TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation

2026-04-29

AI Summary

Existing general-purpose benchmarks struggle to effectively evaluate the reliability of large language models in transportation-specific tasks involving regulatory application, engineering computation, and multimodal scene understanding. Publicly available traffic-related evaluations are limited in scope and lack fine-grained diagnostic capabilities. To address this gap, this work proposes the first open multimodal benchmark tailored for the transportation domain, structured around a three-dimensional taxonomy of role, task, and knowledge, and encompassing four functional areas: vehicles, traffic management, travelers, and planning/design. The benchmark integrates text, images, and point cloud data, employing unified prompt construction, decoding strategies, and scoring criteria, along with capability, modality, and difficulty tags to enable diagnostic evaluation from overall performance down to specific failure modes. Experiments reveal that current models perform reasonably well on textual tasks but exhibit significant deficiencies in multi-step computation, rule-based reasoning, and multimodal understanding—particularly with point clouds—providing a reliable baseline for model selection and safe deployment.

Abstract

Large language models (LLMs) and multimodal large models (MLLMs) are increasingly used for transportation tasks such as regulation question answering, traffic management support, engineering review, and autonomous-driving scene reasoning. Yet transportation workflows are rule-intensive, computation-intensive, safety-critical, and inherently multimodal. Existing general benchmarks provide limited evidence of whether a model can apply regulations correctly, perform verifiable engineering calculations, or interpret traffic scenes reliably, while the small number of public transportation benchmarks remain narrow in scope and rarely support fine-grained diagnosis across text, images, and point-cloud data. To address this gap, we present TRIP-Evaluate, an open multimodal benchmark for large models in transportation. The benchmark organizes 837 items using a role-task-knowledge taxonomy that covers vehicle, traffic-management, traveler, and planning-and-design functions. Each item is annotated with capability, modality, and difficulty labels, enabling diagnosis from overall accuracy down to specific failure modes. The current release includes 596 text items, 198 image items, and 43 point-cloud items. TRIP-Evaluate also standardizes item construction, quality control, prompting, decoding, and scoring to improve cross-model comparability. Results on a diverse panel of models show that text-based performance is improving, but substantial weaknesses remain in multi-step engineering calculation, rule-constrained reasoning, multimodal scene understanding, and point-cloud understanding. Overall, TRIP-Evaluate provides a reproducible, diagnosable, and engineering-aligned evaluation baseline for model selection, regression testing, and safer deployment in transportation applications.

Problem

• transportation
• multimodal benchmark
• large language models
• model evaluation
• safety-critical systems

Innovation

• multimodal benchmark
• transportation AI evaluation
• role-task-knowledge taxonomy
• point-cloud understanding
• rule-constrained reasoning