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Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets

arXiv.org Machine Learning

We assess the demand effects of discounts on train tickets issued by the Swiss Federal Railways, the so-called `supersaver tickets', based on machine learning, a subfield of artificial intelligence. Considering a survey-based sample of buyers of supersaver tickets, we investigate which customer- or trip-related characteristics (including the discount rate) predict buying behavior, namely: booking a trip otherwise not realized by train, buying a first- rather than second-class ticket, or rescheduling a trip (e.g.\ away from rush hours) when being offered a supersaver ticket. Predictive machine learning suggests that customer's age, demand-related information for a specific connection (like departure time and utilization), and the discount level permit forecasting buying behavior to a certain extent. Furthermore, we use causal machine learning to assess the impact of the discount rate on rescheduling a trip, which seems relevant in the light of capacity constraints at rush hours. Assuming that (i) the discount rate is quasi-random conditional on our rich set of characteristics and (ii) the buying decision increases weakly monotonically in the discount rate, we identify the discount rate's effect among `always buyers', who would have traveled even without a discount, based on our survey that asks about customer behavior in the absence of discounts. We find that on average, increasing the discount rate by one percentage point increases the share of rescheduled trips by 0.16 percentage points among always buyers. Investigating effect heterogeneity across observables suggests that the effects are higher for leisure travelers and during peak hours when controlling several other characteristics.


White Paper Machine Learning in Certified Systems

arXiv.org Artificial Intelligence

Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.


Graph Neural Network for Traffic Forecasting: A Survey

arXiv.org Artificial Intelligence

Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.


Want to travel at 600 mph in a tube? It could happen now that Virgin Hyperloop has tested humans in hyperloop system

USATODAY - Tech Top Stories

In the coming years, you may be able to reach your destination by taking an ultra-high-speed pod that zips through a vacuum in a metal tube. Virgin Hyperloop took a step closer to making its high-speed transit technology a reality on Sunday with the first official test of the system with humans inside the pod. The company, part of billionaire business titan Richard Branson's Virgin Group, said the test took place on its 500-meter track outside of Las Vegas. Co-founder and chief technology officer Josh Giegel and Sara Luchian, director of passenger experience, rode in a two-person hyperloop pod for about 15 seconds, reaching a top speed of 107 miles per hour. The eventual goal is to reach speeds of about 1,000 kilometers per hour, or about 621 mph, in yet-to-be-designed 28-person pods.


Deep Reinforcement Learning and Transportation Research: A Comprehensive Review

arXiv.org Artificial Intelligence

Deep reinforcement learning (DRL) is an emerging methodology that is transforming the way many complicated transportation decision-making problems are tackled. Researchers have been increasingly turning to this powerful learning-based methodology to solve challenging problems across transportation fields. While many promising applications have been reported in the literature, there remains a lack of comprehensive synthesis of the many DRL algorithms and their uses and adaptations. The objective of this paper is to fill this gap by conducting a comprehensive, synthesized review of DRL applications in transportation. We start by offering an overview of the DRL mathematical background, popular and promising DRL algorithms, and some highly effective DRL extensions. Building on this overview, a systematic investigation of about 150 DRL studies that have appeared in the transportation literature, divided into seven different categories, is performed. Building on this review, we continue to examine the applicability, strengths, shortcomings, and common and application-specific issues of DRL techniques with regard to their applications in transportation. In the end, we recommend directions for future research and present available resources for actually implementing DRL.


Mobility, Hyperlanes, Bullet Trains, and AI Autonomous Cars - AI Trends

#artificialintelligence

I feel the need, the need for Maglev speed. The Maglev has been considered the fastest commercial High-Speed Rail (HSR) line and whisks passengers at a breathtaking 267 miles per hour from the Pudong airport to the Longyang station in Shanghai, a distance just shy of 20 miles. Named the Maglev because it uses magnetic levitation, it has been a marvel since it first opened in 2004. There are other high-speed rail lines of a research nature that are faster than the Maglev but holds the top record for a commercial in-use line. Let's call high-speed rail lines a more flavorful name, bullet trains. Of course, a bullet train cannot really go as fast as a bullet (which travels around 1,700 mph), though if you are standing on the sidelines when a bullet train goes past it might seem like it is going over a thousand miles per hour. Those of us in the United States don't have many bullet train choices and the preponderance of bullet trains are found in Europe and Asia. If you hold your breath, you might get a chance to someday ride a bullet train in California. That's actually a funny statement because anyone that lives in California knows that we've been pining away to have a bullet train for quite a long time.


Towards a Framework for Certification of Reliable Autonomous Systems

arXiv.org Artificial Intelligence

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.


Within 10 Years, We'll Travel by Hyperloop, Rockets, and Avatars

#artificialintelligence

Try Hyperloop, rocket travel, and robotic avatars. Hyperloop is currently working towards 670 mph (1080 kph) passenger pods, capable of zipping us from Los Angeles to downtown Las Vegas in under 30 minutes. Rocket Travel (think SpaceX's Starship) promises to deliver you almost anywhere on the planet in under an hour. Think New York to Shanghai in 39 minutes. As 5G connectivity, hyper-realistic virtual reality, and next-gen robotics continue their exponential progress, the emergence of "robotic avatars" will all but nullify the concept of distance, replacing human travel with immediate remote telepresence.


The Future of Transportation

#artificialintelligence

Sengupta: Thank you so much for having me today. I'm really excited to be in San Francisco. I don't get to come here that often, which is strange because I live in Los Angeles, but I do like to come whenever I can. For my talk today, I'm going to talk about the future of transportation, specifically on the things that I worked on that I think are kind of the up and coming thing, the thing that I'm working on now and what's going to happen in the future. I think part of my career has always been about just doing fun and exciting new things and all my degrees are in aerospace engineering, ever since I was a little kid, I loved science fiction. I actually am a Star Trek person versus a Star Wars person, but I knew since I was a little kid that I wanted to be involved in the space program, so that's why I decided to go the aerospace engineering route and I wanted to build technology. I got my Ph.D. in plasma propulsion systems. Has anyone heard of the mission called Dawn that's out in the main asteroid belt? My Ph.D. research actually was developing the ion engine technology for that mission. It actually flew and got it to a pretty cool place out in the main asteroid belt looking at Vesta and Ceres. I did that for about five years and then I kind of felt like I had done everything I could possibly do on that front, from a research perspective. My management asked me if I wanted to work on the next mission to Mars. There's very few engineers in the space program who'd be like, "No, I'm just not interested in that." And they're like, "We want you to do the supersonic parachute for it."


Flying robo-taxis eyed for Bay Area commuters

#artificialintelligence

French inventor Frank Zapata grabbed headlines around the world this summer when he flew his hoverboard across the English channel from Pas de Calais, France, to the famous white cliffs of Dover. But Bay Area commuters may soon do Zapata one better by skimming above San Francisco Bay on autonomous, single-passenger drones being developed by a Peninsula start-up company with ties to Google. The automated drones are electrically powered, capable of vertical takeoff and landing, and would fly 10 feet above the water at 20 mph along a pre-determined flight path not subject to passenger controls. The drones' rotors are able to shift from vertical to horizontal alignment for efficient forward movement after takeoff. The company behind all this, three-year-old Kitty Hawk Corp., has personal financial backing from Google founder Larry Page, now CEO of Google's parent, Alphabet, who has long been interested in autonomous forms of transportation.