从稀疏开放网络中学习最小拥堵行驶时间：一种轻量级基于射频的估计器用于城市道路运营
Learning Minimally-Congested Drive Times from Sparse Open Networks: A Lightweight RF-Based Estimator for Urban Roadway Operations
AI总结：
本文提出一种基于射频的轻量级估计器，用于从稀疏开放网络中学习最小拥堵行驶时间，通过整合道路网络数据和稀疏操作特征，提升交通预测的准确性和实用性。
摘要：
准确的道路行驶时间预测是交通系统分析的基础，但广泛依赖于数据密集型拥堵模型或过于幼稚的启发式方法限制了在工程流程中的可扩展性和实际应用。本文开发了一种轻量级估计器，用于最小拥堵的汽车行驶时间，该估计器整合了开放道路网络数据、速度限制和稀疏控制/转弯特征，在随机森林框架内纠正最短路径遍历时间基线的偏差。使用一个城市测试床，该流程：(i) 从志愿者地理数据中构建可行驶网络；(ii) 解决最短路径问题，最小化边遍历时间；(iii) 推导稀疏操作特征（信号、停止、交叉口、让行、环形交叉口；左/右/轻微/U-turn计数）；(iv) 在有限的高质量参考时间上训练回归集成，以在训练集之外推广预测。离样评估显示在均方误差、均绝对百分比误差、均绝对误差、相对偏差和解释方差方面显著优于遍历时间基线，且在最小拥堵条件下没有显著的平均偏差，一致的k折稳定性表明没有过拟合。所提出的方法为交通工程提供了一个实用的中间解决方案：它在大都市规模上保持点对点的准确性，减少资源需求，并在拥堵数据不可用或成本高昂时提供有说服力的性能估计，支持在低交通运行模式下的规划、可达性和网络性能应用。
英文摘要：
Accurate roadway travel-time prediction is foundational to transportation systems analysis, yet widespread reliance on either data-intensive congestion models or overly naïve heuristics limits scalability and practical adoption in engineering workflows. This paper develops a lightweight estimator for minimally-congested car travel times that integrates open road-network data, speed constraints, and sparse control/turn features within a random forest framework to correct bias from shortest-path traversal-time baselines. Using an urban testbed, the pipeline: (i) constructs drivable networks from volunteered geographic data; (ii) solves Dijkstra routes minimizing edge traversal time; (iii) derives sparse operational features (signals, stops, crossings, yield, roundabouts; left/right/slight/U-turn counts); and (iv) trains a regression ensemble on limited high-quality reference times to generalize predictions beyond the training set. Out-of-sample evaluation demonstrates marked improvements over traversal-time baselines across mean absolute error, mean absolute percentage error, mean squared error, relative bias, and explained variance, with no significant mean bias under minimally congested conditions and consistent k-fold stability indicating negligible overfitting. The resulting approach offers a practical middle ground for transportation engineering: it preserves point-to-point fidelity at metropolitan scale, reduces resource requirements, and supplies defensible performance estimates where congestion feeds are inaccessible or cost-prohibitive, supporting planning, accessibility, and network performance applications under low-traffic operating regimes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
