arXiv AI

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support

arXiv:2606. 00991v1 Announce Type: new Abstract: Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations.

arXiv AI

Jun 30

Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline

arXiv:2606. 29014v1 Announce Type: new Abstract: Recent advancements in generative artificial intelligence (AI) and large language models (LLMs) have shown significant promise in automating complex reasoning, summarization, and question-answering tasks.

By Dianwei Chen (Terry), Yuan-Zheng Lei (Terry), Zifan Zhang (Terry), Yuchen Liu (Terry), Xianfeng (Terry), Yang

llms agents nlp fine-tuning benchmarks safety

arXiv Machine Learning

Jul 7

LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs

arXiv:2607. 03651v1 Announce Type: new Abstract: While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context.

By Xiaoyue Liu, Zheng Dong

llms agents

arXiv AI

Jun 2

TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents

arXiv:2606. 01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existing benchmarks' limitations: 1) overemphasis on constraint compliance, neglecting multi-dimensional qualities like spatio-temporal cost; 2) datasets lacking real-world authenticity and coverage in key areas (e.

By Weiyi Chen, Shuaixiong Wang, Ziyun Gao, Kaichun Hu, Wangze Ni, Shimin Di, Chen Jason Zhang, Lei Chen

llms agents benchmarks

arXiv Machine Learning

Jul 1

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.

By Ji Cao, Yu Wang, Tongya Zheng, Jie Song, Qinghong Guo, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song

llms rag benchmarks

arXiv Machine Learning

Jul 30

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs

arXiv:2602. 18600v5 Announce Type: replace Abstract: Systematically evaluating Multimodal Large Language Models (MLLMs) is essential for advancing Artificial General Intelligence (AGI).

By Ziqiao Shang, Ling-Yue Ge, Zian Xu, Zi-Jian Cheng, Shi-Yu Tian, Zhenyu Huang, Wenbo Fu, Weiming Wu, Yang Chen, Xiangwen Zhang, Yulan Hu, Bin Liu, Lan-Zhe Guo

llms multimodal benchmarks

arXiv AI

Jun 16

Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification

arXiv:2606. 16987v1 Announce Type: new Abstract: Accurate Harmonized Tariff Schedule (HTS) code classification is essential for customs clearance, duty assessment, trade statistics, and regulatory compliance in maritime logistics.

By Truong Thanh Hung Nguyen, Khanh Van Quynh Nguyen, Hoang-Loc Cao, Tri Duong, Phuc Ho, Van Pham, Loc Nguyen, Hung Cao

llms agents

arXiv Machine Learning

Jun 2

Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction

arXiv:2606. 00572v1 Announce Type: new Abstract: Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation.

By Oluwaleke Yusuf, Adil Rasheed, Frank Lindseth

llms rag benchmarks safety

arXiv AI

Aug 11

Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios

arXiv:2608. 08281v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different domains, most notable in the automotive sector.

By Julius Wirbel, P. Nicholas Hansen, Line K. H. Clemmensen, Roberto Galeazzi

llms fine-tuning benchmarks

arXiv AI

Jul 28

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning

arXiv:2505. 18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other.

By Jiaxun Cui, Chen Tang, Jarrett Holtz, Janice Nguyen, Alessandro G. Allievi, Hang Qiu, Peter Stone

llms agents robotics

arXiv AI

Jul 14

Beyond Na\"ive Prompting: Strategies for Improved Context-aided Forecasting with LLMs

arXiv:2508. 09904v3 Announce Type: replace-cross Abstract: Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form.

By Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, \'Etienne Marcotte, Valentina Zantedeschi, Alexandre Drouin

llms

arXiv AI

Jul 3

Pre-Flight: A Benchmark for Evaluating Large Language Models on Aviation Operational Knowledge

arXiv:2607. 01829v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for aviation business operations, from documentation and training generation to customer facing assistants.

By Alex Brooker, Tim Hughes

llms benchmarks safety

arXiv AI

Jul 8

LLM4Delay: Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation

arXiv:2510. 23636v4 Announce Type: replace-cross Abstract: Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system.

By Thaweerath Phisannupawong, Joshua Julian Damanik, Han-Lim Choi

llms

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