BusTime：哪种预测模型适合我的巴士到达时间？
BusTime: Which is the Right Prediction Model for My Bus Arrival Time?
摘要：
随着大数据技术的兴起，近年来许多智能交通应用得到了快速发展，其中包括公交车到达时间预测。这种类型的应用程序可以帮助乘客更有效地计划行程，而不会浪费不可预测的公交车站等待时间。许多研究集中在提高各种机器学习和统计模型的预测准确性，而很少有工作证明它们在现实城市环境中部署和使用的适用性。本文试图填补这一空白，提出了一个通用的和实用的评估框架，分析各种广泛使用的预测模型（即延迟，k-近邻，核回归，添加剂模型和递归神经网络使用长短期记忆）的巴士到达时间。特别是，该框架包含一个原始的总线GPS数据预处理方法，需要少得多的输入数据点，同时仍然保持令人满意的预测结果。该预处理方法通过使用基于KD树的最近点搜索方法，使各种模型能够仅预测公交车站的到达时间。在此框架的基础上，使用来自爱尔兰都柏林市的不同尺度的原始公交车GPS数据集，我们还通过分析常用预测模型在训练和预测阶段的实际优势和劣势，为城市管理者提供了初步结果。
英文摘要：
With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions. This type of applications help passengers to plan trips more efficiently without wasting unpredictable amount of waiting time at bus stops. Many studies focus on improving the prediction accuracy of various machine learning and statistical models, while much less work demonstrate their applicability of being deployed and used in realistic urban settings. This paper tries to fill this gap by proposing a general and practical evaluation framework for analysing various widely used prediction models (i.e. delay, k-nearest-neighbour, kernel regression, additive model, and recurrent neural network using long short term memory) for bus arrival time. In particular, this framework contains a raw bus GPS data pre-processing method that needs much less number of input data points while still maintain satisfactory prediction results. This pre-processing method enables various models to predict arrival time at bus stops only, by using a KD-tree based nearest point search method. Based on this framework, using raw bus GPS dataset in different scales from the city of Dublin, Ireland, we also present preliminary results for city managers by analysing the practical strengths and weaknesses in both training and predicting stages of commonly used prediction models.
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。
