Addressing the alignment problem in transportation policy making: an LLM approach

• URL: http://arxiv.org/abs/2510.13139v1
• Date: Wed, 15 Oct 2025 04:36:38 GMT
• Title: Addressing the alignment problem in transportation policy making: an LLM approach
• Abstract summary: 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.<n>Here, we investigate whether large language models (LLMs) can help inform and address this alignment problem.
• Score: 0.3499870393443268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 the promise and limitations of LLMs as tools for solving the alignment problem in transportation decision-making.

Related papers

• Should LLM Agents Decide in Social Simulations? Comparing Finite-State and LLM-Based Decision Policies [44.05636851266385]
  This paper evaluates whether large language models preserve an interpretable reference policy in an online social network simulation.<n>Three open-weight LLMs are tested: LLaMA 3.1, GPT-OSS, and Mistral 24B.<n>Results show that LLMs can approximate the reference policy in some configurations, but do not preserve it reliably.
  arXiv  Detail & Related papers  (2026-06-10T17:35:32Z)
• The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search [0.0]
  Large language models (LLMs) are rapidly assuming an intermediary role in housing search.<n>We conduct a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities.<n>We find that steering is an emergent behavior of the model's interpretive license rather than primarily a static property.
  arXiv  Detail & Related papers  (2026-06-04T20:17:58Z)
• OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents [74.20327254615854]
  Large language model agents interleave reasoning, action selection, and observation to solve sequential decision-making tasks.<n>Existing inference-time adaptation methods for LLM agents mainly rely on prompting or retrieval.<n>We propose OLIVIA, an inference-time action adaptation framework for ReAct-style agents.
  arXiv  Detail & Related papers  (2026-05-11T19:28:20Z)
• LLM Active Alignment: A Nash Equilibrium Perspective [34.54084293479338]
  We develop a game-theoretic framework for predicting and steering the behavior of large language models.<n>Agents choose actively and strategically which groups to align with, yielding an interpretable and behaviorally substantive policy class.
  arXiv  Detail & Related papers  (2026-02-06T16:26:03Z)
• LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling [2.8077601586229908]
  Large language models (LLMs) are increasingly used as behavioral proxies for self-interested travelers in agent-based traffic models.<n>We propose to model each homogeneous traveler group facing the same decision context with a single representative LLM agent.<n>We find that the proposed approach converges rapidly to the user equilibrium.
  arXiv  Detail & Related papers  (2025-11-09T07:36:46Z)
• SAND: Boosting LLM Agents with Self-Taught Action Deliberation [54.48979740613828]
  Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts.<n>We propose Self-taught ActioN Deliberation (SAND) framework, enabling LLM agents to explicitly deliberate over candidate actions before committing to one.<n>SAND achieves an average 20% improvement over initial supervised finetuning and also outperforms state-of-the-art agent tuning approaches.
  arXiv  Detail & Related papers  (2025-07-10T05:38:15Z)
• Aligning LLM with human travel choices: a persona-based embedding learning approach [15.11130742093296]
  This paper introduces a novel framework for aligning large language models with human travel choice behavior.<n>Our framework uses a persona inference and loading process to condition LLMs with suitable prompts to enhance alignment.
  arXiv  Detail & Related papers  (2025-05-25T06:54:01Z)
• Navigating Motion Agents in Dynamic and Cluttered Environments through LLM Reasoning [69.5875073447454]
  This paper advances motion agents empowered by large language models (LLMs) toward autonomous navigation in dynamic and cluttered environments.<n>Our training-free framework supports multi-agent coordination, closed-loop replanning, and dynamic obstacle avoidance without retraining or fine-tuning.
  arXiv  Detail & Related papers  (2025-03-10T13:39:09Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• How Strategic Agents Respond: Comparing Analytical Models with LLM-Generated Responses in Strategic Classification [11.614944245315186]
  We use Strategic Classification to study the interaction between agents and decision-makers.<n>This shift prompts two questions: (i) Can LLMs generate effective and socially responsible strategies in SC settings?<n>We show that even without access to the decision policy, LLMs can generate effective strategies that improve both agents' scores and qualification.
  arXiv  Detail & Related papers  (2025-01-20T01:39:03Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• AI-Driven Day-to-Day Route Choice [15.934133434324755]
  "LLMTraveler" is an agent that learns from past experiences and makes decisions by balancing retrieved data and personality traits.<n>This paper systematically evaluates the LLMTraveler's ability to replicate human-like decision-making through two stages of day-to-day congestion games.<n>This capability offers valuable insights for transportation policymaking, such as simulating traveler responses to new policies or changes in the network.
  arXiv  Detail & Related papers  (2024-12-04T14:13:38Z)
• Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making [85.24399869971236]
  We aim to evaluate Large Language Models (LLMs) for embodied decision making.<n>Existing evaluations tend to rely solely on a final success rate.<n>We propose a generalized interface (Embodied Agent Interface) that supports the formalization of various types of tasks.
  arXiv  Detail & Related papers  (2024-10-09T17:59:00Z)
• From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
  Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
  arXiv  Detail & Related papers  (2024-05-30T09:42:54Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [84.31119464141631]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios.<n>Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.