Urban planning plays a very important role in development modern cities. It effects the economic growth, quality of life, and environmental sustainability. Modern cities face challenges in managing traffic congestion. These challenges arise to due to rapid urbanization. In this study we will explore how AI can be used to understand the traffic and mobility related issues and its effects on the residents sentiment. The approach combines real-time traffic data with geo-located sentiment analysis, offering a comprehensive and dynamic approach to urban mobility planning. AI models and exploratory data analysis was used to predict traffic congestion patterns, analyze commuter behaviors, and identify congestion hotspots and dissatisfaction zones. The findings offer actionable recommendations for optimizing traffic flow, enhancing commuter experiences, and addressing city specific mobility challenges in the Middle East and beyond.

Traffic congestion has long been an ubiquitous problem that is exacerbating with the rapid growth of megacities. In this proof-of-concept work we study intrinsic motivation, implemented via the empowerment principle, to control autonomous car behavior to improve traffic flow. In standard models of traffic dynamics, self-organized traffic jams emerge spontaneously from the individual behavior of cars, affecting traffic over long distances. Our novel car behavior strategy improves traffic flow while still being decentralized and using only locally available information without explicit coordination. Decentralization is essential for various reasons, not least to be able to absorb robustly substantial levels of uncertainty. Our scenario is based on the well-established traffic dynamics model, the Nagel-Schreckenberg cellular automaton. In a fraction of the cars in this model, we substitute the default behavior by empowerment, our intrinsic motivation-based method. This proposed model significantly improves overall traffic flow, mitigates congestion, and reduces the average traffic jam time.

Hyperbole, or exaggeration, is a common linguistic phenomenon. The detection of hyperbole is an important part of understanding human expression. There have been several studies on hyperbole detection, but most of which focus on text modality only. However, with the development of social media, people can create hyperbolic expressions with various modalities, including text, images, videos, etc. In this paper, we focus on multimodal hyperbole detection. We create a multimodal detection dataset\footnote{The dataset will be released to the community.} from Weibo (a Chinese social media) and carry out some studies on it. We treat the text and image from a piece of weibo as two modalities and explore the role of text and image for hyperbole detection. Different pre-trained multimodal encoders are also evaluated on this downstream task to show their performance. Besides, since this dataset is constructed from five different topics, we also evaluate the cross-domain performance of different models. These studies can serve as a benchmark and point out the direction of further study on multimodal hyperbole detection.

Last week, at Google's annual conference dedicated to new products and technologies, the company announced a change to its premier AI product: The Bard chatbot, like OpenAI's GPT-4, will soon be able to describe images. Although it may seem like a minor update, the enhancement is part of a quiet revolution in how companies, researchers, and consumers develop and use AI--pushing the technology not only beyond remixing written language and into different media, but toward the loftier goal of a rich and thorough comprehension of the world. ChatGPT is six months old, and it's already starting to look outdated. That program and its cousins, known as large language models, mime intelligence by predicting what words are statistically likely to follow one another in a sentence. Researchers have trained these models on ever more text--at this point, every book ever and then some--with the premise that force-feeding machines more words in different configurations will yield better predictions and smarter programs.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on'America's Newsroom.' The only thing worse than being stuck in a traffic jam is being stuck in a traffic jam that shouldn't be there. "Phantom Jams" are those backups that occur on highways for seemingly no reason, then dissipate as mysteriously as they appeared. They're usually started by drivers who suddenly brake or change lanes in dense traffic, which is followed by a wave of bad decisions made by the drivers behind. It escalates as more cars arrive at high speeds and have to slow down abruptly.

Every so often, I like to look at some of the more weird conspiracy theories and then try to debunk them. I consider it a media literacy exercise, but there has been one theory that I've come across that is impressively hard to debunk: the "Dead Internet" theory. I think that the best conspiracy theories are the ones that are hardest to debunk, and this one is increasingly getting more difficult to debunk. The core idea is that the Internet itself is actually dead, no human authorship of any content exists. Any actual human content that is created is isolated into its own little heavenbanned bubble. Mainstream platforms, news outlets, social media sites, Internet forums, chatrooms, everything filled with bot generated content to the point that it's impossible to find another human. To be clear, this theory as literally written is absolute nonsense and probably not worth taking too seriously.

Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. Traffic jams without a clear cause, such as an on-ramp or an accident, are called phantom traffic jams and are said to make up 50% of all traffic jams. They are the result of an unstable traffic flow caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans' limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow, and compared it with manual control and full automation. We also investigated the effect of two control types on participants' behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.

Americans wasted a whopping 3.4 billion hours in 2021 thanks to traffic, according to research from connected car analytics company INRIX, which also noted that this equates to 36 hours lost per person. The numbers are clear: Even with drops in traffic thanks to new travel patterns in the wake of the pandemic, we still lose an entire workweek each year to traffic. Soon enough, artificial intelligence (AI) may be able to alleviate--or fully solve--the problem. Today, researchers and companies are working to develop AI-powered systems that tackle the problem of traffic from a number of angles. For instance, Intelligent Traffic Control of Tel Aviv, Israel, has developed a solution that collects data from traffic cams, then regulates traffic signals to optimally route vehicles.

Nvidia has been quick to hop on the artificial intelligence bus一with many of its consumer facing technologies, such as Deep Learning Super Sampling (DLSS) (opens in new tab) and AI-accelerated denoising exemplifying that. However, it has also found many uses for AI in its silicon development process and, as Nvidia's chief scientist Bill Dally (opens in new tab) said in a GTC conference, even designing new hardware. Dally outlines a few use cases for AI in its own development process of the latest and greatest graphic cards (opens in new tab) (among other things), as noted by HPC Wire (opens in new tab). "It's natural as an expert in AI that we would want to take that AI and use it to design better chips," Dally says. "We do this in a couple of different ways. The first and most obvious way is we can take existing computer-aided design tools that we have. For example, we have one that takes a map of where power is used in our GPUs, and predicts how far the voltage grid drops一what's called IR drop for current times resistance drop. Running this on a conventional CAD tool takes three hours."

Did you know there's a specific term for the times when you encounter sudden, inexplicable vehicle congestion on the interstate despite no discernible culprit such as rubbernecking or an accident? It's called a "phantom traffic jam," and was first identified around 12 years ago by researchers in Japan conducting a simple experiment. Despite telling 20 human drivers to all drive at a constant speed around a circular track, even the briefest instances of individuals' pressing their brake pedals compounded on one another, resulting in those recognizable traffic fits and starts. This automotive variation on the "butterfly effect" has been carefully studied ever since, and a research group is now approaching the finish line on a potential solution devoid of any sort of half-baked "self-driving" system. As Associated Press recounts, a recent experiment has shown instances of phantom traffic jams can be reduced by linking cars' into a single communication network via utilizing newer vehicles' adaptive cruise control systems.