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.

AI is being applied to assess the health of civil infrastructure through systems that test the integrity of bridges. A civil engineering assistant professor at The University of Texas at Arlington is working to better understand a bridge's structural health by combining machine learning with traditional monitoring measurements, according to a press release from the University of Texas at Arlington (UTA). The 18-month, $122,000 grant to Dr. Suyun Ham of the Civil Engineering department is part of UTA's membership in the Transportation Consortium of South-Central States (Tran-SET), a U.S. Department of Transportation Center administered by Louisiana State University. He will test his models in Dallas and Fort Worth. The systems in place to monitor bridges today are weight-in-motion systems with sensors that measure vibrations, strain, and deflection.

The Diet on Friday enacted legislative revisions aimed at creating systems to ensure the safety of self-driving vehicles. The revisions to the Road Transport Vehicle Act, approved unanimously by the House of Councilors at a plenary session, call for the applying of vehicle safety standards to self-driving equipment necessary to check the surroundings, including cameras and radars. Under the revised law, special certification will be granted to auto safety inspection business operators capable of undertaking maintenance work for self-driving equipment. The original law did not have provisions that assumed vehicles would ever be self-driving. The revisions also require automakers to provide technical information necessary to carry out inspections of self-driving equipment.

Engineers at Missouri University of Science & Technology in Rolla are developing algorithms that could provide early warnings for motorists about flooded roads. The system could warn drivers to stay off flooded roads. Researchers began the yearlong project to use artificial intelligence to enhance flood evacuation plans in February for transportation agencies in the Midwest, including the Missouri Department of Transportation. The work focuses on the Meramec River basin in eastern Missouri and the areas of Nebraska and northwest Missouri that experienced record-breaking floods in late March from the Missouri River. Artificial intelligence could help deliver that information to motorists faster so to prevent people from being stranded on flooded roads, said Suzanna Long, the chair of engineering management and systems engineering at Missouri S&T.

Steve Marshall is directing the city of Bellevue's efforts to bring a fleet of self-driving vanpools to the region. If it is successful, it could shuttle up to 3,000 commuters along the I-405 corridor from Auburn to Kirkland. Aaron Kunkler/Staff photo As the technology powering self-driving vehicles continues to advance, the cities of Bellevue and Kirkland have a plan to bring a fleet of vans to the Eastside. The CommutePool program was drafted by staff from the two cities, which submitted a grant application to the U.S. Department of Transportation last week asking for $3 million to help fund the $9 million project. Bellevue Transportation Technology Partnership Manager Steve Marshall has been working with large employers in the area to gather support for the electric, self-driving vanpools.

Unmanned aerial vehicles (UAV's) or better called drones, are becoming quite popular. But, with its growth a very few know its proper application and operation. Not everyone knows how they're allowed to use them. For this reason, the Minnesota Department of Transportation is helping people learn about drones. Various people learnt about the technology and there were also flights simulations and Competitive drone racing.

Panelists at Smart Citites NYC '17 discussed how the city of Columbus, Ohio pulled together $500 million in funding. Turning $50 million into $500 million is one of the greatest feats that Columbus, Ohio has ever accomplished. In June 2016, the city of Columbus landed a much-coveted $40 million grant from the US Department of Transportation and $10 million from Vulcan Inc. as part of the federal Smart City Challenge. The city won the funding in large part because of the it's close partnership with the Columbus business community. Columbus has already turned that original $50 million into half a billion dollars, thanks to an array of investments from the private sector, and more is expected.

'The National Highway Traffic Safety Admnnistration, under the U.S. Department of Transportation, has issued its' official report on the crash of a Tesla so-called "Auto-pilot" vehicle which crashed into a big rig last summer. "Problem Description: The Automatic Emergency Braking (AEB) or Autopilot systems may not function as designed, increasing the risk of a crash."'

So far, testing autonomous vehicles on city streets has had mixed results. Uber's plan did not go over well in the company's hometown of San Francisco, but cities like Phoenix and Boston have been a little more receptive to the idea. Now, to solve some of those bureaucratic headaches and foster a little more collaboration at the same time, the US Department of Transportation has laid out 10 autonomous vehicle proving grounds where research teams, automakers and startups can try out their technology before it hits the streets. According to US DOT Secretary Anthony Foxx, the proving grounds will provide more than just the physical roads to drive on -- they'll also form a community where new findings can be shared between the participants. "The designated proving grounds will collectively form a Community of Practice around safe testing and deployment," Foxx said in a statement.

The U.S. Department of Transportation will launch a federal committee on self-driving cars that includes multiple notable technology executives, according to an announcement on Thursday. Via a statement, the group will regularly meet to discuss various aspects of automated technology and the Department of Transportation's role in setting policy. The committee, which includes leading professionals and experts in their field, will hold its first meeting on January 16th, 2017 to immediately begin work on some of the most pressing and relevant matters facing transportation today, including the development and deployment of automated vehicles, and determining the needs of the Department as it continues with its relevant research, policy, and regulations. The committee will be chaired by Los Angeles mayor Eric Garcetti and General Motors CEO Mary Barra. Other members include executives and staffers from companies such as Amazon, Lyft and Zipcar.