sector's most intractable issues is evolved.
New York City transit officials are exploring a controversial plan to use artificial intelligence software to track how many subway riders are wearing face masks, and where. The technology, which is currently being used in Paris, was among a host of ideas presented in a consultant's report released to the public on Monday that could help transit authorities measure the level of face mask compliance at specific subway stations. The list includes several high-tech tools like thermal-scanner temperature checks, which has been adopted in Canada and Singapore, as well as UV lamps and robots that China has deployed on buses to kill the viruses on surfaces. "We're exploring the feasibility of a wide range of tools and approaches for helping keep our employees and customers safe," said Andrei Berman, a spokesman for the MTA, in a statement. "AI is one of those tools and we'll continue to research whether it might be effective, and if so, how it might be deployed in an appropriate manner to continue ensuring best public health practices are followed for the safety of our customers and employees."
Google Maps is working on a new feature that will show you how to reach the nearest public transport connection, according to new leaked screenshots. The new Maps filter will let users choose what mode of transportation they will be using at the very beginning of their daily commute, the screenshots show. Once rolled out, the feature will allow commuters to work out their preferred route to various transport connections, such as the train station, when they return to the workplace after the coronavirus pandemic. The screenshots also reveal an option to get more accurate Uber fares using data from Google Maps and a slightly new design for the Maps interface. 'Google Maps is working on route options with "Connections to Public Transit", such as car and transit, bicycle and transit, auto rickshaw, ride service [and] motorcycle and transit,' said Jane Wong, a Hong Kong-based hacker, tech blogger and software engineer, who leaked the screenshots.
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.
Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator.
In this project, we have developed the ramp activity coordination expert system (races) to solve aircraft-parking problems. By user-driven modeling for end users and near-optimal knowledge-driven scheduling acquired from human experts, races can produce parking schedules for about 400 daily flights in approximately 20 seconds; human experts normally take 4 to 5 hours to do the same. Scheduling results in the form of Gantt charts produced by races are also accepted by the domain experts. After daily scheduling is completed, the messages for aircraft change, and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as the rules, and the scenarios of the graphic user interfaces are designed.
In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of fare evasion depends on the unpredictability and effectiveness of the patrols. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge.
Real-time traffic signal control presents a challenging multiagent planning pro blem, particularly in urban road networks where, unlike simpler arterial settings, there are competing dominant traffic flows that shift through the day. Further complicating matters, urban environments require attention to multimodal traffic flows (vehicles, pedestrians, bicyclists, buses) that move at different speeds and may be given different priorities. For the past several years, my research group has been developing and refining a real-time, adaptive traffic signal control system to address these challenges, referred to as scalable urban traffic control (Surtrac). Combining principles from automated planning and scheduling, multiagent systems, and traffic theory, Surtrac treats traffic signal control as a decentralized online planning process. In operation, each intersection repeatedly generates and executes (in rolling horizon fashion) signal-timing plans that optimize the movement of currently sensed approaching traffic through the intersection.
France is integrating new AI tools into security cameras in the Paris metro system to check whether passengers are wearing face masks. The software, which has already been deployed elsewhere in the country, began a three-month trial in the central Chatelet-Les Halles station of Paris this week, reports Bloomberg. French startup DatakaLab, which created the program, says the goal is not to identify or punish individuals who don't wear masks, but to generate anonymous statistical data that will help authorities anticipate future outbreaks of COVID-19. "We are just measuring this one objective," DatakaLab CEO Xavier Fischer told The Verge. "The goal is just to publish statistics of how many people are wearing masks every day."
UK Power Networks has successfully deployed its artificial intelligence (AI) 'smart' traffic lights in the UK. The smart system, called'autoGreen' makes automatic adjustments to traffic light signals in case of congestion, in order to allow maximum traffic flow during peak hours and while repair and installation work is being carried out. UK Power Networks trialled the system last year in Kent, before deploying it in the South East and East of England - it was noted to have brought down journey times by a'significant' margin, while also improving safety and air quality.