开源框架CrossTraffic让交通分析可复现、可执行，提升准确性与协作效率。
CrossTraffic: An Open-Source Framework for Reproducible and Executable Transportation Analysis and Knowledge Management
- 将交通分析规则转化为可执行的软件模块，支持跨平台调用。
- 知识图谱约束下模型误差极低（MAE<0.50），无效输入检测准确率100%。
- 适合交通工程研究者与政策制定者使用，推动开放协作的交通科学生态。
交通工程常依赖技术手册和分析工具进行规划、设计与运营，但这些方法学的传播与管理仍呈碎片化状态。计算流程多嵌入专有工具，更新不一致，知识传递受限，导致复现性差、互操作性弱，阻碍协同进步。本文提出CrossTraffic，一个将交通方法学与规范知识视为持续部署、可验证软件基础设施的开源框架。该框架提供标准化接口支持跨平台访问的可执行计算核心；基于本体的知识图谱编码工程规则与来源信息，作为分析流程的语义验证层；对话式接口通过结构化调用，使大语言模型能自然语言访问经验证的执行环境，避免无效分析。实验表明，知识图谱约束执行相比仅依赖上下文的方法显著提升数值精度与方法一致性，在多个大语言模型上实现近零误差（MAE<0.50），压力测试中无效输入检测F1值达1.0。其模块化架构支持集成更多交通手册与研究模型，为构建可复现计算核心的开放交通科学生态奠定基础。系统代码已公开于https://github.com/crosstraffic。
原文摘要 · Abstract (English)
Transportation engineering often relies on technical manuals and analytical tools for planning, design, and operations. However, the dissemination and management of these methodologies, such as those defined in the Highway Capacity Manual (HCM), remain fragmented. Computational procedures are often embedded within proprietary tools, updates are inconsistently propagated across platforms, and knowledge transfer is limited. These challenges hinder reproducibility, interoperability, and collaborative advancement in transportation analysis. This paper introduces CrossTraffic, an open-source framework that treats transportation methodologies and regulatory knowledge as continuously deployable and verifiable software infrastructure. CrossTraffic provides an executable computational core for transportation analysis with cross-platform access through standardized interfaces. An ontology-driven knowledge graph encodes engineering rules and provenance and serves as a semantic validation layer for analytical workflows. A conversational interface further connects large language models to this validated execution environment through structured tool invocation, enabling natural-language access while preventing procedurally invalid analyses. Experimental results show that knowledge-graph-constrained execution substantially improves numerical accuracy and methodological fidelity compared with context-only approaches, achieving near-zero numerical error (MAE<0.50) across multiple large language models and perfect detection of invalid analytical inputs in stress testing (F1~=~1.0). Its modular architecture supports the integration of additional transportation manuals and research models, providing a foundation for an open and collaborative transportation science ecosystem with a reproducible computational core. The system implementation is publicly available at https://github.com/crosstraffic.
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