Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

To play this video you need to enable JavaScript in your browser. First Minister John Swinney says he does not have confidence the UK is safe from potential Russian threats. The SNP leader said the UK government, which is in charge of Britain's defences, did not have a sufficient presence in vulnerable areas off Scotland's coast. He said he was very worried by security fears highlighted this week by the head of MI5, Sir Ken McCallum, who named Russia as aggressor number one. Speaking to BBC Scotland News ahead of the SNP's annual conference, Swinney also denied that independence would weaken Scotland's security.

PM says it is'clear' Russia interfered with Brexit vote Andy Burnham has said Russia did interfere with the Brexit referendum and warned that similar activities were taking place in France and Germany. The prime minister told The Rest is Politics podcast, external that upon arriving in No 10, he had asked about possible Russian interference in the 2016 vote, adding: I've had the clear answer that, yes, there was. I think we've been too much on the back foot on that actually and we haven't informed people sufficiently about the extent to which people are seeking to disrupt the political process from overseas, he added. Burnham did not provide further details about the type or extent of Moscow's activity during the Brexit referendum. Britain Unbound, a pro-Brexit campaign group, said Burnham's comments were another crude attempt to undermine the legitimacy of the decision that the British people made in 2016.

Residents of an Oxfordshire village have voted to become independent from the UK in a symbolic referendum held in protest at plans to house more than 1,000 migrants at a former military base. Villagers in Piddington, near Bicester, voted on Tuesday over Home Office proposals to house up to 1,256 male asylum seekers at nearby MoD Bicester. The 350 residents were invited to cast their ballot in a small box outside the village hall, the community hub of Piddington. The referendum was organised by the local parish council and out of 312 votes cast, 285 voted to leave, there were 26 no votes, and one ballot paper was spoiled. Delivering the result, Susannah Parden, vice chair of the parish council said: We are Piddington the village that roars. Following the result, council chair Tim McNally said: This is an example of democracy in operation.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

A propaganda song to get drivers on board even topped Sweden's hit music chart. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A police officer tries to direct traffic as Sweden switches to driving on the other side of the road over night. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . On September 3, 1967, at 4:50 a.m., a lone Swedish taxi driver stopped his car on the street in the light of early dawn.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

We introduce a family of chronologically consistent, instruction-tuned large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the share of predictability that survives once training leakage is removed. Together, these features provide researchers with an easy-to-use generative AI tool useful for a wide range of prediction tasks that is free of lookahead bias.

A key challenge in transportation planning is that the collective preferences of heterogeneous travelers often diverge from the policies produced by model-driven decision tools. This misalignment frequently results in implementation delays or failures. Here, we investigate whether large language models (LLMs)--noted for their capabilities in reasoning and simulating human decision-making--can help inform and address this alignment problem. We develop a multi-agent simulation in which LLMs, acting as agents representing residents from different communities in a city, participate in a referendum on a set of transit policy proposals. Using chain-of-thought reasoning, LLM agents provide Ranked-Choice or approval-based preferences, which are aggregated using instant-runoff voting (IRV) to model democratic consensus. We implement this simulation framework with both GPT-4o and Claude-3.5, and apply it for Chicago and Houston. Our findings suggest that LLM agents are capable of approximating plausible collective preferences and responding to local context, while also displaying model-specific behavioral biases and modest divergences from optimization-based benchmarks. These capabilities underscore both promise and limitations of LLMs as tools for solving the alignment problem in transportation decision-making. Introduction Urban transportation policy plays a central role in shaping regional development. Designing effective policy requires access to multidimensional data and a deep understanding of individual preferences across heterogeneous communities. Conventional approaches typically rely on structured mathematical models that identify an optimal policy under specified objectives and constraints. However, these models often rest on rigid assumptions and oversimplified behavioral representations. As a result, they may produce solutions that are analytically tractable yet poorly aligned with public sentiment or the complex realities of policy implementation. This misalignment frequently contributes to delays--or even failures--in policy approval and execution. Trained on vast corpora of text encompassing news, facts, and human discourse, LLMs possess a rich contextual understanding that could potentially help policymakers infer public preferences and explore trade-offs before implementation. Their ability to interpret unstructured information, reason about competing objectives in natural language, and adapt to specific contexts suggests a new form of decision support that complements the traditional paradigm. In this study, we implement a multi-agent voting framework to examine the potential of LLMs in supporting transportation policy design.