ABSTRACT The First-Mile Last-Mile (FMLM) connectivity is crucial for improving public transit accessibility and efficiency, particularly in sprawling suburban regions where traditional fixed-route transit systems are often inadequate. Autonomous on-Demand Shuttles (AODS) hold a promising option for FMLM connections due to their cost-effectiveness and improved safety features, thereby enhancing user convenience and reducing reliance on personal vehicles. A critical issue in AODS service design is the optimization of travel paths, for which realistic traffic network assignment combined with optimal routing offers a viable solution. In this study, we have designed an AODS controller that integrates a mesoscopic simulation-based dynamic traffic assignment model with a greedy insertion heuristics approach to optimize the travel routes of the shuttles. The controller also considers the charging infrastructure/strategies and the impact of the shuttles on regular traffic flow for routes and fleet-size planning. The controller is implemented in Aimsun traffic simulator considering Lake Nona in Orlando, Florida as a case study. We show that, under the present demand based on 1% of total trips as transit riders, a fleet of 3 autonomous shuttles can serve about 80% of FMLM trip requests on-demand basis with an average waiting time below 4 minutes. Additional power sources have significant effect on service quality as the inactive waiting time for charging would increase the fleet size. We also show that low-speed autonomous shuttles would have negligible impact on regular vehicle flow, making them suitable for suburban areas. These findings have important implications for sustainable urban planning and public transit operations. INTRODUCTION High population and economic growths in the urban regions of the USA are leading to increased traffic congestion, environmental impacts, and crashes. To reduce traffic congestion and associated problems, it is important to increase the use of public transit services which constitute about 1% of the mode share in the USA (1).

Recently there has been a growing interest in industry and academia, regarding the use of wireless chargers to prolong the operational longevity of unmanned aerial vehicles (commonly knowns as drones). In this paper we consider a charger-assisted drone application: a drone is deployed to observe a set points of interest, while a charger can move to recharge the drone's battery. We focus on the route and charging schedule of the drone and the mobile charger, to obtain high observation utility with the shortest possible time, while ensuring the drone remains operational during task execution. Essentially, this proposed drone-charger scheduling problem is a multi-stage decision-making process, in which the drone and the mobile charger act as two agents who cooperate to finish a task. The discrete-continuous hybrid action space of the two agents poses a significant challenge in our problem. To address this issue, we present a hybrid-action deep reinforcement learning framework, called HaDMC, which uses a standard policy learning algorithm to generate latent continuous actions. Motivated by representation learning, we specifically design and train an action decoder. It involves two pipelines to convert the latent continuous actions into original discrete and continuous actions, by which the drone and the charger can directly interact with environment. We embed a mutual learning scheme in model training, emphasizing the collaborative rather than individual actions. We conduct extensive numerical experiments to evaluate HaDMC and compare it with state-of-the-art deep reinforcement learning approaches. The experimental results show the effectiveness and efficiency of our solution.

Ever wanted to take a stroll down Paris' Champs-Élysées? Or maybe take a look around that new restaurant before you make a reservation? Now, Google Maps' Immersive View tool will let you do all this from the comfort of your phone. Rolling out this week, the new AI-powered feature will allow users to explore accurate 3D models of cities and even look around the inside of buildings. By combining millions of street view images and satellite photos, Google has made the tool available in 15 cities including London, Dublin, and Paris.

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considering comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both external and internal spatial-temporal dependence of relevant transportation data. And the external factors are modeled as dynamic attributes by the attribute-augmented encoder for training. AST-GIN model is tested on the data collected in Dundee City and experimental results show the effectiveness of our model considering external factors influence over various horizon settings compared with other baselines.

Ford is trialling a robot charging station for electric vehicles, which could make it easier for mobility-impaired people to charge their cars. The Michigan-based car manufacturer has demonstrated a prototype system, developed by engineers at Dortmund University, Germany. It consists of a robotic arm that extends all the way into a electric car's charging port, operated by the driver via their smartphone from inside the vehicle. After charging, the arm retracts back into place and the driver can be on their way – without having to ever get out of the car. The robotic arm extends all the way into a electric car's charging port, operated by the driver via their smartphone from inside the vehicle A charging station, which could be situated in a car park or a roadside, features a sliding door that conceals the robotic arm.

DUBAI: Six years after the Dubai Roads and Transportation Authority laid the roadmap for driverless vehicles by 2030, smart mobility has swept the landscape with intelligent concepts that are changing the region's social infrastructure. The move has already spurred sustainable cities into high gear with smart transportation such as autonomous shuttles, e-bikes and e-buggies set to own the roads. An excellent example of a fully-integrated residential project is Sharjah Sustainable City. This eco-friendly concept is powering a net-zero energy community with energy-efficient villas that promise to offer sustainable living at no extra cost. Developed by Sharjah Investment and Development Authority in partnership with Diamond Developers, the sustainable city will host the best green technology, including solar-powered smart homes, bio-domes for vertical farming, electric vehicle chargers, driverless shuttles and a biogas plant. "The UAE is the first country in the Gulf Cooperation Council to commit to net-zero by 2050; all growth and development must align with that commitment, which means we have to do our bit," Karim El-Jisr, chief sustainability officer, SSC, told Arab News.

All smartphones and tablets would have to use a common charger under a provisional European Union agreement clinched on Tuesday. The plan would force all companies--most notably Apple Inc.--to make phones, tablets, e-readers and digital cameras use the USB-C charger, negotiators announced. Around 15 product types are included in the scope, including headsets, video-game consoles and headphones. The plan, unveiled last year, was provisionally approved Tuesday and will save consumers an estimated 250 million euros ($267 million) each year according to the European Commission. The European Parliament and 27 EU countries need to sign off on the agreement.

Encouraging many people into electrical conveyance is at the heart of the government's struggle to tackle climate change. Sales of every-electrical conveyance are up 70% on last year, leading to the thought that we have reached a turning point. But there are better causes to remain cagey. One of the UK's popular cars is the every-electrical Tesla Model 3. But its success doesn't alteration the information that just about 1.1% of new cars sold this year are electrical, and that the market for used electrical vehicles barely exists.

A shortage of charging points and strain on energy supplies are now the main stumbling blocks to the rise of driverless electric cars, according to the UK boss of insurer Axa. Amanda Blanc said a lack of rapid charging bays and pressure on the National Grid have overtaken questions about accident liability as the biggest barriers to autonomous vehicles entering the transport mainstream. Blanc, a Tesla driver, said personal experience pointed to problems lying ahead for driverless electric vehicles. There are around 125,000 plug-in electric cars in the UK and 14,000 chargers - 2,620 of them being rapid chargers that can give a car an 80% charge in 30 minutes. Shell has just opened its first charging points for electric vehicles at 10 filling stations, mostly in London and the south-east.

Electric and driverless cars in the UK have been given a major boost by the Chancellor today. A total of £540 million ($716 million) is being invested in electric cars, including £400 million ($530 million) on building more electric car charging points. While there are currently only 4,500 charging stations in the UK, the investment will enable this number to dramatically increase. Philip Hammond said this will pave the way for driverless vehicles, and added that red tape will be cut to allow technology firms to test autonomous cars on public roads by 2021. The budget revealed that £400 million ($530 million) is being invested in electric car charging infrastructure, £100 million ($132 million) is being invested in plug-in car grants and £40 million ($53 million) is being invested in electric car charging research and development.