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NYC Mayor Adams floats 'new tech,' bag checks on subway system to detect weapons

FOX News

WARNING--Graphic footage: Fox News correspondent Bryan Llenas has the latest on the investigation from Brooklyn, New York, on'Special Report.' New York City may be rolling out new technology and periodic bag checks to prevent future terrorist attacks, according to the mayor. New York City Mayor Eric Adams spoke with MSNBC's "Morning Joe" on Wednesday about the previous day's terror attack on the city's subway system. The mayor touched on the possibility of new technology on public transportation to prevent similar acts in the future. "With the gun detection devices – oftentimes when people hear of'metal detectors,' they immediately think of the airport model," Adams said.


3 Questions: Anuradha Annaswamy on building smart infrastructures

#artificialintelligence

How does cloudy weather affect a grid powered by solar energy? How do we ensure that electricity is delivered to the consumer if a grid is powered by wind and the wind does not blow? What's the best course of action if a bird hits a plane engine on takeoff? How can you predict the behavior of a cyber attacker? A senior research scientist in MIT's Department of Mechanical Engineering, Annaswamy spends most of her research time dealing with decision-making under uncertainty.


Smart cities to the hyperloop: This region is investing in a tech-led transport revolution

ZDNet

The Middle East has already been the birthplace of a number of tech-led transport unicorns. Waze, a traffic and navigation app co-created in Israel, was acquired by Google in 2013 for over $1.1 billion. More recently, Uber purchased Careem, a regional ride-hailing app, for $3.1 billion. Opening a bank account for your business is not only necessary -- it's incredibly beneficial to running a smooth operation. Consider these recommendations from ZDNet.


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.


How Network Rail is saving millions by using machine learning - New Civil Engineer

#artificialintelligence

Cost savings of up to £30M were identified on Network Rail's Great Western project through use of machine learning and wider construction industry benefits are set to be unlocked by a new investment from a Google venture business. Network Rail leveraged the savings by using construction risk forecasting platform nPlan, which has just been boosted by a £13.5M investment from GV (formerly Google Ventures). According to nPlan, wider use of machine learning that this investment will support could save the global construction sector almost £730bn each year through spotting delays and recommending improvements with an accuracy and scale previously not possible. The firm has said that such data-led insights would effectively reduce the volatility of and increase investor confidence in construction projects. By using some of the most powerful machine learning capabilities in the world to analyse what worked and what didn't in past projects, we can help our customers work out what's going to derail their own initiatives, and stop problems happening before they even appear." Speaking about the due diligence undertaken by GV ahead of the investment, GV general partner Tom Hulme said that he was "blown away" by the benefits organisations such as Network Rail saw in applying modern machine learning techniques to such a difficult analogue problem".


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.


Virgin Hyperloop shares step-by-step video of its passenger experience

Daily Mail - Science & tech

The idea of hurtling down a vacuum tube in a levitating pod at speeds of over 670 miles an hour may sound like the plot of the latest science fiction blockbuster, but it could soon become a reality. Virgin Hyperloop is developing the futuristic technology, which it claims could transform the way we travel. At the end of last year, the company demonstrated the technology in action, transporting two brave participants for the first time. Now, the tech giant has shared a step-by-step video of the passenger experience on board its Hyperloop system, all the way from arriving at the portal, to taking off on board a hyperloop pod. 'Showing the passenger experience of Virgin Hyperloop is a glimpse of the future, following the success three months ago when people rode in a hyperloop pod for the first time,' said Sultan Bin Sulayem, Chairman of Virgin Hyperloop and Group Chairman and CEO of DP World.


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.


Crowding Prediction of In-Situ Metro Passengers Using Smart Card Data

arXiv.org Machine Learning

The metro system is playing an increasingly important role in the urban public transit network, transferring a massive human flow across space everyday in the city. In recent years, extensive research studies have been conducted to improve the service quality of metro systems. Among them, crowd management has been a critical issue for both public transport agencies and train operators. In this paper, by utilizing accumulated smart card data, we propose a statistical model to predict in-situ passenger density, i.e., number of on-board passengers between any two neighbouring stations, inside a closed metro system. The proposed model performs two main tasks: i) forecasting time-dependent Origin-Destination (OD) matrix by applying mature statistical models; and ii) estimating the travel time cost required by different parts of the metro network via truncated normal mixture distributions with Expectation-Maximization (EM) algorithm. Based on the prediction results, we are able to provide accurate prediction of in-situ passenger density for a future time point. A case study using real smart card data in Singapore Mass Rapid Transit (MRT) system demonstrate the efficacy and efficiency of our proposed method.


TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data

arXiv.org Machine Learning

In this paper, we target at recovering the exact routes taken by commuters inside a metro system that arenot captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategicallypropose two inference tasks to handle the recovering, one to infer the travel time of each travel link thatcontributes to the total duration of any trip inside a metro network and the other to infer the route preferencesbased on historical trip records and the travel time of each travel link inferred in the previous inferencetask. As these two inference tasks have interrelationship, most of existing works perform these two taskssimultaneously. However, our solutionTripDecoderadopts a totally different approach. To the best of ourknowledge,TripDecoderis the first model that points out and fully utilizes the fact that there are some tripsinside a metro system with only one practical route available. It strategically decouples these two inferencetasks by only taking those trip records with only one practical route as the input for the first inference taskof travel time and feeding the inferred travel time to the second inference task as an additional input whichnot only improves the accuracy but also effectively reduces the complexity of both inference tasks. Twocase studies have been performed based on the city-scale real trip records captured by the AFC systems inSingapore and Taipei to compare the accuracy and efficiency ofTripDecoderand its competitors. As expected,TripDecoderhas achieved the best accuracy in both datasets, and it also demonstrates its superior efficiencyand scalability.