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.
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.
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.
The road from marketing-qualified lead to sales-qualified lead can be a long one, and it's a road that seems to have more exits than on-ramps. But this process can -- and must -- be sped up to increase your lead conversion rate. Instead of hiring beyond your plans and budget, consider how automation and AI don't get tired, they can't have too much on their plate, and they never get frustrated in the face of failure. The lead often doesn't even realize they aren't communicating with a flesh-and-blood salesperson when they're speaking to conversational AI. Lead conversion stalls due to slow follow-up on qualified leads -- but conversational AI automation can always respond briskly no matter the volume of leads.
The UK plans to tackle pollution with AI-powered traffic lights that delay the arrival of vehicles in toxic air hotspots. The system collects data on local pollution and traffic flows through roadside sensors, weather forecasts, and Bluetooth devices in cars. An algorithm then analyzes both live and historical data to predict where air pollution will spike within the next hour. When the system forecasts a sharp rise in toxic pollutants, the traffic light timings will automatically change. Drivers on their way to pollution hotspots will be held at red lights for up to 20 seconds longer than usual.
Moovit, an 8-year-old company based in Israel, makes an app that compiles data from public transit systems, ride-hailing services and other resources to help its 800 million users plan the best ways to get around. Intel plans to combine Moovit with Mobileye, a self-driving car specialist that Intel bought for about $15 billion in 2017.
Tesla's inching closer to the self-driving dream with its latest Autopilot update. Last week, Tesla quietly dropped some new abilities to its semi-autonomous driving system called Autopilot. The update gives all Tesla cars with Autopilot activated the option to stop at street lights and stop signs without the driver having to touch the brakes. Stop signs, streetlight symbols, and other road signage information are also now more visible on the screen. But the new Traffic Light and Stop Sign Control feature, which is still in beta, still involves a lot of active participation from the driver.
Washington, DC (CNN Business)Tesla has said its latest version of Autopilot, its autonomous driving software, is able to stop at traffic lights. But some Tesla drivers are learning it doesn't just stop at red lights, it appears to slow down for green lights, too. Last Friday, Tesla drivers first reported receiving a software update that included "Traffic Light and Stop Sign Control," which is designed to slowdown and stop the vehicle for visible traffic lights or stop signs. Tesla (TSLA) describes the software as being in "beta," meaning it's unfinished and still officially in testing. It's designed to gradually improve as the artificial intelligence that powers it learns from the data that's being collected as Tesla cars drive on public roads, according to a notification in Tesla vehicles when the system is first activated.
Traffic forecasting influences various intelligent transportation system (ITS) services and is of great significance for user experience as well as urban traffic control. It is challenging due to the fact that the road network contains complex and time-varying spatial-temporal dependencies. Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies. However, the existing methods often construct the graph only based on road network connectivity, which limits the interaction between roads. In this work, we propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN), a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns. Extensive experiments on a real-world traffic state dataset validate the effectiveness of our method by showing that GLT-GCRNN outperforms the state-of-the-art methods in terms of different metrics.