从稀疏开放数据预测出行时间
Travel Time Prediction from Sparse Open Data
AI总结：
本文提出了一种基于开放数据的免费开源模型，用于在低资源条件下预测出行时间，通过随机森林算法实现合理准确的预测。
摘要：
出行时间预测在交通地理和规划的可达性分析、可持续交通基础设施建设以及主动交通干预中具有核心地位。然而，计算准确的出行时间，尤其是驾驶时间，需要要么有广泛的 technical 能力和定制数据，要么使用如 Google 地图 API 这样的资源，这些资源在分析数千或数百万次行程以进行大都市规模分析时很快变得不可接受。这些障碍尤其挑战了资源较少的研究人员、从业者和社区倡导者。本文主张需要一个中间方法，以在不需大量数据或计算资源的情况下提供合理准确的出行时间预测。它介绍了一个免费、开源的最小拥堵驾驶时间预测模型，其成本、数据和计算需求都极低。该模型通过计算洛杉矶加州城市区域的 naive 出行时间，并利用开放数据中的转弯和交通控制数据，训练并测试了随机森林模型来预测出行时间。验证显示，这种可解释的机器学习方法提供了一种平衡合理精度与最低资源需求的中间技术。
英文摘要：
Travel time prediction is central to transport geography and planning's accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However, calculating accurate travel times, especially for driving, requires either extensive technical capacity and bespoke data, or resources like the Google Maps API that quickly become prohibitively expensive to analyze thousands or millions of trips necessary for metropolitan-scale analyses. Such obstacles particularly challenge less-resourced researchers, practitioners, and community advocates. This article argues that a middle-ground is needed to provide reasonably accurate travel time predictions without extensive data or computing requirements. It introduces a free, open-source minimally-congested driving time prediction model with minimal cost, data, and computational requirements. It trains and tests this model using the Los Angeles, California urban area as a case study by calculating naive travel times from open data then developing a random forest model to predict travel times as a function of those naive times plus open data on turns and traffic controls. Validation shows that this interpretable machine learning method offers a superior middle-ground technique that balances reasonable accuracy with minimal resource requirements.
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。
