Access to metro systems plays a critical role in shaping urban housing markets by enhancing neighborhood accessibility and driving property demand. We present RailEstate, a novel web based system that integrates spatial analytics, natural language interfaces, and interactive forecasting to analyze how proximity to metro stations influences residential property prices in the Washington metropolitan area. Unlike static mapping tools or generic listing platforms, RailEstate combines 25 years of historical housing data with transit infrastructure to support low latency geospatial queries, time series visualizations, and predictive modeling. Users can interactively explore ZIP code level price patterns, investigate long term trends, and forecast future housing values around any metro station. A key innovation is our natural language chatbot, which translates plain-English questions e.g., What is the highest price in Falls Church in the year 2000? into executable SQL over a spatial database. This unified and interactive platform empowers urban planners, investors, and residents to derive actionable insights from metro linked housing data without requiring technical expertise.

Bike-sharing is an environmentally friendly shared mobility mode, but its self-loop phenomenon, where bikes are returned to the same station after several time usage, significantly impacts equity in accessing its services. Therefore, this study conducts a multiscale analysis with a spatial autoregressive model and double machine learning framework to assess socioeconomic features and geospatial location's impact on the self-loop phenomenon at metro stations and street scales. The results reveal that bike-sharing self-loop intensity exhibits significant spatial lag effect at street scale and is positively associated with residential land use. Marginal treatment effects of residential land use is higher on streets with middle-aged residents, high fixed employment, and low car ownership. The multimodal public transit condition reveals significant positive marginal treatment effects at both scales. To enhance bike-sharing cooperation, we advocate augmenting bicycle availability in areas with high metro usage and low bus coverage, alongside implementing adaptable redistribution strategies.

Over the recent years, the protection of the so-called `soft-targets', i.e. locations easily accessible by the general public with relatively low, though, security measures, has emerged as a rather challenging and increasingly important issue. The complexity and seriousness of this security threat growths nowadays exponentially, due to the emergence of new advanced technologies (e.g. Artificial Intelligence (AI), Autonomous Vehicles (AVs), 3D printing, etc.); especially when it comes to large-scale, popular and diverse public spaces. In this paper, a novel Digital Twin-as-a-Security-Service (DTaaSS) architecture is introduced for holistically and significantly enhancing the protection of public spaces (e.g. metro stations, leisure sites, urban squares, etc.). The proposed framework combines a Digital Twin (DT) conceptualization with additional cutting-edge technologies, including Internet of Things (IoT), cloud computing, Big Data analytics and AI. In particular, DTaaSS comprises a holistic, real-time, large-scale, comprehensive and data-driven security solution for the efficient/robust protection of public spaces, supporting: a) data collection and analytics, b) area monitoring/control and proactive threat detection, c) incident/attack prediction, and d) quantitative and data-driven vulnerability assessment. Overall, the designed architecture exhibits increased potential in handling complex, hybrid and combined threats over large, critical and popular soft-targets. The applicability and robustness of DTaaSS is discussed in detail against representative and diverse real-world application scenarios, including complex attacks to: a) a metro station, b) a leisure site, and c) a cathedral square.

On the eve of The Olympics opening ceremony, Paris is a city swamped in security. Packs of police, wearing stab vests, patrol pretty cobbled streets. The river Seine is out of bounds to anyone who has not already been vetted and issued with a personal QR code. Khaki-clad soldiers, present since the 2015 terrorist attacks, linger near a canal-side boulangerie, wearing berets and clutching large guns to their chests. French interior minister Gérald Darmanin has spent the past week justifying these measures as vigilance--not overkill.

Everyone could use a longer battery life on their smartphone. CyberGuy shows you how to change your settings to make your battery last longer. Russian President Vladimir Putin is facing the biggest threat to his authority in decades. It comes after Yevgeny Prigozhin, who leads a private paramilitary group called Wagner, started a violent but brief uprising against Russia that may have long-lasting effects which could bring instability to Putin. If Putin is perceived as weakened by this assault on his leadership, then he is likely to exert aggressive strength in a show of power. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER What few know is that Vladimir Putin has been building a sophisticated facial recognition surveillance system since 2017 that is likely to be put into overdrive against any citizens willing to take on the views of the Russian leadership and military.

A headline in this publication read "Apple's Delhi store is significantly smaller than Mumbai outlet". Many men from Delhi took to the internet challenging their counterparts in Mumbai to show the size of their outlets. Mercifully, the new IT law proposed by the government should help to prevent the spread of any fake news in this regard. Apple will pay a rent of around Rs 40 lakh a month for its second retail store in Delhi. Landlords in Bengaluru have used this as an excuse to hike their rents further.

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated.

This article was first published in the Virginia Mercury. Last week when Virginia's new Secretary of Transportation Sheppard Miller publicly declared his belief that flying cars will be a reality within the next 50 years as a reason that leaders across the commonwealth should "reexamine transit," some might have scoffed. But just as flying cars consumed the fantasies of many mid-century Americans, today plenty of people put their faith in another utopian technology replete with endlessly elusive promises of improved safety and unbridled freedom: autonomous vehicles. As is often the case in the United States, the regulation of autonomous vehicles is largely left to the states, resulting in a patchwork of conflicting and confusing policies where some sort of national approach ought to exist. Any state has the right to craft their own legal framework for the emerging technology but few have -- our commonwealth included.

The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist. This means that the travel demand across different travel modes could be correlated as one mode may receive demand from or create demand for another mode, not to mention natural correlations between different demand time series due to general demand flow patterns across the network. It is expectable that cross-modal ripple effects become more prevalent, with Mobility as a Service. Therefore, by propagating demand data across modes, a better demand prediction could be obtained. To this end, this study explores various machine learning models and transfer learning strategies for cross-modal demand prediction. The trip data of bike-share, metro, and taxi are processed as the station-level passenger flows, and then the proposed prediction method is tested in the large-scale case studies of Nanjing and Chicago. The results suggest that prediction models with transfer learning perform better than unimodal prediction models. Furthermore, stacked Long Short-Term Memory model performs particularly well in cross-modal demand prediction. These results verify our combined method's forecasting improvement over existing benchmarks and demonstrate the good transferability for cross-modal demand prediction in multiple cities.

He said the technology will be rolled out in the coming months across all Metro stations in the emirate. Dubai Police's smart glasses called Rokid T1, and the smart helmets that were used during the COVID-19 pandemic to scan commuters' temperatures, will have more advanced technology in the future like facial recognition to identify wanted people. "Usually, it takes at least five hours to identify a suspect, but with facial recognition technology, it takes less than a minute."