Urban and transportation research has long sought to uncover statistically meaningful relationships between key variables and societal outcomes such as road safety, to generate actionable insights that guide the planning, development, and renewal of urban and transportation systems. However, traditional workflows face several key challenges: (1) reliance on human experts to propose hypotheses, which is time-consuming and prone to confirmation bias; (2) limited interpretability, particularly in deep learning approaches; and (3) underutilization of unstructured data that can encode critical urban context. Given these limitations, we propose a Multimodal Large Language Model (MLLM)-based approach for interpretable hypothesis inference, enabling the automated generation, evaluation, and refinement of hypotheses concerning urban context and road safety outcomes. Our method leverages MLLMs to craft safety-relevant questions for street view images (SVIs), extract interpretable embeddings from their responses, and apply them in regression-based statistical models. UrbanX supports iterative hypothesis testing and refinement, guided by statistical evidence such as coefficient significance, thereby enabling rigorous scientific discovery of previously overlooked correlations between urban design and safety. Experimental evaluations on Manhattan street segments demonstrate that our approach outperforms pretrained deep learning models while offering full interpretability. Beyond road safety, UrbanX can serve as a general-purpose framework for urban scientific discovery, extracting structured insights from unstructured urban data across diverse socioeconomic and environmental outcomes. This approach enhances model trustworthiness for policy applications and establishes a scalable, statistically grounded pathway for interpretable knowledge discovery in urban and transportation studies.

Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive traffic dataset covering 49 states in the United States. The dataset integrates information from diverse sources, including transportation departments, law enforcement, and traffic sensors. This paper specifically emphasizes predicting the number of accidents, utilizing advanced machine learning models such as regression analysis and time series analysis. The inclusion of various factors, ranging from environmental conditions to human behavior and infrastructure, ensures a holistic understanding of the dynamics influencing road safety. Temporal and spatial analysis further allows for the identification of trends, seasonal variations, and high-risk areas. The implications of this research extend to proactive decision-making for policymakers and transportation authorities. By providing accurate predictions and quantifiable insights into expected accident rates under different conditions, the paper aims to empower authorities to allocate resources efficiently and implement targeted interventions. The goal is to contribute to the development of informed policies and interventions that enhance road safety, creating a safer environment for all road users. Keywords: Machine Learning, Random Forest, Accident Prediction, AutoML, LSTM.

This study explores traffic crash narratives in an attempt to inform and enhance effective traffic safety policies using text-mining analytics. Text mining techniques are employed to unravel key themes and trends within the narratives, aiming to provide a deeper understanding of the factors contributing to traffic crashes. This study collected crash data from five major freeways in Jordan that cover narratives of 7,587 records from 2018-2022. An unsupervised learning method was adopted to learn the pattern from crash data. Various text mining techniques, such as topic modeling, keyword extraction, and Word Co-Occurrence Network, were also used to reveal the co-occurrence of crash patterns. Results show that text mining analytics is a promising method and underscore the multifactorial nature of traffic crashes, including intertwining human decisions and vehicular conditions. The recurrent themes across all analyses highlight the need for a balanced approach to road safety, merging both proactive and reactive measures. Emphasis on driver education and awareness around animal-related incidents is paramount.

Autonomous vehicles could be on UK roads as soon as 2026, the transport secretary has said, as ministers seeks to capture as much as £42bn of the international self-driving market within the coming decade. "This technology exists, it works, and what we're doing is putting in place the proper legislation so that people can have full confidence in the safety of this technology," Mark Harper told BBC Radio 4's Today programme on Wednesday. Asked if people would be able to travel in self-driving vehicles "with your hands off the wheel, doing your emails" in 2026, Harper replied: "Yes, and I think that's when companies are expecting – in 2026, during that year – that we'll start seeing this technology rolled out." Responding to a question by the former Top Gear presenter James May – who was Today's guest editor – about why the government was supporting the development of autonomous driving, Harper claimed there were "a few" reasons. He said: "I think it will actually improve road safety. We already have a very good road safety record in Britain but there are still several thousand people a year killed on our roads. "It's a big economic opportunity for Britain to get what will be a big global share of market.

This research project delves into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, and multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple and logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time-series forecasting. With this approach, we've achieved superior predictive accuracy, marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast.

Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.

Over the past few years, the topic of Artificial Intelligence has been on the lips of many people around the globe. The possibilities of AI-powered systems are limitless, which is the reason for its heavy demand and interest. The infusion of AIs into various sectors of the economy would not just create a fast, efficient, and effective working process but will also aid in ensuring safety and accountability, depending on how it is utilized. Several AI innovations have emerged in recent years to aid in everyday activities and unique platforms such as SafetyConnect have risen to help unlock the world of safe driving for employees of large enterprises. SafetyConnect is an AI-powered Field Force Driving and Work Safety Suit for enterprises.

What are the types of technologies driving the future of mobility? The future of the technology is electronic, shared, connected, autonomous and driven by data. To read data, we need technologies as artificial intelligence (AI). AI will be a key driver for mobility in the future. It will help an autonomous vehicle read the road, comprehend the radar, understand the computer vision and make decision, as well as also be used for route optimisation – going from A to B. When AI is added to mobility, it will generate comfort, convenience and entertainment.

A van with technology that can automatically detect drivers holding a mobile phone at the wheel or not wearing a seatbelt is being trialled in the UK for the first time. National Highways are working with Warwickshire police to try out the "sensor test vehicle" on motorways and major A roads, and drivers who are caught may be prosecuted. The initial three-month trial will determine how the technology can be further deployed in future. Insp Jem Mountford, of Warwickshire police, said: "We are really excited to see the impact that this new technology has on the behaviour of drivers in Warwickshire. "During the trial the most serious breaches may be prosecuted, with others receiving warning letters, giving us the opportunity to explain how they have been caught and asking them to change their behaviour.

You could soon see self-driving buses and delivery vans on UK roads as the government launches a £40m ($50m) competition to bring this technology to the market. The funding to kick-start commercial self-driving services, such as delivery vehicles and passenger shuttles, will help bring together companies and investors so that sustainable business models to be rolled out nationally and exported globally. The Commercialising Connected and Automated Mobility competition will provide grants to help roll out commercial use self-driving vehicles across the UK from 2025. Types of self-driving vehicles that could be deployed include delivery vans, passenger buses, shuttles and pods, as well as vehicles that move people and luggage at airports and containers at shipping ports. The competition aims to unlock a new industry that could be worth £42bn to the UK economy by 2035, potentially creating 38,000 new skilled jobs.