Fox News chief Washington correspondent Mike Emanuel has the latest on Transportation Secretary Sean Duffy's statements about recent air traffic control incidents on'Special Report.' Transportation Secretary Sean Duffy recently announced that artificial intelligence (AI) is being used to detect and address air traffic risks, following a slew of near-misses and fatal plane crashes across the country. Duffy told FOX 5 DC that officials are implementing AI to "identify and address potential air traffic risks nationwide," potentially aiding in preventing tragedies like the fatal Jan. 29 midair collision at Ronald Reagan Washington National Airport (DCA) that claimed the lives of 67 people. Following the Potomac River crash, which involved a commercial plane and an Army Black Hawk helicopter, Duffy announced a plan to build a new "state-of-the-art" traffic control system that will equip locations with better technology to reduce outages, improve efficiency and reinforce safety. Duffy told FOX 5 that when investigators were looking into how to prevent collisions, they asked themselves, "Are there any other DCAs out there?" Transportation Secretary Sean Duffy speaks during a news conference following up on the issuance of the National Transportation Safety Board preliminary report on the mid-air collision near Ronald Reagan Washington National Airport, on Tuesday, March 11.

The increasing automation of traffic management systems has made them prime targets for cyberattacks, disrupting urban mobility and public safety. Traditional network-layer defenses are often inaccessible to transportation agencies, necessitating a machine learning-based approach that relies solely on traffic flow data. In this study, we simulate cyberattacks in a semi-realistic environment, using a virtualized traffic network to analyze disruption patterns. We develop a deep learning-based anomaly detection system, demonstrating that Longest Stop Duration and Total Jam Distance are key indicators of compromised signals. To enhance interpretability, we apply Explainable AI (XAI) techniques, identifying critical decision factors and diagnosing misclassification errors. Our analysis reveals two primary challenges: transitional data inconsistencies, where mislabeled recovery-phase traffic misleads the model, and model limitations, where stealth attacks in low-traffic conditions evade detection. This work enhances AI-driven traffic security, improving both detection accuracy and trustworthiness in smart transportation systems.

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the research questions: are we able to identify cyber attacks by only analyzing traffic flow patterns. In this research, the cyber attacks are focused particularly when lights are randomly turned all green or red at busy intersections by adversarial attackers. Despite challenges stemming from imbalanced data and overlapping traffic patterns, our best model shows 85\% accuracy when detecting intrusions purely using traffic flow statistics. Key indicators for successful detection included occupancy, jam length, and halting durations.

Transportation Secretary Sean Duffy delves into his take on DEI, DOGE, infrastructure projects and his first weeks in his new role on'My View with Lara Trump.' Transportation Secretary Sean Duffy announced plans to bolster airport air traffic control systems with the latest technology over the next four years, while also using artificial intelligence (AI) to identify "hot spots" where close encounters between aircraft occur frequently. The announcement came after an update on an investigation into a crash near Ronald Reagan Washington National Airport in Arlington, Virginia, when a U.S. Army helicopter and an American Airlines-operated passenger jet collided over the Potomac River Jan. 29. "We're here because 67 souls lost their lives on Jan. 29," Duffy told reporters Tuesday, noting that the National Transportation Safety Board (NTSB) unveiled its preliminary findings into the crash earlier in the day. The findings noted that, over the last 2½ years, there have been 85 near misses or close calls at Reagan National. Close calls were identified as incidents when there are less than 200 feet of vertical separation and 1,500 feet of lateral separation between aircraft.

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also deliver tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics.

This system is a real-time adaptive traffic control system, which combines artificial intelligence (AI) and traffic theory to optimize highly dynamic traffic flow in complex real-world urban road networks. As the lead inventor of the system, Dr. Xie has created its core control engine, which combines schedule-driven intersection control (SchIC) with decentralized coordination mechanisms (in the sense of Internet of Smart Intersections, an instance of smart IoT). He has also designed and realized the strengthening strategies to enable the real-world operations of the system in the field. His relevant research work also includes: multimodal traffic control (assisted with machine learning and computer vision techniques), integration with decentralized route choice models and dynamic congestion pricing protocols, vehicle-to-infrastructure (V2I) communication with connected vehicles, energy efficiency optimization, and data-driven self-learning and active congestion management based on performance measurement. The system has been running since June 2012.

If you take humans out of the driving seat, could traffic jams, accidents and high fuel bills become a thing of the past? As cars become more automated and connected, attention is turning to how to best choreograph the interaction between the tens or hundreds of automated vehicles that will one day share the same segment of Europe's road network. It is one of the most keenly studied fields in transport – how to make sure that automated cars get to their destinations safely and efficiently. But the prospect of having a multitude of vehicles taking decisions while interacting on Europe's roads is leading researchers to design new traffic management systems suitable for an era of connected transport. The idea is to ensure that traffic flows as smoothly and efficiently as possible, potentially avoiding the jams and delays caused by human behaviour.

For all the mishegas about self-driving cars in the sunny, techie Silicon Valley, the future of the automobile may still live in a colder clime. At least, the Wolverine State hasn't loosened its grip on the future of traffic. Ann Arbor plays home to the University of Michigan, and with the football games, Kid Rock concerts, and daily commuters comes traffic, and lots of it. On the average weekday, the 125,000-person town swells to hold 200,000 people, most of whom travel in by personal car. The city is exploring buses, commuter rail, and carpool options to clear up its roads, but knows it can't drive the car out of its home state anytime soon.

The UK government is due to publish its long awaited Digital Strategy later today, about a year later than originally slated. Existing delays having been compounded by the shock of Brexit. Drafts of the strategy framework seen by TechCrunch suggest its scope and ambition vis-a-vis digital technologies has been pared back and repositioned vs earlier formulations of the plan, dating from December 2015 and June 2016, as the government recalibrated to factor in last summer's referendum vote for the UK to leave the European Union. Since the earlier drafts were penned there has also of course been a change of leadership (and direction) at the top of government. And Prime Minister Theresa May appointed a new cabinet, including digital minister, Matt Hancock, who replaced Ed Vaizey.

Conventional urban traffic control systems have been based on historical traffic data. Later advancements made use of detectors, which enabled the gathering of real time traffic data, in order to re organize and calibrate traffic signalization programs. Further evolvement provided the ability to forecast traffic conditions, in order to develop traffic signalization programs and strategies pre computed and applied at the most appropriate time frame for the optimal control of the current traffic conditions. We, propose the next generation of traffic control systems based on principles of Artificial Intelligence and Context Awareness. Most of the existing algorithms use average waiting time or length of the queue to assess an algorithm's performance. However, a low average waiting time may come at the cost of delaying other vehicles indefinitely. In our algorithm, besides the vehicle queue, we use'fairness' also as an important performance metric to assess an algorithm's performance.