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.

翻译：旅行时间预测是交通地理与规划可达性分析、可持续交通基础设施供给以及主动交通干预措施的核心。然而，计算精确的旅行时间（特别是驾车出行）需要大量技术能力和定制数据，或依赖如谷歌地图API等资源，这些资源在大都市尺度分析所需的数千乃至数百万次行程计算中会迅速变得极其昂贵。此类障碍尤其对资源有限的研究人员、从业者和社区倡导者构成挑战。本文主张需要一种折中方案，以在无需大量数据或计算资源的前提下提供合理准确的旅行时间预测。文章介绍了一种免费、开源、成本极低、数据需求和计算要求最小的低拥堵驾车时间预测模型。该模型以加利福尼亚州洛杉矶都市区为案例，通过开放数据计算初始旅行时间，进而构建随机森林模型，将旅行时间预测为初始时间叠加转弯与交通管制开放数据的函数，并以此完成训练与测试。验证结果表明，这种可解释的机器学习方法提供了一种优越的折中技术，在合理精度与最小资源需求之间取得了平衡。