数据驱动的混合车辆公交车队路径级能耗预测
Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets
摘要：
由于对环境影响、运营成本和能源安全的担忧日益增加，公共交通机构正在寻求通过使用电动汽车（EV）来减少燃料使用。然而，由于电动汽车的前期成本很高，大多数机构只能负担内燃机和电动汽车的混合车队。充分利用这些混合车队对各机构提出了挑战，因为优化运输路线的车辆分配、安排充电等需要准确预测电力和燃料的使用。基于传感器的技术、数据分析和机器学习的最新进展可以弥补这种情况;然而，据我们所知，目前还没有一个框架可以将所有相关数据整合到公共交通的路线级预测模型中。在本文中，我们提出了一种新的框架，用于混合车辆运输车队的路线级能源使用的数据驱动预测，我们使用从田纳西州查塔努加市公共交通管理局CARTA的公交车队收集的数据进行评估。我们提出了一个数据收集和存储框架，我们用它来捕获系统级数据，包括交通和天气状况，以及高频车辆级数据，包括位置跟踪，燃料或电力使用等。我们提出了特定于域的方法和算法，用于集成和清理来自各种来源的数据，包括街道和高程地图。最后，我们在我们的集成数据集上训练和评估机器学习模型，包括深度神经网络、决策树和线性回归。我们的研究结果表明，神经网络提供了准确的估计，而其他模型可以帮助我们发现能源使用与道路和天气条件等因素之间的关系。
英文摘要：
Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs). However, because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. Making the best use of these mixed fleets presents a challenge for agencies since optimizing the assignment of vehicles to transit routes, scheduling charging, etc. require accurate predictions of electricity and fuel use. Recent advances in sensor-based technologies, data analytics, and machine learning enable remedying this situation; however, to the best of our knowledge, there exists no framework that would integrate all relevant data into a route-level prediction model for public transit. In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN. We present a data collection and storage framework, which we use to capture system-level data, including traffic and weather conditions, and high-frequency vehicle-level data, including location traces, fuel or electricity use, etc. We present domain-specific methods and algorithms for integrating and cleansing data from various sources, including street and elevation maps. Finally, we train and evaluate machine learning models, including deep neural networks, decision trees, and linear regression, on our integrated dataset. Our results show that neural networks provide accurate estimates, while other models can help us discover relations between energy use and factors such as road and weather conditions.
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。
