Air taxis are poised to be an additional mode of transportation in major cities suffering from ground transportation congestion. Among several potential applications of air taxis, we focus on their use within a city to transport passengers to nearby airports. Specifically, we consider the problem of determining optimal locations for skyports (enabling pick-up of passengers to airport) within a city. Our approach is inspired from hub location problems, and our proposed method optimizes for aggregate travel time to multiple airports while satisfying the demand (trips to airports) either via (i) ground transportation to skyport followed by an air taxi to the airport, or (ii) direct ground transportation to the airport. The number of skyports is a constraint, and the decision to go via the skyport versus direct ground transportation is a variable in the optimization problem. Extensive experiments on publicly available airport trips data from New York City (NYC) show the efficacy of our optimization method implemented using Gurobi. In addition, we share insightful results based on the NYC data set on how ground transportation congestion can impact the demand and service efficiency in such skyports; this emerges as yet another factor in deciding the optimal number of skyports and their locations for a given city.
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.
As network data become increasingly available, new opportunities arise to understand dynamic and multilayer network systems in many applied disciplines. Statistical modeling for multilayer networks is currently an active research area that aims to develop methods to carry out inference on such data. Recent contributions focus on latent space representation of the multilayer structure with underlying stochastic processes to account for network dynamics. Existing multilayer models are however typically limited to rather small networks. In this paper we introduce a dynamic multilayer block network model with a latent space represention for blocks rather than nodes. A block structure is natural for many real networks, such as social or transportation networks, where community structure naturally arises. A Gibbs sampler based on P\'olya-Gamma data augmentation is presented for the proposed model. Results from extensive simulations on synthetic data show that the inference algorithm scales well with the size of the network. We present a case study using real data from an airline system, a classic example of hub-and-spoke network.
Fascinating footage has been released of a robot's-eye-view of a driverless vehicle trial at Heathrow Airport, side-by-side with how a human driver would see the routes it took. The clip comes from a'cargopod' vehicle that spent three and a half weeks running autonomously along a cargo route around the airside perimeter. The trial collected over 200km of data for Heathrow, cargo operator IAG Cargo and the software firm providing the self-driving tech, Oxford-based Oxbotica. Fascinating footage has been released of a robot's-eye-view of a driverless vehicle trial at Heathrow Airport, side-by-side with how a human driver would see the routes it took The clip comes from a'cargopod' vehicle, pictured, that spent three and a half weeks running autonomously along a cargo route around the airside perimeter The trial was designed to further understanding about how autonomous vehicles could work in an airside environment so opportunities for their use can be maximised. Lynne Embleton, CEO at IAG Cargo, said: 'Technology is evolving at an incredible pace.
Police arrested the rapper Coolio on Saturday after authorities said they found a loaded, stolen firearm in his carry-on bag at a security checkpoint inside Los Angeles International Airport. Around 10:50 a.m., airport police responded to Terminal 3 after receiving a report about a prohibited item in the screening area, spokeswoman Alicia Hernandez said in a statement. Police took possession of a carry -on bag on the X -ray screening belt and detained a 39-year-old man who claimed the bag, Hernandez said. Authorities soon discovered that the bag "contained items belonging to one of the suspect's traveling companions," who had left the screening area and boarded a departing plane, Hernandez said. Police then detained Coolio, 53, "who upon questioning claimed ownership and possession of the carry -on bag," Hernandez said.